Data Access

Surface Radiation Budget Data

Total-sky Downward Shortwave Flux

Clear-sky Downward Shortwave Flux

Total-sky Net Shortwave Flux

Total-sky Downward Longwave Flux

Clear-sky Downward Longwave Flux

Total-sky Net Longwave Flux

Cloud Percentage

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

Data Set Overview

The surface radiation budget (SRB) is a basic climate and biosphere parameter which affects the surface heat and moisture budget as well as biological productivity. This data set covers the spectral range of 0.2-50 micrometers, and is divided into two regions: the shortwave (SW, 0.2-5.0) and the thermal longwave (LW, 5.0-50) micrometers. Both the downward and net radiation at the surface are given. It consists of monthly means covering the period (July 1983-June 1991) and was developed by the Radiation Sciences Branch of the Atmospheric Sciences Division at NASA Langley Research Center, Hampton, Virginia. The irradiances are calculated using computationally fast radiative transfer algorithms whose primary input data come from the International Satellite Cloud Climatology Project (ISCCP) C1 products (Darnell et al., 1996; Rossow and Schiffer, 1991). This data set, like the ISCCP products, was developed on an equal area world grid (about 280 km by 280 km). We have regridded the original products to a 1x1 degree grid for easy comparison with the other Climatology Interdisciplinary Data Collection parameters. There have been extensive validation efforts, including comparisons with surface measurements (Gupta et al., 1993a; Whitlock et al., 1995, and Darn ell et al., 1996). Pinker et al. (1995) discuss a number of research fields in which this data will be very useful. The Global Energy and Water-cycle Experiment (GEWEX) SRB Project chose the Langley short- and long-wave SRB algorithms as one pair of two shortwave and two longwave algorithms to produce SRB fluxes for the twelve year study period (July'83 through June'95).

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: Surface Radiation Budget: A Long-term Global Dataset of Shortwave and Longwave Fluxes by Darnell, W. L., W. F. Staylor, N. A. Ritchey, S. K. Gupta, and A. C. Wilber. Thanks are also given to the Distribute Active Archive Center (Code 902.2) at the:

Goddard Space Flight Center, Greenbelt, MD, 20771, for putting these data in their present format and distributing them. These distribution and production activities were sponsored by NASA's Earth Science enterprise.

Original Archive
This data set was developed by the Radiation Sciences Branch of the Atmospheric Sciences Division at NASA Langley Research Center, Hampton, Virginia, and can be obtained from there in its original format. It should be noted that the present data set is different than the 46-month data set of daily and monthly mean surface shortwave fluxes produced at the SRB Satellite Data Analysis Center also at the NASA Langley Research Center. This latter data set was produced under the sponsorship of the World Climate Research Program and can be obtained from the Langley Distributed Active Archive Center (DAAC)

Future Updates
This data set will be updated as new data is made available.

The Data

The original SRB products were generated using the ISCCP C1 (3-hourly) parameters as the chief input. Like ISCCP, they were calculated on an approximately equal area world grid (280x280 km^2).

The monthly mean data presented here has been regridded by the Goddard DAAC from the original equal area grid to a 1x1 degree latitude-longitude equal angle grid that starts at (89.5N, 179.5W) and runs eastward and southward to latitude 89.5 S. This has been done for easy comparison to the other Climatology Interdisciplinary Data Collection products.

Characteristics

Parameters

Parameter	Description	Units
SWCS	clear-sky downward SW flux	W/m^2
SWDWN	all-sky downward SW flux (insolation)	W/m^2
SWNET	total sky net SW flux (absorbed)	W/m^2
LWCS	clear-sky downward LW flux	W/m^2
LWDWN	all-sky downward LW flux	W/m^2
LWNET	all-sky net LW flux	W/m^2
CP	cloud fraction	percent

• Temporal Coverage: July 1983 - June 1991
• Temporal Resolution: Monthly means
• Spatial Coverage: Global
• Spatial Resolution: 1 degree x 1 degree

The following equations can be used to compute additional SRB parameters:

Surface albedo,

AS = 1 - (SWNET/SWDWN)

SW & LW surface cloud forcing

SWCF = SWDWN - SWCS

LWCF = LWDWN - LWCS

Surface total cloud forcing and total net radiation

TOTCF = SWCF + LWCF

TOTNET = SWNET + LWNET

Surface cloud forcing is defined as the difference between the average (all-sky) surface flux and the clear-sky surface flux. The surface albedo (AS) is defined as the ratio of the reflected SW to the incident SW.

