• Akunuri, R., J. Pritchard, and L. Dennis, cited. 2009: Orbit-by-orbit microwave derived products (TDR) interface control document. NOAA/OSDPD. [Available online at http://www.osdpd.noaa.gov/PSB/SHARED_PROCESSING/TDR.html].

    • Search Google Scholar
    • Export Citation
  • Cao, C., X. Wu, A. Wu, and X. Xiong, 2007: Improving the SNO calibration accuracy for the reflective solar bands of AVHRR and MODIS. Proc. SPIE, 6684 , 668408. doi:10.1117/12.735132.

    • Search Google Scholar
    • Export Citation
  • Christy, J. R., R. W. Spencer, W. B. Norris, W. D. Braswell, and D. E. Park, 2003: Error estimates of version 5 of MSU–AMSU bulk atmospheric temperature. J. Atmos. Oceanic Technol., 20 , 613629.

    • Search Google Scholar
    • Export Citation
  • Colton, M. C., and G. A. Poe, 1999: Intersensor calibration of DMSP SSM/Is: F-8 to F-14, 1987-1997. IEEE Trans. Geosci. Remote Sens., 37 , 418439.

    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., and M. J. Manton, 1998: Performance of satellite rainfall estimation algorithms during TOGA COARE. J. Atmos. Sci., 55 , 15371557.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R., F. Weng, N. C. Grody, and A. Basist, 1996: An eight-year (1987–1994) time series of rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurements. Bull. Amer. Meteor. Soc., 77 , 891905.

    • Search Google Scholar
    • Export Citation
  • Grody, N. C., K. Y. Vinnikov, M. D. Goldberg, J. T. Sullivan, and J. D. Tarpley, 2004: Calibration of multisatellite observations for climate studies: Microwave Sounding Unit (MSU). J. Geophys. Res., 109 , D24104. doi:10.1029/2004JD005079.

    • Search Google Scholar
    • Export Citation
  • Haddad, Z. S., E. A. Smith, C. D. Kummerow, T. Iguchi, M. R. Farrar, S. L. Durden, M. Alves, and W. S. Olson, 1997: The TRMM “Day-1” radar/radiometer combined rain-profile algorithm. J. Meteor. Soc. Japan, 75 , 799809.

    • Search Google Scholar
    • Export Citation
  • Hilburn, K. A., and F. J. Wentz, 2008: Intercalibrated passive microwave rain products from the Unified Microwave Ocean Retrieval Algorithm (UMORA). J. Appl. Meteor. Climatol., 47 , 778794.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multi-satellite Precipitation Analysis (TMPA): Quasi-global, multi-year, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8 , 3855.

    • Search Google Scholar
    • Export Citation
  • Iacovazzi Jr., R. A., and C. Cao, 2007: Quantifying EOS Aqua and NOAA POES AMSU-A brightness temperature biases for weather and climate applications utilizing the SNO method. J. Atmos. Oceanic Technol., 24 , 18951909.

    • Search Google Scholar
    • Export Citation
  • Iacovazzi Jr., R. A., and C. Cao, 2008: Reducing uncertainties of SNO-estimated intersatellite AMSU-A brightness temperature biases for surface-sensitive channels. J. Atmos. Oceanic Technol., 25 , 10481054.

    • Search Google Scholar
    • Export Citation
  • Iacovazzi Jr., R. A., C. Cao, and S-A. Boukabara, 2009: Analysis of Polar-orbiting Operational Environmental Satellite NOAA-14 MSU and NOAA-15 AMSU-A relative measurement biases for climate change detection. J. Geophys. Res., 114 , D09107. doi:10.1029/2008JD011588.

    • Search Google Scholar
    • Export Citation
  • Iguchi, T., T. Kozu, R. Meneghini, J. Awaka, and K. Okamoto, 2000: Rain-profiling algorithm for the TRMM Precipitation Radar. J. Appl. Meteor., 39 , 20382052.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., W. S. Olson, and L. Giglio, 1996: A simplified scheme for obtaining precipitation and vertical hydrometer profiles from passive microwave sensors. IEEE Trans. Geosci. Remote Sens., 34 , 12131232.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and Coauthors, 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor., 39 , 19651982.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and Coauthors, 2001: The evolution of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40 , 18011820.

    • Search Google Scholar
    • Export Citation
  • Kunkee, D. B., S. D. Swadlley, G. A. Poe, Y. Hong, and M. F. Werner, 2008: Special Sensor Microwave Imager Sounder (SSMIS) radiometric calibration anomalies—Part I: Identification and characterization. IEEE Trans. Geosci. Remote Sens., 46 , 10171033.

    • Search Google Scholar
    • Export Citation
  • Mears, C. A., and F. J. Wentz, 2005: The effect of drifting measurement time on satellite-derived lower tropospheric temperature. Science, 309 , 15481551.

    • Search Google Scholar
    • Export Citation
  • NOAA/NCDC, cited. 2009: SSM/I TDR documentation. [Available online at http://ncdc.noaa.gov/oa/rsad/ssmi/ssmi-tdr-documentation.pdf].

  • NRC, 2004: Climate Data Records from Environmental Satellites. National Academy Press, 136 pp.

  • Olson, W. S., and Coauthors, 2006: Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I: Improved method and uncertainties. J. Appl. Meteor. Climatol., 45 , 702720.

    • Search Google Scholar
    • Export Citation
  • Prabhakara, C., R. A. Iacovazzi Jr., J-M. Yoo, and G. Dalu, 2000: Global warming: Evidence from satellite observations. Geophys. Res. Lett., 27 , 35173520.

    • Search Google Scholar
    • Export Citation
  • Smith, E. A., and Coauthors, 1998: Results of WetNet PIP-2 project. J. Atmos. Sci., 55 , 14831536.

