19970307 060
PL-TR-96-2224
SUPPORT OF ENVIRONMENTAL
REQUIREMENTS FOR CLOUD ANALYSIS AND
ARCHIVE (SERCAA): FINAL REPORT
Gary B. Gustafson
Robert P. d'Entremont
Ronald G. Isaacs
Atmospheric and Environmental Research, Inc.
840 Memorial Drive
Cambridge, MA 02139-3794
14 August 1996
Final Report
14 August 1992 - 13 August 1996
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14 August 1996 | Final 14 August 1992-13 Aug.1996
4. TITLE AND SUBTITLE , ^ j
Support of Environmental Requirements for Cloud
Analysis and Archives (SERCAA): Final Report
5. FUNDING NUMBERS
PE 35160F
PRDSPO TA GR
WU AA
F19628-92-C-0149
6. AUTHOR(S)
G.B. Gustafson, R.P. d'Entremont and
R.G. Isaacs
7. PERFORMING ORGANIZATION NAME(S) AND ADORESS{E$)
Atmospheric and Environmental Research, Inc.
840 Memorial Drive
Cambridge, MA 02139-3794
8. PERFORMING ORGANIZATION
REPORT NUMBER
9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)
Phillips Laboratory
29 Randolph Road
Hanscom AFB, MA 01731-3010
Contract Manager: Allan Bussey/GPAB
10. SPONSORING/MONITORING
AGENCY REPORT NUMBER
PL-TR-96-2224
11. SUPPLEMENTARY NOTES .
Sponsored by joint Department of Defense, Department of Energy, and
Environmental Protection Agency Strategic Environmental Research and
Development Program (SERDP)
12a. DISTRIBUTION/AVAILABILITY STATEMENT
Approved for public release; distribution
unlimited
12b. DISTRIBUTION CODE
13. ABSTRACT (Maximum 200 words)
Support of Environmental Requirements for Cloud Analysis and Archives (SERCAA) was a
four-year, two-phase research and development program. Program goals were to produce a
global multispectral, multisensor cloud analysis capability for retrieval of spatial, radiative,
microphysical, and environmental cloud properties through analysis of sensor data from multiple
operational environmental satellites. Program accomplishments during Phase 1 included
development of three customized cloud-detection algorithms for analysis of NOAA/AVHRR,
DMSP/OLS, and geostationary sensor data; a new algorithm for discrimination of vertical cloud
layers within a 25 km grid cell; and an innovative analysis-integration algorithm for optimdly
combining the results of the source-specific cloud analysis modules. During Phase 2, retrieval
capabilities were expanded to include cloud particle size, emissivity/transmissivity, optical depth,
and altitude. Additional work toward characterization of cloud-free bidirectional reflectance
provides a basis for enhanced analysis of visible-channel data in the cloud detection process.
SERCAA algorithms have been selected for operational implementation at the Air Force Global
Weather Central.
14. SUBJECT TERMS
cloud, nepnanalysis, satellite meteorology, cirrus
IS. NUMBER OF PAGES
80
16. PRICE CODE
17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION
OF REPORT OF THIS PA<iE. ^ OF ABSTRACT
Unclassified Unclassified Unclassified
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Unlimited
NSN 7540-01*280-5500 Standard Form 298 (Rev 2-89)
Pfe«f«be<J by AN$i Sid Z39-18
298-102
TABLE OF CONTENTS
Page
Executive Summary.viii
1.0 Introduction. 1
2.0 Program Requirements. 2
3.0 SERCAA Phase I Overview. 3
3.1 DMSP/OLS. 4
3.2 NOAA/AVHRR. 6
3.3 Geostationary. 8
3.4 Cloud Layering and Typing. 9
3.5 Cloud Analysis Integration. 10
4. SERCAA Phase n. 12
4.1 Cloud Detection Using Visible and Near-Infirared Bidirectional
Reflectance Distribution Models. 12
4.1.1 Spectral Tests for Cloud-Clearing. 14
4.1.1.1 Bright Clouds Test. 14
4.1.1.2 Cold Cloud Tests. 14
4.1.1.3 NIR-Visible Ratios. 15
4.1.1.4 Low Clouds and Fog. 15
4.1.1.5 Thin Cirrus. 15
4.1.1.6 Cloud Shadows. 16
4.1.1.7 Adjacency Effects. 16
4.1.2 BRDF Models. 17
4.1.3 BRDF Retrieval Estimates..'.. 17
4.1.4 Cloud Detection Results. 18
4.2 Cirrus Radiative and Spatial Properties. 21
4.2.1 Observation of Cirrus from Satellite. 21
4.2.2 Passive Irrfrared Physics of Cirrus Cloud Signatures. 22
4.2.3 Cirrus Retrievals Using 6.7-p,m Water Vapor Imager Data. 24
4.2.4 Cirrus Retrieval Validation Results. 24
4.2.5 Summary. 26
4.3 Optical and Microphysical Parameters for Water Droplet Clouds. 29
4.4 Cloud Environment. 34
4.5 Implementation Changes for Cloud Typing and Layering. 40
4.6 Analysis Consistency. 41
4.7 Daytime Cirrus/Low-Cloud Discrimination Using MWIR Data. 49
iii
TABLE OF CONTENTS
(continued)
Page
5. Future Research Topics. 50
5.1 New Sensors. 50
5.2 Advanced Retrieval Techniques. 52
5.2.1 Interpretation of Visible to Midwave-Infrared Data. 52
5.2.2 Transmissive Cirrus over Warm and Reflective
Backgrounds. 54
5.2.3 Cloud Typing. 55
5.2.4 Cloud Altitude. 55
5.2.5 Temporal Consistency. 57
5.3 Validation. 59
6. Summary. 61
7. References. 61
Appendix A AER SERCAA Related Papers and Technical Presentations for
1993-1996 . 69
IV
LIST OF FIGURES
Figure Page
1 The New England Cartesian grid used for the BRDF study. 14
2 AVHRR sample image valid 1221 UTC 12 Sept 1994 (left), and its
associated manually digitized cloud mask generated using Eqs. (l)-(6). 17
3 17-Day composite cloud mask for AVHRR morning overpasses
between 3-20 September 1994. 19
4. Map of Ambrals kernel combinations obtained for the 17-day AVHRR
Sept 94 dataset. 20
5 TPQ-11 35-GHz radar returns and coincident satellite-based SERCAA
MWIR/LWIR retrievals for cirrus clouds over Hanscom AFB, 2315
UTC 15 Sept 95. 25
6 TPQ-11 35-GHz radar returns and coincident satellite-based SERCAA
Water-Vapor/LWIR retrievals for cirrus clouds over Hanscom AFB,
2315 UTC 15 Sept 95. 27
7 Plots of SERCAA and RTNEPH-derived cirrus altitudes for the cirrus
samples shown in Figures 5 and 6. 28
8 Modified gamma droplet size distributions for stratocumulus (dot,dot)
and altostratus (solid). Distributions are fi:om Stephens (1979) and
Diem (1948), respectively. 30
9 Plots of nighttime MWIR/LWIR brighmess temperature differences as
a function of cloud top altitude for the mid-latitude summer
atmosphere. 31
10 Plots of nighttime MWIR/LWIR brighmess temperature differences as
a ftmction of satellite view angle for altostratus in a tropical
atmosphere. 32
11 Schematic of a prototype infi’ared cloud detection scheme that
integrates microwave derived information. 37
12 Typical Tb signatures for clear over ocean (solid), partly cloudy over
ocean (dash-dot), cloudy over ocean (dash), clear over land (solid),
partly cloudy over land (dash-dot), and cloudy over land (dash) for
data collected during May and July, 1992 for the east and west coast
of the United States. 39
V
LIST OF FIGURES
(continued)
Figure Page
13 Typical Tb signatures for clear (dash), light precipitation (solid) and
heavy precipitation (dot) observed in Typhoon Oliver, February 4,
1993 in the western Pacific Ocean. 39
14 NOAA-14 10.7-pm brightness temperature image over New England
for November 1994. High tones denote low brightness temperatures. 42
15 SERCAA Phase-1 cloud layer analysis for the image in Figure 14. This
analysis was generated using LWIR brightness temperatures as input
to the clustering and unsupervised classification routines. Each square
represents a 16th-mesh box. White boxes contain two cloud layers,
and gray boxes contain only one. 43
16 SERCAA Phase-2 cloud layer analysis for the image in Figure 14. This
analysis was generated using SERCAA Phase-2 pixel-resolution
corrected altitudes as input to the clustering and unsupervised
classification routines. Each square represents a 16th-mesh box.
White boxes contain two cloud layers, and gray boxes contain only
one. 44
17 SERCAA evaluation regions of interest. 45
18 SERCAA analyses of total cloud firaction over the western Pacific
Ocean and eastern Asia on 26 March 1993. Top row contains hourly
analyses starting at 1700 UTC and ending at 2000 UTC. Bottom row
shows a simple persistence forecast based on the analysis at 1600
UTC. Greyshade is proportional to analyzed cloud amount. 48
VI
LIST OF TABLES
Table Page
1 BRDF cloud detection accuracy (%), measured against the manually
digitized 1-km cloud mask. 21
2 Regression coefficients for Eq. (14) that yield droplet mode radius as a
function of the MWIR-LWIR brightness temperature pair and the
satellite look angle, for each model atmosphere. 33
3 Sensor data suite. 35
4 Summary of operational environmental satellite imaging sensor
characteristics. 46
5 Planned environmental satellite systems with primary sensor
characteristics. 51
vii
Executive Summary
This final report describes a two-part research and development program to produce a
global multispectral, multisensor cloud analysis capability for retrieval of spatial, radiative,
microphysicd and environmental cloud properties through analysis of sensor data from
multiple environmental satelhtes. Jointly sponsored by the Department of Defense,
Department of Energy, and Environmental Protection Agency through the Strategic
Environmental Research and Development Program (SERDP) and the Air Force P hilli ps
Laboratory (PL), the Support of Environmental Requirements for Cloud Analysis and
Archive (SERCAA) program achieved the project goals through development and testing of
a series of cloud analysis algorithms. A key attribute of the SERCAA algorithms is then-
ability to analyze and integrate data from multiple environmental satellite systems consisting
of the U.S. military (DMSP) and civilian (NOAA/POES) polar orbiting sensor platforms
and international geostationary systems from the U.S. (GOES), Japan (GMS), and Europe
(METEOS AT), "nie program was broken into two development phases. Phase I focused
on analysis of imaging-sensor data for cloud detection and retrieval of cloud spatial
properties including location, extent, fractional amount, cloud-top altitude, and cloud type.
Important elements are multiple cloud detection algorithms designed to best exploit the
information content from each class of satellite, a new layering and typing algorithm based
on generalized image processing classification techniques, and a unique analysis integration
algorithm that optimally combines analyzed cloud products from each data source into a
single consistent analysis. Phase n used the cloud spatial information as the first step in
retrieval of cloud emissivity, phase, and particle size. Emissivity algorithms use
multisj^tral infrared data to simultaneously solve multiple radiative transfer equations for
the emission term. From emission, a first-order estimate of optical depth inferred. For
accurate estimation of cloud emission, it is first necessary to know cloud phase. A
deterministic phase algorithm was developed that exploits unique cloud signatures at mid¬
wave and long-wave infrared wavelengths from liquid water and ice cloud. While imaging
sensors provide insufficient information to retrieve a cloud drop distribution, a useful bulk-
property parameterization (e.g., mode radius) can be retrieved. A mode-radius retrieval
algorithm was develop^ for water droplets based on theoretically-derived look-up tables of
expected monochromatic infrared radiance values as a function of atmospheric profile, view
angle, and cloud altitude. Monochromatic radiances were convolved with sensor response
functions for IR channel confi^rations on available environmental satellites. Where
available, collocated sounding instruments were used in addition to the imager data to
retrieve temperature and moisture profile information in the vicinity of cloud. A unified
retrieval algorithm was used wherein aU available sensor information is used to produce an
optimal retrieval.
1.0 Introduction
A global cloud detection and analysis technology, operating continuously and in real¬
time, which more completely exploits the information in present satellite sensors, has been
a recognized and stated requirement to support the operations of the United States
Department of Defense (DoD) for a number of years. Also, the environmental monitoring
and global change research communities have identified clouds as having a preponderant
impact on climate and climate change. They have given top priority to improved global
cloud specification although real-time cloud product generation is not a requirement for
these groups. Progress firom the global change community has occurred, specifically the
International Satellite Cloud Climatology Project (ISCCP). However, fiscd and
technological constraints have prevented the development and implementation of a multi¬
satellite multispectral cloud an^ysis technology capable of utilizing in an operational mode
the abundant sensor information presently on orbit. The need has been identified, the
measurement capabilities have been in place, the missing component has been an advanced
satellite-based operational cloud retrieval technology.
This report describes the Support of Environmental Requirements for Cloud Analysis
and Archive (SERCAA) research and development program which has produced such a
technology and provides a technical description of the component algorithms. The program
was accepted by the DoD portion of the Strategic Environmental Research and
Development Program (SERDP) and supported under the Remote Sensing Thrust as a
project of the “Definition and Demonstration of Remote Sensing Capability to Contribute to
Environmental Understanding and Support for Environmental Issues”, administered by the
Defense Support Projects Office (DSPO) through a contract with the Air Force Phillips
Laboratory.
The overall SERCAA program objective was to provide a satellite-based global cloud
analysis capability for determining the spatial, radiative and microphysical properties of
clouds. The two-phases of the program have resulted in development and demonstration of
cloud algorithms for retrieval of: (1) continuously updated cloud spatial properties, and (2)
augmented cloud optical, microphysical, and environmental properties. The combined
algorithms form a cloud analysis model that is intended to serve a large number of
environmental users, both within and outside of DoD. The SERCAA model can be used to
help in determining and validating the distribution of clouds and their associated
hy^ological and radiative effects on climate and on global climate change. It will also
constitute a substantial upgrade to the presently operating Real-Time Nephanalysis
(RTNEPH) cloud analysis model at the Air Force Globd Weather Center (AFGWC). In
this role SERCAA is a fundamental component in the Cloud Depiction and Forecast System
(CDFS) being carried out by the Air Weather Service of the U. S. Air Force.
Under Phase I, the objective of improved satellite cloud detection capabilities for
global applications was addressed through development of an integrated ensemble of global
cloud andysis algorithms applicable to sensor data from both civilian and military satellites.
The SERCAA multiplatform, multisensor, multispectral cloud product analysis algorithms
(Isaacs et al., 1993; Gustafson et al., 1994) incorporate high-resolution sensor data into a
real-time cloud analysis. Multispectral cloud analysis techniques are used for the detection
and specification of clouds, especially cirrus and low clouds. Geostationary sensor data
augment polar-orbiting data to improve the timeliness of analyses, particularly in tropical
regions. The steps required to process the raw sensor data, collected from each of the
satellite platforms, into each of the individual cloud analysis products include total cloud
algorithms for the Defense Meteorological Satellite Program (DMSP) Operational Linescan
System (OLS), the National Oceanic and Atmospheric Administration (NOAA) Advanced
Very High Resolution Radiometer (AVHRR), and geostationary platforms; cloud layer and
1
type algorithms; and a new analysis integration algorithm. SERCAA data products
include: total cloud cover fraction, number of cloud layers (up to four floating layers),
cloud layer coverage fraction, cloud type, cloud height, and analysis confidence level.
These products will be provided on a 16* mesh (24 km grid tme at 60 degrees latitude)
polar stereographic grid.
In Phase n, the goal was to extend the multiple-source optimal satellite retrieval
strategy that was successfully apphed for cloud spatial characteristics (cloud amount,
height, layers, and type), to Ae augmented cloud properties (optical, microphysical,
environmental) specification. This was accomplished by emphasizing infrared and
microwave sounder data in conjunction with cloud detection from visible and infrared
satellite imagers. Optical properties are emissivity, reflectivity, and optical depth;
microphysical attributes are particle size distribution and phase; environmental parameters
are water vapor profiles, temperature profiles and cloud liquid water.
2.0 Program Requirements
In the U.S. Global Change Research Program, the role of clouds has been identified
as the number one science priority and also the most important environmental factor
(Correll, 1990) affecting global climate change processes. The reliable determination of
cloud parameters on a global scale is fundamental to understanding that role (Schiffer and
Rossow, 1983). The U.S. Interagency Committee on Earth and Environmental Sciences
(CEES) has also specified clouds and the hydrologic cycle as elements sharing the highest
scientific priority for global change research. The two-decade-long Global Energy and
Water Cycle Experiment (GEWEX), part of the World Climate Research Programme
(WCRP), has singled out the cloud-radiation feedback process as primary among its most
critical problems (WMO, 1989). GEWEX emphasizes that satellite detection of cloud
physical and radiative properties on a global basis will require significant enhancement if
the goal is to achieved of specifying the complex role clouds have in climate and rlimatp!
change (Chahine, 1992). Also, the American Meteorological Society Council (AMS,
1992) has stated, “Clouds are the most critical factor causing uncertainties in model
estimates of global warming.”
The requirements for detailed representative global cloud analyses have increased
over the last decade. Advancement of cloud forecasting models and the accurate simulation
of cloud effects on the radiative feedback processes in global/medium-range numerical
weather prediction (NWP) and climate models is impeded by the lack of accurate cloud
analyses (Isaacs et al., 1983). Applied meteorology’s goal of improved short and medium
range cloud forecasting is limited in significant part to Ae accuracy of cloud parameteriza-
tions employed by forecast modelers. In the summer of 1983, the ISCCP started as a part
of the WCRP. One of the prim^ objectives of the ISCCP is to derive and validate a
global cloud climatology that will allow for improved parameterizations of clouds and
therefore their associated radiation feedback processes in climate models (Schiffer and
Rossow et al., 1985).
The impact of clouds on Department of Defense (DoD) activities is also of first order.
Improved cloud forecasting and improved cloud data handling and analysis are among the
highest prioritized Air Force requirements. The DoD policy of quantifying the
environment’s impact on new systems before they enter the acquisition process has placed
increased emphasis on having accurate and multi-parameter cloud archives against which
system performance can be evaluated. The need for superior global cloud analysis and
archive products is critical. Presently the best, and for certain cloud characteristics, the
only operational real-time global cloud analysis is the RTNEPH (Hamill et al., 1992),
2
residing at the Air Force Global Weather Central (AFGWC). It became operational in
1983, replacing the earlier 3-Dimensional Nephanalysis, or 3DNEPH. Unfortunately, the
cloud analysis techniques and database management contained in the RTNEPH make
minimal use of existing advanced technologies. Currently, only Operational Linescan
System (OLS) visible and infrared channel radiances, or single-channel Advanced Very
High Resolution Radiometer (AVHRR) infrared data, are accepted for processing. Even
this limited data set is then degraded in both spatial and spectral resolution and
geometrically transformed to fit a standardized fixed grid structure. Restrictions on the
satellite data sources made available for processing and the amount of ad hoc tuning and
manual interaction required by the current RTNEPH introduce serious limitations for
climate research purposes. Additionally, RTNEPH products are immediately available only
to government, and in particular DoD weather forecast facilities. RTNEPH information is
archived at the National Climatic Data Center (NCDC), Asheville, NC by the United States
Air Force Combat Climatology Center (USAFCCC). From a climate standpoint, the
quality and therefore the usefolness of these archive products is limited by the lack of data
source information and the lack of continuity in analysis algorithms in the RTNEPH over
the past two decades.
Recent advances in satelhte cloud detection using multispectral analysis techniques
have demonstrated a superior quantification of cloud characteristics utilizing near-in^ed
(NIR) and medium-wavelength infrared (MWIR) data from the AVHRR sensor. For
example, the location, height, amount, and optic^ properties of cirms clouds can be better
quantified; low cloud and fog can also be more confidently detected at night and during the
da 5 ^me over melting snow (d’Entremont, 1986; d’Entremont and Thomason, 1987;
d’Entremont et al., 1990; Stowe et al., 1991; d’Entremont et al., 1992). Application of
these retrieval techniques was successfully achieved within DoD under the TACNEPH
development project conducted by researchers at the Phillips Laboratory, Geophysics
Directorate (PL/GPA) and AER (Gustafson and d’Entremont, 1992). The objective of
TACNEPH was to produce a stand-alone, deployable satellite cloud analysis capability on a
regional scale to support tactical operations (Gustafson et al., 1993b). The TACNEPH
heritage lies in the global Real Time Nephanalysis or RTNEPH model (Hamill et al., 1992)
run operationally at the Air Force Global Weather Central (AFGWC).
Incorporation of improved satellite cloud detection capabilities for global applications
was a primary goal of SERCAA. The three principal accomplishments of SERCAA were:
1) to incorporate high-resolution sensor data from multiple military and civilian satellites,
polar and geostationary, into a real-time cloud analysis model, 2) to demonstrate
multispectral cloud andysis techniques that improve the detection and specification of
clouds, especially cirrus and low clouds, and 3) to provide augmented parameter,
algoritlun, and data base specifications for an improved cloud retrieval model including
cloud environmental, radiative, and microphysical properties.
