Use of ScaRaB Measurements for Validating a GOES-Based TOA Radiation Product

Alexander Trishchenko Canada Centre for Remote Sensing, Ottawa, Ontario, Canada

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Zhanqing Li Canada Centre for Remote Sensing, Ottawa, Ontario, Canada

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Abstract

Lack of calibrated radiation measurements at the top of the atmosphere (TOA) between major spaceborne radiation missions entails inference of the TOA radiation budget from operational weather sensors. The inferred data are subject to uncertainties due to calibration, narrow- to broadband conversion, etc. In this study, a surrogate TOA earth radiation budget product generated from GOES-7 (Geostationary Operational Environmental Satellite) imagery data for use in the U.S. Atmospheric Radiation Measurement (ARM) program was validated using measurements from the ScaRaB radiometer flown on board the METEOR-3/7 satellite. Comparisons were made between coincident and collocated shortwave and longwave radiative quantities derived from GOES and ScaRaB sensors over an ARM experimental locale in the South Great Plains of Oklahoma, during April and July 1994. The comparisons are proven to be instrumental in validating the calibration and narrow- to broadband conversion used to obtain broadband radiative quantities from GOES digital counts. Calibrations for both visible and infrared window channels have small uncertainties, whereas narrow- to broadband conversion of shortwave measurements contains large systematic errors. The caveat stems from use of a quadratic conversion equation instead of a linear one, as was found from ScaRaB narrow- and broadband measurements. The ensuing errors in the estimates of broadband albedo depend on scene brightness, underestimation for bright scenes, and overestimation for dark scenes. As a result, the magnitude of the TOA cloud radiative forcing is underestimated by about 14 W m−2 or 7.5% on a daytime mean basis. After correcting this error, the ratio of cloud radiative forcing (a measure of the impact of clouds on atmospheric absorption) derived from ARM measurements turns out to be 1.07, which is in even closer agreement with radiative transfer models than found from previous studies using original GOES products.

Corresponding author address: Dr. Z. Li, Canada Centre for Remote Sensing, 588 Booth St., Ottawa, ON K1A 0Y7 Canada.

Zhanqing.Li@CCRS.NRCan.gc.ca

Abstract

Lack of calibrated radiation measurements at the top of the atmosphere (TOA) between major spaceborne radiation missions entails inference of the TOA radiation budget from operational weather sensors. The inferred data are subject to uncertainties due to calibration, narrow- to broadband conversion, etc. In this study, a surrogate TOA earth radiation budget product generated from GOES-7 (Geostationary Operational Environmental Satellite) imagery data for use in the U.S. Atmospheric Radiation Measurement (ARM) program was validated using measurements from the ScaRaB radiometer flown on board the METEOR-3/7 satellite. Comparisons were made between coincident and collocated shortwave and longwave radiative quantities derived from GOES and ScaRaB sensors over an ARM experimental locale in the South Great Plains of Oklahoma, during April and July 1994. The comparisons are proven to be instrumental in validating the calibration and narrow- to broadband conversion used to obtain broadband radiative quantities from GOES digital counts. Calibrations for both visible and infrared window channels have small uncertainties, whereas narrow- to broadband conversion of shortwave measurements contains large systematic errors. The caveat stems from use of a quadratic conversion equation instead of a linear one, as was found from ScaRaB narrow- and broadband measurements. The ensuing errors in the estimates of broadband albedo depend on scene brightness, underestimation for bright scenes, and overestimation for dark scenes. As a result, the magnitude of the TOA cloud radiative forcing is underestimated by about 14 W m−2 or 7.5% on a daytime mean basis. After correcting this error, the ratio of cloud radiative forcing (a measure of the impact of clouds on atmospheric absorption) derived from ARM measurements turns out to be 1.07, which is in even closer agreement with radiative transfer models than found from previous studies using original GOES products.

Corresponding author address: Dr. Z. Li, Canada Centre for Remote Sensing, 588 Booth St., Ottawa, ON K1A 0Y7 Canada.

Zhanqing.Li@CCRS.NRCan.gc.ca

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  • Barkstrom, B. R., and G. L. Smith, 1986: The earth radiation budget experiment: Science and implementation. Rev. Geophys.,24, 379–390.

  • Bess, T. D., G. L. Smith, R. N. Green, D. A. Rutan, R. S. Kandel, P. Raberanto, and M. Viollier, 1997: Intercomparison of scanning radiometer for radiation budget (ScaRaB) and Earth Radiation Budget Experiment (ERBE) results. Preprints, Ninth Conf. Atmospheric Radiation, Long Beach, CA, Amer. Meteor. Soc., 203–207.

