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Release 3/3.1 Model Documentation

GEWEX SW Algorithm

The GEWEX SW algorithm (hereafter GSW) Rel.-3 is a greatly modified version of the method described originally in Pinker and Laszlo (1992) and follows the steps below.

First, extensive look-up tables of clear and cloudy sky atmospheric transmissivity and reflectivity over a zero-albedo surface are produced for five SW bands (0.2-0.4, 0.4-0.5, 0.5-0.6, 0.6-0.7, and 0.7-4.0 μm) for a range of values of column ozone, column water vapor, surface elevation, aerosol and cloud optical depths, aerosol composition, and solar zenith angle using a delta-Eddington method.

Next, clear-sky, clear-sky composite, and cloudy-sky narrowband (NB) visible radiances from
ISCCP DX data are logarithmically averaged on a 3-hourly basis. The three NB radiances are then
converted to NB reflectances by normalizing to the incoming solar irradiance. The NB reflectances are then converted to broadband (BB) reflectances using linear slope and offset factors determined by simulation over five scene types (water, vegetation, desert, snow/ice, cloud). Anisotropic conversion factors from ERBE for the above scene types are then used to convert all three BB reflectances to corresponding TOA BB albedos.

For deriving surface albedos, 33 surface types from Matthews et al. (1985) are grouped into 12 types for which spectral surface albedos are available from Briegleb et al. (1986) that are used only to provide the shape of the albedo spectrum for each surface type. Absolute value of the surface albedo for each surface type is then determined by scaling Briegleb et al. (1986) spectral values by the factor required to produce the same clear-sky composite BB TOA albedo as obtained from ISCCP clear-sky composite radiance. Values of TOA albedo computed using the above surface albedo and the look-up tables are matched with those derived from clear-sky and cloudy-sky ISCCP radiances by adjusting aerosol and cloud optical depths.

Finally, clear- and cloudy-sky fluxes are derived using all of the above information. All-sky fluxes are obtained as the sum of clear and cloudy fluxes weighted by the cloud fraction.

GEWEX LW Algorithm

The GEWEX LW algorithm (hereafter GLW) Rel-3.1 is an improved version of the delta-two/four-stream combination approximation model outlined originally in Fu et al. (1997) and used for the Release-2.0 version of SRB dataset (Stackhouse et al. 2004). Primary improvements for this version are in the use of infrared (IR) radiative parameterization for ice clouds originally based on Fu et al. (1998) and in the absorption in water vapor continuum region based on Kratz and Rose (1999).

GLW uses cloud top information from ISCCP to map cloud vertical distribution on to GEOS-4 temperature profiles. Cloud top pressure information for each pixel within a grid box is then used to identify the high, middle, or low layer in which the cloud resides in accordance with ISCCP definitions. Middle and low cloud layers can have both ice and water types based on a cloud top temperature threshold of less than 260 K for ice clouds consistent with ISCCP criterion. Cloud optical depths for daytime are available from ISCCP data but those for nighttime are inferred using the Fu et al. (1993; 1998) ice and water cloud parameterizations. Optical depths of like phase clouds within each layer are averaged logarithmically to conserve effective emission. Base pressures are assigned to cloud layers assuming constant pressure heights consistent with average thicknesses of these clouds (e.g., Dowling and Radke 1990). Clouds amounts of different types within each layer are added up to arrive at the cloud amount for the layer and cloud amounts of the three layers within a grid box are treated with random overlap for the purpose of computing radiative fluxes. Probabilities of occurrence are computed for each of the 16 possible configurations of the five ISCCP cloud types (middle water over low ice is not allowed).

Fluxes are computed in 12 spectral bands of the model and spectrally integrated separately for each cloud configuration. Finally, the flux for the grid box is computed by weighting these fluxes by the probability of each cloud configuration.

Langley Parameterized SW Algorithm (LPSA)

The LPSA, described in detail in Gupta et al. (2001), consists of physical parameterizations which account for the attenuation of solar radiation in simple terms separately for clear atmosphere and clouds. Surface insolation, Fsd, is computed as:

Fsd = Ftoa Ta Tc

where Ftoa is the corresponding TOA insolation, Ta is the transmittance of the clear atmosphere, and Tc is the transmittance of the clouds (Darnell et al. 1992).

Clear-sky transmittance, was computed as:

Ta = ( 1 + B ) exp(-τz)

where B represents the backscattering of surface reflected radiation by the atmosphere, and τz is the broadband extinction optical depth at solar zenith angle z . Cloud transmittance, was computed using a threshold method (see Gupta et al. 2001) as:

Tc = 0.05 + 0.95 [ (Rovc-Rmeas) / (Rovc-Rclr) ]

where Rovc, Rclr and Rmeas represent values of overcast, clear, and measured reflectances for the grid box respectively.

Langley Parameterized LW Algorithm (LPLA)

The LPLA, described in Gupta et al. (1992), is a fast parameterization developed from an accurate narrowband radiative transfer model (Gupta 1989) where clear-sky component of the downward LW flux (DLF) is computed as:

Fclr = (a0 + a1 w + a2 w2 +a3 w3) x Te3.7

where w (kg/m2) is the column water vapor, Te (K) is the effective emitting temperature of the atmosphere, and a0, a1, a2 and a3 are regression coefficients.

A cloud radiative effect (CRE) term is computed as:

Fcre = Tcb4 /(b0 + b1 wc + b2 wc2 + b3 wc3)

where Tcb is the cloud-base temperature, wc is the water vapor amount below the cloud base, b0, b1, b2, and b3 are regression coefficients.

Finally, all-sky DLF (Fall) is computed as:

Fall = Fclr + FcreAc

where Ac is the fractional cloud amount derived from the ISCCP data.


Each of the algorithms use cloud parameters derived from the DX data of the International Satellite Cloud Climatology Project (ISCCP; Rossow and Schiffer,1999, BAMS, 80, 2261-2287) and temperature and moisture profiles taken from 4-Ddata assimilation products provided by the Data Assimilation Office at NASA GSFCand produced with the Goddard Earth Observing System model version 4 (GEOS-4). GEOS-4 is used in Rel. 3.0. Surface emissivities used in several algorithms are taken from a map developed at NASA LaRC (Wilber et al. 1999; see reference above). Column ozone values for the entire duration of this dataset were obtained primarily from the Total Ozone Mapping Spectrometer (TOMS) archive. For the early period (July 1983-November 1994), TOMS data came from NIMBUS-7 and Meteor-3 satellites. There was an interruption of about 20 months (December 1994-July 1996) after which TOMS data from EP-TOMS became available in August 1996 and continued until December 2004. From January 2005 to June 2006, Stratosphere Monitoring Ozone Blended Analysis (SMOBA) is used. All gaps in TOMS data, including those over the polar night areas every year, were filled with column ozone values from TIROS Operational Vertical Sounder (TOVS) data. Surface albedos are derived with a parameterization using monthly climatological clear-sky TOA albedos which are based on ERBE measurements during the 1985-1989 period.