Source
Starting in the mid-1980s the algorithms used to calculate these products were developed by the Radiation Sciences Branch of the Atmospheric Sciences Division at NASA Langley Research Center. The shortwave algorithm was developed by W. F. Staylor (Darnell et al., 1992). The current model is a modified version of an earlier model by Darnell et al. (1988). The longwave algorithm is known as the Gupta algorithm (Gupta et al., 1992). They are both computational fast radiative transfer algorithms which utilize the International Satellite Cloud Climatology Project (ISCCP)-C1 (daily) data (Rossow and Schiffer, 1989; Rossow and Garder 1993) as their primary input data. Using data from American, European, and Japanese operational meteorological satellites ISCCP determines the global cloud cover every three hours. The ISCCP C1 data set also contains daily meteorological information required by the atmospheric radiative transfer programs used as an aid in identifying the clouds and their properties. The mean cloud parameters are determined for 6596 approximately equal area (280 km by 280 km) regions which comprise a fixed world grid. The flux estimates rely principally on the cloud parameters, water vapor burden, temperature profile, and surface temperature from the C1 data (Darnell et al., 1992).

The ISCCP data set started with July 1983 and is still continuing. However the "C" data products just run from July'83 to June'91. The project is now using an improved 'D' cloud algorithm to process new data and to reprocess the old data (Rossow et al., 1996).

Format

• File Size: 259200 bytes, 64800 data values
• Data Format: IEEE floating point notation
• Headers, trailers, and delimiters: none
• Fill value: None
• Continent mask: none (data valid over land and water)
• Orientation: North to South

  Start position: (179.5W, 89.5N)

  End position: (179.5E, 89.5S)

Name and Directory Information

Naming Convention

The file naming convention for the SRB data files is

srb.xxxxxx.1nmegg.[yymm].ddd

where

srb = data product designator

xxxxxx = parameter name ( swdwn, swcs, swnet, lwdwn, lwcs, lwnet, and cp)

1 = number of levels

n = vertical coordinate, n = not applicable

m = temporal period, m = monthly

e = horizontal grid resolution, a = 1 x 1 degree (regrided from 2.5 by 2.5 degree, at equator, equal area)

gg = spatial coverage, gg = global (land and ocean)

yy = year

mm = month

ddd = file type designation, (bin=binary, ctl=GrADS control files)

Directory Path

/data/radiation_clouds/srb/PARMS/YYYY/

where PARMS is parameter name, 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

Theoretical Basis of Data
Determination of the surface radiation budget (SRB) requires information concerning the surface conditions (temperature, reflectivity, emissivity) , the overlying atmosphere ( composition, transmissivity, temperature, etc.), and the top-of-the-atmosphere insolation. Clouds and water vapor are the most important short term atmospheric variables. Atmospheric radiative transfer programs can then use this information to calculate the SRB. In the Langley SRB most of the required input information is taken from the ISCCP-C1 data set. Their algorithms do not require detailed radiative transfer calculations. Rather, parameterized models are set up which depend on important physical parameters such as those mentioned above (Darnell et al., 1992). This permits rapid calculations to be performed.

In the Staylor SW algorithm downward SW flux at the surface is the product of insolation at the TOA, clear-sky atmospheric transmittance and cloud transmittance. Daily insolation requires time integration of the instantaneous values from sunrise to sunset. Insolation at the TOA is a product of the cosine of the solar zenith angle and the distance-corrected solar flux, which is calculated daily using 1365 W/m**2 as the solar flux at 1 Astronomical Unit ( i.e. the mean Earth-Sun distance). The surface albedo was estimated for snow free regions from the ERBE top-of-the-atmosphere albedos.

In the Gupta LW algorithm the most important variables are the surface temperature, atmospheric water vapor, cloud fraction, cloud base altitude and cloud base temperature. The last two are not included among the ISCCP parameters since satellites see only cloud tops. Lacking other information Gupta (1989) estimated climatological cloud base pressures given the ISCCP cloud top pressures. A thickness equivalent to 50 mb was assumed for low clouds (cloud tops below 700 mb). For middle clouds (cloud tops between 700 mb and 400 mb) a thickness equivalent to 100 mb was assumed in the tropics (30 N-30 S latitude) and 50 mb for higher latitudes. For high clouds ( cloud top pressures less than 400 mb) a thickness equivalent to 50 mb was again assumed. In the cases where the cloud base pressure was close to or greater than the surface pressure adjustments were made (Gupta et al., 1992; Darnell et al., 1992). From the cloud base pressures thus obtained, cloud base temperatures were calculated from temperature profiles in the ISCCP-C1 data set. This gives a good mean result but in some instances produces considerable instantaneous and even mean regional error.

