CIDC FTP Data
ISCCP D2 IDC Data on FTP

Data Access

ISCCP D2 Cloud Data
Mean cloud fraction(%) Mean cloud top pressure Mean cloud top temperature mean cloud optical thickness Mean cloud water path
Clear sky surface reflectance Clear sky surface temperature

IR low cloud fraction(%) IR low cloud top pressure IR low cloud top temperature
IR mid cloud fraction(%) IR mid cloud top pressure IR mid cloud top temperature
IR high cloud fraction(%) IR high cloud top pressure IR high cloud top temperature

Cumulus,liquid,
low cloud fraction(%) 		Stratocumulus,liquid,
low cloud fraction(%) 		Stratus,liquid,
low cloud fraction(%)
Cumulus, ice,
low cloud fraction(%) 		Stratocumulus,ice ,
low cloud fraction(%) 		Stratus,ice,
low cloud fraction(%)

Altocumulus,liquid,
mid cloud fraction(%) 		Altostratus,liquid,
mid cloud fraction(%) 		Nimbostratus,liquid,
mid cloud fraction(%)
Altocumulus,ice,
mid cloud fraction(%) 		Altostratus,ice,
mid cloud fraction(%) 		Nimbostratus,ice,
mid cloud fraction(%)

Cirrus
high cloud fraction(%) 		Cirrostratus
high cloud fraction(%) 		Deep convective
high cloud fraction(%)

Ice/snow cover TOVS surface pressure TOVS near-surface air temperature TOVS precipitable water(1000- 685 mb) TOVS precipitable water (685 - 310 mb)

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Readme Contents

Data Set Overview
Sponsor
Original Archive
Future Updates

The Data
Characteristics
Source

The Files
Format
Name and Directory Information
Companion Software

The Science
Theoretical Basis of Data
Processing Sequence and Algorithms
Scientific Potential of Data
Validation of Data

Contacts
Points of Contact

References

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Data Set Overview

Cloud cover is an extremely important climate parameter. Though only some clouds bring precipitation, all affect the heat exchange between the Sun, Earth and cold space, and they are also quite variable in time, from region to region, and in the effects they produce. Clouds modulate the solar irradiance incident on the Earth's surface (the insolation) and this affects the productivity of plants both on land and in the water as well as the surface temperature and heat budget. Numerous efforts to produce cloud climatologies from both surface and satellite observations have been made. At present the most important of these is the ongoing International Satellite Cloud Climatology Project (ISCCP). A combination of satellite-measured radiances, TOVS atmospheric temperature/humidity and ice/snow data are used by ISCCP to produce a global dataset on cloud and surface variables. An overview of the Project and the data products is given in Rossow and Schiffer (1991); the algorithm and its effectiveness are described by Rossow and Garder (1993a&b) while Rossow et al. (1993) compare the resulting products to other cloud climatologies. The ISCCP D-series, which is described in this readme and more extensively in Rossow et al. (1996), is a revised version of the C-series dataset. The ISCCP D2-series products are gridded data averaged over each month. These data set(presently covering the period 1986-1987 & 1989-1992) are originally produced on an equal area map grids which has a constant 2.5 degree latitude increments and varaiable longitude increments ranging from 2.5 degree at the equator to 120 degree at the pole. The Goddard DAAC has regridded a subset (36 out of the original 130 parameters) of these dataset to 1x1 degree equal angle grid for inclusion in the Interdisciplinary data collection.

Sponsor
The production and distribution of this data set are funded by NASA's Earth Science enterprise. The data are not copyrighted; however, we request that when you publish data or results using these data please acknowledge as follows:

The authors wish to thank William B. Rossow, and the Goddard Institute for Space Studies (GISS),New York, NY, USA, for the production of this data set, and the Distributed Active Archive Center (Code 902) at the Goddard Space Flight Center, Greenbelt, MD, 20771, for putting these data in their present format and distributing them. These distribution activities were sponsored by NASA's Earth Science enterprise.