3.0 SERCAA Phase I Overview
The focus of the Phase I effort (Gustafson et al., 1994) was development of an
integrated nephanalysis model to retrieve cloud spatial properties using imaging sensor
measurements from three categories of environmental satelUtes as the primary data source.
NOAA Polar Orbiting Environmental Satellites (POES) carry the five-chaimel AVHRR
imaging sensor. DMSP satellites use the two-channel OLS. The constellation of
international geostationary satellites include two United States Geostationary Operational
Environment^ Satellites (GOES) which carry five-channel imagers, the Japanese
Geostationary Meteorological Satellite (GMS) which uses a four-channel instrument, and
the European Space Agency’s Meteorological Satellite (METEOSAT) with a three-charmel
3
sensor. Data from each category of satellite are analyzed using separate cloud detection
algorithms specifically designed to exploit the specific information content of each system.
Satellite sensor data are processed as diey are received from the Satellite Data Handling
System by the CDFS n. For polar-orbiting satellites, the basic processing unit is one orbit
of data, for geostationary satellites the amount of data received from any one satellite varies
according to a data acquisition schedule defined by the operating nation(s).
The SERCAA nephanalysis model consists of four processing levels. Level 1 is
ingest processing that includes unpacking of the telemetry stream, sensor data calibration,
and Earth location. Level 2 is cloud detection performed on a pixel-by-pixel basis using
sensor-specific algorithms to analyzed data transmitted from each satellite. Level 3 is cloud
layering and typing to provide a vertical stratification of the cloudy pixels detected in Level
2. Level 3 output is remapped to the standard AFGWC polar-stereographic grid projection
at a resolution of 16^ mesh. Level 1,2, and 3 are event driven processes triggered by the
receipt of new data from any of the satellite sources. Level 4 is merge processing wherein
the most recent analyzed products from each satellite are combined to produce a single
optimal integrated analysis. When implemented in CDFS H, a fifth level of processing will
be added that uses hourly integrated cloud analyses to initialize a short-term and a long-term
cloud forecast model. Levels 4 and 5 will be clock driven processes run hourly under a
user-defined schedule.
Three cloud detection algorithms are used to analyze data from the different imaging
sensor systems utilized in CDFS n. Each cloud algorithm is designed to exploit the unique
strengths of the respective sensor system. DMSP/OLS data have the highest spatial
resolution and a constant instantaneous field of view (IFOV) across a scan line but are
limited to two broad band channels, one visible and one infrared. NOAA/AVHRR data
have a relatively coarse spatial resolution that degrades toward the edge of scan balanced by
five iiarrow band channels with high spectral resolution. Geostationary sensors vary by
satellite but have at least one visible and one infrared channel in common. GOES carries a
five-channel imager, GMS a four channel, METEOSAT three, and INSAT two.
3.1 DMSP/OLS
The OLS two-channel radiometer has broad-band channels in the visible to near
infrared (0.40-1.10 |xm) and the long wave infrared (10.5-12.6 ^im). Analysis of OLS
data is accomplished through a dual threshold technique that operates on two dimensional
visible-infrared data during daytime and on single-channel IR data at night. Separate cutoff
thresholds are established to discriminate completely cloud free and cloud filled pixels.
Threshold determination, and hence algorithm accuracy, is critically dependent on an ability
to accurately predict the background visible and infrared radiance that the sensor would
measure in the absence of cloud. An independent analysis of surface skin temperature
produced by an analysis model such as SFCTMP (Kopp et al., 1994a) is used as a first
guess to this dear-column infrared brightness temperature prediction. The first guess is
adjusted by using a nearby cotemporal satellite measurement to establish a reference
temperature correction to account for differences between the modeled and satellite
observed teinperatures. Once established, the reference temperature correction is used to
adjust the skin temperature field to predict the dear-column brighmess temperature for all
pixels within a local analysis region.
The reference temperature correction factor calculation requires collocated IR sensor
data and modeled skin temperature fields. Satellite data are segmented into a series of
small, local analysis regions. The size of each region is determined by the relative spatial
scales of the AFGWC skin temperature database and the satellite sensor data. For each
4
cloud analysis grid box a separate surface skin temperature correction is computed by
obtaining Ae difference between the observed satellite brightness temperature for a
reference pixel and the corresponding collocated and time interpolated skin temperature. If
the reference pixel can be established to be cloud free then the reference temperature
difference is used to predict the clear scene brightness temperature for all remaining pixels
in the analysis region. To maximize the likelihood that the reference pixel is cloud free it is
selected as the pixel with the largest IR brightness temperature within the analysis region.
The critical step in estimation of the clear scene brightness temperature is insuring that
the reference pixel is free from cloud contamination. This is done by comparing the
magnitude of reference temperature difference against the range of expected temperature
differences for a given time, location and satellite. Statistics on clear scene temperature
differences are produced as byproducts of the nephanalysis algorithm and are maintained in
a database stratified by location, surface type (land, water, desert), satellite, and time of
day. Cloudy pixels are eliminated by running the nephanalysis algorithm using
conservative cloud detection thresholds. Assumed natural variability of this quantity for a
given satellite pass is calculated from a time weighted average of difference statistics
accumulated over the previous ten days. If the reference temperature difference lies within
the averaged hmits, it is assumed to fall within the naturally occurring variability for cloud
free conditions and the reference value is assumed to be cloud free.
If the reference pixel can be established to be cloud free, then the predicted clear scene
brightness temperature for any pixel within the analysis box is computed by adding the
reference temperature difference to the time interpolated AFGWC surface skin temperature
at the desired pixel location. If the reference pixel is determined to be cloud contaminated
then a default correction based on the time weighted mean of the historical temperature
differences is used as the reference temperature.
Visible channel dear-scene statistics are maintained globally on an 8* mesh grid
using a technique developed for the RTNEPH (Kiess and Cox, 1988). Similar to the IR
procedure described above, visible-channel clear scene statistics are computed as a
byproduct of the nephanalysis. For each 8* mesh grid cell, all clear scene visible-channel
pixel values are accumulated and the mean calculated. The visible background database is
updated continuously as new data are received. To avoid anomalous vdues from
corrupting the database, information is added only if the new value does not vary from the
magnitude of the stored value by more than a preset threshold. If the newly computed
mean reflectance value passes this criteria then the database is updated using a weighted
average of the previous and new values. Thus the clear column visible background
database contains a continuously updated record of the satellite observed clear column
visible count at each grid cell location.
The OLS cloud algorithm is based on a statistical threshold approach designed to
operate using either a single infrared thermal window channel alone or in combination with
a visible channel. Each requires the predicted clear scene bandpass weighted brightness
temperature and, during daytime, the visible background values described above. An
empirically derived dynamic correction factor is used to collectively account for all sources
of error in the predicted dear-scene values without the need to understand and quantify the
individual contributions. Error sources include sensor calibration errors, incomplete
modeling of atmospheric transmission, and poor or unrepresentative estimates of the dear-
scene skin temperature.
Separate cloud detection criteria are used for processing combined visible-infrared
data and infrared data alone. Use of visible data is restricted to conditions where the solar
zenith angle is less than a preset threshold that is defined to discontinue visible-data proces-
5
sing well before the daylight terminator. Objective processing of daytime OLS visible data
beyond this cutoff is problematic because of rapid changes made to visible sensor gain
control performed on-board the satellite. Visible sensor gain is controlled by the value of
the scene solar elevation (i.e., the solar elevation at the viewed point on the Earth). Gain
changes are relatively smooth at high solar elevations, however, as the scan approaches the
terminator the granularity of incremental changes in the solar elevation increases resulting in
rapid changes in sensor gain adjustment (Bunting and d'Entremont, 1982). Under daytime
conditions, when usable visible and infrared data are available, a bispectrd algorithm is
executed. During nighttime conditions a single channel IR test is used.
The OLS single channel IR algorithm is a three step procedure: 1) predict the clear
scene bandpass weighted brightness temperature; 2) compare satellite observed IR
brightness temperatures to the predicted temperature; and 3) based on the magnitude of the
difference between the two temperatures, classify each pixel as either cloud-filled or cloud-
free. The dynamic threshold approach described above is used to predict the brightness
temj^rature that the satellite would measure for a clear scene. Two separate thresholds are
applied to the corrected temperature to account for any remaining uncertainties in the clear
scene estimate. One threshold is used to establish a cutoff value for completely cloud filled
pixels and the second for completely cloud free. Data points that lie between the two cutoff
values are treated as cloud filled but the analysis is assumed to be less reliable.
The two channel OLS algorithm is an extension of the single channel approach into
two spectral dimensions. When usable visible data are available, data from both OLS
sensor channels are analyzed simultaneously using two pairs of cutoff values, one pair for
each channel. Pixels witih lower brightness temperatures than the IR cutoff and/or higher
brightness values than the visible cutoff are classified as cloud. Warm bright regions
require an additional a priori background type classification to remove ambiguity caused by
similarities in the radiative signatures of desert, snow or ice backgrounds with low cloud.
Data points that fall between all four cutoff values are classified as cloud filled but are
considered less reliable.
3.2 NOAA/AVHRR
The AVHRR sensor is a five-channel radiometer with two channels sensitive to
reflected solar, chaimels 1 centered at 0.6 |im and 2 at 0.9 pm; two channels that measure
only emitted IR energy, channels 4 at 10.5 |xm and 5 at 12.0 |im; and one channel that is
pnsitive to both reflected solar and emitted IR, channel 3 at 3.7 pm. The multispectral
information content from these channels was used to develop a cloud algorithm that is less
dependent on accurate characterization of the cloud-free background than the OLS
algorithm. The AVHRR multispectral algorithms are based on the approach of Saunders
and Knebel (1988) wherein a hierarchy of cloud detection and background discrimination
tests, each exploiting a different cloud radiative signature, are run in series on each pixel
within the scene. The algorithms are capable of operating on all five AVHRR channels
simultaneously or on any combination of channels. A cloud analysis is produced through
interpretation of results from all available cloud and background tests.
Separate snow and sun glint tests are used to identify problematic background
conditions. Generally, tests that rely on reflected solar radiation (i.e., AVHRR channels 1,
2, or 3) can misclassify sun glint, snow, or ice as cloud. The main exception are tests that
use channel 3 data over frozen water backgrounds since ice and snow are relatively non-
reflective at these wavelengths, however, sun glint affects all three channels. Some
information on snow cover and sea ice is available from the SNODEP model and from the
Navy, however, experience has demonstrated that these databases have insufficient
6
accuracy to support the nephanalysis. To supplement the available databases snow, ice and
sun glint algorithms were developed using the multispectral sensor data alone. Snow and
ice are detected by comparing the solar component of daytime channel 3 data to channel 1
visible data. Cloud surface tend to be reflective at both wavelengths, however snow and
ice surfaces have a significantly lower reflectivity at the channel-3 mid wave IR (MWIR)
wavelengths than in die visible. Thus for cloud, both channels will exhibit a strong
reflected solar signature while for snow or ice surfaces the signature is much greater in the
visible channel. Similar to snow and ice, sun glint from water surfaces can produce a
cloud-like signature in the visible to MWIR channels. Specular reflection from water
surfaces occurs when the sensor is scanning toward the sun and the satellite zenith angle
matches the solar zenith angle. Although these angles can be calculated with sufficient
precision to identify the specular point, accurate identification of sun glint from water
surfaces remains a problem. This is due to the well-known occurrence of sun glint well
away from the specular point under conditions when the water surface is agitated. Since
information on sea state and wind direction is not direcfly available, a multispectral sun
glint algorithm was developed to test for conditions that resemble cloud in the reflected
solar tests but do not in tests that rely on emitted IR radiation only. However, this
condition alone is not sufficient to detect glint since some liquid water clouds can exhibit
the same characteristics. An additional criteria requires that the magnitude of the channel 3
data approach sensor saturation, a condition that normally occurs only in ghnt conditions
since the derived channel 3 brighmess temperature is very sensitive to even small amounts
of reflected solar.
AVHRR cloud tests generally rely on channel differences or ratios to discriminate
cloud signatures from those of terrestrial back^ounds. Due to the unique radiative charac¬
teristics of low clouds and fog at 3.7 |im relative to long wave IR channels, comparison of
channel 3 and 4 brightness temperatures is a powerful low cloud discrimination technique
during both day and nighttime. At 3.7 |im liquid water clouds radiate as gray bodies (i.e.,
both emit and reflect energy) whereas at 10.5 ^m they are nearly black (d'Entremont et al.,
1987). As a result, liquid water cloud emissivity can be significantly less than 1 resulting
in a lower nighttime equivalent blackbody brightness temperature computed from channel 3
relative to channel 4. However, during the daytime the combined emitted and reflected
solar components cause the computed brighmess temperature to be relatively large
compared to channel 4 where there is only emitted radiation. Additionally, for a partially
filled IFOV (e.g., cold cloud and warm background in the same IFOV) the relative
contribution to the derived brighmess temperature from warm and cold surfaces differs
between me channels 3 and 4 due to me highly non-linear shape of me Planck function
between me two channel wavelengms. At 3.7 |jm me emitted energy from me warm
background is me dominant contribution to me computed brighmess temperamre whereas at
11 pm me relative contributions from me background and cloud are roughly equal. This
signature is useful for detecting broken and transmissive high (cold) cloud at night,
particularly optically thin cirrus.
Omer cloud tests use relative differences between me split visible (1 and 2) and split
long wave IR channels (4 and 5). Relative visible and near IR clear scene albedo measure¬
ments will differ depending on background. Over water bom channel reflectances tend to
be low, but enhanced atmospheric aerosol scattering at me shorter channel 1 wavelengths
generally results in a slightly higher scene reflectance. Over land me signature reverses
except in cases of extreme aerosol loading since vegetated surfaces reflect preferentially in
me longer channel 2 wavelengms. Clouds reflect approximately equally in bom channels
and as such tend to obliterate the background signatures. Thus a split visible cloud test
checks for roughly equal channel 1 and 2 reflectance values. However, me absolute
magnirnde of the measured channel 1 and 2 radiances can vary significantly over a scene
depending on me relative reflectivity of the observed surface, solar geometry, and
7
anisotropic effects making selection of a channel-difference threshold problematic. To
cancel these effects out of the cloud detection algorithm the ratio of the two channels is used
as opposed to a channel difference to discriminate the background signatures from the
cloud. A cloud signature is assumed to be a channel ratio of approximately 1.0. Split IR
data (channels 4 and 5) are used to detect ice cloud and small particles along cloud edges,
independent of time of day. Even in cloud-free conditions, inter-channel brightness
temperature differences are expected to exist due primarily to preferential water vapor
absoiption at channel 5 wavelengths. However, in the presence of cloud the differences
increase beyond the level predicted theoretically. Inoue (1987) recognized that this
departure was caused by unequal extinction from thin ice clouds at 11 and 12 jim, with the
^eater extinction occurring at 12 )nm. Prabhakara et al. (1988) extended this signature to
include both liquid water and ice clouds when the droplet or particle size was smaller than
the channel wavelength. Saunders and Kriebel (1988) developed a test to exploit these
signatures through a theoretically derived look-up table of expected clear scene channel
differences. When the channel differences exceed the theoretically predicted amount cloud
is assumed to be present in the volume.
3.3 Geostationary
The cloud detection algorithm for geostationaty satellite platforms employs a hybrid
approach to identify cloudy pixels within an analysis scene. The approach consists of three
algorithms that ^e applied in series: temporal differencing, dynamic thresholding, and
spectral discriminant tests. The procedure is applicable to any geostationary satellite with at
least one visible and one long wave IR channel.
The first level of processing utilizes a temporal differencing technique to identify new
cloud development or cloud motion over either previously clear background or lower level
cloud. The algorithm exploits the high temporal resolution of geostationary data by testing
for rapid changes in infrared brightness temperature and/or visible reflectance in collocated
pixels taken from sequential satellite images. Cloud detection is accomplished by identi¬
fying pixels that change by an amount greater than the dear-scene conditions are expected
to change. Expected changes in dear-scene background characteristics are computed from
time series of surface skin temperature and visible background brightness databases.
During daytime conditions a bispectral technique is employed that simultaneously evaluates
the change in infrared brightness temprature and visible reflectance. At night only the
change m IR brightness temperature is evaluated. A pixel is flagged as cloud filled if the
change in collocated pixel values over a one hour time period exceeds the expected change
by greater than a predetermined threshold amount. Otherwise, the pixel is flagged as cloud
free.
The temporal difference test is only expected to identify some fraction of the cloud in
a scene, however the confidence level for these clouds is very hi gh The dynamic
thresholding algorithm uses information on the radiative characteristics of cloud identified
through temporal differencing to detect nearby cloud with similar characteristics. The
image is segmented into a regular grid and the minimum and maximum brightness tempera¬
tures (visible reflectance) of the pixels classified as cloudy by the temporal differencing test
within each grid cell are identified. The maximum temperature is used to define an infrared
brightness temperature cloud threshold for the remaining pixels within a cell. The
threshold is set at the maximum temperature minus an offset to eliminate anomalous values.
Separate cloud thresholds are calculated for each grid cell and all pixels within the cell with
a lower brightness temperature or higher visible count are classified as cloudy.
8
Following the temporal differencing and dynamic threshold processing, it is still
possible that clouds with spatial and spectral attributes that have not changed over the
analysis time interval remain undetected. The final step in the geostationary processing
applies a series of spectral discriminant tests to the remaining unclassified pixels. Spectral
discriminant tests use sensor channel information in the same manner as the OLS and
AVHRR algorithms to detect spectral signatures of cloud. Since geostationaiy sensors have
different configurations depending on platform, the suite of tests employed depends on the
set of data channels available. Since aU geostationary imaging sensors have at least one
visible and one long wave IR chaimel, single-channel visible and IR threshold tests can be
applied universally.
The recently launched GOES-8 and 9 satellites, along with GMS-5 are the first in a
series of next-generation geostationary systems that have introduced multispectral imaging
sensors that have potentid for significantly improving geostationary cloud detection. New
channels include two split-window LWIR channels at 10.2 -11.2pm and 11.5 - 12.5pm,
and on GOES, a mid-wavelength infrared (MWIR) channel at 3.8 - 4.0pm. The new satel¬
lites provide all channels simultaneously on a full-time basis. The geostationary algorithm
exploits these new channels, when available, through the addition of multispectral cloud
tests similar to the AVHRR tests described above. The algorithm automatically determines
which tests are applicable based on the individual sensor configurations.
3.4 Cloud Layering and Typing
Vertical stratification of the cloud data into layers is accomplished using a single cloud
layering algorithm for all geostationary and polar orbiter satellite data. The layering
algorithm only operates on LWIR channel data from pixels previously classified as cloudy
by the appropriate Level 2 processing algorithm. A three-step procedure is used: first a
maximum likelihood classification algorithm is applied to separate the LWIR brightness
temperature data into thermally homogeneous layers. Next each pixel is assigned a cloud
type using local spatial information. The final step consists of accumulating die pixel data
into 16* mesh grid cells, clustering of the pixels into local cloud layers, and assigning a
predominant cloud type to each layer.
Cloud layering begins by stratifying the cloudy LWIR brightness temperature data
over a large region (on the order of a few thousand scan lines) using a generic
unsupervised clustering algorithm. The clustering process defines homogeneous
groupings within the spectral domain of the input data. User input controls the minimum
spectral distance between pixel temperatures and the cluster centroid but the maximum
number of layers is left a free parameter. Once clusters are defined a Bayesian maximum
likelihood classifier is used to assign each pixel to a cluster. Clusters are now treated as
vertical slices through the atmosphere based on an inverse relationship between cluster-
mean brightness temperature and height.
Cloud typing occurs by analyzing data one layer at a time starting from the top-most
(coldest) layer. Each pixel and its contiguous neighbors are grouped using a region
growing routine. The number of contiguous pixels in each group is used to assign a cloud
type classification of either stratiform or cumuliform. A number threshold is used to make
Ae assignments such that groups with pixel counts below the threshold are classified
cumuliform and those above as stratiform. The region growing and type assignment
process continues for each group in the layer until all pixels assigned to that layer by the
maximum likelihood classifier have been processed. Subsequent processing moving from
high to lower (cold to warmer) layers is accomplished by combining all the pixels from
higher layers with the pixels of the current layer before the region growing process occurs.
9
Cloud type assignment occurs as before but only for pixels in the current layer, pixels in
higher layers that were previously assigned a cloud type retain that classification even if the
new grouping places them in a different category. The inherent assumption is that
cumuhform clouds have a greater vertical extent than stratiform while stratiform have the
larger horizontal range. By processing one layer at a time from the top down, features such
as cumulus towers embedded in a lower stratiform deck will be correctly classified.
The final processing step occurs after all cloudy pixels have been assigned a cloud
type. In this step cloudy pixel LWIR data are first accumulated into 16* mesh grid cells by
simply identifying, based on latitude and longitude, which grid cell each pixel belongs to.