Processing Sequence and Algorithms
The equations are discussed in Darnell et al. (1992) with additional detail concerning the longwave algorithm given in Gupta (1989) and Gupta et al. (1992). The following summary is taken from the NASA Langley SRB MODEL DOCUMENTATION signed by N. A. Ritchey, March 20, 1996.

SW Algorithm

In the Staylor SW algorithm atmospheric transmittance is a function of surface pressure, surface albedo, aerosols, and the effective clear-sky atmospheric optical depth. The first three terms account for the atmospheric backscatter of surface reflected rays. The effective clear-sky atmospheric optical depth is a vertical attenuation factor for solar energy and it is the sum of all absorption and scattering processes. These processes include absorption and scattering due to gases and aerosols. The broadband absorption due to water vapor and ozone, and Rayleigh attenuation are estimated using the method of Lacis and Hansen (1974). The broadband absorption due to oxygen and carbon dioxide are approximated using the method of Yamamoto (1962). Aerosol attenuation is based on World Climate Program aerosol models (World Climate Research Program, 1983). It should be noted that the Rayleigh and aerosol attenuation terms are concerned only with backscattering and/or absorption, but not with forward scattering of flux which reaches the surface.

Cloud transmittance is based on a threshold technique which relates boundary values of TOA reflectances for overcast and clear-sky conditions and actual measured conditions (from ISCCP). Overcast reflectances are estimated from a model by Staylor (1985) using the cosines of viewing zenith angle and solar zenith angle, and overcast coefficients. These coefficients are determined monthly for each ISCCP satellite using data for non-snow covered, totally overcast regions having mean cloud optical depths within the top 5 percent of all observations. Clear-sky reflectances are determined by one of several methods depending on the snow cover and surface type. Over oceans, the cosines of viewing zenith angle and solar zenith angle, along with clear-sky coefficients are used. These coefficients are determined for totally-clear oceans for each satellite every month. For snow-free land regions or land regions in which the snow cover does not fluctuate by more than 10 percent during the month, daily TOA clear-sky reflectance values are computed from the clear-sky pixels. The monthly minimum value is used for the entire month. If the snow cover changes by more than 10 percent during the month (determined for 5-day intervals), then the above procedure is applied to the 5-day periods. Measured instantaneous reflectances are the pixel-weighted average of the clear and cloudy reflectances. If no value exists for a day (occurs most frequently in polar regions), a fill value is provided by one of two methods. If a value exists for a longitudinally adjacent region for that day, it is used. If it does not exist, then the previous day's value is used. This procedure is expanded spatially, then temporally until a non-fill value is found.

Daily surface albedo for all-sky conditions is a function of the daily overcast albedo, the daily clear-sky albedo and cloud transmittance. Data from Budyko (see Payne 1972) and Ter-Markariantz (see Kondratyev 1973) were used to estimate clear-sky surface albedos over oceans. Estimates of daily overcast albedos over oceans are based on the fact that under overcast conditions the effective zenith angle of the diffuse rays is about 53 degrees for all zenith angles (cosine = 0.6) and therefore is a constant value of 0.065. Monthly average clear-sky ERBE TOA albedos were used to estimate surface albedos over snow/ice-free land. This approach avoided the need for spectral conversions from narrowband to broadband and from radiances to albedos (Staylor and Wilber 1990). The ERBE data cover the period March 1985 through December 1988 and were used as such for those months. The ERBE derived surface albedos were found to vary less than 1 percent interannually. Therefore, a multi-year monthly average ERBE clear-sky TOA albedo was used to derive surface albedo for the corresponding months outside the ERBE period.

SW cloud forcing is the difference between the total-sky net SW flux and the clear-sky net SW flux.

LW Technique

The LW radiative fluxes (both LWDWN and LWNET) are computed using a fast parameterization which is based on detailed radiative transfer computations (Gupta 1989; Gupta et al. 1992). The inputs for the computation are taken from the ISCCP-C1 datasets. LWDWN is computed as

LWDWN = LWCS + F2 * AC,

where LWCS is the clear-sky LWDWN, F2 is the cloud forcing factor, and AC is the fractional cloud cover. LWNET is computed as

LWNET = LWDWN - SIGMA * TS**4,

where SIGMA is the Stefan-Boltzman constant (5.67E-08 W/(m**2 K**4)), and TS is the surface temperature.

