NASA Climatology Interdisciplinary Data Collection/NASA Climatology Interdisciplinary Data Collection - Disc 1.iso/readmes/readme.larc_srb

Text File | 1998-03-04 | 35KB | 749 lines

                               [CIDCFTP Data]
                           [SRB IDC Data on FTP]

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

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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

                                   [rule]

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).

The Files

     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/inter_disc/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

The Science

     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.

Contacts

     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: [e-mail]
          301-614-5224 (voice)
          301-614-5268 (fax)

References

     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
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