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Algorithm Descriptions

Shortwave Algorithm

The NASA/GEWEX Surface Radiation Budget shortwave product (GSW) issued its Release 3.0 in 2011, covering the period from July 1983 through December 2007. (Stackhouse et al., 2011). The core of the code was based on the methodology described in Pinker and Laszlo (1992).

Release 4-IP features multiple updates from Rel. 3. The GSW algorithm uses lookup tables (LUT) of spectral atmospheric transmissivities and reflectivities based on a range of input values (atmosphere or cloud optical depth, cosine solar zenith angle, column water vapor, column ozone, surface elevation). Rel4-IP recalculates these lookup tables using the LaRC CERES modified Fu and Liou algorithm. There are now 18 spectral bands up from the 5 of Rel. 3. Cloud phase is now included, so separate LUT for liquid and ice cloud are generated. Aerosol now includes asymmetry parameter and single scattering albedo as inputs, with the MACv1 dataset providing monthly values of each, allowing a continuous range of aerosol composition (homogenous mixture assumed).

The spectral albedo of ocean has been changed in Rel. 4-IP. Rel. 3 used Briegleb (1986) for ocean albedo which varies direct reflectance with sun angle only. Rel. 4-IP has adopted the scheme of Jin et al. (2004) which incorporates wind speed, aerosol optical depth, and chlorophyll concentration. The new schema give a much lower ocean albedo at shallow sun angles. There is now spectral variation with a higher albedo at visible wavelengths and lower albedo in the near infrared.

Snow and ice spectral albedos have been changed, from the Warren and Wiscombe (1980) approach in Rel. 3, to Jin (1994). The Rel. 3 approach treated all snow and ice the same, whereas theRel. 4-IP approach differentiates snow (generally brighter) from ice (generally darker).

Total Solar Irradiance in Rel. 3 was fixed at Wm-2. Rel. 4-IP uses the daily time series from SORCE/TIM v17 (Kopp and Lean, 2011), with gaps filled by linear interpolation. Values vary within around 1 Wm-2 around a mean of 1360.8 Wm-2.

Flow diagram of SW algorithm

Longwave Algorithm

Like the shortwave algorithm, the NASA/GEWEX longwave algorithm (GLW) released its Rel. 3.1 in 2011. It was an improved version of the delta-two/four-stream combination approximation model outlined originally in Fu et al. (1997).

Rel. 4-IP is also using the Fu-Liou algorithm, but now is using the CERES LaRC Fu-Liou (Rose et al., 2013) which includes updates to cloud physics, gaseous constituents, and surface properties. Sensitivity tests were conducted from on the year 2007 between this version and that used for Rel. 3.1. These tests have shown that the surface pristine-sky (clear-sky for Rel. 3.1) surface downward flux has the largest algorithm change of +2.9Wm-2 for the global annual average. The Pristine OLR change is much smaller, at -0.36 Wm-2.

GLW uses cloud top information from ISCCP-H to map cloud vertical distribution onto 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. All cloud layers can have both ice and water types based on a cloud top temperature threshold of less than 253 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 20 physically possible configurations of the six ISCCP cloud types.

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.

Flow diagram of LW algorithm

  • 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.

The newly available hourly data will provide users the ARD needed to model the energy performance of building systems, providing information directly amenable to decision support tools introducing the industry standard EPW (EnergyPlus Weather file). One of POWER’s partners, Natural Resource Canada’s RETScreen™, will be simultaneously releasing a new version of its software, which will have integrated POWER hourly and daily ARD products. For our agroclimatology users, the ICASA (International Consortium for Agricultural Systems Applications standards) format for the crop modelers has been modernized.

POWER is releasing new user-defined analytic capabilities, including custom climatologies and climatological-based reports for parameter anomalies, ASHRAE® compatible climate design condition statistics, and building climate zones. The ARD and climate analytics will be readily accessible through POWER's integrated services suite, including the Data Access Viewer (DAV). The DAV has been improved to incorporate updated parameter groupings, new analytical capabilities, and the new data formats. Updated methodology documentation and usage tutorials, as well as application developer specific pages, allow users to access to POWER Data efficiently.

+Visit the POWER Program Site to Learn More.