Umupervised clustering of the LWIR data is then repeated but this time on a floating
window of 3X3 grid cell arrays and with the maximum number of clusters set to four.
Individual pixels are then assigned to each cluster using a maximum likelihood classifier,
but only for the pixels in the center grid cell of the 3X3 array. The moving 3X3 window
of 16* mesh grid cells is used to establish local cloud cluster attributes for the center grid
cell that mitiimize artificial discontinuities across grid cell boundaries. The populated
clusters are interpreted as cloud layers and cluster statistics are used to compute fractional
cloud amount, mean cloud top temperature, and predominant cloud type for each layer.
Note that in this process each grid cell will have a maximum of four cloud layers. In this
way each 16* mesh grid cell is populated, cloud layers defined, and layer statistics
computed. Level 3 output parameters for each grid cell include the valid time of the satellite
data, total cloud amount and, for each layer, fractional cloud amount, mean cloud top
temperature, and a stratiform/cumuliform designation.
3.5 Cloud Analysis Integration
As discussed above, separate cloud analysis algorithms are used to analyze cloud
spatial properties from each sensor system. At the end of Level 3 processing, three
independent gridded cloud analyses have been produced: one derived from DMSP/OLS
data, one from NOAA/AVHRR data, and the third from the constellation of geostationary
sensors. The analyses at each cell in the three 16* mesh hemispheric grids may have a
different valid time based on the time of the input satellite measurements. To create the
integrated analysis, analyzed cloud data from each of the three analysis grids are input into
M analysis integration algorithm to produce a single, optimal analysis based on the relative
timeliness and accuracy of the input data.
The general conceptual approach to the inte^ation problem is one that utilizes both
rule-based concepts as well as principles from statistical objective analysis (Lorenc, 1981;
H^ll and Hoffman, 1993). The unique nature of the satellite-derived cloud parameters
drive the way in which the cloud analysis data are combined. For example, some cloud
parameters such as cloud type and number of layers are discrete quantities and cannot be
"averaged" in any physically meaningful way. Computational concerns also argue for
applying rule-based ideas which allow the preferential selection of one satellite analysis
over dl others thereby avoiding weighted averaging of the data as much as possible.
Additionally, the tradeoff between the relative timeliness and accuracy of the input analyses
is a major consideration in the development of the integration algorithms. For instance,
analyses derived from DMSP or AVHRR visible and infrared imagery are potentially more
accurate than those obtained from lower spatial resolution geostationary data. However,
there may be large time gaps between consecutive analyses derived from polar orbiting da t a
while new geostationary analyses are generally available at least once per hour.
Integration of total cloud amount precedes integration of layer quantities since the
estimates of total cloud fraction are believed to be more reliable than any individual layer
10
fraction (due to small sample sizes and the potential for height assignment errors).
Processing occurs independently for each grid cell. First a series of rules are applied to
determine if any one of the input analyses is superior to the other two. If none of the input
grids have been updated since the time of the previous Worldwide Merged Analysis then
die previous analysis is persisted. If new analyses are available, a check is made to
determine if more than one are timely. If only one timely analysis is available, the merged
total cloud fraction is set to the value of this analysis. If more than one analysis satisfies
timeliness requirements, these analyses are examined to determine if they are all either
completely cloudy or completely cloud free. If so, total cloud fraction is set to either 100 or
0 percent, respectively. If multiple timely analyses exist that are neither all completely clear
nor completely cloudy, then an estimated error of each sensor analysis is used to determine
if the most recent analysis also has the lowest estimated error. Only when all these
conditions fail is an 01 algorithm used to obtain a blended estimate of total cloud fraction
from multiple input analyses. Averaging weights for the OI are based on estimated analysis
errors computed for each available sensor an^ysis.
Analysis errors for each sensor analysis are computed assuming an empirically
defined initial analysis error plus an additional error growth function that is linear with
time. Values for initial analysis error and error growth rate are sensor-dependent and are
main ta ined for both polar orbiting and geostationary platforms. These are tunable
parameters, and as such can be used to adjust the merge processing to correct for
inconsistencies.
Once integration of total cloud fraction is complete, merging of all other layer
parameters is performed. A rules-based procedure similar to diat used for total cloud is
used to determine if the layer parameters from one analysis is superior to the others. In
cases of only one timely analysis, all analyses indicating clear conditions, or when the most
timely analysis is also the most accurate, Aen layer cloud parameters are set in the same
way as total cloud amount described above. However, when all timely analyses indicate
100 percent cloud cover or in cases where the OI technique is used to determine total cloud
amount the integration of layer parameters requires a more complex algorithm. This is
because the vertical distribution of cloudiness and type are determined independently for
each analysis and, given the different sensor characteristics, are likely to be different. In
these cases one sensor analysis is selected as most reliable and is designated as a master or
template profile into which all other timely analyses are blended. For discretely varying
quantities such as number of layers and cloud type the integrated analysis profile will
simply assume the values of the master analysis. For the continuously varying parameters
of layer cloud fraction and cloud top temperature, an OI blending is performed by
combining layers that most closely match in cloud top temperature.
The integration algorithm is designed to use additional information provided by
multispectral observations from the NOAA/AVHRR and multispectral geostationary
sensors. In particular, cloud algorithms developed for these sensors are assumed to better
detect the presence of low cloud and transmissive cirrus. Once the integration of all cloud
parameters has been performed, the analysis grid box is then checked for the possible
addition of these cloud types. This is achieved by imposing separate, less strict timeliness
constraints for these special-case clouds. This effectively extends the length of time that
these observations will affect subsequent merged analyses. Similarly, conventional,
surface-based observations of low cloud are also used to extend the length of time satellite-
based low-cloud analyses are used in the merged analysis.
Final output parameters for each 16* mesh grid cell are now computed. Total cloud
amount is the total number of cloudy pixels in the grid cell divided by the total number of
pixels. Layer fractional cloud amount is computed in the same way as total cloud for the
11
coldest (top) layer. Cloud top altitude is computed by interpolating temperature/height
profile information to the mean cloud top temperature. Cloud type is assigned the most
prominent cloud type in the master analysis. A representative time is assigned as the time
of the most recent sensor-specific analysis used to produce the merged analysis.
4. SERCAA Phase II
Existing cloud analysis technology does not combine the cloud information measured
by diverse sensors. For example, the input satellite data which support the SERCAA
Phase I algorithm suite (Gustafson et al., 1994) utilizes only cloud imager data.
Furthermore, outputs of the SERCAA Phase I algorithm treat only cloud spatial properties
including cloud coverage, cloud layering, and cloud type. The success of overall cloud
depiction and forecast improvements for short and extended ranges is dependent on
characterization of atmospheric state. The SERCAA Phase I algorithms provide the basis
to address requirements for improved specification of cloud initial state for short range
forecasts by improving initialization of cloud macrophysics: cloud cover, layers, and
heights. For these reasons one objective of the Phase n effort has been to identify
approaches to synergistically combine information from both cloud imagers and other
sensors such as sounders aboard current platforms to enhance overall cloud analyses.
These algorithms complete the cloud depiction with enhanced parameters such as
cloud environment, microphysics, and radiative parameters which are required for cloud
forecasting over longer time scales.
4.1 Cloud Detection Using Visible and Near-Infrared Bidirectional
Reflectance Distribution Models
The Earth's surface varies widely on a global scale, from deserts to tropical forests to
permanent snow and ice cover. Vegetated surfaces and snow cover also vary for a given
Earth location as a function of time, both diumally and seasonally. Surface-based
observations of these characteristics are extremely limited both spatially and temporally,
arid are of limited use in monitoring changes in the Earth's surfaces over longer term,
climatic time scales. Satellite sensor observations provide the only truly global
measurements of the Earth's surface. Thus observations and monitoring of the Earth's
surface are best provided by satellite radiance measurements.
The manner in which a particular surface type reflects incident solar energy (as a
function of solar position and the direction from which that surface is viewed) is a
fundamental property of the surface. This prope^ is depicted by means of a bidirectional
reflectance distribution function (BRDF), which is a mathematical representation of a
surface's dependence of reflected energy on the illumination and viewing geometries.
BRDFs describe the manner in which surfaces reflect incident solar radiation as a function
of the satellite view and solar illumination geometries. Knowledge of BRDFs allows for
directional correction (normalization) of satellite radiance data via anisotropic correction
factors to a standard view and illumination geometry. Such a capability helps to reduce
errors in computed derivatives from visible and near-infrared radiance data such as
vegetation indices. BRDFs are of importance in remote sensing for computing location-
dependent albedos of the Earth's surface on a global scale.
Modeled BRDFs allow for prediction of visible and near-infrared satellite radiances
for a particular location on the Earth's surface prior to the actual measurements of the
satellite radiances themselves. Thus, the BRDF-predicted dear-scene anisotropic
12
correction factors are of particular use in the SERCAA cloud detection model for analyzing
visible data from multiple satellite observations of the Earth's surface over varying times
and viewing geometries.
In order to estimate an Earth-surface BRDF, a series of reflected radi^ce
measurements from the surface over a range of viewing and solar illumination geometries
are required. It is important that these observations not be influenced (contaminated) by
clouds or their shadows. Thus corrections are performed to ensure that the cloud-free
AVHRR radiance observations used to compute surface BRDFs contain only surface
effects. In this study, BRDF models are retrieved to estimate the dear-scene radiance for
each 1-km pixel in a series of AVHRR satellite image scenes of New England during a 17-
day period in September 1994. Each scene has differing solar illumination and satellite
viewing geometries. The observed radiance in each AVHRR pixel is compared to its
corresponding BRDF-predicted dear-scene radiance for the appropriate view and
illumination geometries and any value that exceeds the predicted clear-scene value is
flagged as cloud. Cloud detection results are then compared digitally to manually cloud-
cleared AVHRR data taken over New England.
T his study has five main steps: (1) collocation of all AVHRR data to a common 1-km
Cartesian grid projection using orbital prediction models and visual landmarks (e.g., Cape
Cod) observable in the images to map the data; (2) cloud clearing using spectral signature
cloud detection tests similar to those of Saunders and Kriebel (1988) and Gustafson et al.
(1994); (3) correction of the clear-scene 0.63-|j,m visible and 0.86-p.m near-infrared
AVH]^ radiances for atmospheric scattering effects using a coupled surface-atmospheric
radiative transfer model; (4) fitting six semi-empirical BRDF models to the cloud-cleared
and atmospherically corrected clear-scene radiances; and (5) application of the BRDF
models to cloud detection.
The NOAA AVHRR dataset used in this study consists of 17 morning-descender
orbits of NOAA-10 and NOAA-12 between 3 and 20 September 1996. These data are first
mapped to a 400 x 402 Cartesian grid centered over New England, shown in Figure 1.
Grid resolution is 1.1 and 0.75 km in the column (north-south) and row (east-west)
directions, respectively, corresponding to the nominal along- and cross-track AVHRR
HRPT scanner FOV resolutions. Choosing these grid attributes minimizes pixel over- and
undersampling during the remapping process. Using orbital prediction models and manual
rubber-sheeting software, the two-dimensional remapping residuals for over 500,000
pixels were on average only 0.3 km, or approximately 30 percent of pixel size. This high
remapping accuracy minimizes any BRDF retrieval inaccuracy that may be caused by pixel
collocation errors.
The visible and NER data are next atmospherically corrected using surface visibility
observations to define the aerosol profile in the MODTRAN atmospheric radiative transfer
code. In general, atmospheric scattering effects cause the upwelling satellite-measured
reflectance to be less than that of the surface itself. Next are described the cloud-clearing
tests used on the New England data.
4.1.1 Spectral Tests for Cloud-Clearing
Seven tests that exploit the spectral attributes of the AVHRR data were used in this
study to detect cloud and cloud-contaminated pixels. Each test was run individually on a
pixel-by-pixel basis. If any of the tests detected cloud, then that pixel was considered
cloudy and not used in subsequent BRDF clear-scene characterizations. In the following
13
Figure 1 . The New England regional area analyzed in the BRDF study.
paragraphs, Ai denotes the AVHRR Chi 0.63-iim visible albedo; A 2 denotes the Ch2
0.86-pm near-infrared (NIR) albedo; T 3 denotes the 3.7-|im Ch3 middle-wavelenglh
infrared (MWIR) brightness temperature; and T 4 and T 5 denote the 10.7- and 1 l.S-pm
Ch4/Ch5 long-wavelength infrared (LWIR) brightness temperatures, respectively. The
nadir resolution of all five AVHRR channels is 1.1 km.
4.1.1 Spectral Tests for Cloud-Clearing
Seven tests that exploit the spectral attributes of the AVHRR data were used in this
smdy to detect cloud and cloud-contaminated pixels. Each test was run individually on a
pixel-by-pixel basis. If any of the tests detected cloud, then that pixel was considered
cloudy and not used in subsequent BRDF dear-scene characterizations. In the following
paragraphs, Ai denotes the AVHRR Chi 0.63-p.m visible albedo; A 2 denotes the Ch2
0.86-jxm near-infrared (NIR) albedo; T 3 denotes the 3.7-pm Ch3 middle-wavelength
infrared (MWIR) brightness temperature; and T 4 and T 5 denote the 10.7- and 11.8-|im
Ch4/Ch5 long-wavelength infrared (LWIR) brightness temperatures, respectively. The
nadir resolution of all five AVHRR channels is 1.1 km.
4.1.1.1 Bright Clouds Test
The visible brightness, or “obvious bright” cloud detection test is a single-channel
threshold test that is used to discriminate relatively high cloud albedos from a uniformly
low-albedo New England background. If
Ai > Ai,Land, (1)
14
then the pixel is cloudy. Ai,Land was chosen to be 0.05, which is a liberally high estimate
for vegetated surfaces. This’ test is designed to ensure that clouds with obviously high
reflectivities do not go undetected by subsequent spectral tests.
4.1.1.2 Cold Cloud Tests
The “obvious cold” cloud detection test is a single-channel LWIR threshold test that is
used to discriminate very cold clouds from a warm background. If
T4 < TLand, ( 2 )
then the pixel is cloudy. Ttand was chosen to be 273 K, which for September in New
England is liberally low. Like the bright test (Eq. 1), this test is designed to ensure that
clouds with obviously low temperatures do not go undetected by subsequent spectral tests.
4.1.1.3 NIR-Visible Ratios
This ratio test compares the relative magnitudes of visible and NIR albedos using an
interchannel ratio. For clouds, the AVHRR Chi and Ch2 albedos are very close to each
other, while for vegetated land surfaces the Ch2 NIR albedos are much higher then the
corresponding Chi visible albedos. Thus if
Rlo < A 2 / Ai < Rni, (3)
then the pixel is cloudy. Clear-land ratios generally have values greater than one, while
water ratios have values less than one. In this study Rlo = 0-80 and Rhi =1.25 generated
very good cloud detection results. The ratio test defined by Eq. (3) also detects coastlines
and sub-pixel-sized ponds and lakes quite well, since they are mixed fields of view (i.e.,
water and land).
4.1.1.4 Low Clouds and Fog
The low cloud and fog test exploits the different radiative characteristics of water
droplet clouds in the MWIR and LWIR spectral regions. Low clouds effectively reflect
M\\TR solar energy as well as emit MWIR thermal energy, while at LWIR wavelengths the
clouds essentially only emit thermal energy. Since the upwelling MWIR radiance contains
both a solar and terrestrial emission component, the result is that the Ch3 observed
brightness temperature for a daytime low cloud is higher than the corresponding Ch4
brightness temperature. Thus if
T 3 - T 4 > 5 K, (4)
then the pixel is classified as cloud-filled. All liquid water droplet clouds will reflect
enough solar MWIR energy to make T 3 higher than T 4 in the daytime.
4.1.1.5 Thin Cirrus
Split-window LWIR T 4 - T 5 brightness temperature differences exhibit small but
persistent thin cirrus cloud signatures. There are three radiative effects that combine to
15
provide this result: (1) ice particle emissivities are slightly lower at 11.Slim than at 10.7|xm;
(2) atmospheric water vapor attenuation is stronger at 11.8|xm; and (3) there is a slightly
stronger dependence of Planck radiances on temperature at 10.7)i.m, so that for thin cirrus
pixel radiances that have contributions from warm backgrounds and cold clouds, the
10.7p.m T4 is higher for any given combination of cloud and underlying surface
temperatures, all other effects neglected. Thus if
T4 - T5 > Tci, (5)
then the pixel is classified as cloud-filled. For this study the thin-cirrus LWIR brightness
temperature difference Tq was chosen to be between 2 and 2.5 K, with higher differences
for longer atmospheric path lengths and higher water vapor concentrations.
4.1.1.6 Cloud Shadows
Cloud shadows as well as clouds contaminate upwelling surface-reflected energy, so
their effects must be removed from the AVHRR data before using them to retrieve surface
BRDF models. Cloud shadows have distinctively low NIR albedos in comparison to
vegetated land albedos at 0.86p,m. Thus if
A 2 < A2,Land, (6)
the pixel is classified as containing cloud shadow, or a pond or lake. For this study,
A2,Land was chosen to be 0.042.
4.1.1.7 Adjacency Effects
To ensure that the scattering effects of cloud edges into adjacent cloud-free pixels do
not enter into the Earth-surface BRDF model calculations, any pixel that is adjacent to a
cloud edge is flagged as cloud-contaminated and is not used in subsequent BRDF
retrievals. Figure 2 illustrates the cloud mask obtained for the NOAA-12 AVHRR image
collected at 1221 UTC on 12 September 1994. The mask represents the combined results
of each of the cloud tests, and in this case masks out 48 percent of the land pixels in the
image.
4.1.2 BRDF Models
Each of the BRDF models tested under this study is semi-empiiical in nature; such a
model has the general form
I — fiso + fgeo Kgeo + fvol Kvol> (7)
where I is the directionally dependent radiance; the scalars fx are multivariate regression co¬
efficients whose values are influenced by the sub-pixel morphologic and spectral reflec¬
tance attributes of the vegetated surface; and the kernels Kx are deterministic predictors that
carry information pertaining to the basic shape of the BRDF, and are analytic functions of
16
Figure 2. AVHRR sample image valid 1221 UTC 12 September 1994 (left), and its
associated manually digitized cloud mask generated using Eqs. 1-6.
the solar illumination and satellite viewing geometries. Kernels are independent of wave¬
length and the parameters fiso, fgeo. and fyoi- If the surface being viewed reflects isotropi¬
cally, I is constant for all observations at a ^ven solar zenith angle and Eq. (7) would
reduce to I = fjso (the iso subscript denotes isotropic), independent of view and illumination
geometries. The component of diffuse reflectance by the vegetated surface is represented
fgeo> and includes such effects as shadowing by vegetation canopy elements arranged in
particular geometric fashions. Scattering of radiation (e.g., by leaves or pine needles)
within the canopy volume is represented by fyoi- In general, a BRDF that is estimated
using satellite measurements will be a nontrivial linear combination of these isotropic, geo¬
metric, and volume-scattering components. Alternatively stated, the fx parameters in Eq.
(7) will be nonzero. In theory, the relative magnitudes of these parameters designate the
comparative importance of the three reflective effects that define the overall characteristics
of the BRDF of a surface (after Roujean et al., 1992). The kernels Kx are approximations
to deterministic, physical models of radiative transfer in vegetated canopies. Accordingly,
BRDF models of the form given by Eq. (7) are semi-empirical in nature. For a given scene
they describe the association between observed cloud-free radiances and the view and
illumination geometries under which those radiances were measured. Although the
association is (strictly speaking) statistical in nature, the hope is that the deterministic origin
of the kernels argues for the cause of association in a more physically rigorous maimer.
Using the cloud-cleared and atmospherically corrected New England AVHRR LAC
data taken during September 1994, six semi-empirical BRDF models were tested on a
pixel-by-pixel basis for their ability to accurately characterize the directional dependences
observed in the satellite radiance measurements. The models are (1) the Ross Radiative
Transfer (RT) model (Ross and Marshak, 1985) for thick canopies; (2) the Ross RT model
for thin canopies; (3) die Roujean geometric optical (GO) model with no mutual shadowing
(Roujean et 1992); (4) the Li GO model for dense canopies, which is based on the Li-
Strahler GO model (Li and Strahler, 1992) with mutual shadowing, driven by illuminated
crowns; (5) the Li GO model for sparse canopies, which is based on the Li-Strahler GO
17
model (Li and Strahler, 1992), driven by shadows on the ground; and (6) the modified
Walthall model (based on W^thall et al., 1985). Each of Siese BRDF models has the
general form specified by Eq. (7).