Details of the development and application of the parameterizations of LWCS and F2 in terms of the meteorological parameters are given in Gupta (1989) and Gupta et al. (1992). A very brief description of the parameterizations is presented here.

The clear-sky LWDWN (LWCS) is computed as

LWCS = ( A0 + A1 * V + A2 * V**2 + A3 * V**3 ) * TE**3.7,

where V = ln W, and W is the total water vapor burden of the atmosphere. TE is an effective emitting temperature of the lower troposphere, and is computed as

TE = KS*TS + K1*T1 + K2*T2,

where TS is the surface temperature, T1 is the mean temperature of the first layer in the ISCCP-C1 data (surface to 800mb), and T2 is the same for the second layer (800mb to 680mb). KS, K1, and K2 are weighting factors with values of 0.60, 0.35, and 0.05 respectively. The regression coefficients A0, A1, A2, and A3 have the following values:

A0 = 1.791E-07,
A1 = 2.093E-08,
A2 = -2.748E-09,
A3 = 1.184E-09.

The cloud forcing factor (F2) is computed as

F2 = TCB**4 / ( B0 + B1 * WC + B2 * WC**2 + B3 * WC**3 ),

where TCB is the cloud-base temperature, WC is the water vapor burden below the cloud base, and B0, B1, B2, and B3 are regression coefficients with the following values:

B0 = 4.990E+07,
B1 = 2.688E+06,
B2 = -6.147E+03,
B3 = 8.163E+02.

All fluxes represented here are in W/m**2, temperatures in K, and water vapor burdens in kg/m**2. Cloud-base pressure is obtained by combining cloud-top pressure (available from ISCCP-C1 data) with climatological estimates of cloud thickness which depend upon cloud height and latitude. TCB and WC are computed from the available ISCCP-C1 data using the procedure described in Gupta (1989). The above equation for F2 is used as such when pressure difference between the surface and cloud base is greater than 200 mb. When pressure difference is less than or equal to 200 mb, a modified form of this equation as described in Gupta et al. (1992) is used.

LW cloud forcing is the difference between the total-sky net LW flux and the clear-sky net LW flux.

Total Flux

Total net flux and total cloud forcing are the sum of LW and SW components. For further information, the user is referred to Darnell et al. (1992) and Gupta et al. (1993b).

Re-gridding process done by the Goddard DAAC

Physical Lay Out of Original Data: Each input data file represented the monthly means for the entire globe. Within each file, each line consists of seven radiation parameters. The data are gridded using the ISCCP method of equal area gridding. The equal area map is defined by the area of a 2.5 x 2.5 degree cell at the equator. There are 6596 cells in this map grid. All map cells are determined by a constant 2.5 degree increment in latitude and a variable longitude increment. The longitude increment is selected to provide an integer number of cells in a latitude zone and to give a cell area as close to that of the equatorial cell as possible.

Logical Lay Out of Original Data: Within each file, the data start at the 0 deg. longitude, and -90 deg. latitude, progressing eastward to 360 deg. longitude, and then northward to 90 deg. latitude.

Processing Steps done by the Goddard DAAC; Regrid each latitude and longitude band of data by implementing the following steps:

1. Replicated every data value in each latitude band 360 times, assigning them to a temporary array. For latitude band #1, there were 3 values, each value is replicated 360 times producing a temporary array of 1080 data values. The number of original values in a latitude band increases as you move toward the equator, where there were 144 data values. If the latitude band originally had 144 data values, this would also be replicated 360 producing a temporary array of 51840 data values.

2. For latitude band #1 the first three (temporary array) data values are summed and then divided by the number of original values (3) for that latitude band, with consideration given to the weighting of fill values to data values for that cell. Should the contribution of fill values be 50% or more, then the cell was assigned that fill value, if not then the cell was assigned only the average of the data values composing that cell. This was repeated 359 more times, for every three (temporary array) data values, in affect performing a linear interpolation of the data within the latitude band. If the latitude band had 144 data values, every 144 (temporary array) data values would be summed and then divided by 144.

3. Step 1 and 2 were repeated until all latitude bands have been interpolated.

4. A similar method, discussed above, was used for regridding each longitude band of data. The difference was that the number data values in each longitude band did not vary (there always 144 data values), and the replication was 180.