Original Archive
The original ISCCP D2 cloud data set was produceded by the Goddard Institute for Space Studies at New York, NY. This data set in its original format can be obtained from NASA Langley Research Center, Distributed Active Archive Center . This is the long term archive for the data and also the source for detailed information concerning the ISCCP D series and other data products.

Note: The format of the data in the GISS and LaRC archives is different than that stored at the Goddard DAAC. The Goddard DAAC regridded the original equal area grid to a 1 by 1degree (lat/lon) equal angle grid. For more details see Processing Sequence and Algorithms.

Future Updates
Additional years of the ISCCP D-2 data are being processed.

The Data

The monthly mean data is presented on 1x1 degree latitude-longitude world grid that starts at (89.5N, 179.5W) and runs eastward and southward to latitude 89.5 S.

The original ISCCP D1 (daily) and D2 (monthly mean) products were calculated on an approximately equal area world grid (280x280 km^2) which is equivalent to a 2.5x2.5 degree latitude-longitude grid at the equator. The latitude band widths were fixed to 2.5 degree and grid size along the longitude was varied to provide an integer number of cells in a latitude zone and grid cell area as close to an equatorial grid cell as possible. Map grids started from south pole to north pole. In each latitude zone, all longitudes were indexed in order from the Greenwich meridian eastward(0 - 360 degree) before going to the next latitude zone. Here we have interpolated a subset (36 out of the original 130 parameters) of the equal area monthly mean products to a 1x1 degree grid for easy comparison to the other Interdisciplinary Data Collection products.

Characteristics

Source
ISCCP was established in 1982 as part of the World Climate Research Programme (WCRP) to collect and analyze satellite radiance measurements to infer the global distribution of clouds, their properties, and their diurnal, seasonal, and interannual variations. Data collection began on 1 July 1983 and is currently planned to continue through 30 June 2000.

The first phase of the project produced the gridded, 3-hourly (stage C1) and monthly (stage C2) datasets (Rossow, and Schiffer 1991, Rossow et al. 1989). A subset of the monthly mean C2 data, consisting of six parameters and covering the period of July 1983 to June 1991, is available as part of the Interdescipline Dataset Collection. In the second phase of the project new versions of products (D-series) have been produced, with the addition of a 30 km research product. The processing of the D-series data is on-going.

There are four principle product levels:

The resulting datasets and analysis products are being used to improve understanding and modeling of the role of clouds in climate, with the primary focus being the elucidation of the effects of clouds on the radiation balance. These data can also used to support many other cloud studies, including understanding of the hydrological cycle.

Data are collected from the suite of weather satellites operated by several nations and processed by several groups in government agencies, laboratories, and universities. For each operational satellite, a Satellite Processing Center (SPC) collects the raw satellite data and sends it to the Global Processing Center (GPC). The Correlative Data Center coordinates the delivery of other satellite and conventional weather data to the GPC. The Satellite Calibration Center (SCC) normalizes the calibration of the geostationary satellites with respect to a polar orbiter satellite standard. All ISCCP data products are archived at the NASA Langley Research Center, Distributed Active Archive Center.

The satellites involved in the D-version products are listed in the table below:

Temporal and Regional Coverage
Satellite
Sensor
start
end
Longitudes
NOAA-7AVHRR07/8301/85 global
NOAA-8AVHRR10/8306/84 global
NOAA-9AVHRR02/8510/88 global
NOAA-10AVHRR12/8608/91 global
NOAA-11AVHRR11/8809/94 global
NOAA-12AVHRR09/91----- global
NOAA-14AVHRR02/95----- global
GOES-5VISSR07/8307/84 112W-50W
GOES-6VISSR07/8301/89 135W-98W
GOES-7VISSR04/87 04/92135W-98W
05-92-----112W-50W
GOES-8I-M Imager03/95---- 112W-50W
GOES-9I-M Imager--------- 135W-98W
METEOSAT-2MIR07/8307/88 60W-60E
METEOSAT-3MIR 08/8806/8960W-60E
02/9004/9060W-60E
05/9204/95112W-50W
METEOSAT-4MIR 07/8901/9060W-60E
05/9001/9460W-60E
METEOSAT-5MIR06/95----- 60W-60E
GMS-1VISSR02/8405/84 140E
GMS-2VISSR 07/8301/84140E
07/8409/84140E
GMS-3VISSR10/8411/89 160W-80E
GMS-4VISSR12/8908/91 160W-80E
GMS-5VISSR09/91---- 160W-80E
INSAT-1VHRR04/8803/89 74.5E


