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[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
[rule]
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: daacuso@daac.gsfc.nasa.gov
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
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
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