5. The resulting array of data values were then split and shifted from 0 longitude -> 360 longitude to -180 longitude -> 180 longitude.

6. These data were then flipped from -180 longitude, -90 latitude to -180 longitude, 90 latitude.

Scientific Potential of Data
The surface radiation budget forms the major component of the surface atmosphere energy exchange. As such it is important in studies of the surface temperature, the hydrology cycle, climate and biological productivity.

Information on the surface radiation budget is needed by several international research projects such as the Global Energy Water Cycle Experiment (GEWEX; Chahine, 1992). The GEWEX SRB Project chose the Langley SW and LW algorithms as one pair of two SW and two LW SRB algorithms to produce SRB fluxes for the twelve year study period (July'83 - June'95).

The effect of surface insolation variations on the sea surface temperature has been studied by Seager and Blumenthal (1994) and Liu et al. (1994).

Several other potential uses varying from agronomy to atmospheric physics are discussed by Pinker et al. (1995).

Validation of Data
There has been extensive work done to validate the results and recent descriptions are given in Darnell et al. (1992), Gupta et al. (1992 & 1993a), and Whitlock et al. (1995). The studies include comparison with more detailed radiative transfer models, comparison with the results of other surface radiation budget algorithms and with surface measurements. Ideally, a final error analysis would include comparison with accurate surface measurements. Unfortunately only a few regions of the world have even a mediocre network of surface measurement sites (Whitlock et al., 1995). Wielicki et al. (1995) also state that in most cases the surface measurements of insolation are more accurate than those of downward longwave flux. With these caveats, recent comparison of both the SW and LW calculated fluxes were generally within 10 W/m^2 of the mean monthly surface measurements. Larger errors were found where there are larger uncertainties in the input data such as over snow or ice covered surfaces and where the surface measurement site data did not represent the entire grid box (Darnell et al., 1996). Larger errors in downward SW flux were also found over African and South American locations where aerosols from biomass burning are not accounted for in the SW model (Konzelman et al., 1995). The ground measurements were obtained from the Swiss Federal Institute of Technology's Global Energy Balance Archive and NOAA's Climate Monitoring and Diagnostics Laboratory.

Errors in the fluxes come from the radiation modeling and from the meteorological data. The clear sky results are normally more accurate than cloudy sky fluxes.

Several investigators are presently working on the problem of calculating surface radiation fluxes. The SW surface fluxes have been also calculated by Pinker and Lazlo (1992) for this same period; these are available from the NASA/Langley DAAC. We have also made the su rface insolation, calculated by Bishop and Rossow (1991; see also Bishop et al., 1994), as part of the Climatology Interdisciplinary Data Collection. Zhang et al. (1995) calculated both the SW & LW fluxes for this period but only for every third month. All of these investigators used the same ISCCP data set as the basic source of climate data. Now days, global circulation models (GCMs) also calculate the surface radiation budget, but they normally also calculate the cloud cover. As an example the Climatology Interdisciplinary Data Collection includes a summary of the multiyear output of the 4-D data assimilation produced by the Goddard Data Assimilation Office. This summary includes the net surface SW & LW fluxes.

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

EOS Distributed Active Archive Center(DAAC)

Code 902.2

NASA Goddard Space Flight Center

Greenbelt, Maryland 20771

Internet: daacuso@daac.gsfc.nasa.gov

301-614-5224 (voice)

301-614-5268 (fax)

Bishop, J. K. B., J. McLaren, Z. Garraffo, and W. B. Rossow, 1994: Documentation and description of surface solar irradiance data sets produced for SeaWiFS, A draft document dated (10/30/94), 23 pages, available on the internet at: http://www.giss.nasa.gov/Data/SeaW iFS/

Bishop, J. K. B., and W. B. Rossow, 1991: Spatial and temporal variability of global surface solar irradiance, J. Geophys. Res., 96, 16,839- 16,858.

Chahine, M. T., 1992: The hydrological cycle and its influence on climate, Nature, 359, 373-380.

Darnell, W. L., W. F. Staylor, S. K. Gupta, and F. M. Denn, 1988: Estimation of surface insolation using Sun-synchronous satellite data, J. Climate, 1, 820-835.

Darnell, W. L., W. F. Staylor, S. K. Gupta, N. A. Ritchey, and A. C. Wilber, 1992: Seasonal variation of surface radiation budget derived from International Satellite Cloud Climatology Project C1 data, J. Geophys. Res., 97, 15,741-15,760.