Notes: NOAA-7, 9, 11 & 14 were/are afternoon polar orbiting satellites, while NOAA 8, 10 12 were/are morning polar orbiting satellites. NOAA-14 was launched as a afternoon satellite, because NOAA-13 launched failed. METEOSAT-3 was re-positioned, in May 1992, to a GOES-East position.

The Files

Compressed:

The data files have been compressed using Lempel-Ziv coding. Files with a .gz ending are compressed versions of the .bin file. When decompressing the files use the -N option so that the original .bin file name ending is restored. For additional information on decompression see aareadme file in the directory:

software/decompression/

Uncompressed:

The ISCCP subset presented here consists of 3132 datafiles (=87 monthly mean data filess per parameter x 36 parameters). Though size of single data file is only .259 MB but if whole 4 years worth of data is downloaded it would require ~476 MB of disk storage.

Format

Name and Directory Information

Naming Convention:

The file naming convention for the monthly files is

isccp_d2.pppppp.1nmegg.[yymm].ddd

where
isccp_d2 = data product designator
pppppp = parameter (cldfrc,cldtmp,cldprs,cldtau,...)
1 = number of levels(=1)
n = pressure levels for vertical coordinate, (n=not applicable)
m = temporal period, (m = monthly)
e = horizontal grid resolution, (e = 1 x 1 degree)
gg = spatial coverage, gg = global (land and ocean)
yy = year
mm = month
ddd = file type designation, (gz=compressed, bin=binary, ctl=GrADS control file)

NOTE: When decompressing the data files be sure to use the -N option. This will restore the original .bin filename. For additional information on decompression see the format section of this readme and the aareadme file in the directory:

software/decompression/

Directory Path:

/data/radiation_clouds/isccp_d2/pppppp/yyyy/

where pppppp is the parameter and yyyy is year.

Companion Software
Several software packages have been made available on the CIDC CD-ROM set. The Grid Analysis and Display System (GrADS) is an interactive desktop tool that is currently in use worldwide for the analysis and display of earth science data. GrADS meta-data files (.ctl) have been supplied for each of the data sets. A GrADS gui interface has been created for use with the CIDC data. See the GrADS document for information on how to use the gui interface.

Decompression software for PC and Macintosh platforms have been supplied for datasets which are compressed on the CIDC CD-ROM set. For additional information on the decompression software see the aareadme file in the directory:

software/decompression/

Sample programs in FORTRAN, C and IDL languages have also been made available to read these data. You may also acquire this software by accessing the software/read_cidc_sftwr directory on each of the CIDC CD-ROMs

The Science
Theoretical Basis of Data
The ISCCP cloud analysis has two main components, cloud detection and radiative analysis, respectively. The cloud detection component has four major steps, the first two were part of the C-series cloud product (Rossow and Garder 1993a), and the last two are new to the D-series. The first three steps, of the cloud detection component, produce the refined clear-sky radiances, while the fourth step is a final threshold test using the refined clear-sky radiances. A brief synopsis of the four steps are as follows:

  1. The first estimate of the clear sky radiance values is derived by performing a series of test (Rossow et al. 1989) using space-time variations of the IR and VIS radiances. The VIS radiance are normalized and are expressed as fractional values of the sensor measurement when looking directly at the Sun. These unitless radiances go to zero as the solar zenith angle approaches 90 degrees; reflectance, on the other hand normally increase.