4.1.3 BRDF Retrieval Estimates
Cloud-free AVHRR data were then fit to the BRDF models to determine their respec¬
tive abilities to describe the observed directional dependence of the radiances. Recall that
the kernels Kx in Eq. (7) are analytic functions of scene-solar and satellite zenith and azi¬
muth angles. Each cloud-free AVHRR radiance I for a given pixel is first associated with
its set of kernels given that pixel's view and illumination geometries. Next the 17-day full
set of these radiance-kernel triplets is assembled for each pixel in the New England 402 x
400 Cartesian grid. Finally, the fx parameters in Eq. (7) are computed in a least-squares
sense to provide the best available overall fit to the semi-empirical BRDF models. Compar¬
ative goodness-of-fit measures include statistical R2 hypothesis testing that demonstrates
both the strength (via test statistic) and significance of association (via the magnitude of R)
between the observed directional AVHRR radiance measurements lobs the overall
BRDF models that predict lobs as a function of the satellite view and solar illumination
geometries (Eq. 7).
Figure 3 shows the cloud-clearing results for the 17-day New England dataset. In
order to determine the semi-empirical BRDF model that best describes surface anisotropic
reflection attributes, a minimum of eight clear- pixel observations must be available for a
given pixel. Only 35 percent of the pixels in the New England grid have this minimum
number of cloud-free observations. The majority of pixels with enough cloud-free radiance
observations occurred in southern and eastern New England. Clouds inhibited BRDF
retrievals primarily in the Green and White Mountains.
Figure 4 shows Ae results of the best-fit BRDF models in the New England grid
projection. At this point the best-fit multivariate regression coefficients fx, the kernels Kx,
and the model choices “x” have been determined for each pixel. Thus a mathematical
representation of the anisotropic reflectance attributes of each surface in the grid has been
obtained. Tfre representation is written in the form given by Eq. (7) as an analytic function
of the satellite view and solar illumination geometries, and dierefore can be used to predict
the dear-scene reflectance of a pixel’s surface prior to observation of that surface by
satellite. Next, the satellite-observed radiance will be compared to the predicted radiance
and any values that exceed the predicted dear-scene value will be flagged as cloud.
4.1.4 Cloud Detection Results
The retrieved BRDFs were next used to cloud-clear an AVHRR test image taken
during the same 17-day time period. First, Eq. (7) was used to predict the visible (Chi)
and NIR (Ch2) satelUte-observed reflectances for a number of pixels in the test image.
Next, the BRDF prediction was directly compared to the radiance observation of each test
pixel. Any observed value that exceeds the predicted dear-scene value is flagged as cloud.
Cloud detection results are then compared digitally to the manuaUy digitized AVHRR cloud
mask obtained during the cloud-clearing process.
Table 1 lists the results of the BRDF cloud clearing. Let “r” denote the BRDF dear-
scene reflectance (which is linearly proportional to lobs Eq. (7)). When r is compared
directly to the visible satellite observation, a correct determination of cloud presence is
achieved 66 percent of the time. If the threshold r is allowed to vary to within 5 percent of
18
Figure 3. 17-Day composite cloud mask for AVHRR morning overpasses between 3-20
September 1994.
19
AMBRALS KERNELS 0.63-0.86 um
AVHRR 3-20 SEPT 1994
ROSS-THIN LI-SPARSE
^ROSS-THIN LI-DENSE
|| R 0 S S “ T H I C K L I - S P A R S E
ROSS-THICK LI-DENSE
COK & MUHK
II UNDERSAMPLED
Figure 4. Map of Ambrals kernel combinations obtained for the 17-day AVHRR Sept 94
dataset.
its actual value, cloud detection accuracy increases dramatically to 89 percent. Table 1 lists
a nearly perfect cloud detection accuracy when r is allowed to vary to up to 20 percent of its
BRDF-predicted value. Even higher accuracies are listed for AVHRR NIR Ch2, which
usually has brighter backgrounds that are more easily and more often confused with cloud
than those of AVHRR Chi.
Table 1. BRDF cloud detection accuracy (%), measured against the manually digitized
1 -km cloud mask.
Threshold_Visible_Near-IR
r
66
83
1.05r
89
90
l.lOr
92
97
1 .20r
98
99
These test results offer promise for single-channel, threshold-type cloud detection
using reflected solar radiance data. BRDF techniques present alternatives to current state-
of-the-art single-channel visible cloud detection algorithms that have trouble performing in
scenes with Wgh solar zenith angles and surfaces &at exhibit strong anisotropic reflectance
attributes, since the BRDF cloud^no-cloud thresholds explicitly depend on the solar illumi¬
nation and satellite viewing geometries. Current operational cloud detection models such as
the RTNEPH (Hamill et al., 1992) attempt to define dear-scene background reflectances
for polar-orbiting satellite data using a nearly constant value that does not account for
variations in solar zenith angle and view geometries over the course of several days.
The test results also hold promise for significantly increasing the amount of early-
morning and late-aftemoon visible and NIR satellite data that are used operationally by
cloud models such as the RTNEPH. This is because dear-scene reflectances at these times
of day vary significantly for the smallest of changes in sun-satellite positions, so much so
that constant-threshold background reflectivities simply cannot provide accurate cloud
detection results.
4.2 Cirrus Radiative and Spatial Properties
The transmissive nature of cirrus clouds turns out to be its most important (in a
climate sense) and elusive (in a retrieval sense) attribute to specify. If the semi-transparent
nature of cirrus clouds is not accounted for, its altitude is consistently underestimated when
using passive infrared brightness temperature data. Although it is generally agreed that
cirrus has a net warming effect on climate, determination of the magnitude of this effect
depends critically on the accurate specification of cirrus radiative and spatial attributes. For
example, in the case of very thin (sub-visual) cirrus, ice particles have a more significant
interaction effect with incident solar and upwelling thermal radiation than does upper
tropospheric water vapor (Smith et al., 1990).
4.2.1 Observation of Cirrus from Satellite
There are many sources of passive satellite data that can be used to detect and analyze
cirrus attributes. Among the earliest are visible and infrared data of the 1960s from the
TIROS series of polar orbiting satellites, augmented in the early 1970s by geostationary
GOES data. Current GOES-Next Imager and Sounder channels useful for detection of
21
cirrus include 3.9, 6.7, 11.2, 12.7, and CO 2 13.3 - 14.5|ini spectral bands. The 3.9, 6.7,
and 11.2 and 12.7|j,m channels will be discussed in detail shortly; the water vapor
band has proven useful for detection of very thin cirrus over warm backgrounds such as
deserts and tropical oceans, and has use in discriminating water-droplet clouds from ice
clouds in daytime 3.7/6.7-|Xm imagery.
More recent TIROS sensors include the Advanced Very High Resolution Radiometer
(AVHRR), a five-channel passive radiometer with detectors that measure upwelling visible
(0.63p,m), near-infrared (NIR, 0.86|xm), middle wavelength IR (MWIR, 3.7p,m), and split
longwave IR (LWIR, 10.7 and 11.8|xm) energy both day and night. The sounder instru¬
ments collectively known as TOYS (JIROS Operational Vertical Sounder) also collect data in
the wings of the ISjim CO 2 absorption band that are useful for detection of thin cirrus and
specification of their height. There are also very high spatial resolution (500 m) Defense
Meteorological Satellite Program data available in visible/NIR (0.4 - l.l^m) and LWIR (10 -
12 |xm) bands that are helpful in ascertaining the small-scale spatial attributes of cirrus.
4.2.2 Passive Infrared Physics of Cirrus Cloud Signatures
The upwelling spectral thermal radiance lobs measured by a downward poin tin g
radiometer for a field of view completely filled by a non-reflective, thin cirrus cloud is
lobs ~ (1 ■ £) Isfc + £ Icld > (8)
where e is the bulk cirrus emissivity, Igfc is the upwelling radiance emitted by the under¬
lying surface and clear atmosphere, and Lid includes the cirrus blackbody radiance plus the
radiance emitted by the atmosphere above the cloud. In practice, reflection from cirms
clouds at thermal infrared wavelengths is ignored. This is reasonable not only because the
cinus bulk reflectivity is low, but also because there is only minor downwelling thermal
emission incident on the top of cirrus clouds to be reflected back to space. In theory the
specification of Lfc in equation ( 8 ) requires information on many of the physical properties
of the atmosphere and surface that underlies the cirrus cloud: the temperature Tsfc and
atinospheric traiismittance t (for water vapor attenuation) are two of the more important
attributes. As discussed later, Lfc is specified using nearby measurements of cirrus-free
pixels.
The two unknowns of interest in Eq. ( 8 ) are the cirrus bulk emissivity £ and the cirrus
Planck blackbody emission Lid. which is a known function of the cirrus effective
temperature Tdd- In contrast there is only one known in Eq. ( 8 ), namely the radiance
measurement Lbs- In order to specify these two unknowns, additional measurement
information is needed. This is achieved first by considering Eq. ( 8 ) for simultaneous
radiance measurements at two different infrared wavelengths.
For purposes of discussion, assume that the radiance data are being measured
AVHRR MWIR Channel 3 and LWIR Channel 4 sensors. The two cirrus radiance
equations are then
, Iobs,3 = (1 - £ 3 ) Isfc,3 + £3 Icld,3
and
lobs,4 = (1 ■ £ 4 ) Isfc,4 + £4 Icld,45
where the 3 and 4 subscripts denote the 3.7 and 10.7|j.m AVHRR Channels 3 and 4
radiances, respectively. Eqs. (9a) and (9b) are two equations, but with the second equation
a tlmd unknown £4 has been introduced. A third equation is needed that contains no new
variables and that relates at least two of the three unknowns already established. This is
by the
(9a)
(9b)
22
done by assuming a relationship between the cirrus bulk optical depths 63 and 64 as
follows. First, radiative transfer calculations are available that compute bulk cirrus optical
depth as a function of wavelength and hexagonal ice particle size for varying cirrus cloud
thicknesses (Takano and Liou, 1989; Hunt, 1973). Once computed, a simple linear
regression between corresponding pairs of the two optical depths is performed to obtain a
relationship of the form
63 = m 84 +b , ( 10 )
where m and b are the regression slope and intercept, respectively. The slope m is
nonzero; the intercept b, however, turns out to be very close to zero since the two optical
depths are close to each other for optically very thin cirrus. Thus Eq. (10) is generally of
the form
53 = m54. (11)
The value of the regression slope (m) is dependent on the effective ice crystal size which in
turn is dependent on cirrus temperature (after Ou et al, 1993). Values range from m=2.603
for Tcid= 210 K to m=1.088 for Tcid=253 K.
Considering the radiative properties of citrus within a satellite field of view in a bulk
sense, the cirrus cloud optical depth 8 is related to the citrus transmissivity t by the relation
8 = -Int, ( 12 a)
so that Eq. (11) can be rewritten
lnt 3 = mlnU. ( 12 b)
Since it is assumed that the citrus cloud is non-reflective, e +1 = 1 so that
In (1 - £ 3 ) = m In (1 - £ 4 ) . ( 12 c)
Finally, solving for £3 in terms of £4 yields
£3 = l-(l-e4)“. (13)
This is the third of the three-set equation, so that there are now three equations (9a),
(9b), and (13) in three unknowns £ 3 , £ 4 , and Tdd- The three measurements consist of the
linear regression slope m, and the satellite-measured radiances Iobs,3 and Iobs,4- This three-
equation system forms the basis for the SERCAA cirrus retrieval techniques that analyze
multispectral infrared satellite radiances.
In practice, nearby cirras-free pixels are used to obtain accurate estimates of Isfc ,3 and
Isfc,4- Atmospheric emission above the cirrus is neglected. Cirrus reflectivity is negl^ted
as well. As previously mentioned, this does not introduce into the retrieval process a major
source of error at night, but during the daytime incident solar radiances at the shorter 3.7-
pm wavelengths noticeably affect cirrus radiance measurements. The daytime problem is a
challenging one. Although cirrus reflectivities are relatively small, incoming 3.7-pm solar
radiation is strong enough so that measured radiances are solar-contaminated. Subsequent¬
ly, Eq. (9a) is no longer accurate and the retrieval process becomes considerably more
complex because of it. Thus,the use of Eqs. (9a), (9b), and (13) are presently restricted to
nighttime scenes when there is no incident solar energy being reflected back to space by
either the ciirus cloud itself or the underlying background. Research is ongoing to separate
out the solar component in the 3.7pm daytime data (Ou et al., 1993). Another major
constraint is that in assigning a single cloud temperature Tdd to the cirrus, it is assumed that
23
the cloud is a thin sheet that lies precisely at one atmospheric level. Clearly this is not the
case. Lidar backscatter returns from cirrus clouds consistently show their complex
structure on both horizontal and vertical scales. In midlatitudes their altitudes range from 6
to 13 km, and their thicknesses anywhere from 1 to 5 km and, on occasion, even higher.
Thus the assignment of a single cirrus temperature is a gross one which in the case of
multispectral infrared radiance retrievals results in the assigmnent of one effective cirrus
cloud dtitude. However, the severity of this constraint affects only the cirrus altitude
determination. Its effects on the bulk optical properties of the cloud are far less detrimental.
Nonetheless, it is important to remember that current satellite-retrieved cirrus altitudes are
not an accurate assessment of the true levels at which the cirrus lie, but rather are only
correct in a radiatively bulk, energy-average sense. For this reason the cirrus temperature
Tcid Md corresponding altitude ZcW are labeled as effective properties, since they afford
little inference on the detailed vertical structure of the cloud.
In practice Eqs. (9) and (13) can also be used with AVHRR channels 3 and 5. Thus
for every triplet of AVIBIR infrared satellite radiance measurements Iobs,3» Iobs,4» and
Iobs,5 it is possible to retrieve at sensor resolution the 3.7,10.7, and 11 . 8 ^m cIots bulk
emissivities £3, £ 4 , £5 and optical depths 83, 84 , and 85 along with cirrus effective
temperature (altitude) Tdd (Zdd).
4.2.3 Cirrus Retrievals Using 6.7-p,m Water Vapor Imager Data
In hght of the 3.7-)Lim daytime solar contamination issue, ongoing SERCAA studies
are revealing that 6.7-|xm water-vapor channel data can be used in place of those taken at
3.7pm. This finding is significant in that (a) the complexity of retrieving cirrus properties
during daytime (due to mixed solar/thermal energy at 3.7-pm) is eliminated, and (b) there
are 6.7-pm water vapor chaimels on all currently operational meteorological satellites, both
geostationary and polar, save DMSP. Thus wiA the development of a 6.7-pm cirrus
optical depth and effective altitude algorithm comes a capability for cirrus spatial and optical
property analyses that are truly global in nature and that span the full diurnal cycle. The
water-vapor cirms retrieval physics is identical to the MWIR retrieval physics in that Eq.
(9a) is written for 6.7-pm (vice 3.7-pm) satellite radiance observations. Initial retrieval
results over a 10 -case data set compare quite well with ground- and aircraft-based
observations of cirrus clouds.
4.2.4 Cirrus Retrieval Validation Results
The figures that follow compare directly the SERCAA sateUite-based cirms retrievals
with coincident ground-based 35-GHz active radar observations of cirrus base, top, and
optical thickness. The ground-based data were collected by the PL Atmospheric Sciences
Division TPQ -11 radar as a part of the Space-Based Infrared Systems (SBIRS) field
experiment conducted over Hanscom AFB, MA and Madison, WI during mid-September
1995. Three examples are shown of cirms altitude retrievals plotted against radar altitude
observations for 2330 UTC at Hanscom AFB on 16 September 1995. Figure 5 has a
predominantly blue background, is labeled G08_ORH_259_2315 across the top, and has
the label 3.7-iim - 10.7-|im Emissivity in the vertical on the right-hand side. The colors on
the shde are representative of the reflected power received as a function of altitude by the
active 35-GHz radar, with warm colors (reds, oranges, yellows) corresponding to strong
returns (optically thick clouds) and cool colors (green, cyan) corresponding to weaker
returns (optically thin clouds). Dark blue denotes cloud-free radar observations. Yellow
diamonds denote the SERCAA 3.7-10.7-|im satellite-retrieved cirms altitudes that
correspond with their respective coincident radar observations. As can be seen, the
24
Altitude (km)
OGS ORH_259_2315
2315
Time (HHMM)
^ ^altitude
emissivity
Figure 5 . SERCAA cirrus altitude retrievals using MWIR/LWIR-channel data compared
to coincidence TPQ-11 35 GHz radar returns over Hanscom AFB, 2315 UTC
15 Sept 95.
25
3.7/xm — Emlsslvii
retrievals match well with observation. Corresponding cirrus effective emissivities are
plotted in red squares against the right-hand vertical (emissivity) axis. It is seen that the
retrieved emissivities are higher where the radar returns are stronger and vice versa.
Figure 6 is similar to the first, except it has the label 6.7-|xm - 10.7-|im Emissivity
next to the vertical axis on the right-hand side. Yellow diamonds denote the SERCAA 6.7-
10 .7-|im satellite-retrieved cirrus altitudes that correspond with their respective coincident
radar observations. (The 3.7-|im data and the 6.7-|i,m data from these two examples are
both from the GOES-8 Imager.) The water-vapor retrievals also match well with
observation. However, there is a bias of sorts in the water-vapor altitude retrievals that
weights them much more closely to the true tops of the transmissive cirrus, in comparison
to the respective 3.7-|jm MWIR retrievals. This bias is due primarily to the fact that in the
cirrus environment there is more atmospheric water vapor than in the dear-column (i.e.,
cirrus-free) regions. The net result is that the 6.7-|im cirrus brightness temperature
observations are more strongly influenced by this excess water vapor than are the
corresponding 3.7-|J,m MWIR measurements (since 3.7-)i.m is an atmospheric window
region wherein water-vapor absorption effects are minimal). However, there are two
useful attributes to this bias. The first is that for the SBIRS program, cirrus cloud top
altitudes are of more value than the radiative center-of-mass altitudes that the 3.7-|im data
provide. The second is that in differencing the MWIR and water-vapor altitude retrievals,
information on cirrus physical thickness can be inferred that has to now been unobtainable
using passive infrared retrieval techniques.
Figure 7 is a simple plot of AVHRR-retrieved 3.7-|im cirrus effective altitudes with
the corresponding 35-GHz radar observations. The radar-observed cirrus tops and bases
are plotted as brown and cyan lines, respectively. The blue diamonds are the AVHRR
altitude retrievals; they compare well with observation, as did their GOES Imager counter¬
parts. The green squares correspond to blackbody altitude retrievals obtained using 10.7-
)j,m brightness temperatures that have been uncorrected for cirrus transmissive effects (i.e.,
assuming the cirrus is a blackbody cloud). This is the technique typically used by most
cloud analysis models to compute cloud altitude for both opaque and transmissive clouds.
Altitudes retrieved in this manner agree reasonably well with tme cirrus altitudes wherever
cirrus transmissivities are low (optically thick cirrus), as is seen on the left side of the plot.
However, where cirrus is optically thin, the comparison differences are dramatic: on the
right half of the plot are citrus blackbody retrievals that differ by as much as 6 km from the
true, SERCAA-retrieved cirrus altitudes. It is important to note that the green squares
denote state of the art in the current Air Force operational global cloud analysis model, the
RTNEPH (Hatnill et al., 1992), as well as in the Phase-I SERCAA algorithms being
implemented as a part of CDFS-II.
4.2.5 Summary
As a part of SERCAA cloud analysis development efforts, new and irmovative multi-
spectral infrared cirrus analysis techniques are being developed and successfully applied
and validated to real-time cirrus detection and analysis scenarios. Integration of these and
other retrieval techniques into the overall SERCAA cloud analysis allows the strongest and
most reliable attributes of each technique to be combined into one comprehensive cloud
analysis product that is more realistically representative of cirrus spatial and optical proper¬
ties than the current RTNEPH. With increasing amounts of multispectral infrared satellite
data becoming available, the goal is to continue to retrieve from these data high quality
augmented radiative, spatial, and microphysical properties of cirrus clouds in red-time and
for climatological purposes at finer spatial and temporal resolution.
26
Altitude (km)
G0S_0RH_259_2315
2315
Time (HHMM)
A A
altitude
emisslvity
Figure 6. SERCAA cirrus altitude retrievals using Water-Vapor/LWIR-channel data
compared to coincidence TPQ-11 35 GHz radar returns over Hanscom AFB,
2315 UTC 15 Sept 95. Contrast to Figure 5 whose MWIR-channel data were
used in place of Water-Vapor-channel data.
27
6.7^m — ^0.7fLm Emissivity
22.40 22.60 22.80 23.00 23.20 23.40 23.60 23.80
Time (z)
« Ch3,Ch4 effective altitude ■ blackbody altitude-cloud top-cloud bottom
Figure 7. Plots of SERCAA and RTNEPH-derived cirrus altitudes for the cirrus samples
shown in Figures 5 and 6.
24.00
28
4.3 Optical and Microphysical Parameters for Water Droplet Clouds
A comprehensive radiative transfer scattering theory was applied to model the
response of NOAA AVHRR and GOES-Next satellite imager data to realistic variations in
cloud microphysical, optical, and radiative processes. In this maimer the sensitivity of
multispectral MWIR-LWIR measurements to variations in the microphysical properties of
liquid water-droplet clouds was assessed. Results of these theoretical computations can be
applied in the development of retrieval concepts to infer cloud drop size distributions,
MWIR emissivity, and normalized cloud liquid water content (LWC).
