• Doelling, D. R., Lukashin C. , Minnis P. , Scarino B. , and Morstad D. , 2012: Spectral reflectance corrections for satellite intercalibrations using SCIAMACHY data. IEEEGeosci. Remote Sens. Lett, 9, 119123, doi:10.1109/LGRS.2011.2161751.

    • Search Google Scholar
    • Export Citation
  • Harries, J. E., and Coauthors, 2005: The Geostationary Earth Radiation Budget Project. Bull. Amer. Meteor. Soc., 86, 945960.

  • Kato, S., and Loeb N. G. , 2003: Twilight irradiance reflected by the earth estimated from Clouds and the Earth’s Radiant Energy System (CERES) measurements. J. Climate, 16, 26462650.

    • Search Google Scholar
    • Export Citation
  • Kato, S., and Loeb N. G. , 2005: Top-of-atmosphere shortwave broadband observed radiance and estimated irradiance over polar regions from Clouds and the Earth’s Radiant Energy System (CERES) instruments on Terra. J. Geophys. Res., 110, D07202, doi:10.1029/2004JD005308.

    • Search Google Scholar
    • Export Citation
  • Keyes, D. F., Doelling D. R. , Young D. F. , Rose F. G. , Rutan D. A. , Nordeen M. L. , and Boghosian J. S. , 2006: The validation of the 5-year Terra-based monthly CERES radiative flux and cloud product. Proc. 12th Conf. on Atmospheric Radiation, Madison, WI, Amer. Meteor. Soc., P3.1. [Available online at https://ams.confex.com/ams/Madison2006/techprogram/paper_112731.htm.]

  • Lazzara, M. A., and Coauthors, 1999: The Man Computer Interactive Data Access System: 25 years of interactive processing. Bull. Amer. Meteor. Soc., 80, 271284.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., Priestley K. J. , Kratz D. P. , Geier E. B. , Green R. N. , Wielicki B. A. , Hinton P. O. R. , and Nolan S. K. , 2001: Determination of unfiltered radiances from the Clouds and the Earth’s Radiant Energy System (CERES) instrument. J. Appl. Meteor., 40, 822835.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., Manalo-Smith N. , Kato S. , Miller W. F. , Gupta S. K. , Minnis P. , and Wielicki B. A. , 2003: Angular distribution models for top-of-atmosphere radiative flux estimation from the Clouds and the Earth’s Radiant Energy System instrument on the Tropical Rainfall Measuring Mission Satellite. Part I: Methodology. J. Appl. Meteor., 42, 240265.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., Kato S. , Loukachine K. , and Smith N. M. , 2005: Angular distribution models for top-of-atmosphere radiative flux estimation from the Clouds and the Earth’s Radiant Energy System instrument on the Terra satellite. Part I: Methodology. J. Atmos. Oceanic Technol., 22, 338351.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., Kato S. , Loukachine K. , Smith N. M. , and Doelling D. R. , 2007: Angular distribution models for top-of-atmosphere radiative flux estimation from the Clouds and the Earth’s Radiant Energy System instrument on the Terra satellite. Part II: Validation. J. Atmos. Oceanic Technol., 24, 564584.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., Wielicki B. A. , Doelling D. R. , Smith G. L. , Keyes D. F. , Kato S. , Manalo-Smith N. , and Wong T. , 2009: Toward optimal closure of the earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748766.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., Lyman J. M. , Johnson G. C. , Allen R. P. , Doelling D. R. , Wong T. , Soden B. J. , and Stephens G. L. , 2012: Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent within uncertainty. Nat. Geosci., 5, doi:10.1038/ngeo1375.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., Smith W. L. Jr., Garber D. P. , Ayers J. K. , and Doelling D. R. , 1994: Cloud properties derived from GOES-7 for spring 1994 ARM intensive observing period using version 1.0.0 of ARM Satellite Data Analysis Program. NASA Reference Publ. 1366, 61 pp.