  2. The first threshold test is performed by determining which radiance measurements deviate from the first clear sky values by an amount greater than the uncertainty in the estimated clear radiances.

  3. Additional test are performed to remove some infrequent errors in the clear sky radiances that occur under certain circumstances. These test are performed based on the results of the first two test. Estimates of the daytime clear solar reflectance for the polar orbiting satellites NIR channel, are also acquired.

  4. The final step involves repeating a threshold test using the final refined clear sky radiances, produced from the first three step, with three changes:

    • the IR thresholds for land surfaces are reduced by 2.0 K,
    • the VIS threshold test is changed to a test of reflectance values instead of radiance values, and
    • NIR threshold test is performed for polar orbiting data over ice and snow-covered surfaces only

The first threshold test is done only on the IR and VIS, comparing them to the clear sky radiance derived in the first step. Any observed radiance which varies from the corresponding clear sky radiance by more than the threshold values is regarded as cloudy All remaining values are called clear. The threshold values vary (see table below) depending on surface type.

First Cloud Threshold Values (Rossow et al., 1996)
RADIANCE SURFACE TYPES
1 2 3 4
IR (K) 2.5 4.0 6.0 8.0
VIS (fraction of Sun looking measurement) 0.03 0.03 0.06 0.06
IR SURFACE TYPES:
Type 1 = "low variability" water - all open water except Type 2,
Type 2 = "high variability" water - water within 75 km of a coastline, water within 50 km of sea ice, or sea ice-covered water,
Type 3 = "low variability" land - all open land including land within 50 km of a coastline or snow-covered land except Type 4,
Type 4 = "high variability" land - high topography pixels (height > 1750 m), all pixels within 300 km regions that are rough topography (standard deviation of heights > 1000 m) or that are high topography (mean height > 2500 m), or permanently ice-covered locations (Iceland, Greenland and Anarctica).
VIS SURFACE TYPES:
The VIS types are basically divided into two groups. The first group consists of all open water including near-coast and sea ice-covered water. This group uses the threshold value 0.03. The second group consist of all land type include snow and ice covered land. This group uses the threshold value 0.06.

In the fourth step the threshold test is repeated on the final clear sky radiance using the threshold values, for IR, VIS, NIR and TNIR (NIR brightness temperature) listed below.

Final Cloud Detection Threshold Interval Values
(Rossow et al., 1996)
WAVELENGTH SURFACE TYPES
1 2 3 4
IR (K) 2.5 3.5 4.0 6.0
VIS Reflectance 0.030 0.030 0.060 0.090
VIS Radiance Limit 0.025 0.025 0.040 0.040
TNIR (K) 8.0 8.0 8.0 8.0
NIR Reflectance 0.045 0.045 0.055 0.055
Surface types for the IR test:
Type 1 = open water,
Type 2 = near-coastal water, sea ice margin and sea ice,
Type 3 = open land, and
Type 4 = near-coastal land, high topography, snow margin, and snow and ice-covered land.
Surface types for the VIS & NIR test:
The same four surface types are used for the VIS and NIR tests, except the sea ice margin and sea ice are changed to Type 3.

In the radiation analysis component of the ISCCP cloud analysis, surface properties are retrieved from the final clear sky radiance, and used with the atmospheric data to do further analyses of individual pixel radiances. From this analysis surface properties and cloud properties are deduced for each individual pixel, based on whether the threshold tests indicate clear or cloudy conditions.

Information on cloud types are derived based on ranges of values (see table below) from the Cloud Top Pressure (cldprs) and Cloud Optical Thickness (cldtau).