Calculations were made for the nighttime MWIR and LWIR chaimels of NOAA
AVHRR. Atmospheric properties including relevant gas absorption profiles and aerosol
scattering extinction profiles (parameterized by surface visibility) are evaluated using the
multiple scattering version (Isaacs et al., 1987) of the Phillips Laboratory LOWTRAN-7
radiative transfer model (Kneizys et al., 1983). LOWTRAN standard atmospheres (tropi¬
cal, mid-latitude summer/winter, subarctic, and U.S. Standard) drive the calculation of gas
absorption as a function of altitude within the atmosphere. Aerosol properties (optical
thickness, single-scattering albedo, phase functions) are also specified within LOWTRAN-
7 by selecting an aerosol model (e.g., rural, maritime). Analogous cloud properties are
based on Mie scattering calculations (Lon^n and Shettle, 1988) using specified cloud-
type-dependent modified gamma droplet size distributions for the cloud particles. Complex
index of refraction data of Hale and Querry (1973) are used. These input data are
processed by an interface routine that generates an input file for a discrete ordinate method
(DOM) multiple scattering radiative transfer model that computes scattering, absorption,
and thermal emission in a vertically inhomogeneous, non-isothermal atmosphere (Stanmes
et al., 1988). This interface merges the gaseous, aerosol, and cloud (if present) optical
properties profiles and produces the required input profiles for use by the radiative transfer
model. The DOM model also accounts for bidirectional reflection and thermal emission
from the Earth's surface. Reflectance functions for ocean, soil, ice, and vegetation are
used to provide the required surface properties. AVHRR upwelling radiances have been
calculated as a function of viewing angle, solar zenith angle, and atmospheric state (e.g.,
clouds, water vapor). These results were then mapped to the input cloud microphysicS
properties in order to determine whether a measurable multispectral MWIR/LWER signature
could be expected to uniquely specify cloud water droplet size distributions.
The DOM adding-doubling radiative transfer model was used to simulate the
dependence of AVHRR radiances on cloud droplet size and cloud LWC. The effects of
upwelling bandpass-weighted MWIR-LWIR radiances were determined using the modified
gamma distribution of Deirmendjian (1969). This distribution characterizes cloud
microphysical attributes in terms of two parameters: cloud LWC and droplet mode radius
rm- This droplet distribution is then used by the Mie scattering code to generate the angular
distribution of monochromatic optical depth for the desired cloud layer attributes. Mie
calculations are subsequently used by the adding-doubling model to generate theoretically-
expected AVHRR upwelling radiances.
For low- and mid-level clouds, the particle density distributions of Stephens (1979)
for stratocumulus (Sc) and Diem (1948) for altostratus (As) were used to specify the cloud
microphysical parameters rm and LWC. Each distribution is plotted in Figure 8.
Radiative transfer model results indicated little if any LWIR sensitivity to cloud liquid
water content (i.e., cloud thickness) for a given cloud type and particle size distribution. In
other words, water-droplet-cloud thermal emission at MWIR and LWIR wavelengths is
independent of LWC starting at values of total liquid water as low as 17 g m-2. Thus for
clouds with integrated LWC of 17 g m-2 or higher, the modeled MWIR-LWIR brightness
29
DROPLET RADIUS r (um)
Figure 8 . Modified gamma droplet size distributions for stratocumulus (dot,dot) and altostratus
(solid). Distributions are from Stephens (1979) and Diem (1948), respectively.
temperatme differences are not strongly dependent on water content. The dependence of
particle size distribution was investigated by parametrically varying the mode radius rm
from 2 to 10 |xm. For a given view angle, there is considerable sensitivity of brightness
temperature to droplet mode radius. Figure 9 shows this sensitivity for a mid-latitude
summer atmosphere and a cloud of 1 -km thickness whose top is at 1 , 2 , 3 , 4 , 5 , and 6 km.
Note that in general, larger drop sizes result in lower absolute-magnitude brighmess
temperature differences.
Figure 10 shows the angular dependence of brightness temperature to cloud droplet
mode radius in a tropical atmosphere, for As clouds. Note in general that the absolute
magnitude of the MWIR-LWIR brightness temperature difference is larger for higher
satellite viewing angles, i.e., for longer atmospheric paths. This is primarily due to the
increased and propo^onally higher atmospheric water vapor attenuation effects at the
LWIR wavelengths in comparison to the MWIR wavelengths.
Results of the radiative transfer computations were next summarh^d in multivariate
linear regression form to obtain a closed-form relation between droplet mode radius rm and
the MWIR-LWIR brighmess temperature difference T 3 -T 4 and satellite view angle 0 as a
function of atmospheric type: tropical, mid-latitude summer, mid-latitude winter, subarctic
summer, and subarctic winter. The best associations were obtained using a regression
equation of the form
rm = ao + ai (T 3 - T 4 )2 cosB + a 2 cos 0 + a 3 (T 3 - T 4 ) + a 4 (T 3 - T 4 ) 2 . (14)
The coefficients aj are listed in Table 2 as a function of atmosphere (e.g., tropical,
mid-latitude winter) and cloud top altitude Zcid> which is obtainable from the water-vapor-
attenuation corrected LWIR brighmess temperature. The regression errors are smaller than
10 percent. It is then possible to use Eq. (14) for a given satellite scene to determine the
drop size distribution and cloud-thickness-normalized LWC given the measured MWIR-
LWIR brightness temperature difference from AVHRR.
30
T3 - T4 : Mid-latitude Summer Atmosphere
2 4 6
Mode Radius ((xm)
Figure 9. Plots of nighttime MWIR/LWIR brightness temperature differences as a
function of cloud top altitude for the mid-latitude summer atmosphere.
T3 - T4 : Tropical Atmosphere - As Clouds
Table 2. Regression coefficients for Eq. (14) that yield droplet mode radius as a
function of the MWIR-LWIR brightness temperature pair and the satellite look
angle, for each model atmosphere.
Mid-Latitude Summer
CldHgt
(km)
aO
ai
a2
a3
34
0-lkm
13.8768654
0.0309042
-4.8211699
1.5821823
0.0426597
l-2km
14.2211437
0.0234482
-4.9754000
1.5184215
0.0401019
2-3km
13.8027706
0.0231690
-4.9726338
1.4157032
0.0346063
3-4km
13.2925663
0.0199931
-4.7398143
1.3609697
0.0342111
4-5km
14.4715405
0.0254101
-5.0803456
1.6378285
0.0490300
5-6km
14.3149967
0.0244705
-5.0080938
1.6739343
0.0532231
Subarctic Summer Atmosphere
Cld Hgt
(km)
ao
ai
a2
a3
a4
0 -lkm
13.7166748
0.0312264
-4.7909217
1.5540760
0.0404421
l-2km
13.9235210
0.0243387
-4.8280582
1.5504421
0.0433875
2-3km
13.5413494
0.0217502
-4.8088727
1.4445802
0.0377909
3-4km
13.2689419
0.0212684
-4.7162375
1.4258769
0.0381388
4-4km
14.5154686
0.0256623
-5.1110134
1.7134761
0.0546654
5-6km
13.7188549
0.0208300
-4.6964335
1.6611785
0.0558874
Tropical Atmosphere
Cld Hgt
(km)
ao
ai
a2
a3
34
0 -lkm
11.9033222
0.0329830
-3.9397399
1.4436375
0.0387454
l-2km
13.6360912
0.0269514
-4.9876447
1.4249316
0.0330761
2-3km
14.2441406
0.0236156
-5.0845866
1.4694586
0.0373121
3-4km
13.0778732
0.0172403
-4.6029153
1.3105589
0.0330673
4-5km
14.2904015
0.0229082
-5.0103798
1.5672971
0.0453061
5-6km
14.1846523
0.0251303
-4.9360580
1.6399782
0.0507642
33
4.4 Cloud Environment
Current operational, automated cloud analysis approaches underutilize available
meteorological satellite remote sensing data resources. For example, onboard the two U.S.
polar orbiting satellite platforms, DMSP and NOAA, there are two different multispectral
imagem, four different microwave sounders, and a multi-channel infrared sounder. Table
3 provides a compilation of the multispectral, multisensor data sources currently available
from the DMSP, NOAA, and geostation^ platforms. Current approaches also lack the
potential to fully exploit the next generation of satellite sensors. The plaimed National
Polar-orbiting Oj^rational Environmental Satellite System (NPOESS) and Earth Orbiting
System (EOS) will add an even greater number of sensor suites. The amount and accuracy
of cloud information potentially obtainable by passive remote sensing is dependent upon
the number of quasi-independent radiance measurements used in the analysis. The spectral
diversity of satellite sensors is great, providing the potential to infer significant high quality
cloud information.
This section provides a discussion of algorithmic approaches to utilize combined data
sets of DMSP cloud imager and microwave imager (SSNhl) and microwave sounder
(SSM/T-1 and SSM/T-2) data to support the inference of cloud environment parameters
including vertical temperature and moisture profiles and cloud liquid water. We also
address approaches to deal with some current deficiencies in nephanalysis which can be
treated by combining cloud imager and other sensor data sets. These include the "black"
stratus problem over land, inference of land surface temperatures in the presence of cloud,
and the identification of precipitation (including thunderstorms). All of die above
approaches might be considered for eventual operational implementation as extensions to
the existing SERCAA Phase 1 algorithm suite. Here we focus on DMSP based
approaches. Much of this work is based on applications of the unified retrieval approach
for DMSP developed under separate funding.
The cloud spatial properties (coverage, layering, and type) resulting from the objec¬
tive analysis of cloud imager data by the SERCAA Phase I algorithm suite result from
threshold, contrast, multispectral, and temporal tests. For example, the determination of
cloud presence in an individual pixel might be determined by whether satellite-measured
radiance at visible wavelengths is greater than a predetermined background value or
whether the radiance in neighboring channels is similar indicating a reflective (white) cloud.
The analysis does not consider meteorological factors in determining the presence of cloud
although it is understood that cloud environment, i.e. temperature, moisture, and attendant
circulations will determine the thermodynamic characteristics for cloud presence. This
problem, i.e., that of diagnosing cloud presence and properties from the properties of the
generating cloud environment, is a vety active area of research for general circulation
modelers including those specifically interested in cloud forecasting (see Zivkovic and
Louis, 1992; Nehrkom and Zivkovic, 1996). The availability of simultaneous measures of
cloud spatial properties afforded by the SERCAA Phase I algorithms and cloud environ¬
ment data such as the vertical profiles of temperature and moisture alone would be
extremely useful to this community.
There are other reasons for supplementing the information available from cloud
imaging sensors with that from collocated sounding sensors. The specification of
simultaneous vertical temperature and moisture profiles provides important information in
the determination of other desired cloud parameters such as cloud top height and more
complete information of cloud layering and type. Additionally, cloud liquid water content
available from microwave sensors can also be used to help in cloud typing.
34
Table 3. Sensor data suite
Satellite
Sensor
Channel Wavelength/Frequency
DMSP
OLS
0.4-1.1 and 10.5-12.6 |i,m
SSM/I
19.35 V&H, 22.235 V, 37.00 V&H, 85.5 V&H GHz
SSM/T
50.5, 53.2, 54.35, 57.9, 58.4, 58.825, 59.4 GHz
(O 2 absorption band)
SSM/T2
91.5, 150, 183*1, 183*3, and 183*7
(H 2 O absorption band)
NOAA
AVHRR
0.58-0.68, 0.725-1.10, 1.58-1.641, 3 . 55 . 3 . 93 ,
10.3-11.3, 11.5-12.5 pm
HIRS
0.69, 3.76, 4.00, 4.13, 4.40, 4.46, 4.52, 4.57, 6.72, 7.33, 9.71,
11.11, 12.55, 13.35, 13.64, 13.97, 14.22, 14.49, 14.71, 14.95 pm
(CO 2 , N 2 O, O 3 , and H 2 O absorption bands)
MSU
50.30, 53.74, 54.96, and 57.95 GHz
(O 2 absoiption band)
GOES
imager
0.55-0.75, 3.8-4.0, 6.5-7.0, 10.2-11.2, 11.5-12.5 pm
GMS
imager
0.5-0.75, 10.5-12.5 pm
METEOSAT
imager
0.5-0.9, 5.7-7. 1 , 10.5-12.6 pm
1 1.6 |xm channel will first fly on NOAA K -1997
Temperature profile data can be used to determine cloud top heights (or equivalently,
pressures). When cloud is determined to be present in a pixel, the cloud imager equivalent
black body brightness temperatures (EBBT) for that cloudy pixel is available. In order to
transform this EBBT into a cloud top height, two procedures are required. First,
estimation of water vapor continuum effects on the infrared window measurements can be
made based on the relevant column integrated water vapor amount obtained from the
moisture profile (see below). Second, Ae temperature profile can be used to assign a cloud
top height (or pressure) to the corrected EBBT.
The SERCAA Phase I cloud layering approach clusters cloud imager equivalent black
body brightness temperatures (EBBT) into defined layers. Layers are characterized by then-
presence and the resultant cloud top EBBT. In analog to the pixel-by-pixel discussion of
cloud top height in the preceding paragraph, temperature profile information can be used to
make layer height (pressure) assignments as required.
Vertical moisture profiles provide both the vertical distribution of water vapor and the
total integrated water vapor. In conjunction with the retrieved temperature profiles, the
vertical moisture profiles provide simultaneous relative humidity information. Integrated
water vapor from the cloud top to space is necessary to estimate water-vapor-continuum
35
effects on infrared window measurements. The impact of water vapor absorption is to
decrease the EBBT seen by the sensor. This effectively raises the cloud top height deter¬
mined if water vapor absorption were not considered. In tropical atmospheres where the
burden of water vapor is large, this effect may be important in the determination of cloud
top heights, especially for low clouds (since most of the water vapor is near the surface).
Microwave imager (SSM/I) and millimeter wave moisture sounder (SSM/T-2) data
can be used to infer integrated cloud liquid water content. Integrated cloud liquid water
content available from microwave sensors can also be used to help in cloud typing. In
general integrated cloud liquid water content is largest for bright, low clouds and smallest
for darker, higher clouds.
In our previous work, we have described a unified retrieval (UR) algorithm
applicable to the Defense Meteorological Satellite Program SSM/T-1, SSMyT-2, SSM/I
microwave sensors which employs physical retrieval concepts and utilizes the frill
multispectral information content of the available data (Isaacs, 1987; Moncet and Isaacs,
1992,1994; Moncet et al., 1996). The UR algorithm is a general non-linear physical
retrieval algoritlm for the simultaneous retrieval of temperature profiles, water vapor
profiles, cloud liquid water content, surface temperature and surface emissivity from the
DMSP multi-sensor platforms. The UR concept offers the distinct advantage of
simultaneously characterizing the atmospheric and surface state vector from the available
multifrequency sensor observations by combining the traditionally separate functions of
atmospheric sounding and surface property imaging. Through the physical retrieval
process, it intrinsically recognizes the dependence of atmospheric properties on the
observed background and surface properties on atmospheric transmission effects.
The potentid usefulness of cloud environment information available from DMSP
microwave sounding sensors to enhance IR based automated cloud detection is illustrated
by examining the problem of detecting so-called "black stratus" clouds (Moncet et al.,
1996). Black stratus, which is problematic to most IR cloud detection schemes including
the SERCAA Phase 1 algorithms, refers to low level clouds that form within an inversion
layer at night as the surface cools. Common at high latitudes, particularly during winter,
these clouds are difficult to detect using satelhte data because their radiative signature is
greater than of the underlying surface. IR threshold satellite cloud detection schemes
generally require surface-to-cloud contrast in either an infrared-channel brightness
temperature or visible-channel brightness in order to detect a cloud. However, since the
black stratus are warmer than the surface any infrared signature that can be detected by the
satellite will be in the wrong direction (i.e., clouds are assumed to be colder than the
surface). Similarly, since black stratus tend to occur under limited or no sunlight
con(htions (e.g., high latitude winter) or over reflective snow or ice backgrounds, reliance
on visible cloud signatures is severely restricted.
Working collaboratively with the SERCAA Phase n team, Moncet et al. (1994)
formulated an algorithm to treat “black stratus” detection. The algorithm illustrated in
Figure 11 uses both IR imager brightness temperatures applied to a simple threshold
algorithm similar to the one currently used in SERCAA Phase I for the processing of the
36
OLS
IR Brightness
Temperature
Microwave Derived
Temperature Profile
and
Surface Skin Temperature (Tskin)
NO INVERSION
INVERSION
Tskln - A T^
IR<Tskin’ ^^0
iR >Tskin+AT q
lR<Tskin- AT-j
<IR <
■ikin+ AT2
IR >Tskin+A T 2
Cloud
Clear
Cold Cloud
Clear
Warm Cloud
(Type 4)
(Typel)
(Types)
(Type 2)
(TypeO)
Figure 11. Schematic of a prototype infrared cloud detection scheme that integrates
microwave derived information.
OLS infrared data. Additionally, however, it uses microwave sensor retrieved temperature
profiles and surface skin temperatures to identify and classify inversion conditions. Like
the SERCAA Phase 1 OLS algorithm, a cloud is detected if the IR measured brightness
temperature is colder than the estimated surface skin temperature by some pre-set margin
under so-called “normal” conditions. However, when an inversion is present, the
algorithm also detects a low cloud if the IR brightness temperature is warmer than the
surface temperature (i.e. the black stratus condition).
Moncet et al. (1994) illustrate an example applying this algorithm over snow/ice
fields based on data from the DMSP SSM/T-2 Calibration/validation effort (Falcone et al.,
1992) using DMSP 5D-2 data. While additional validation of the algorithm is required, the
approach integrates well into the overall SERCAA algorithm suite framework.
The existing imager based cloud analysis approaches for the infi*ared LWIR channels
are based on discriminating cloud (usually cold; except in the case of black stratus
described above) from the warmer, cloud-free background. The determination of cloud
free background temperatures is supported by the cloud imager LWIR data itself (using
adjacent cloud free pixels). This becomes problematic in overcast situations. One
approach is to rely on model predicted surface temperatures (as in the use of the SCFTMP
model by AFGWC). Practice indicates that these may be inaccurate resulting in over or
underanalysis of cloud. Kopp et al. (1994b) discussed the use of SSM/I derived surface
temperatures in improving results of the RTT^PH model at AFGWC. In their application,
SSM/T surface temperatures are used along with those of SFCTMP based on a weighting
determined within SCFTMP.
As a result of the UR approach described above, surface temperatures may be
retrieved over ocean and land in fully overcast situations using the combined DMSP micro-
wave sensor suite. Over the ocean standard deviations of between 2 and 3 K are obtained
between the retrieved sea surface temperatures (SSTs) and Navy SST fields. Over land,
there is little sensitivity of retrieved surface skin temperatures to cloud. This provides the
basis for retrieval of surface skin temperature in completely overcast conditions. Results
indicate that it would be worth investigating whether coupling OLS information with the
microwave skin temperature retrievals would be helpful in enhancing IR based low cloud
37
detection capabilities over land (Moncet et al., 1996). An advantage of the UR approach
over that of using SSM/I derived temperatures alone is the retrieval of surface emissivity
which decouples the surface temperature results from those of surface type.
Convective storms represent a significant weather aviation hazard and are given
special treatment in operational cloud typing. At AFGWC, the approach adopted to identify
thunderstorms is the convective-stratiform technique (CST) of Adler and Negri (1988).
The CST technique is based on the use of satellite imageiy alone. In particular it uses the
spatial properties of LWIR EBBT's to differentiate between convective and stratiform
situations. A description of the approach applied can be found in Kopp et al. (1996). One
immediate suggestion based on our experience with the DMSP microwave sounder and
imager data is the use of these data to help characterize the presence and vertical extent and
hence intensity of thunderstorms and convective precipitation.
A number of studies have indicated that multifrequency microwave observations of
convective systems can be used to identify both liquid and glaciated hydrometeors (see
Wilheit et al., 1982; and Hakkarinen and Adler, 1988; Adler et al., 1990; Kummerow et
al., 1991, Petty, 1994; Petty, 1995; Petty and Miller, 1995). These studies have generally
tended to focus on window channel observations (i.e. 91 GHz and channels away from the
center of the 183.31 GHz water vapor line). Microwave brighmess temperatures (Tbs)
tend to be dommated by emission from rain below the freezing level, while higher
frequency millimeter brightness temperatures are affected by scattering from frozen
hydrometeors. These signatures thus provide gross information on the vertical structure of
storms (Spencer et al., 1989).
The key to using window channel data is associating these signatures with consistent
vertical temperature stracture information and exploiting sounding channel sensitivity to the
s^e phenomenon. Notably, precipitation estimates from SSM/I are obtained using
microwave window channels and thus assume a model of the vertical distribution and
phase of precipitation. Recent aircraft observations of precipitating convective systems at
microwave and millimeter wave frequencies indicate the potential for inferring the vertical
distribution of precipitation proj^rties (Wilheit et al., 1982; Hakkarinen and Adler, 1988).