Darnell, W. L., W. G. Staylor, N. A. Ritchey, S. K. Gupta, and A. C. Wilber,1996: Surface Radiation Budget: A Long-term Global Dataset of Shortwave and Longwave Fluxes, EOS Transactions, Electronic Supplement

Gupta, S. K., 1989: A parameterization for longwave surface radiation from Sun-synchronous satellite data. J. Climate, 2, 305-320.

Gupta, S. K., W. L. Darnell, and A. C. Wilber, 1992: A parameterization for longwave surface radiation from satellite data: recent improvements, J. Appl. Meteorol., 31, 1361-1367.

Gupta, S. K., A. C. Wilber, W. L. Darnell, and J. T. Suttles, 1993a: Longwave surface radiation over the globe from satellite data: An error analysis, Int. J. Remote Sens., 14, 95-114.

Gupta, S. K., W. F. Staylor, W. L. Darnell, A. C. Wilber, and N. A. Ritchey, 1993b: Seasonal variation of surface and atmospheric cloud radiative forcing over the globe derived from satellite data. J. Geophys. Res., 98, 20761-20778.

Kondratyev, K. Y., 1973: Radiation characteristics of the atmosphere and the Earth's surface. NASA TTF-678, 580pp.

Konzelman, T., D. R. Cahoon, and C. H. Whitlock, 1995: Impact of biomass burning in equatorial Africa on the downward surface shortwave irradiance: Observations versus calculations. Submitted to J. Geophys. Res. May 1995.

Lacis, A. A. and J. E. Hansen, 1974: A parameterization for the absorption of solar radiation in the earth's atmosphere. J. Atmos. Sci., 31, 118- 133.

Liu, W. T., A. Zhang, and J. K. B. Bishop, 1994: Evaporation and solar irradiance as regulators of sea surface temperature in annual and interannual changes. J. Geophys. Res., 99, 12,623-12637.

Payne, R. E., 1972: albedo of the sea surface. J. Atmos. Sci., 29, 959-970.

Pinker, R. T., and I. Laszlo, 1992: Modeling surface solar irradiance for satellite applications on a global scale, J. Appl. Meteorol., 31, 194- 211.

Pinker, R. T., I. Laszlo, C. H. Whitlock, and T. P. Charlock, 1995: Radiative flux opens new window on climate research, EOS, 76, 145.

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, 1993: Cloud detection using satellite measurements of infrared and visible radiances for ISCCP, J. Climate, 6, 2341-2369.

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, draft document dated January 1996, 115 pages, available on the internet at : http://isccp.giss.nasa.gov/docu ments.html

Seager, R., and M. Benno Blumenthal, 1994: Modeling tropical Pacific sea surface temperature with Satellite-derived solar radiative forcing, J. Climate, 7, 1943-1957.

Staylor, W. F., 1985: Reflection and emission models for clouds derived from Nimbus 7 Earth radiation budget scanner measurements. J. Geophys. Res., 90, 8075-8079.

Staylor, W. F., and A. C. Wilber, 1990: Global surface albedos estimated from ERBE data. Proceedings of AMS Conf. on Atmospheric Radiation, July 23-27, 1990, San Francisco, CA, pp 231-236.

Whitlock, C. H., T. P. Charlock, W. F. Staylor, R. T. Pinker, I. Laszlo, A. Ohmura, H. Gilgen, T. Konzelman, R. C. DiPasquale, C. D. Moats, S. R. LeCroy, and N. A. Ritchey, 1995: First global WCRP shortwave surface radiation budget data set, Bull. Am. Meteorol. Soc., 76, 905-92.

Wielicki, B. A., R. D. Cess, M. D. King, D. A. Randall, and E. R. Harrison, 1995: Mission to Planet Earth: Role of clouds and radiation in climate, Bull. Amer. Meteor. Soc., 76, 2125-2153.

World Climate Research Program, 1983: Experts meeting on aerosols and their climate effects. A. Deepak and H. E. Gerber editors, WCP-55, 107 pp.

Yamamoto, G., 1962: Direct absorption of solar radiation by atmospheric water vapor, carbon dioxide, and molecular oxygen. J. Atmos. Sci., 19, 182-188.

Zhang, Y.-C, W. B. Rossow, and A. A. Lacis, 1995: Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets: 1. Method and sensitivity to input data uncertainties, J. Geophys. Res., 100, 1149-1165.

NASA	Goddard	GDAAC	CIDC