Cloud Types
(Rossow et al., 1996)
NAME CLDPRS RANGE (mb) CLDTAU RANGE TYPE
LOW
Cumulus > 680 <= 3.55 liquid, ice
Stratocumulus > 680 3.55 - 22.63 liquid, ice
Stratus > 680 > 22.63 liquid, ice
MIDDLE
Altocumulus 440 - 680 <= 3.55 liquid, ice
Altostratus 440 - 680 3.55 - 22.63 liquid, ice
Nimbostratus 440 - 680 > 22.64 liquid, ice
HIGH
Cirrus <= 440 <= 3.55 ice
Cirrostratus <= 440 3.55 - 22.63 ice
Deep Convection <= 440 > 22.63 ice

The ISCCP products include the infrared estimates for both day and night observations but the combined VIS/IR/NIR products, cloud optical thickness estimate and surface reflectance are available only during the day. The combined VIS/IR/NIR products are more accurate. Therefore a correction is made to the infrared only nighttime cloud products when the mean diurnal total cloud fraction is calculated. The correction is determined from a comparison of daytime VIS/IR/NIR and IR only cloud fractions.

Processing Sequence and Algorithms
The ISCCP project collects visible (~0.6 micrometers), near infrared (~3.7 micrometers) and thermal infrared (~11 micrometers) data from several geostationary weather satellites and from the National Oceanic and Atmospheric Administration (NOAA) operational meteorological satellites which are in Sun-synchronous near polar orbits. A subset of the monthly mean (D2) products was incorporated into the Interdiscipline Data Collection. A hierarchy of satellite data, which indicated a preference for data from one satellite over another, was used if data were available from more than one satellite for one location during a single time step. Geostationary satellite data were given higher preference over the NOAA polar orbiting data, for latitudes of 55 degrees and below. Data with the smallest satellite zenith angle are preferred in cases where geostationary data overlaps. NOAA polar orbiting data are used above 55 degrees latitude, due to the high satellite zenith angle of geostationary data above that latitude. The geostationary satellites involved are the GOES 5-9, METEOSAT 2-5 and the GMS 1-5, and INSAT-1. Due to numerous problems 3-hourly coverage from 55 N to 55 S is not always available in some regions. This is particularly true around India. The NOAA 7-14 satellites, which observe all regions on the Earth at least twice a day, were used to fill data gaps in these regions.

Atmospheric effects on the satellite radiances were taken into account, in the ISCCP D series processing, by using atmospheric temperature and humidity profile data and ozone column abundance data in the radiative model. The TIROS Operational Vertical Sounder (TOVS) atmospheric datasets and the NOAA GFDL temperature/humidity and NIMBUS 4 BUV ozone climatology datasets were used for this purpose. In our interdisciplinary data collection, we have included four parameters of the TOVS datasets that are used in the ISCCP algorithm and are part of the ISCCP-D dataset.

A merged ice/snow cover dataset was develop by ISCCP to help differentiate between clear and cloudy scenes in high latitude and higher elevation regions. The input datasets used in this merged ice/snow cover product are the U.S. Navy weekly sea ice analyses (from paper maps) through 1991, sea ice derived from daily analysis of the SSM/I microwave measurements after 1991, and NOAA's Synoptic Analysis Branch northern hemisphere weekly snow and ice cover charts. Three other datasets along with the ice/snow cover were used to specify different surface types as a function of latitude/longitude:

  1. Masaki (1972) land/water/coast classification
  2. U.S. Navy topographic height dataset from NCAR
  3. Matthews (1983) land vegetation types

The current version of the ISCCP C2-series was released 1991. Since then the C-series data has gone through an extensive review and a number of improvements have been recommended. The D-series data was developed as a result of this process. A synopses of the changes between the C-series and D-series datasets is listed below.