The channel set of the DMSP microwave sensor suite (Falcone and Isaacs, 1987) provides
information content from 19.35 to 183 GHz which can be exploited to provide such
inforaiation. Since the multifrequency response of liquid precipitation differs from that of
glaciated precipitation, an indication of the freezing level is also possible. Examining the
effect of clouds and precipitation on SSM/T-2 channels, non-precipitating clouds over
water generally displayed warmer 91 GHz Tbs compared to clear FOVs (Figure 12). There
was no difference between Tb signatures in the SSM/T-2 channels for cloud-filled FOVs
over land. Griffin et al. (1994) showed a distinct SSM/T-2 Tb signature for precipitation
(data set used was that from a developing typhoon in the western Pacific Ocean) (Figure
13). The presence of light rain over water caused the warmest Tb to shift to 150 GHz. As
the rain rate and scattering in the FOV increased, the 183il GHz Tb became the warmest
of the thr^ atmospheric channels. This work and that of Pickle et al. (1996) suggest that
DMSP microwave imager and sounder data would be useful in delineating precipitating
cloud types, particularly thunderstorms.
38
Tb [K] § Tb [K]
SSM/T.2 Channels [GHz]
12. Typical Tb signatures for clear over ocean (solid), partly cloudy over ocean
(dash-dot), cloudy over ocean (dash), clear over land (solid), partly cloudy
over land (dash-dot), and cloudy over land (dash) for data collected during
May and July, 1992 for the east and west coast of the United States.
Figure 13. Typical Tb signatures for clear (dash), light precipitation (solid) and heavy
precipitation (dot) observed in Typhoon Oliver, February 4,1993 in the
western Pacific Ocean.
39
I*
4.5 Implementation Changes for Cloud Typing and Layering
The SERCAAI cloud typing algorithm provided a broad classification of pixels into
one of ^o types of clouds, cumuliform or stratiform, based on their spectral and spatial
properties (Gustafson et al., 1994). Cloud-filled longwave IR pixels were segregated into
cloud classes using an unsupervised classification approach. The classes were then
processed in a top-down fashion by applying a multi-pass, region growing segmentation
routine in concert with variance-based texture information. The segmentation routine
resulted in the formation of regions, or groups of connected pixels, whose size was the
basis of the cloud type determination. The underlying assumption gove rnin g the process is
that cumuliform clouds are represented by small, connected and compact regions of pixels
while stratiform clouds extend over larger regions of connected pixels wi thin a given
temperature class. Therefore, a size threshold was applied to each new region and regions
were then sorted into cumuliform or stratiform types.
While this implementation provided an accurate reflection as to the nature of cloud
formations in a scene, the calculation of the texture derivative and the region-growing
feature of the segmentation routine proved to be computationally intensive. An improved
technique has been developed and implemented in which composite temperature classes are
segmented using a fast, single-pass algorithm based solely on pixel contiguity. Pixels in
each composite mask are analyzed in turn with respect to their imm ediafe. neighbors and are
assigned either a new region identification (ED) number or that belonging to their top or left
neighbor. Ultimately, aU pixels that ^e connected in one of the four adjoining spatial
^ections (left, right, top, bottom) will belong to the same region. As in the ori gin al
implementation, the number of pixels in a region determines Ae cloud type label it will
receive. Regions less than a given threshold size are assigned a cumuliform label; aft others
receive a stratiform label. By omitting both the multi-pass segmentation technique and the
derivation of a texture layer, the new routine successfully operates several orders of
magnitude faster than its predecessor despite the continued iterative, top-down processing.
In addition to the improved speed and performance of the SERCAA Phase I cloud
typing algorithm, a new techmque for discriminating among cloud layers was also
developed and tested. The SERCAA Phase-1 cloud layering algorithm was achieved by
performing a second, local-scale unsuj^rvised classification (clustering) layering process
that operates on the cloudy pixels within a 3x3 floating window of l/16th-mesh grid cells
centered on the grid cell currently being analyzed (Gustafson et al., 1994). This process is
conceptually simil^ to the large-scale layering procedure just described; unsupervised
clustering and maximum likelihood classification are performed on the cloudy pixels that
lie within the floating window. Under Phase 1, these pixel values were linearly
proportional to the cloudy pkel's LWIR brightness temperature. This technique worked
well for opaque clouds whose brightness temperatures are very close to the true physical
teinperature of the cloud. However, for thin cirrus the LWIR brightness temperature,
being a mix of cold cloud and warm background, is not representative of the physical
temperature of the cloud. Thus cirrus that has varying optical thicknesses in the horizontal
but whose tops are essentially at the same vertical level was being separated into too many
layers because of the large variation in cirrus brightness temperatures. In this case the
relatively large spread of LWIR brightness temperatures misleads the clustering algorithm
into separating the cirrus into too many layers when the spread in cloud top altitudes is not
nearly as large.
To alleviate this problem, tests were conducted using the SERCAA Phase 2 retrieved
cloud top altitudes instead of the raw brightness temperatures as input to the layer
unsupervised clustering and maximum likelihood classification (MLC) routines. Recall that
SERCAA Phase 2 cloud top altitudes include water-vapor-attenuation corrected altitudes for
40
opaque clouds, and transmissive cirrus corrections for thin ice-particle clouds. These
altitudes were passed to the clustering and MLC routines in tandem with the original LWIR
brightness temperatures, and the results were subjectively compared.
Figure 14 contains an LWIR NOAA-14 image over New England in November
1994. Note that the image contains a large cirrus shield of varying brightness
temperatures. The corresponding LWIR brightness-temperature layer analysis is shown in
Figure 15. In Figure 15, gray 16th-mesh boxes have one cloud layer in them while white
16th-mesh boxes have two. Note that in the left side of the image the SERCAA Phase 1
algorithm has found two layers where there is a large variation in cirrus LWIR brightness
temperatures. However, the Phase 2 height analysis indicates that the cirrus tops are all
within 1-2 km of each other over this region. When the Phase-2 altitudes were fed into the
clustering procedure, far fewer 16th-mesh boxes were analyzed as containing two layers,
as is shown in Figure 16. Note that in the heart of the thin cirrus shield, the Phase 2
altitude-generated layer algorithm assigns only one layer to the cirrus. In Figure 15, the
corresponding brightness-temperature layer analysis seems overdone, again being confused
by the large variations in brightness temperature that are caused by variation in cirrus
optical properties across the scene. Overall the results that were obtained for two dozen
cases using the altitude-generated layers were noticeably improved in areas of thin cirrus.
However, it is recommended that the technique be tested over many more samples and that
some sort of objective measure of goodness be established to judge whether layers should
be generated from altitudes or brightness temperatures.
4.6 Analysis Consistency
The existing RTNEPH cloud analysis process (Kiess and Cox, 1988; Hamill et al,
1992) was designed to produce quarter orbit based cloud analysis products based on
individual polar orbiting passes from two-charmel (VIS and IR) data, primarily supplied
from the DMSP/OLS. To address known deficiencies in the RTNEPH's polar platform a
primary objective of the SERCAA effort was to incorporate both polar multispectral data
from the NOAA TIROS Advanced Very High Resolution Radiometer (AVHRR) and
hourly geostationary data from GOES, Meteosat, and GMS (Neu et al., 1994). The
availability of the multispectral data significantly improves the ability to detect low stratus
and thin cirrus, while geostationaty data complements that of the polar platforms by
providing higher temporal sampling of diurnal cloud development and dissipation,
particularly in the tropics. A key element of SERCAA is the integration algorithm to
optimally combine the most recently available cloud product analyses from all platforms
into a single optimal analysis.
As discussed in Section 3 above, the design philosophy for the SERCAA analysis
inte^ation algorithm was to provide an optimal 16* mesh merged cloud analysis valid at a
specific instant in time based on the analyzed cloud products derived from the most recent
observations available from the three independent classes of environmental satellites. The
key attribute of the integration scheme is that the RMS error at each grid cell will be
minimized independent of the cells around it. Thus, the algorithm design does not include
logic to enforce meteorological consistency between temporally or spatially adjacent grid
cells. This was neither the requirement nor the design philosophy during SERCAA.
However, evaluation of SERCAA-derived integrated cloud products using real satellite data
has revealed that meteorological consistency between hourly-generated products can be
degra,ded by numerous factors including: changes in the mix and relative age of input
satellite data, variations in solar illumination and satellite viewing geometry, and the t 5 q)e of
cloud present.
41
Figure 14. NOAA-14 10.7-ixm brightness temperature image over New England for
November 1994. High tones denote low brightness temperatures.
42
Figure 15. SERCAA Phase-1 cloud layer analysis for the image in Figure 14. This
analysis was generated using LWIR brightness temperatures as input to the
clustering and unsupervised classification routines. Each square represents a
16th-mesh box. White boxes contain two cloud layers, and gray boxes contain
only one.
43
Figure 16. SERCAA Phase-2 cloud layer analysis for the image in Figure 14. This
andysis was generated using SERCAA Phase-2 pixel-resolution corrected
altitudes as input to the clustering and unsupervised classification routines.
Each square represents a 16th-mesh box. White boxes contain two cloud
layers, and gray boxes contain only one.
44
The SERCAA analysis and integration algorithms were evaluated using a series of
ten-day satellite data sets obtained from NOAA 11 and 12, DMSP FIO and FI 1, GOES-7,
GMS-4, and METEOSAT 3,4, and 5 for the months of March and July from 1993 and
1994. The algorithms were applied over three regions of interest including eastern Asia
and tropical Indonesia, central and northern South America, and north-central Africa and
the Mediterranean that were selected for geographic and climatic diversity (Figure 17).
Results from this real-data study were analyzed to establish baseline characteristics of both
the analysis and integration algorithms.
Generally, results of the source-specific analysis algorithms showed good agreement
with manual interpretation of satellite imagery generated from the input sensor data
(Heideman et al., 1994; 1995). Examination of the final integrated results showed the
complementary nature and relative strengths of the different data sources. However, under
some conditions, inconsistencies were found to exist between contemporaneous polar- and
geostationary-based cloud analyses. These are manifested as either spatially and/or
temporally inconsistent cloud fields in the integrated products. Gustedson et al. (1995)
investigated the source of these inconsistencies and concluded that they were due, at least in
part, to differences in the inherent information content provided by each of the satellite
systems used as data sources. Table 4 summarizes some of the relevant satellite sensor
characteristics. The unique information content of each source with respect to cloud
detection capability is a well understood (see Isaacs and Barnes, 1988) function of number
of channels, channel band passes, sensor resolution, and solar illumination geometry.
Figure 17. SERCAA evaluation regions of interest.
45
Table 4. Summary of operational enviromnental satellite imaging sensor characteristics
Satellite
Sensor
Channel
Number
Channel
()im)
IFOV
Resolution
(km)^
Bits
per
Pixel
Minimum
Repeat
Cycle (h)^
Maximum
Repeat
Cycle (h)^
DMSP
OLS
1
0.40-1.10
2.7-4.8
6
3.5
8.5
2
10.5-12.6
2.7-4.8
8
NOAA
AVHRR-2
1
0.58-0.68
4.0-18.0
10
5.5
6.5
2
0.72-1.10
4.0-18.0
10
3
3.71-4.18
4.0-18.0
10
4
10.3-11.3
4.0-18.0
10
5
11.5-12.5
4.0-18.0
10
GOES I/M
imager
1
0.55-0.75
1.0-3.5
10
0.25
3.0
2
3.80-4.00
4.0-14.0
10
3
6.50-7.00
8.0-28.0
10
4
10.2-11.2
4.0-14.0
10
5
11.5-12.5
4.0-14.0
10
METEOSAT-5
VISSR
1
0.55-0.75
2.5-9.0
8
0.5
0.5
2
5.7-7.1
5.0-17.0
8
3
10.5-12.6
5.0-17.0
8
GMS-5
VISSR
1
0.5-0.75
1.25-4.5
6
1.0
1.0
2
6.50-7.00
5.0-17.0
8
3
10.5-11.5
5.0-17.0
8
4
11.5-12.5
5.0-17.0
8
Ipield of View on the ground varies from satellite subpoint (highest resolution) out to edge of scan
(lowest resolution).
^Assumes a two satellite constellation for each polar system; DMSP in early and mid morning orbits and
NOAA in early morning and afternoon orbits. Geostationary minimum repeat cycles are for selected
subregions, maximum cycles are for full disk.
Also given the current constellation of gaps of polar satellites, up to four hour gaps can
occur between the crossing times of consecutive orbits. While it is these differences that
the SERCAA analysis integration algorithm exploits in producing a combined analysis, the
different sensor characteristics can lead to different analyses of the same scene from
multiple platforms.
Figure 18 illustrates the essential nature of the temporal consistency issue using
SERCAA analyses produced under contract with the DNA. The analyses shown cover a
large region over the western Pacific Ocean and eastern Asia and are valid for the period
1600 to 2200 UTC on 26 March 1993. The top row of panels show a 4-h sequence of
integrated SERCAA analyses (gray shade is proportional to total cloud fraction with white
representing completely overcast) beginning 1700 and ending at 2200 UTC. The bottom
row contains a simple persistence forecast based on the SERCAA analysis valid at 1600
UTC. The 1 and 2-h persistence forecasts correspond fairly well with the verifying
SERCAA analyses because the available input satellite analyses during these time, period
were limited to GMS only. However at 1900 and 2000 UTC the SERCAA analysis
46
abruptly changes in appearance, and this is directly attributable to the addition of low cloud
detected by the available AVHRR analyses valid at 1820 and 1958 UTC, respectively.
While it is true that the later analyses in the above example provide a more complete
picture of the actual clouds, the sudden appearance of analyzed low cloud fields over areas
covered by the AVHRR sensor are not consistent with the analyses from other sensors.
Note that this is strictly a consequence of the increase sensitivity to those types of clouds in
the multispectral AVHRR data relative to the other data sources. However, since the
overall time series is not meteorologically consistent, even though the analysis is
compatible with the SERCAA goal of providing the best analysis based on the most
recently available data, it may negatively impact user assessment of the accuracy and
usefulness of the cloud analyses. This problem, however, is not unique to SERCAA:
even in the current RTNEPH with its much more restricted input data, cloud fields are
updated abruptly over areas covered by more recent satellite passes, and the cloud analyses
also exhibit spatial artifacts of the data coverage. A potentially more serious concern is the
effect of temporal inconsistencies on the initialization and verification of cloud forecast
models.
In adopting the SERCAA algorithm suite as the basis of the CDFSII-NEPH, an often
stated assumption was that the most accurate cloud depiction would support the best cloud
forecast. In the current suite of forecast models in use at GWC, which are all advective in
nature, there are no technical difficulties in using cloud analyses with temporal or spatial
discontinuities in the cloud fields for initialization. The difficulty arises when one tries to
verify the resulting forecasts against subsequent cloud analyses. As an illustration of this
problem, a simple persistence forecast from the hour preceding the cloud analyses is shown
in Figure 18 alongside the verifying analyses. It is clear that for hours 1 and 2 of the
forecast, neither Ae analysis nor the forecast show the low cloud fields because of the lack
of multispectral AVHRR data. As a result, the verification provides an unrealistically
optimistic assessment of the forecast accuracy. Only at hours 3 and 4 would the detection
of the low cloud in the verifying analyses result in an apparent sudden reduction in forecast
skill to its more realistic value. In this case the apparent reduction in forecast skill is an
artifact of the verification procedure, caused by an unrealistically high apparent forecast
skill at hours 1 and 2. Other scenarios are possible in which the apparent forecast skill is
reduced because of temporal inconsistency in the analysis. For example, in a situation
where an analysis at the initial time contains multispectrally-detected low cloud, but later
analyses do not as the multispectral data age out of the an^ysis. In this scenario, forecasts
generated from the initial andysis will be penalized for (correctly) predicting low clouds not
present in the (later) verification analyses. Here the apparent forecast skill reduction is
caused by an unrealistically pessimistic assessment of the forecast skill by the verification
procedure. Similar problems would occur with advective forecasts since analysis
inconsistencies at the start and end point of the cloud trajectories would result in artificially
low verification score.
As noted, the potential for a high level of temporal inconsistency in CDFS n has
potentially significant implications for the apparent accuracy of cloud forecasts initialized
from these fields. It is important to point out that, given the above example, the issue of
cloud forecast accuracy is as much an issue of validation data set characteristics as it is one
of forecast quality itself. A possible mitigation strategy would be development of a new
integration dgorithm with modified design criteria that emphasized consistency over fidelity
to the input data. Validation methodology should also be reviewed to determine if
requirements are better served by validation criteria that consider temporal consistency of
both the input and verification data. This is addressed in Section 6.2.5 below.
47
Figure 18. SERCAA analyses of total cloud fraction over the western Pacific Ocean and eastern Asia on 26 March 1993. Top
row contains hourly analyses starting at 1700 UTC and ending at 2000 UTC. Bottom row shows a simple
persistence forecast based on the analysis at 1600 UTC. Grey shade is proportional to analyzed cloud amount.
4.7 Daytime Cirrus/Low-Cloud Discrimination Using MWIR Data
Nighttime cirrus cloud detection tests compare the 3.7-|im and 10.7-|xm brightness
temperatures; generally, MWIR temperatures are higher then corresponding LWIR
temperatures, with the magnitude of the difference dependent on cirrus cloud altitude and
optical depth. At night, thin cirrus MWIR-LWIR spectral signatures are unambiguous.
There are three main reasons why citrus detection using multispectral infrared
measurements is successful at night. The most dominant effect has to do with the nature of
the dependence of the Planck function on temperature at the MWIR and LWIR
wavelengths. Proportionally less energy comes from the warmer part of the scene as
wavelength increases; this effect is due solely to the exponential dependence of the Planck
function on temperature at 3.7 and 10.7|jm. The second effect is that of the varying
emissivities of ice particle clouds themselves among the three wavelengths. In general,
cirrus emissivities increase with increasing wavelength from 3.7 to 12|j,m. This means
that, on the basis of emissivity alone, increasingly more of the upwelling radiant energy in
a cirras-filled pixel comes from the colder cloud. This is an analogous but weaker effect to
that of the Planck function in that it amplifies the brightness temperature differences that
comprise the thin cirrus signature. Finally, a third effect that causes brightness
temperatures for cirrus pixels to decrease with increasing wavelength is that of varying
atmospheric water vapor attenuation. In general, atmospheric water vapor attenuation is
stronger at longer wavelengths. This operates in the same sense as do the previous two,
increasing the difference between 3.7 and 10.7-|im-channel brighmess temperatures,
although typically it is the weakest effect of the three.
In the daytime, solar energy adds complexity to the cirrus MWIR-LWIR signature.
At 3.7|im there is a noticeable solar component to incident MWIR energy; this component
can be either reflected or transmitted by thin cirrus. Any transmitted energy continues
downward to the underlying surface where it is reflected upward and re-transmitted by the
cirrus. These effects cause the 3.7|im brighmess temperature to be even higher for thin
cirrus during the day, so that the MWIR-LWIR difference remains positive. However,
during daytime the magnitude of this positive 3.7-10.7fim difference is ambiguous. Low
water droplet clouds such as cumulus, stratus, and fog also have very high brightness
temperatures at 3.7p,m because they are relatively efficient reflectors of solar energy at
MV^ wavelengths. Thus low clouds have a combination of thermal emission and solar
reflection of MWIR radiation that causes their brighmess temperamres to be high as well.
In turn, me MWIR-LWIR brighmess temperature difference is often as high for low,
opaque clouds as it is for high, transparent citrus.
For meteorological satellites wim 6.7-|xm water vapor sensors, this problem is
surmountable. Such channels sense predominantly upwelling thermal energy mat has been
absorbed and re-emitted by atmospheric water vapor, wim little or no contribution to me
upwelling radiance coming from low clouds or me Earth's surface. The only type of
clouds mat influence strongly me 6.7-|im merrnal energy are cirrus clouds. The weighting
function of a 6.7-nm sensor peaks relatively high in me atmosphere, not often any lower
man cirrus altitudes. Thus water-vapor brighmess temperatures for thin cirrus are similar
to me true physical temperamre of the cirrus itself, while for low clouds they are very
different. It is very conceivable merefore mat a 6.7-10.7-|im brighmess temperamre
difference will be routinely small for cirrus, but large for low clouds and fog.
It is be possible to quantify this relationship through manual cloud-clearing of
daytime multispectral GOES-Next imagery and comparing me observed cirrus water-
49
vapor/LWIR differences with those that correspond to water-droplet clouds. Thus a
potential daj^time ice/water-cloud discrimination test is to first check for large MWIR-LWIR
differences; this detects thin cirrus and stratus and fog. The second check is then to
determine how large the water-vapor/LWIR difference is: if small, then cirrus; if large,
then low cloud. Such a suite of daytime tests will allow for confident daytime cloud phase
discriminations.