  • Minnis, P., Nguyen L. , Doelling D. R. , Young D. F. , and Miller W. , 2002a: Rapid calibration of operational and research meteorological satellite imagers. Part I: Evaluation of research satellite visible channels as references. J. Atmos. Oceanic Technol., 19, 12331249.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., Nguyen L. , Doelling D. R. , Young D. F. , Miller W. , and Kratz D. P. , 2002b: Rapid calibration of operational and research meteorological satellite imagers. Part II: Comparison of infrared channels. J. Atmos. Oceanic Technol., 19, 12501266.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., Doelling D. R. , Nguyen L. , Miller W. , and Chakrapani V. , 2008: Assessment of the visible channel calibrations of the TRMM VIRS and MODIS on Aqua and Terra. J. Atmos. Oceanic Technol., 25, 385400.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Coauthors, 2011: CERES edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data—Part I: Algorithms. IEEE Trans. Geosci. Remote Sens., 49, 43744400, doi:10.1109/TGRS.2011.2144601.

    • Search Google Scholar
    • Export Citation
  • Morstad, D. L., Doelling D. R. , Bhatt R. , and Scarino B. , 2011: The CERES calibration strategy of the geostationary visible channels for CERES clouds and flux products. Earth Observing Systems XVI, J. J. Butler, X. Xiong, and X. Gu, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 8153), doi:10.1117/12.894650.

  • National Research Council, 2007: Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. National Academies Press, 456 pp.

  • Puschell, J. J., and Coauthors, 2003: Design and characterization of the Japanese Advanced Meteorological Imager (JAMI). Optical Spectroscopic Techniques and Instrumentation for Atmospheric and Space Research V, A. M. Larar, J. A. Shaw, and Z. Sun, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 5157), 58–74, doi:10.1117/12.509806.

  • Schmit, T. J., Gunshor M. M. , Menzel W. P. , Gurka J. J. , Li J. , and Bachmeier A. S. , 2005: Introducing the next-generation Advanced Baseline Imager on GOES-R. Bull. Amer. Meteor. Soc., 86, 10791096.

    • Search Google Scholar
    • Export Citation
  • Stamnes, K., Tsay S.-C. , Wiscombe W. , and Jayaweera K. , 1988: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. Appl. Opt.,27, 25022509.

  • Suarez, M. J., Ed., 2005: Documentation and validation of the Goddard Earth Observing System (GEOS) data assimilation system—Version 4. Technical Report Series on Global Modeling and Data Assimilation, Vol. 26, NASA Tech. Memo. NASA/TM-2005-104606, 181 pp.

  • Sun, W., Loeb N. G. , Davies R. , Loukachine K. , and Miller W. F. , 2006: Comparison of MISR and CERES top-of-atmosphere albedo. Geophys. Res. Lett., 33, L23810, doi:10.1029/2006GL027958.

    • Search Google Scholar
    • Export Citation
  • Thompson, S. L., and Warren S. G. , 1982: Parameterization of outgoing infrared radiation derived from detailed radiative calculations. J. Atmos. Sci., 39, 26672680.

    • Search Google Scholar
    • Export Citation
  • Viollier, M., Kandel R. , and Raberanto P. , 2004: Combination of ScaRaB-2 and CERES with Meteosat-5 to remove time sampling bias and improve radiation budget estimations in the INDOEX region. J. Geophys. Res., 109, D05105, doi:10.1029/2003JD003947.

    • Search Google Scholar
    • Export Citation
  • Weinreb, M. P., Jamieson M. , Fulton N. , Chen Y. , Johnson J. X. , Bremer J. , Smith C. , and Baucom J. , 1997: Operational calibration of Geostationary Operational Environmental Satellite-8 and -9 imagers and sounders. Appl. Opt., 36, 68956904.

    • Search Google Scholar
    • Export Citation
  • Wielicki, B. A., Barkstrom B. R. , Harrison E. F. , Lee R. B. III, Smith G. L. , and Cooper J. E. , 1996: Clouds and the Earth’s Radiant Energy System (CERES): An Earth Observing System experiment. Bull. Amer. Meteor. Soc., 77, 853868.

    • Search Google Scholar
    • Export Citation
  • Wu, A., Xiong X. , Doelling D. R. , Morstad D. L. , Angal A. , and Bhatt R. , 2013: Characterization of Terra and Aqua MODIS VIS, NIR, and SWIR spectral band calibration stability. IEEE Trans. Geosci. Remote Sens., in press.