Highlights of differences between the C-series and D-series cloud data
(Rossow et al., 1996)
Radiance Calibrations Revised VIS and IR calibrations to eliminate spurious changes between different reference polar orbits. (Brest et al. 1996)
Revised normalizations of geostationary satellite calibrations to eliminate occasional short-term deviations. (Brest et al. 1996)
Cloud Detection Improved cirrus detection over land by lowering IR threshold from 6 K to 4 K
Improved polar cloud detection over ice and snow surfaces by lowering VIS threshold from 0.12 to 0.06 and by using threshold test on 3.7 micrometer radiances
Improved detection of low clouds at high latitudes by changing to VIS reflectance threshold test
Radiation Model Improved treatment of cold (top temperature < 260 K) clouds by using ice polycrystal scattering phase function to retrieve optical thickness and top temperature
Improved retrieval of cloud optical thickness over ice and snow surfaces using 3.7 micrometer radiances
Improved retrieval of cloud top temperatures by including effects of IR scattering
Improved retrieval of surface and cloud top temperatures by adopting new treatment of water vapor continuum absorption in IR
Gridded Product Contents Better resolved variations of optically thicker clouds by adding 6th optical thickness category
Added correct cloud water path parameter
Reported actual average values of cloud top temperature, pressure, optical thickness and water path for each of nine cloud types defined by cloud top pressure and optical thickness in the 3-hourly dataset
Reported separate cloud properties for liquid and ice forms of low and middle-level clouds
Provided conversion of cloud top pressures to cloud top heights above mean sea level based on atmospheric temperature profile
added cloud amount frequency distribution to monthly dataset
Increase Resolution Archived pixel-level cloud products with resolution of 30 km and 3 hr

The ISCCP D2 data sets described in this readme have been reformatted for a subset of parameters, so that they are consistent with other CIDC data sets.

Rergridding from an equal area world grid to 1 x 1 degree equal angle grid

For consistency with the other data sets in the Goddard DAAC's Interdisciplinary Data Collection, the original data in HDF format was extracted from the long term data archive at Langley Research Center (LaRC) DAAC and converted from 8-bit quantities (scaled integer values) into 32-bit floating point quantities (unscaled values) and regridded to 1 x 1 degree equal angle grid from their original approximate 280 km x 280 km equal area grid maps.

In the regridding process the original data at equal area grid with constant 2.5 degree latitudinal increments and variable longitudinal increments, ranging from 2.5 degree at the equator to 120 degree at the pole were replicated as many time as needed to produce a 1 by 1 degree latitude-longitude equal angle product. A weighted average was used in cases where two equal area grid cells overlapped a single one degree grid cell. This weighting was based on the percent area each original equal area grid cells covered in the 1 degree area. Changes in grid area due to changing latitude were taken into consideration in this procedure. This regridding method is different from the one used by ISCCP to convert their data from approximate 280km xx 280 km equal area to 2.5 by 2.5 degree equal angle. Their method did not use a weighted average in cases where grid cells overlapped, but instead chose one of the grid cell values over another. As a result the values from the statistical files in the original dataset can not be related to all of the grid values in this regridded dataset.

Also, the south to north orientation was reversed, and for each latitude zone, data along the longitude was made to start from 180 west going towards east,again for conformity to the existing criteria, and gif images, created from the resultant files, were visually inspected to assure that the data was free of artifacts introduced by these procedures.

Scientific Potential of Data
The monthly mean summary of the data set given here can be used for many types of climate studies including:

Validation of Data
The ISCCP D-series product was produced in part as a result of intensive research done on the C-series data, in which over 200 research articles have been written. Similar research is continuing with the D-series product and includes comparison of observations from an on-going series of field experiments.

Rossow et al. (1996) discuss the differences between the ISCCP C series and D series algorithms, as well as on-going validation efforts being made on the D-series product. The ISCCP project has documented known and fixed data errors in their dataset.