5. Future Research Topics
The scope of the SERCAA program was large and multifaceted. However, as with
any large research program, issues came up during the period of the project that are of
interest to Air Force mission requirements but that were outside the scope of the current
program. Many of these issues are not being adequately addressed by existing or currently
planned capabilities. There were also lessons learned that could potentially improve future
operational capabilities. This section provides an overview of these topics and a discussion
of possible approaches and how they might be used to address Air Force requirements.
5.1 New Sensors
Over the coming decade a number of new environmental satellite and sensor systems
are scheduled for launch. Table 5 provides a list of proposed satellites and respective
characteristics of the primary sensors that are potentially useful for cloud applications.
Extant satellites, depending on the suite of onboard sensors, provide varying levels of
information that can be exploited for cloud property retrieval. The SERCAA cloud analysis
m(^el currently being implemented for operational Air Force use as part of CDFS n is
unique in that it analyzes data from multiple satellites and accommodates differences in
information content through the use of multiple cloud analysis algorithms, each tailored to a
specific satellite sensor system (Gustafson et al., 1994). Thus as new satellites are
launched, the SERCAA model is intended to be easily expandable to include the new data
sources simply through the addition of another source-specific analysis algori thm
However, for this or any other plan to improve cloud analysis accuracy using new sensor
data to succeed it is necessary that the appropriate algorithms be developed prior to
introduction of the data into the analysis system.
High spectral resolution data from new satellites offer the potential for significantly
advancing the state of the art for analysis of cloud from space. Numerous studies have
addressed the advantages of adding specific channels or higher resolution sensors to the
existing constellation of environmental satellites (e.g., Di Girolamo and Davies, 1995;
King et al., 1992; Eyre and Menzel, 1989; Arking and Childs, 1985). These new data
sources are applicable for addressing specific weaknesses in current cloud analysis
algorithms. For example, discrimination and accurate altitude assignments for optically
thin cirrus is extremely difficult during daytime conditions. However, 8.55 fxm channel
data, has been demonstrated to provide sufficient information, when combined with data
from split long wave IR channels, to develop a robust, daytime thin cirrus detection
algorithm (Strabala et al., 1994; Ackerman et al., 1990). An 8.55 pm channel is planned
for instraments on the ADEOSI and EOS/AM platforms, and data in case-study quantities
are available now from the MODIS Airborne Simulator (MAS). Similarly, improved cloud
altitude assignments of high cloud can be made from temperature sounding instruments
(Wylie et al., 1994; Menzel et al., 1992) and multispectral IR instruments (dEntremont et
al., 1995; dEntremont et al., 1992) such as HIRS-S, MODIS, OCTS, AVHRR-3, and
NPOES/Imager.
50
Table 5. Planned environmental satellite systems with primary sensor characteristics
Proposed
Initial
Satellite
Launch Date
Sensors
Characteristics
GOES I-M
1994
Imager
moderate-resolution 5 channel radiometer in visible to thermal IR
Sounder
12-channel infrared sounding radiometer
GMS-5
1995
Imager
moderate-resolution 4 channel radiometer in visible to thermal IR
NOAA K-N
1996
AVHRR-3
high-resolution 6-channel radiometer in visible-thermal IR
AMSU
15-channel microwave temperature sounding radiometer with 12 channels
in the 50-60 GHz oxygen absorption band
HIRS-3
20-channel infrared temperature and moisture sounder with channels along
CO 2 and water vapor absorption lines
MHS
5-channel microwave moisture sounder with 3 channels along the 183
GHz water vapor absorption line
ADEOSI
1996
NSCAT
Ku band active scatterometer for measurement of ocean-surface wind
velocity (speed and direction)
OCXS
high-resolution 12-channel radiometer in visible-thermal infrared
AVNIR
very-high-resolution 5-channel radiometer in visible to near infrared with
daytime cross-track pointing, stereo capability
TRMM
1997
CERES
moderate resolution radiometer with 3 channels covering broad band
visible to long wave IR, visible to short wave IR and long wave IR
LIS
staring telescope/filter imager at 0.777 fim for lightning detection
EOS/AM
1998
CERES(2)
moderate resolution dual radiometer, each with 3 channels, covering broad
band visible to long wave IR, visible to short wave IR and long wave IR
MISR
moderate resolution, multiple-angle imaging radiometer with 4 channels
in visible to near-IR
MODIS
moderate resolution scanning radiometer with 36 channels covering
visible to long wave IR
ASTER
very-high-resolution, 3-instrument radiometer with 3 visible to near IR
channels, 6 short wave IR channels, and 5 thermal IR channels
MOPITT
low resolution scanning spectrometer with 4 channels in gaseous
absorption bands
METEOR
1998
SAGEm
limb scanning and solar and lunar occultation spectroradiometer with 9
channels covering visible to long wave IR
ADEOSn
1999
SeaWinds
Ku band active scatterometer for measurement of ocean-surface wind
velocity (speed and direction)
AMSR
scanning microwave radiometer with 6 frequencies in 6-90 GHz for
humidity profiling and precipitation/surface temperature retrieval
GU
scanning radiometer with 35 channels covering ultraviolet to thermal IR
EOS/PM
2000
AIRS
high spectral resolution infrared sounder using a 2300 channels
spectrometer between 0.4 and 15.4 |j,m for retrieval of temperature and
humidity profiles and surface temperature
AMSU
see NOAA K-N above
MHS
5-channel microwave humidity sounding radiometer with 3 channels
along the 183 GHz water vapor absorption line
CERES(2)
see EOS/AM above
MODIS
see EOS/AM above
METOP
2001
AVHRR-3
see NOAA K-N above
AMSU
see NOAA K-N above
HIRS-3
see NOAA K-N above
MHS
see NOAA K-N above
NPOESS
2006
imager
TBD
sounder(s)
TBD
51
Changes and advances in satellite sensor design, and how they affect the cloud
algorithm performance, is an ongoing issue. The recent launches of GOES 8 and 9, the
first two satellites in the GOES I-M series of next generation of the U.S. operational geo¬
stationary program, provide the opportunity to evaluate the impact of new sensor channels
using data available now. These satellites have a sensor design that is a significant depar¬
ture from the previous series (Menzel and Purdom, 1994). The principal instrument
package consists of completely separate and autonomous imaging and sounding sensors
with additional channels and enhanced resolution. Existing geostationary cloud analysis
algorithms will require modifications to exploit the additional channels. The potential
benefits from making these modifications are both large and relatively easily identified since
the new sensor configuration is similar to the current AVHRR sensor which has been
studied extensively. Advantages of a multispectral sensor over the previously-available
two-channel system include improved low cloud detection, cloud phase determination,
transmissive cirrus identification and characterization of cloud radiative and microphysical
properties.
The Japanese Meteorological Agency has also recently launched a new geostationary
satellite, GMS-5. The imaging sensor is modified over the previous version with the
addition of a pair of split long wave infrared window channels in place of a single broad
band channel. This offers the potential for improved thin cirrus detection, one of the
recognized weaknesses of current geostationary-data analysis algorithms.
5.2 Advanced Retrieval Techniques
Existing and planned objective cloud analysis capabilities at the Air Force Global
Weather Central (AFGWC) make use of multispectral data from multiple satellite systems.
However in a number of import areas, gaps or weakness exist in the analysis
methodology that affect the ability to adequately address Air Force requirements;
1) quantitative interpretation of da54ime sensor channel data in the visible to midwave-
infrared (MWIR) spectrum; 2) discrimination of transmissive cirrus over hot and bright
backgrounds; 3) accmate cloud typing as a first-order determinant of cloud microphysical
and radiative properties; 4) use of remotely sensed cloud radiative and environment
characteristics to improve retrieval accuracy for cloud altitude; and 5) use of inferred cloud
physical characteristics to provide temporal consistency within time series of analyzed
cloud parameters derived from multiple sateUites.
5.2.1 Interpretation of Visible to Midwave-Infrared Data
All satellite-based cloud analysis techniques require some knowledge of the terrestrial
back^ound radiative characteristics at the various sensor wavelengths in order to accurately
discriminate cloud. Reliance on this knowledge vanes by technique and information
content of the satellite data. However, quantitative background information is difficult to
obtain globally since Earth surface radiative characteristics vary widely with geography and
surface cover (e.g., deserts, tropical forests, permanent snow and ice cover). Vegetated
surfaces and snow cover also vary for a given Earth location as a function of time, both
diumally and seasonally.
The problem of background surface characterization is particularly acute for data
channels sensitive to reflected solar radiation. Changes in solar illumination with time of
day, anisotropic reflection, and similarities between reflective terrestrial backgrounds and
cloud all contribute to make objective cloud detection using these channels complex and
52
often problematic. However, visible to midwave infrared sensor data are extremely useful
for objective cloud detection since they generally exhibit a strong liquid-water cloud
signature (i.e., the clouds are good reflectors). For two-channel sensors like the OLS and
NffiTEOS AT satellites, the visible channel adds significantly to low cloud detection
accuracy during daytime. Also for the new AVHRR-3 sensor scheduled for launch in 1996
(Table 5), loss of the 3.7 |im channel during sunlight hours will place increased reliance on
Ae visible and near-IR (MR) channels for daytime low cloud detection.
Proposed research topics related to improved use of visible to MWIR data fall
generally in the area of better cloud-free background characterization. The ratio of two-
channel visible/near IR data has been used successfully to detect reflective clouds
(Gustafson et al., 1994; Stowe et al., 1991; Saunders and Kriebel, 1988). The principle
advantage of using a ratio, instead of the absolute magnitude of a single visible data channel
compared to some threshold, is that as long as the channels are spectrally close, some
uncertainties in the measurements (e.g., solar illumination, geometry) tend to cancel out.
However, problems persist with reflective backgrounds erfiibiting ratio signatures similar
to cloud (e.g., deserts, snow and ice). Development of databases of dear-scene ratio
values to characterize the problematic backgrounds offer the potential to improve cloud
discrimination. This approach retains the advantages of using channel ratios while relying
on locally adaptive thresholds to account for changes in background characteristics.
Single-charmel algorithms have traditionally used this type of approach to characterize the
background reflectance (e.g., Hamill et al., 1992; Rossow and Schiffer, 1991).
The AVHRR is the only extant sensor system that contains narrow-band chaimels in
both the visible and near IR. All other environmental satellite systems are limited to a
single, broad band "visible" chaimel covering that spectral range. Reflectance of most
terrestrial surfaces at these wavelengths is anisotropic resulting in potentially large changes
in measured cloud-free radiance as a result of relatively small changes in the solar and/or
viewing geometry. This uncertainly in the cloud-free radiance has constrained the accuracy
of single-chaimel techniques. Bi-directional Reflectance Distribution Functions (BRDF)
are potentially useful for describing changes in surface reflectivity as a function of
geometric considerations. Earlier work investigating the application of deterministic BRDF
models to real-world conditions (d'Entremont et al., 1995; d'Entremont et al., 1996) has
demonstrated their feasibility of using BRDF models for predicting the bandpass weighted
dear-scene visible and near IR reflectance of known surfaces. BJ^F-derived dear-scene
reflectance information can be applied to visible and NIR cloud detection algorithms to
improve cloud-retrieval accuracy. Improved characterization of dear-scene reflectance
conditions is also expected to increase the amount of visible to MWIR data that can be used
for quantitative evaluation by objective cloud analysis algorithms.
Knowledge of MWIR surface reflectance characteristics provides the potential for
significantly improving the accuracy of operational cloud analysis models that use daytime
3.7-3.9 |Xm data. During daylight hours surfaces both reflect solar energy and emit thermal
energy at these wavelengths. If surface-reflectance effects can be successfully separated
from thermal effects, improved detection and characterization of cloud (particularly thin
transmissive cirrus) and more accurate skin temperature retrievals can be expected. MWIR
reflectivity (r) can be expressed as a function of emissivity (e) only (i.e., r = 1-e), since the
transmissivity of terrestrial surfaces at these wavelengths can be assumed to be zero. Thus,
if the directional dependence of the surface emissivity can be calculated, then the directional
dependence of the surface reflectivity is also known. LWIR emissivity is assumed to be
1.0 for most terrestrial surfaces, therefore the physical surface skin temperature can be
retrieved by applying an atmospheric correction to the observed LWIR brightness tempera¬
ture. This can then be used to compute the coincident MWIR emissivity from the corres¬
ponding atmospherically corrected MWIR brightness temperature. Directional dependence
53
could be determined from a series of observations of the same geographic location over
multiple orbits (i.e., varying view angles). GOES-8 and GOES-9 data along with NOAA
AVHQRR data are all useful sources of coincident LWIR and MWTR observations.
5.2.2 Transmissive Cirrus over Warm and Reflective Backgrounds
When cirrus optical depth is sufficiently small, transmission of background radiation
through the cloud can reduce the cloud-back^ound contrast to a level that cannot be
detected by automated cloud algorithms. This can occur in visible, MWIR, and LWIR
image bands, and is particularly severe over reflective and warm backgrounds such as
desert. Under such conditions it may be possible to determine the presence of these
transmissive cirrus using wavelengths wWe the atmosphere is opaque such as the 6.7-p,m
water-vapor band.
Water vapor imaging chaimels centered near 6.7 pm are available on most
geostationary satellites and on IR sounders on the NOAA polar platforms. At these
wavelengths the sensors measure upwelling thermal radiation emitted primarily by water
vapor in the atmosphere, with little or no contribution from the underlying terrestrial
surface. Under cloud-free conditions, the absolute magnitude of a 6.7-pm brightness
temperature is dependent on the amount of water vapor that is present along the
atmospheric path. If an estimate of the water vapor profile is available (e.g., from IR and
microwave satellite sounders), a predicted dear-scene 6.7 pm brightness temperature can
be calculated using radiative transfer code. Any cloud in a satellite-observed 6.7 pm field
of view would produce a negative departure from the corresponding predicted brightness
temperature. \\^e this approach is identical in form to standard single-channel threshold
cloud tests, it effectively eliminates any problematic surface contributions that can act to
mask weak cloud signatures. Sensitivity of this test can be evaluated using nighttime cases
where conventional multispectral tests perform well for detecting thin cirms. This will
provide an experience base that can be used to determine the usefulness of the water vapor
channels for upper-level cloud detection. It is possible to employ this technique not only
with 6.7-pm data but with any water-vapor imagery in the 6.5-7.5-pm spectral regions
such as are typically available on infrared moisture sounders. Using adthtional water vapor
sounding channels also offers the potential for discriminating multiple cloud layers at
different levels in the atmosphere.
Water vapor imagery is also potentially useful for improving cloud forecasts. Cross¬
correlation short-term cloud forecasting techniques have been demonstrated to produce
useful 0 to 6-hour forecast products (Hamill and Nehrkom, 1993). These techniques
generate short-term trajectory forecasts for a particular region using cloud-track wind
vectors that have been computed by correlating cloudy areas in time series of geostationary
data. Regions in later images that correlate most strongly with a cloudy region in an earlier
image are used to define a displacement vector that is used to generate a trajectory cloud
forecast. One of the problems in relying on cloud-track winds from satellite imagery is that
no displacement vector data can be retrieved in cloud-free areas. No vectors can be
obtained since there are no wind tracers in the clear regions from which to infer horizontal
atmospheric motion. This in turn poses a problem when trying to advect clouds into
previously cloud-free regions. One possibility for inferring motions in cloud-free regions
is to study the correlation of water-vapor imagery patterns from one time to the next in
cloud-free regions. This would provide a truly satelUte-only short-term cloud forecast
capabiUty and could potentially be extended to derive wind profiles using water vapor
channels that peak at differing levels in the atmosphere. With wind profile information it
could then be feasible to advect low, middle, and high clouds separately with multiple wind
fields that vary with altitude.
54
5.2.3 Cloud Typing
Cloud type information is often used as a surrogate to infer cloud physical and optical
properties (e.g., physical and optical depth, drop or ice Ctystal size distribution, transmis¬
sion) due to the difficulty of retrieving these parameters directly from environmental
satellite data. However, the cloud type retrievals themselves may be suspect. For
example, the RTNEPH model currently identifies eight cloud types from only one visible
and one IR channel (Hamill et al., 1992). Numerous approaches to cloud typing have been
tried with varying degrees of success. A common technique discriminates type through a
measure of the smface spatial texture generally evaluated through the local variance of
infrared brightness temperature or visible reflectance (e.g., Lamaei, 1994; Hawkins and
d'Entremont, 1990; Ebert 1989). Recently, neural networks have been used to assimilate
multispectral satellite sensor data to retrieve a number of parameters including cloud type
(e.g., Bankert, 1994, Lee et al., 1990). Garand (1988) stratified multispectrd satellite data
into a taxonomy of classes and used a knowledge-base system to identify different cloud
types. In SERCAA a hybrid technique is used 5iat first vertically stratified LWIR data and
ttien applies standard image processing segmentation and classification techniques to dis¬
criminate cumuliform from stratiform cloud. Current Air Force requirements call for nine
cloud types; cirrus, ckrostratus, altocumulus, altostratus, stratocumulus, stratus, cumulus,
cumulonimbus, and nimbostratus. Robustness is a major issue in that any typing algorithm
must be applicable to data from all available sensor systems over globally varying condi¬
tions. While most cloud typing techniques use some form of nephanalysis algorithm to
provide cloud spatial information (e.g., location, extent, altitude), typically no use is made
of cloud physical or optical properties available from advanced andysis algorithms. How¬
ever, numerous approaches exist to retrieve this type of information from existing sensor
systems (e.g., Ou et al., 1993; d'Entremont et al., 1993; Nakajima and King, 1989).
Processing time and efficiency are primary factors in operational implementations of
cloud typing algorithms. If neural nets were to be shown feasible for cloud type retrieval
this could significantly reduce the system resources required to perform that function.
Also, depending on the quality of the training set and the information content in the satellite
data, neural nets may be able to discriminate more cloud t 5 q)es than traditional retrieval
techniques. It is important to note that the accuracy and robustness of neural networks are
strongly tied to the amount and accuracy of the available training data; they are positively
correlated.
5.2.4 Cloud Altitude
Cloud altitude is typically derived from LWIR brightness temperature data that are
assumed to be representative of the cloud-top temperature (usually either an average or
median brightness temperature computed from all cloudy pixels). Some temperature/height
profile is then used to establish the cloud top altitude or pressure. Inherent assumptions are
that derived LWIR brightness temperatures are representative of the cloud top temperature,
that atmospheric transmission between the cloud and satellite can be ignored, and Aat the
available temperature/height profile information is sufficiently accurate for the desired
retrieval accuracy.
More accurate and timely temperature and moisture profiles would improve not only
fixing the height of the retrieved cloud-top temperature but also the accuracy of that
temperature through modeling of the LWIR attenuation caused by the atmosphere above the
cloud. Use of satellite-derived soundings provide much improved coverage in comparison
to conventional radiosonde measurements, both in terms of geographic density and
temporal frequency. Also, since they are coincident with the satellite imaging sensor
55
measurements used for the cloud analyses, satellite-derived soundings are the optimal
information source for quantifying the environment in which clouds are embedded. The
sounder and imager data are best analyzed together since 1) the accuracy of the retrieved
soundings is dependent on the presence of cloud (microwave-based soundings are less
affected than IR based, particularly for non precipitating clouds) and 2) both cloud
detection and altitude calculations are potentially improved with better understanding of the
vertical structure of the atmosphere.
Cloud top altitude is typically determined by comparing a measured cloud-top LWIR
brightness temperature with its coincident atmospheric temperature profile and choosing the
profile altitude that corresponds with the brightness temperature observation. Not ac¬
counted for are the possible transmissive nature of the clouds and the effects of atmospheric
water vapor attenuation on upwelling LWIR brightness temperatures. In-situ or NWP-
derived profiles of atmospheric temperature and water vapor are not always available in
real-time. However, retrievals of atmospheric temperature and water vapor from collocated
satellite LWIR and microwave sounder data ehminate the need for external data sources.
The first step in correcting LWIR brightness temperature measurements for water
vapor attenuation is to compute the atmospheric transmittance profile. Such computations
can be quickly made using band-model transmittance codes such as those of Weinreb and
Hill (1980) and Weinreb and Neuendorffer (1973). These models are well suited for this
purpose since they can be customized for a satellite band-pass and response function, and
they are fast, computationally efficient, and accurate. Input for these models includes
atmospheric temperature and water vapor profiles that can be obtained from coincident
satellite sounder data and/or NWP upper-air analysis fields.
Once a transmittance profile is obtained, predicted infrared brightness temperature can
be computed as a function of altitude for a cloud at that altitude. If a blackbody cloud is
placed at some level in the atmosphere, the measured brightness temperature for that cloud
will in general be lower than the actual cloud top temperature due to attenuation by the
atmosphere above the cloud. This attenuation is computable by evaluating the radiative
transfer equation in the forward direction using the band-model transmittance profile
con-esponding to cloud-satellite atmospheric path. Thus for black clouds, true cloud top
altitude as a function of satellite-measured brightness temperature can be computed a priori
and used in lookup-table form to adjust satellite-measured cloud top temperature, and
subsequently the cloud top height, for water vapor attenuation.