    • Search Google Scholar
    • Export Citation
  • Xiong, X., Sun J. , Wu A. , Chiang K. , Esposito J. , and Barnes W. , 2005: Terra and Aqua MODIS calibration algorithms and uncertainty analysis. Sensors, Systems, and Next-Generation Satellites IX, R. Meynart, S. P. Neeck and H. Shimoda, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 5978), doi:10.1117/12.627631.

  • Young, D. F., Minnis P. , Doelling D. R. , Gibson G. G. , and Wong T. , 1998: Temporal interpolation methods for the Clouds and the Earth’s Radiant Energy System (CERES) experiment. J. Appl. Meteor., 37, 572590.

    • Search Google Scholar
    • Export Citation
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Geostationary Enhanced Temporal Interpolation for CERES Flux Products

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  • 1 NASA Langley Research Center, Hampton, Virginia
  • | 2 SSAI, Hampton, Virginia
  • | 3 NASA Langley Research Center, Hampton, Virginia
  • | 4 SSAI, Hampton, Virginia
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Abstract

The Clouds and the Earth’s Radiant Energy System (CERES) instruments on board the Terra and Aqua spacecraft continue to provide an unprecedented global climate record of the earth’s top-of-atmosphere (TOA) energy budget since March 2000. A critical step in determining accurate daily averaged flux involves estimating the flux between CERES Terra or Aqua overpass times. CERES employs the CERES-only (CO) and the CERES geostationary (CG) temporal interpolation methods. The CO method assumes that the cloud properties at the time of the CERES observation remain constant and that it only accounts for changes in albedo with solar zenith angle and diurnal land heating, by assuming a shape for unresolved changes in the diurnal cycle. The CG method enhances the CERES data by explicitly accounting for changes in cloud and radiation between CERES observation times using 3-hourly imager data from five geostationary (GEO) satellites. To maintain calibration traceability, GEO radiances are calibrated against Moderate Resolution Imaging Spectroradiometer (MODIS) and the derived GEO fluxes are normalized to the CERES measurements. While the regional (1° latitude × 1° longitude) monthly-mean difference between the CG and CO methods can exceed 25 W m−2 over marine stratus and land convection, these regional biases nearly cancel in the global mean. The regional monthly CG shortwave (SW) and longwave (LW) flux uncertainty is reduced by 20%, whereas the daily uncertainty is reduced by 50% and 20%, respectively, over the CO method, based on comparisons with 15-min Geostationary Earth Radiation Budget (GERB) data.

Corresponding author address: David Doelling, NASA Langley Research Center, Mail Stop 420, Hampton, VA 23681. E-mail: david.r.doelling@nasa.gov

Abstract

The Clouds and the Earth’s Radiant Energy System (CERES) instruments on board the Terra and Aqua spacecraft continue to provide an unprecedented global climate record of the earth’s top-of-atmosphere (TOA) energy budget since March 2000. A critical step in determining accurate daily averaged flux involves estimating the flux between CERES Terra or Aqua overpass times. CERES employs the CERES-only (CO) and the CERES geostationary (CG) temporal interpolation methods. The CO method assumes that the cloud properties at the time of the CERES observation remain constant and that it only accounts for changes in albedo with solar zenith angle and diurnal land heating, by assuming a shape for unresolved changes in the diurnal cycle. The CG method enhances the CERES data by explicitly accounting for changes in cloud and radiation between CERES observation times using 3-hourly imager data from five geostationary (GEO) satellites. To maintain calibration traceability, GEO radiances are calibrated against Moderate Resolution Imaging Spectroradiometer (MODIS) and the derived GEO fluxes are normalized to the CERES measurements. While the regional (1° latitude × 1° longitude) monthly-mean difference between the CG and CO methods can exceed 25 W m−2 over marine stratus and land convection, these regional biases nearly cancel in the global mean. The regional monthly CG shortwave (SW) and longwave (LW) flux uncertainty is reduced by 20%, whereas the daily uncertainty is reduced by 50% and 20%, respectively, over the CO method, based on comparisons with 15-min Geostationary Earth Radiation Budget (GERB) data.

Corresponding author address: David Doelling, NASA Langley Research Center, Mail Stop 420, Hampton, VA 23681. E-mail: david.r.doelling@nasa.gov
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