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David Doelling (NASA)

Title: Research Scientist
Mission/Group/Project: CERES
Technical Focus Area: Climate Science
Study Topics: geostationary derived broadband fluxes, diurnal flux modelling, imager inter-calibration


David Doelling is a Physical Scientist at NASA Langley Research Center since 2008 and has over 30 years of experience in remote sensing. He is the lead for the Clouds and the Earth’s Radiant Energy System (CERES) responsible for the diurnal averaging and spatial gridding of CERES footprint cloud and radiative flux parameters. He incorporates hourly geostationary (GEO) derived broadband fluxes in order to infer the diurnal cycle in between CERES Terra and Aqua measurements. He is also a member of the Geostationary Earth Radiation Budget (GERB), Megha-Tropiques, Deep Space Climate Observatory (DSCOVR) science teams, projects similar to CERES that measure broadband fluxes. Prior to working on CERES he was a member of the Earth Radiation Budget Experiment (ERBE) team. He has studied the orbital sampling errors for proposed satellites as a member of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) science definition team. In addition, he has performed limited scan angle sampling studies for the Radiation Budget Instrument (RBI) project. He is an expert in simultaneous nadir overpass (SNO) inter-calibration, as well as deep convective clouds (DCC), desert and polar ice invariant target calibration. He has pioneered the use of DCC as an invariant calibration target and spectral band adjustment factors (SBAF) derived from SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). He is the Global Spaced-based Inter-calibration System (GSICS) visible calibration lead, which is an international effort designed to uniformly calibrate operational satellite sensors across the many agencies. He is leading the GSICS effort to apply his DCC calibration technique across all current operational GEOs. He has calibrated 38 GEO visible imagers, all 16 AVHRR sensors and many LEO imagers. For CERES, he monitors closely the calibration stability of MODIS, and VIIRS and closely with Jack Xiong’s NASA calibration team.


Select Publications:

  • Hewison, T.J.; Doelling, D.R.; Lukashin, C.; Tobin, D.; O. John, V.; Joro, S.; Bojkov, B. Extending the Global Space-based Inter-Calibration System (GSICS) to Tie Satellite Radiances to an Absolute Scale. Remote Sens. 2020, 12(11), 1782;

  • Rajendra Bhatt, David R. Doelling, Conor O. Haney, Douglas A. Spangenberg, Benjamin R. Scarino, Arun Gopalan, “Clouds and the Earth’s Radiant Energy System strategy for intercalibrating the new-generation geostationary visible imagers,” J. Appl. Rem. Sens. 14(3) 032410 (7 August 2020);

  • Doelling, D.R., C. Haney, R. Bhatt, B. Scarino, A. Gopalan, Geostationary Visible Imager Calibration for the CERES SYN1deg Edition 4 Product, Remote Sens. 2018, 10(2) 288;

  • Doelling, D.R., M. Sun, L.T. Nguyen, M.L. Nordeen, C.O. Haney, D.F. Keyes, P.E. Mlynczak, 2016, Advances in Geostationary-Derived Longwave Fluxes for the CERES Synoptic (SYN1deg) Product, J. Atmos. Oceanic Technol. Vol. 33, March 2016: 503-521, https://DOI: 10.1175/JTECH-D-15-0147.1

  • Scarino, B. R., D. R. Doelling, P. Minnis, A. Gopalan, T. Chee, R. Bhatt, C. Lukashin, 2016, An Online Interface for Calculating Spectral Band Adjustment Factors Derived from SCIAMACHY Hyper-spectral Data, IEEE Trans. Geosci. Remote Sens., Vol. 54, No. 5, 2529-2542, https://doi: 10.1109/TGRS.2015.2502904

  • Doelling, D. R., D. Morstad, B.R. Scarino, R. Bhatt, and A. Gopalan, 2013, The Characterization of Deep Convective Clouds as an Invariant Calibration Target and as a Visible Calibration Technique, IEEE Trans. Geosci. Remote Sens., Vol 51, No. 3, 1147-1159, https://doi: 10.1109/TGRS.2012.2225066.

  • Doelling, D., N. Loeb, D. Keyes, M. Nordeen, D. Morstad, C. Nguyen, B. Wielicki, D. Young, and M. Sun, 2013: Geostationary Enhanced Temporal Interpolation for CERES flux products. J. Atmos. Oceanic Technol. Vol. 30, No. 6, June 2013: 1072-1090, https://doi:10.1175/JTECH-D-12-00136.1


  • NASA Public Service Medal, 2002
  • NASA Exceptional Achievement Medal, 2019

National/International Leadership:

  • CERES TISA lead
  • GSICS visible calibration lead


  • M.S., Atmospheric Science, University of Washington, Seattle, WA
  • B.S., Meteorology, University of Utah, Salt Lake City, UT

Related Websites:

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SD Profiles Contact
  • 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.