For transmissive cirrus clouds, the non-blackbody effects on upwelling LWIR
radiances must also be accounted for. This has been accomplished u si ng physical
techniques such as those employed by d'Entremont et al. (1993), Ou et al. (1993)
dEntremont et al. (1990) and Rao et al. (1995). These models all use a deterministic
approach to account for the low bias in retrieved cirrus altitudes caused by an incorrect
blackbody assumption. However, the topic of cirrus radiative and spatial property retrieval
using daytime MWIR and LWIR data remains problematic. During nighttime, the physical
retrieval of cimis emissivity and effective altitude (two unknowns) is accomplished nstn g a
coincident pair of MWIR and LWIR thermal-only radiance measurements (two knowns).
The retrieval process capitalizes on the thermal properties of cirrus clouds at the 3.7-|im
and 10.7-p.m wavelengths. During daytime this retrieval is complicated by the addition of a
reflected solar component to the measured MWIR radiances, essentially adding a thir d
unknown to the system without the addition of a third measurement. Thus for daylight
observations there are three unknowns (emissivity, altitude, and 3.7-|j,m solar reflection)
and only two measurements (a combined solar-plus-thermal MWIR radiance and a thermal-
only LWIR radiance). Before the cirrus optical and radiative properties can be retrieved,
the solar component of the MWIR radiance observation must be removed. Rao et al.
56
(1995) have presented and successfully implemented a deterministic technique for removal
of the MWIR solar component. Their technique requires a priori computations of very
complex processes that model the MWIR bidirectional scattering properties of ice crystals.
An alternative solution to the daytime MWIR-LWIR cirrus retrieval problem requires
separation of the solar and thermal MWIR components from the overall MWIR
measurement. Recall that the fundamental cirrus retrieval model requires as input the
upwelling MWIR and LWDR thermal-only radiances. Cirrus effective altitude can be
independently retrieved both day and night using data along the wing of an LWIR
absorption band using a technique such as CO 2 Slicing (Wylie and Menzel, 1989; Menzel
et al., 1992,1993). CO 2 slicing generates cirrus effective altitude analyses using 15-|xm
sounder data that are available with GOES and NOAA sounder data. Using this approach,
the effective altimde can be eliminated from the list of unknowns and the imager M^TR-
LWIR retrievals of the cloud effective emissivity can proceed.
A cirrus BRDF for 3.7-|im reflected solar energy using coincident measures of CO 2
effective altitude and MWIR-LWIR radiances could be used to specify the reflected-solar
MWIR radiance component in situations where CO 2 Slicing analyses are not available. As
discussed above, daytime retrieval of cirrus attributes is initially posed as a problem with
three unknowns and two measurements, which in general has no mathematically unique
solution. Using CO 2 Slicing to eliminate effective citrus altitude from the unknowns, the
reflected-solar portion of the MWIR cirrus radiance can then be retrieved whenever
coincident CO 2 Slicing analyses are available. Tabulated retrievals over a range of view
and illumination angles will be used to constmct a cirrus MWIR BRDF that will be used
where CO 2 Slicing analyses are not available. The resultant two-channel cirrus retrievals
can be compared to results using the technique of Rao et al. (1995). Also, to check for
consistency, the cirrus BRDFs can be directly and quantitatively compared with the full¬
blown radiative transfer (scattering by ice crystals) calculations of Rao et al. (1995). Other
issues that will require investigation are the potential dependencies of measured cirrus
BRDFs on effective ice crystal size (effective temperature) and optical depth (effective
emissivity).
5.2.5 Temporal Consistency
As discussed in Section 4.6 above, integrated analyses produced by combining data
from multiple satellite platforms to produce a single unified or integrated cloud analysis can
exhibit inconsistencies between collocated results obtained from different satellite platforms
at approximately the same time. The problem consists of combining data from high spatial
and spectral resolution polar-orbiting satellites with high temporal resolution geostationary
data. Under some conditions geostationary-based analyses tend to identify less cloud than
corresponding polar-based andyses. Investigation of a limited number of cases has
revealed no apparent problems with the accuracy of any of the individual, source-specific
analyses. However, when combined the temporal consistency of the resultant integrated
analysis can be degraded. This in turn may negatively impact subsequent applications that
use tire cloud analysis data as input, such as cloud forecast models. Depending on the
source, age and mix of satellite data available over a given time period, certain clouds can
appear in an integrated analysis only to later disappear and then reappear as different data
sources are used and subsequently aged out of the analysis.
Inconsistencies between polar- and geostationary-based analyses are most likely due
to inherent differences in their respective scan methodologies and major sensor
characteristics (i.e., the available information content varies with platform). Table 4
summarizes the imaging sensor characteristics of the currently operational environmental
57
satellites. From the table it can be seen that each data source exhibits characteristic
strengths that contribute to the relative accuracy of derived cloud products. OLS data have
high spatial resolution but are relatively low in spectral information content and temporal
resolution. AVHRR has the greatest spectral information content and its repeat cycle is
consistent with synoptic scale events, but the spatial resolution is relatively coarse.
Geostationary data exhibit the greatest tempor^ resolution, but the spatial resolution is
significantly de^aded for a large fraction of the coverage area, and spectral information
content varies widely with satelhte.
An investigation of the magnitude and frequency of inconsistencies between cloud
analyses derived from different satellite systems and of their root causes is needed to
characterize the problem more completely. To date only a few cases have been documented
where differences between the polar- and geostationary-based analyses exist. It is not
known how often this situation occurs or whether it is limited only to specific types of
cloud (e.g., low cloud, transmissive cirrus). The cases that have been identified are
consistent with conditions described above where the accuracy of the individual source-
sp^ific analyses have been confirmed through visual inspection yet discrepancies between
coincident geo- and polar-based analyses exist. One possible explanation is the relatively
coarse resolution, both spatially and spectrally, of geostationary compared to the polar-
orbiter data. Geostationary sensors used in the earlier studies have a spatial resolution of 5
km at the subpoint that degrades to approximately 17 km at the edge of scan. Also they
were only two-channel instruments with a radiometric resolution of no better than 8 bits.
This is in contrast to the five-channel AVHRR data with a 10 bit radiance quantization
range and 4 km footprint degrading only near the edges of scan, and the OLS with an 8 bit
quantization and a roughly fixed 2.7 km footprint throughout the scan. The data quality
hypothesis can be tested using the new GOES-8 data now available in real-time at PL. The
imaging sensor on GOES-8 has additional charmels plus improved spatial and spectral
resolution that should increase cloud detection sensitivity and possibly reduce differences in
the derived cloud analyses as compared to polar-based analyses. However, even if the
improved data quality from the new GOES satellites does resolve the geostationary vs.
polar consistency issue for those platforms, the problem will remain for the other
international geostationary satellites (i.e., Meteosat and GMS).
To the extent inconsistencies exist between cloud data analyzed from different
satellites, and the inconsistencies are detrimental to subsequent applications, mitigation is
probably best achieved through modifications to the integration algorithm used to combine
the separate analyses. This could be made more effective if inconsistency proble ms are
linked to a particular range of conditions or cloud types that can be identified algorithmi¬
cally. An approach capable of quantifying system related impacts on analysis charac¬
teristics derived from geostationary satelhtes is to selectively degrade GOES 8 capabilities
and observe the impact on cloud analysis accuracy and consistency. GOES 8 has improved
radiometric and spatial resolution plus additional spectral capabilities (i.e., additional
channels) relative to other geostationary systems. Sensitivity to system capabilities can be
derived by parametrically varying GOES 8 system parameters (e.g., sensor or spectral
resolution, channel configuration) and tabulating the change in retrieved cloud properties.
The principal objective of such sensitivity studies would be to identify cloud types or
characteristics for which retrieval accuracy are most heavily impacted by the degraded
system capabilities of geostation^ satellites. Using this information, a mitigation strategy
could be developed to best exploit the relative strengths of the individual satellites. For
example, if it is demonstrated that geostationary analyses poorly resolve, say, low stratus
cloud, then the inte^ation algorithm could be modified to selectively persist low cloud
identified from earlier polar passes, even in cases where more recent geostationary analyses
indicate clear. Another strategy is to use a short-term cloud forecast algorithm such as
58
cross correlation (Hamill and Nehrkom, 1993) to extend the influence of the polar-based
analyses for longer time periods. A similar approach can be applied to analyzed data from
nearly-coincident NOAA and DMSP scans (e.g., at high latitudes) to identify any cloud
types that are sensitive to sensor-specific system characteristics.
5.3 Validation
A recurring criticism of objective satellite-based cloud analysis algorithms is the
general lack of formal validation. There are several reasons for the paucity of quantitative
validation work, however, most derive from a single cause: the absence of independently
derived or measured cloud parameter databases with an established accuracy sufficient to be
classified as ground truth.
A need exists for an established procedure and accepted objective measures of
algorithm accuracy over a range of conditions. The initial step in development of an
algorithm validation methodology is to first identify certain fundamental study attributes:
• goal: what information is required from the validation study, what is the end
user's application and what information is needed to determine if the subject
algorithm can provide that information with sufficient accuracy, does the
accuracy metric need to be absolute or relative to some known standard (e.g., an
existing legacy technique);
• scope: does the subject cloud algorithm need to be applied globally or
regionally, only over certain background conditions (e.g., ocean, land, desert,
snow), day and night, to specific satellite or sensor types (e.g., polar,
geostationary, infrared, microwave);
• supporting data: what supporting data are available for use as part of the
validation study, is there a truth data set that is accepted by the end user, what is
the scale, resolution, projection of the supporting data relative to the satellite
data, are the supporting or truth data parameters directly relatable to the algorithm
output parameters; and
• results: is there specific quantitative information required by the end user, what
statistical measures or form of results are required, are these results sufficient to
meet the goals.
Comparisons with trath sets, if they are available, provide an absolute metric that is
potentially useful for Air Force programs such as SERCAA and CDFS n that need to
gauge algorithm performance in terms of absolute accuracy. Given the requirements placed
on cloud algorithms for near real time global analyses, any ground truth data set would
need to be representative of globally varying surface and atmospheric conditions for a full
range of seasonal and diurnal conditions. The only practical data source capable of
providing the required level of coverage is environmental satellites. Thus to date, most
validation studies that require an absolute accuracy metric have involved some form of
subjective comparison between satellite-derived algorithmic results and the corresponding
satellite imagery; and even those studies tend to be limited to validation of cloud spatial
properties (e.g., coverage, amount, height).
Other Air Force programs have requirements for cloud climatologies, rather than real¬
time analyses. This drives a need to establish a measure of relative accuracy between
available climatological data and to quantify any differences. Intercompaiisons over
59
extended time periods between results from real-ti me cloud analysis algorithms and
coincident data being ingested into candidate climatological data sets are potentially useful
for establishing any characteristic biases or artifacts in the climatologies, particularly if the
absolute accuracy of the cloud algorithms is previously established. Radiative and
microphysical retrieval algorithms can realistically only be validated against in-situ and/or
active remotely sensed data. Well calibrated lidars and radars can provide necessary
information on cloud altitude and optical properties but are typically only available from
intensive field study programs for a limited range of meteorological conditions and a single
geographic region for perhaps one season. These data need to be collected from as many
locations and time periods as possible to provide a more complete validation of the retrieval
algorithms.
An acceptable vahdation methodolo^ must identify factors or techniques that can
minimize problems associated with the limited availability of truth data and that can provide
the required accuracy information. There is evidence that using a man-computer technique
to analyze satellite imagery is an acceptable approach for developing ground truth
information for some applications. Gustafson et al. (1993) produced a quantitative
me^ure of the absolute accuracy of total cloud amount retrievals produced by a set of
tactic^ cloud analysis algorithms. Their approach used direct comparison with ground
tmth images produced through manual analysis of satellite imagery following a formalized
technique developed by Gustafson and Felde (1988). This technique has also been
successfully applied to previous studies of the RTNEPH (d'Entremont et al., 1989) and an
SSM/I cloud algorithm (Gustafson and Felde, 1989).
Comparison of results from multiple cloud models to determine the optimal approach
for a paiticidar application is another common validation requirement (e.g., Rossow et al.,
1985). During the recent cloud algorithm development program conducted at the Phillips
Laboratory, a validation study was designed and executed to establish a relative accuracy
metric for comparison of cloud algoritlun results against a known standard (P hilli ps
Laboratory, 1994). The focus of diis study was to identify the impact on analysis accuracy
of the differing analysis approaches used by the respective algorithms. Two validation
criteria were established: 1) did differences between the two techniques exceed a specified
level of statistical significance and 2) where they were different, which was more accurate.
To minimize the impact on comparison statistics of minor algorithmic differences such as
output resolution and precise positioning of cloud edges, the output data were first gridded
and stratified into three categories: clear, partly cloudy, and completely cloudy. Direct
comparison statistics were then computed using collocated output pairs taken from the two
algorithms at matching analysis times. A ^2 test was used to demonstrate that the
computed differences were not attributable to random chance. A dichotomous test using a
single-blind manual evaluation was then applied to establish if one analysis was superior
for those cases where the results differed. A similar approach was used by Welch et al.
(1992) to intercompare results from three independent cloud classification schemes and
establish a relative accuracy metric for each algorithm.
The above-cited studies provide useful insight into possible validation approaches to
detemune the accuracy or applicability of candidate cloud algorithms to a given application.
Note that in each case, available supporting data were used in the way best suited to meet
the goals of the individual studies. This insured that the information required to establish
the usefulness of the analysis approach to the desired application would be provided. In
some cases the amount of available data was too small to allow for comprehensive
conclusions, however in each case the study met the stated goals.
60
6. Summary
Development of improved automated nephanalysis capabilities from multiplatform
sensor data is an important goal of the Strategic Environmental Research and Development
Program (SERDP). One such SERDP initiative is the Support of Environmental Require¬
ments for Cloud Analysis and Archive (SERCAA) project. SERCAA as a two-phase
research and development program has provided both the next generation nephanalysis
model for CDFSII and a new global cloud algorithm for use in determining the radiative
and hydrological effects of clouds on climate and global change. SERCAA cloud analysis
products will be available to a wide community of users both within and outside of the
Department of Defense. The principal accomplishments of SERCAA are: 1) incorporation
of high-resolution sensor data from multiple military and civilian satellites, polar and geo¬
stationary, into an integrated real-time cloud analysis model, 2) demonstration of multi-
spectral cloud analysis techniques that improve the detection and specification of clouds,
especially cirrus and low clouds, and 3) development of cloud microphysical and radiative
property retrieval algorithms and support database specifications for an improved cloud
model.
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Appendix A
AER SERCAA-Related Papers and Technical Presentations for 1993 - 1996:
AMS 8*** Conference on Satellite Meteorology and Oceanography - 28
January - 2 February 1996, Atlanta, GA
Evaluation of SERCAA integration algorithm for analysis of multiplatform/multisensor
satellite-derived cloud parameters. Gary Gustafson, Christopher Grassotti, Robert
dEntremont (AER).
Cloud detection and land-surface albedos using visible and near-infrared bi-directional
reflectance distribution models. Robert d'Entremont (AER), Crystal Schaaf (PL),
and Alan Strahler (BU).
Retrieval of cirrus radiative and spatial properties using coincident multispectral imager
and sounder satellite data. Robert d'Entremont (AER), Donald Wylie (UW), Szu-
Cheng Ou, Kuo-Nan Liou (UU).
Retrieval of cloud spatial, microphysical, radiative and environment parameters from
multisource satellite.data using SERCAA. Ronald Isaacs, Gary Gustafson, Robert
d'Entremont (AER).
Cloud Impacts on DoD Operations and Systems (CIDOS 95) - 24-26
October 1995, Hanscom AFB, MA
Cloud data sets derived from combined geostationary and polar-orbiting environmental
satellite sensors using the SERCAA cloud model. Gary Gustafson, Robert
d'Entremont, Daniel Peduzzi (AER)
Enhanced satellite cloud analysis by the development of a higher resolution (6-km)
global geography data set. Lt. Rad Robb (PL), Daniel Peduzzi (AER), Joan Ward
(SRC)
Cloud detection using visible and near-infrared bidirectional reflectance distribution
models. Robert d'Entremont (AER), Crystal Schaaf (PL), Alan Strahler (BU)
SERCAA Phase 11: cloud radiative, microphysical, and environmental properties.
Ronald Isaacs, Gary Gustafson, Robert d'Entremont, David Hogan (AER), Maj.
Michael Remeika, James Bunting (PL)
Retrieval of cirrus radiative and spatial properties using coincident multispectral imager
and sounder satellite data. Robert d'Entremont (AER), Donald Wylie (UW), Szu-
Cheng Ou, Kuo-Nan Liou (UU).
Support of enviromnental requirements for cloud analysis eind archive (SERCAA)
integrated cloud analysis validation. Kermeth Heideman (PL), Robert dEntremont,
Jeanne Sparrow, Anthony Lisa, Gary Gustafson (AER).
69
European Symposium on Satellite Remote Sensing II, Conference on Passive
Infrared Remote Sensing of Clouds and the Atmosphere III, 25-29 September
1995, Paris, France.
Multi-spectral multi-platform satellite cloud and cloud environment retrievals for
SERCAA. Ronald G. Isaacs, Gary B. Gustafson, and Robert P. d'Entremont
Strategic Environmental Research and Development Program Symposium -
12-14 April 1994, Washington, D.C.
Multi-spectral multi-platform satellite cloud and cloud environment retrievals from
SERCAA. Ronald G. Isaacs, Gary B. Gustafson, and Robert P. d'Entremont
(AER), James T. Bunting and Maj. Michael F. Remeika (PL)
European Symposium on Satellite Remote Sensing - 26-30 September
1994, Rome, Italy
Integration of multiplatform/multisensor satellite global cloud analyses within
SERCAA. C. Grassotti, R. Isaacs, G. Gustafson (AER).
Improved cloud analysis for CDFS n through the SERCAA research and development
program. R.G. Isaacs, G.B. Gustafson, R.P. d'Entremont (AER), T.J. Neu
(AFGWC), J.W. Snow (PL).
AMS Tth Conference on Satellite Meteorology and Oceanography, 6-10
June 1994, Monterey, CA
Cloud Cover Determination Using the DMSP OLS. Gary B. Gustafson, Daniel C.
Peduzzi, and Jean-Luc Moncet (AER).
Validation of the SERCAA Cloud Analysis Algorithm. Kenneth F. Heideman (PL) and
Jeanne M. Sparrow (AER).
Analysis of Geostationary Satellite Imagery Using a Temporal Differencing Approach.
Robert P. d'Entremont, Gary B. Gustafson, and Brian T. Pearson (AER).
Analysis Integration Within SERCAA: Optimizing the Analysis of
Multiplatform/Multisensor Satellite-Derived Cloud Parameters. C. Grassotti, T.
Hamrll, R. Isaacs, G. Gustafson, D. Johnson (AER).
Improved Cloud Analysis for CDFS E Through the SERCAA Research and
Development Program. Thomas J. Neu (AFGWC), Ronald G. Isaacs and Gary B.
Gustafson (AER), and J. William Snow (PL).
70
Cloud Impacts on DoD Operations and Systems (CIDOS 93) - 16-19
November 1993, Ft. Belvoir, VA
Satellite Cloud Analysis Programs at the Air Force Phillips Laboratory: An Overview:
Part 1 Tactical Nephanalysis (TACNEPH). Gary B. Gustafson, Ronald G. Isaacs
(AER), Robert P. d'Entremont and James T. Bunting (PL).
Satellite Cloud Analysis Programs at the Air Force Phillips Laboratory: An Overview:
Part 2 Support of Environmental Requirements for Cloud Analysis and Archives
(SERCAA). R. G. Isaacs and G. B. Gustafson (AER) and J. W. Snow and R.
P. d'Entremont (PL).
Validation of TACNEPH Cloud Detection Algorithms. Jeanne M. Sparrow, Gary B.
Gustafson, Anthony S. Lisa and Robert P. d'Entremont (AER).
Remote Sensing of Cloud Thickness and Base from Multispectral Cloud Imager Data.
Ronald G. Isaacs, Alberto Bianco, Gary Gustafson, and Charles Sarkisian (AER).
A Short-Term Cloud Forecast Scheme Using Cross Correlations. Thomas M. Hamill
and Thomas Nehrkom (AER).
Robust Database Management for Virtual-Application Environments. James S.
Belfiore (AER).
SPIE Conference on Passive Infrared Remote Sensing of Clouds and the
Atmosphere - 13-15 April 1993, Orlando, FL
Remote sensing of cloud for defense and climate studies: an overview (invited paper).
R.G. Isaacs (AER).
Validation of infrared cloud detection algorithms developed for TACNEPH. G.B.
Gustafson, R.G. Isaacs, J.M Sparrow (AER), J.T. Bunting, and R.P.
d'Entremont (PL).
71