Contacts

Points of Contact
For information about or assistance in using any DAAC data, contact

EOS Goddard Distributed Active Archive Center (DAAC)
Code 902
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
e-mail: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)

For questions about ISCCP science, contact

Dr. William B. Rossow
NASA Goddard Institute for Space Studies
2880 Broadway
New York, NY 10025 USA
e-mail: clwbr@giss.nasa.gov
(212) 678-5567

The long term archives for the ISCCP data products are at:
(Not including the Stage DX 30 km resolution data)

ISCCP Central Archives
National Climatic Data Center
Federal Building 151 Patton Ave.
Asheville, NC 28801-5001
email: satorder@ncdc.noaa.gov
(704) 271-4800 (option #5) (voice)
(704) 271-4876

(Including the Stage DX 30 km resolution data)

Langley DAAC
Mail Stop 157B
NASA Langley Research Center
Hampton, VA 23681-0001
e-mail: userserv@eosdis.larc.nasa.gov
telnet eosdis.larc.nasa.gov
(804) 864-8656 (voice)
(804) 864-9807 (fax)

References

Brest, C.L., and W.B. Rossow, 1992. Radiometric calibration and monitoring of NOAA AVHRR data for ISCCP. Int. J. Remote Sens., 13:235-273.

Brest, C.L., W.B. Rossow, and M.D. Roiter, 1996. Update on ISCCP calibration for visible and infrared radiances. J. Atmos. Ocean. Tech., (submitted).

Kyle, H. L., M. Weiss, and P. Ardanuy, 1995. Cloud, surface temperature, and outgoing longwave radiation for the period from 1979 to 1990, J. Climate, 8:2644-2658.

Masaki, G.T., 1972 (rev., 1976). The Wolf Plotting and Contouring Package. GSFC Computer Program Lib. #A00227, Computer Sciences Corporation, Goddard Space Flight Center, Greenbelt, MD, 187 pp.

Matthews, E., 1983. Global vegetation and land use: New high-resolution data bases for climate studies. J. Clim. Appl. Meteor., 26:170-202.

Rossow, W. B., and R. A. Schiffer, 1991. ISCCP cloud data products, Bull. Amer. Meteor. Soc., 72:2-20.

Rossow, W. B., and L. C. Garder, 1993a. Cloud detection using satellite measurements of infrared and visible radiances for ISCCP, J. Climate, 6: 2341-2369.

Rossow, W. B., and L. C. Garder, 1993b. Validation of ISCCP cloud detection, J. Climate, 6: 2370-2393.

Rossow, W. B., and Y.-C Zhang, 1995. Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets: 2. Validation and first results, J. Geophys. Res., 100:1167-1197.

Rossow, W. B., E. Kinsella, A. Wolf, and L. Garder, 1987. International satellite Cloud Climatology Project (ISCCP) Description of Reduced Resolution Radiance Data. In, WMO/TD No. 58, (eds), World Meteorological Organization, Geneva, 143 pp.

Rossow, W.B., L.C. Garder, and A.A. Lacis, 1989. Global seasonal cloud variations from satellite radiance measurements. Part I: Sensitivity of Analysis. J. Climate, 2:419-458.

Rossow, W. B., A. W. Walker, and L. C. Garder, 1993. Comparison of ISCCP and other cloud amounts, J. Climate, 6:2394-2418.

Rossow, W. B., A. W. Walker, D. E. Beuschel, and M. D. Roiter, 1996. International Satellite Cloud Climatology Project (ISCCP): documentation of new cloud datasets, 115 pages, available on internet at : http://isccp.giss.nasa.gov/documents.html

Sohn, B. J., and E. A. Smith, 1992. Global energy transports and the influence of cloud on transport requirements: A satellite analyses, J. Climate, 5:717-734.

NASA GSFC Goddard DAAC cidc site
NASAGoddardGDAACCIDC

Last update:Mon Aug 18 17:10:29 EDT 1997
Page Author: Page Author: James McManus -- mcmanus@daac.gsfc.nasa.gov
Web Curator: Daniel Ziskin -- ziskin@daac.gsfc.nasa.gov
NASA official: Paul Chan, DAAC Manager -- chan@daac.gsfc.nasa.gov