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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.


  • CAPABLE/CRAVE Full Site Photo from left to right site enclosures: 1196A NASA LaRC, MPLnet, Virginia DEQ
    CAPABLE/CRAVE Full Site Photo from left to right site enclosures: 1196A NASA LaRC, MPLnet, Virginia DEQ

  • NASA LaRC NAST-I and HU ASSIST side-by-side for intercomparison
    NASA LaRC NAST-I and HU ASSIST side-by-side for intercomparison

  • Virginia DEQ, NASA and Penn State-NATIVE Enclosures (from right to left)
    Virginia DEQ, NASA and Penn State-NATIVE Enclosures (from right to left)

  • Ozone-sonde away.
    Ozone-sonde away.
  • About to lift.
    About to lift.
PurpleAir PA-II-SD Air Quality Sensor
Laser Particle Counters
Type (2) PMS5003
Range of measurement 0.3, 0.5, 1.0, 2.5, 5.0, & 10 μm
Counting efficiency 50% at 0.3μm & 98% at ≥0.5μm
Effective range
(PM2.5 standard)*
0 to 500 μg/m³
Maximum range (PM2.5 standard)* ≥1000 μg/m³
Maximum consistency error (PM2.5 standard) ±10% at 100 to 500μg/m³ & ±10μg/m³ at 0 to 100μg/m³
Standard Volume 0.1 Litre
Single response time ≤1 second
Total response time ≤10 seconds
Pressure, Temperature, & Humidity Sensor
Type BME280
Temperature range -40°F to 185°F (-40°C to 85°C)
Pressure range 300 to 1100 hPa
Humidity Response time (τ63%): 1 s
Accuracy tolerance: ±3% RH
Hysteresis: ≤2% RH

Pandora capabilities










Total Column O3, NO2, HCHO, SO2, H2O, BrO

0.01 DU

0.1 DU



Virginia Department of Environment Quality in-situ instrumentation






Thermo Scientific 42C (Molybdenum converter)

60 s

NO and NOx

50 pptv


Teledyne API 200EU w/ photolytic converter
(EPA) PI-Szykman

20 s


50 pptv


Thermo Scientific 49C (VADEQ)

20 s


1 ppbv


Thermo Scientific 48i (VADEQ)

60 s


40 ppbv


Thermo Scientific 43i (VADEQ)

80 s


0.2 ppbv


Thermo Scientific 1400AB TEOM (VADEQ)

600 s

PM2.5 (continuous)


1 3%

Thermo Scientific Partisol Plus 2025 (VADEQ)

24 hr

PM2.5 (filter-based FRM)- 1/3 days



Large area view.
Latitude: 37.1038
Longitude: -76.3872
Elevation: 3 m Above sea level
Scenes: urban, marsh, bay, river and farm.


  • The inner red circle is a 20km CERES foot print centered on the BSRN-LRC site.
  • The pink circle represents a possible tangential 20km foot print.
  • The middle red circle represents the area in which a 20km foot print could fall and still see the site.
  • Yellow is a sample 40 deg off nadir foot print.
  • The outer red circle is the region which would be seen by a possible 40 deg off nadir foot print.
The BSRN-LRC sun tracker at the NASA Langley Research Center on a snowy day (02/20/2015) The BSRN-LRC sun tracker at the NASA Langley Research Center on a snowy day (02/20/2015)
CAPABLE-BSRN Google Site Location Image

Team Satellite Sensor G/L Dates Number of obs Phase angle range (°)
CMA FY-3C MERSI LEO 2013-2014 9 [43 57]
CMA FY-2D VISSR GEO 2007-2014
CMA FY-2E VISSR GEO 2010-2014
CMA FY-2F VISSR GEO 2012-2014
JMA MTSAT-2 IMAGER GEO 2010-2013 62 [-138,147]
JMA GMS5 VISSR GEO 1995-2003 50 [-94,96]
JMA Himawari-8 AHI GEO 2014- -
EUMETSAT MSG1 SEVIRI GEO 2003-2014 380/43 [-150,152]
EUMETSAT MSG2 SEVIRI GEO 2006-2014 312/54 [-147,150]
EUMETSAT MSG3 SEVIRI GEO 2013-2014 45/7 [-144,143]
EUMETSAT MET7 MVIRI GEO 1998-2014 128 [-147,144]
CNES Pleiades-1A PHR LEO 2012 10 [+/-40]
CNES Pleiades-1B PHR LEO 2013-2014 10 [+/-40]
NASA-MODIS Terra MODIS LEO 2000-2014 136 [54,56]
NASA-MODIS Aqua MODIS LEO 2002-2014 117 [-54,-56]
NASA-VIIRS NPP VIIRS LEO 2012-2014 20 [50,52]
NASA-OBPG SeaStar SeaWiFS LEO 1997-2010 204 (<10, [27-66])
NASA/USGS Landsat-8 OLI LEO 2013-2014 3 [-7]
NOAA-STAR NPP VIIRS LEO 2011-2014 19 [-52,-50]
NOAA GOES-10 IMAGER GEO 1998-2006 33 [-66, 81]
NOAA GOES-11 IMAGER GEO 2006-2007 10 [-62, 57]
NOAA GOES-12 IMAGER GEO 2003-2010 49 [-83, 66]
NOAA GOES-15 IMAGER GEO 2012-2013 28 [-52, 69]
VITO Proba-V VGT-P LEO 2013-2014 25 [-7]
KMA COMS MI GEO 2010-2014 60
AIST Terra ASTER LEO 1999-2014 1 -27.7
ISRO OceanSat2 OCM-2 LEO 2009-2014 2

The NASA Prediction Of Worldwide Energy Resources (POWER) Project improves the accessibility and usage NASA Earth Observations (EO) supporting community research in three focus areas: 1) renewable energy development, 2) building energy efficiency, and 3) agroclimatology applications. The latest POWER version enhances its distribution systems to provide the latest NASA EO source data, be more resilient, support users more effectively, and provide data more efficiently. The update will include hourly-based source Analysis Ready Data (ARD), in addition to enhanced daily, monthly, annual, and climatology ARD. The daily time-series now spans 40 years for meteorology available from 1981 and solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning 20 years from 2001.

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