  • Cess, R. D., and Coauthors, 1995: Absorption of solar radiation by clouds: Observations versus models. Science,267, 496–499.

  • ——, M. H. Zhang, Y. Zhou, X. Jing, and V. Dvortsov, 1996: Absorption of solar radiation by clouds: Interpretation of satellite, surface, and aircraft measurements. J. Geophys. Res.,101, 23 299–23 309.

  • Charlock, T. P., and T. L. Alberta, 1996: The CERES/ARM/GEWEX Experiment (CAGEX) for the retrieval of radiative fluxes with satellite data. Bull. Amer. Meteor. Soc.,77, 2673–2683.

  • Diekmann, F. J., and G. L. Smith, 1989: Investigation of scene identification algorithms for radiation budget measurements. J. Geophys. Res.,94, 3395–3412.

  • Green, R. N., F. B. House, P. W. Stackhouse, X. Wu, S. A. Ackermann, W. L. Smith, and M. J. Johnson, 1990: Intercomparison of scanner and nonscanner measurements for the Earth Radiation Budget Experiment (ERBE). J. Geophys. Res.,95, 11 785–11 798.

  • Imre, D. G., E. H. Abramson, and P. H. Daum, 1996: Quantifying cloud-induced shortwave absorption: An examination of uncertainties and recent arguments for large excess absorption. J. Appl. Meteor.,35, 1991–2010.

  • Jackobowitz, H., V. Soule, H. L. Kyle, F. B. House, and the ERB Nimbus-7 Experiment Team, 1984: The Earth Radiation Budget (ERB) experiment: An overview. J. Geophys. Res.,89, 5021–5038.

  • Kandel, R. S., J.-L. Monge, M. Viollier, L. A. Pakhomov, V. I. Adas’ko, R. G. Reitenbach, E. Raschke, and R. Stuhlmann, 1994:The ScaRaB project: Earth radiation budget observations from Meteor satellites. Adv. Space Res.,14, 47–57.

  • Kneizys, F. X., E. P. Shettle, L. W. Abrieu, J. Chetwynd, G. Anderson, W. Gallery, J. Selby, and S. Clough, 1988: User’s Guide to LOWTRAN-7. AFGL-TR-88-0177, AFGL (OPI), Hanscom Air Force Base, MA, 140 pp.

  • Li, Z., 1996: On the angular correction of satellite radiation measurements: The performance of ERBE angular dependence model in the Arctic. Theor. Appl. Climatol.,54, 235–248.

  • ——, and H. G. Leighton, 1991: Scene identification and its effect on cloud radiative forcing in the Arctic. J. Geophys. Res.,96, 9175–9188.

  • ——, and ——, 1992: Narrowband to broadband conversion with spatially autocorrelated reflectance measurements. J. Appl. Meteor.,31, 421–432.

  • ——, H. W. Barker, and L. Moreau, 1995: The variable effect of clouds on atmospheric absorption of solar radiation. Nature,376, 486–490.

  • Masuda, K., H. G. Leighton, and Z. Li, 1995: A new parameterization for the determination of solar flux absorbed at the surface from satellite measurements. J. Climate,8, 1615–1629.

  • Minnis, P., and E. F. Harrison, 1984: Diurnal variability of regional cloud and clear-sky radiative parameters derived from GOES data. Part III: November 1978 radiative parameters. J. Climate Appl. Meteor.,23, 1023–1051.

  • ——, D. F. Young, and E. F. Harrison, 1991: Examination of the relationship between outgoing infrared window and total long-wave fluxes using satellite data. J. Climate,4, 1114–1133.

  • ——, W. L. Smith, D. P. Garber, J. K Ayers, and D. R. Doelling, 1995: Cloud properties derived from GOES-7 for spring 1994 ARM intensive observation period using version 1.0.0 of ARM satellite data analysis program. NASA Ref. Publ. 1366, NASA/Langley Research Center, Hampton, VA, 58 pp.

  • Monge, J.-L., J. Mueller, R. Kandel, and L. A. Pakhomov, 1994: Calibration results: Summary of FM1 parameters. SBRT-6310-RES1-3, 23 pp. [Available from LMD, Ecole Polytechnique, 91128 Palaiseau, Cedex, France.].

  • Rao, C. R. N., and J. Chen, 1994: Post-launch calibration of the visible and near-infrared channels of the Advanced Very High Resolution Radiometers on NOAA-7, -9, -11 spacecraft. NOAA Tech. Rep., 22 pp. [Available from NOAA, U.S. Department of Commerce, Washington, DC 20233.].

  • Raschke, E., T. H. Vonder Haar, W. R. Bandeen, and M. Pasternak, 1973: The annual radiation ballance of the earth–atmosphere system during 1969–70 from Nimbus-3 measurements. J. Atmos. Sci.,30, 341–364.

  • Rossow, W. B., Y. Desormeaux, C. L. Brest, and A. Parker, 1992: International Satellite Cloud Climatology Project (ISCCP). Radiance calibration report. WMO/TD 520, 104 pp. [Available from NASA/Goddard Space Flight Center, Institute for Space Studies, 2880 Broadway, New York, NY 10025.].

  • ——, C. L. Brest, and M. D. Roiter, 1995: International Satellite Cloud Climatology Project (ISCCP). New radiance calibrations. WMO/TD, 736, 71 pp. [Available from NASA/Goddard Space Flight Center, Institute for Space Studies, 2880 Broadway, New York, NY 10025.].

  • Rutan, D., and T. P. Charlock, 1997: Spectral reflectance, directional reflectance, and broadband albedo of the earth’s surface. Preprints, Ninth. Conf. Atmospheric Radiation, Long Beach, CA, Amer. Meteor. Soc., 466–470.

  • Smith, W. L., L. Nguyen, and P. Minnis, 1997: Cloud radiative forcing derived from ARM surface and satellite measurements during ARESE and the spring ARM/UAV IOP. Preprints, Ninth Conf. Atmospheric Radiation, Long Beach, CA, Amer. Meteor. Soc., 1–4.

  • Stephens, G. L., 1978: Radiation profiles in extended water clouds. Part I: Theory. J. Atmos. Sci.,35, 2111–2122.

  • Stokes, G. M., and S. E. Schwartz, 1994: The Atmospheric Radiation Measurement (ARM) program: Programmatic background and design of the cloud and radiation test bed. Bull. Amer. Meteor. Soc.,75, 1201–1221.

  • Suttles, J. T., and Coauthors, 1988: Angular Radiation Models for Earth–Atmosphere System. Vol. I—Short-Wave Radiation. NASA Ref. Publ., 1184, NASA/Langley Research Center, Hampton, VA, 114 pp.

  • ——, B. A. Wielicki, and S. Vemury, 1992: Top-of-atmosphere radiation fluxes: Validation of ERBE scanner inversion algorithms using Nimbus-7 ERB data. J. Appl. Meteor.,31, 784–796.

  • Vesperini, M., and Y. Fouquart, 1994: Determination of broadband short-wave fluxes from the Meteosat visible channel by comparison to ERBE. Beitr. Phys. Atmos.,67, 121–131.

  • Viollier, M., R. S. Kandel, and P. Raberanto, 1995: Inversion and space-time-averaging algorithms for ScaRaB (Scanner for Earth Radiation Budget). Comparison with ERBE. Ann. Geophys.,13, 959–968.

  • Whitlock, C. H., and Coauthors, 1990: AVHRR and VISSR satellite instrument calibration results for both cirrus and marine stratocumulus IFO periods. FIRE Sci. Rep. NASA CP 3038, NASA/Langley Research Center, Hampton, VA, 451 pp.

  • Wielicki, B. A., and R. N. Green, 1989: Cloud identification for ERBE radiative flux retrieval. J. Appl. Meteor.,28, 1133–1146.

  • ——, B. R. Barkstrom, E. F. Harrison, R. B. Lee III, G. L. Smith, and J. E. Cooper, 1996: Clouds and the Earth’s Radiant Energy System (CERES): An earth observing system experiment. Bull. Amer. Meteor. Soc.,77, 853–868.

  • Wydick, J. E., P. A. Davis, and A. Gruber, 1987: Estimation of broad-band planetary albedo from operational narrow-band satellite measurements. NOAA TR/NESDIS 27, 32 pp. [Available from NOAA, U.S. Department of Commerce, Washington, DC 20233.].

  • Ye, Q., and J. A. Coakley Jr., 1996: Biases in Earth radiation budget observations. Part 2. Consistent scene identification and anisotropic factors. J. Geopyhys. Res.,101, 21 253–21 263.

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