  • Sohn, B. J., and E. A. Smith, 2003: Explaining sources of discrepancy in SSM/I water vapor algorithms. J. Climate, 16 , 32293255.

  • Sun, N., and F. Weng, 2008: Evaluation of Special Sensor Microwave Imager/Sounder (SSMIS) environmental data records. IEEE Trans. Geosci. Remote Sens., 46 , 10061016.

    • Search Google Scholar
    • Export Citation
  • Sun, N., F. Weng, R. Ferraro, and S. Yang, 2009: Prototype demonstration of special sensor microwave imager (SSM/I) based climate data records (CDRs) during 1992-2006 after inter-sensor calibration. Proc. Fourth Int. Precipitation Working Group (IPWG) Workshop, Beijing, China, National Satellite Meteorological Center and Chinese Meteorological Administration, 319–323.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., J. Fasullo, and L. Smith, 2005: Trends and variability in column-integrated atmospheric water vapor. Climate Dyn., 24 , 741758.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.

  • Weng, F., and N. C. Grody, 1994: Retrieval of cloud liquid water using the Special Sensor Microwave Imager (SSM/I). J. Geophys. Res., 99 , (D12). 2553525551.

    • Search Google Scholar
    • Export Citation
  • Weng, F., S. Yang, N. Sun, and B. Yan, 2009: SSM/I intersensor calibration produces improved climate trends. GSICS Quart., 3 , 23.

  • Wentz, F. J., 1988: User’s manual: SSM/I antenna temperature tapes. Remote Sensing Systems Rep. 032588, 36 pp.

  • Wentz, F. J., 1991: User’s manual: SSM/I antenna temperature tapes (revision 1). Remote Sensing Systems Rep. 120191, Santa Rosa, CA, 73 pp.

    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., 1993: User’s Manual: SSM/I antenna temperature tapes (revision 2). Remote Sensing Systems 120193, Santa Rosa, CA, 36 pp.

    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., and M. C. Schabel, 1998: Effects of satellite orbital decay on MSU lower tropospheric temperature trends. Nature, 394 , 661664.

    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., L. Ricciarduli, K. Hilburn, and C. Mears, 2007: How much more rain will global warming bring? Science, 37 , 233235.

  • Wolff, D. B., and B. L. Fisher, 2008: Comparisons of instantaneous TRMM ground validation and satellite rain-rate estimates at different spatial scales. J. Appl. Meteor. Climatol., 47 , 22152237.

    • Search Google Scholar
    • Export Citation
  • Wolff, D. B., and B. L. Fisher, 2009: Assessing the relative performance of microwave-based satellite rain-rate retrievals using TRMM ground validation data. J. Appl. Meteor. Climatol., 48 , 10691099.

    • Search Google Scholar
    • Export Citation
  • Yamamoto, M. K., F. A. Furuzawa, A. Higuchi, and K. Nakamura, 2008: Comparison of diurnal variations in precipitation systems observed by TRMM PR, TMI, and VIRS. J. Climate, 21 , 40114028.

    • Search Google Scholar
    • Export Citation
  • Yan, B., and F. Weng, 2006: Recalibration of DMSP SSM/I for weather and climate applications. Abstracts, 15th Int. TOVS Studies Conf., Maratea, Italy, Int. TOVS Working Group. [Available online at http://cimss.ssec.wisc.edu/itwg/itsc/itsc15/report/ITSC-XV_WG_Report_final.pdf].

    • Search Google Scholar
    • Export Citation
  • Yan, B., and F. Weng, 2008: Intercalibration between Special Sensor Microwave Imager/Sounder and Special Sensor Microwave Imager. IEEE Trans. Geosci. Remote Sens., 46 , 984995.

    • Search Google Scholar
    • Export Citation
  • Yan, B., and F. Weng, 2009: Assessments of F16 Special Sensor Microwave Imager and Sounder antenna temperature at lower atmospheric sounding channels. Adv. Meteor., 420985. doi:10.1155/2009/420985.

    • Search Google Scholar
    • Export Citation
  • Yang, S., and E. A. Smith, 2008: Convective–stratiform precipitation variability at seasonal scale from eight years of TRMM observations: Implications for multiple modes of diurnal variability. J. Climate, 21 , 40874114.

    • Search Google Scholar
    • Export Citation
  • Yang, S., and Coauthors, 2006: Precipitation and latent heating distributions from satellite passive microwave radiometry. Part II: Evaluation of estimates using independent data. J. Appl. Meteor. Climatol., 45 , 721739.

    • Search Google Scholar
    • Export Citation
  • Yang, S., K-S. Kuo, and E. A. Smith, 2008: Persistent nature of secondary diurnal modes of precipitation over oceanic and continental regimes. J. Climate, 21 , 41154131.

    • Search Google Scholar
    • Export Citation
  • Yang, S., F. Weng, N. Sun, and B. Yan, 2009: Special Sensor Microwave Imager (SSM/I) intersensor calibration and impact on environmental data records. Proc. Fourth Int. Precipitation Working Group (IPWG) Workshop, Beijing, China, National Satellite Meteorological Center and Chinese Meteorological Administration, 366–373.

    • Search Google Scholar
    • Export Citation
  • Zou, C-Z., M. D. Goldberg, Z. Cheng, N. Grody, J. T. Sullivan, C. Cao, and T. Tarpley, 2006: Recalibration of Microwave Sounding Unit for climate studies using simultaneous nadir overpasses. J. Geophys. Res., 111 , D19114. doi:10.1029/2005JD006798.

    • Search Google Scholar
    • Export Citation
  • Zou, C-Z., M. Gao, and M. D. Goldberg, 2009: Error structure and atmospheric temperature trends in observations from the microwave sounding unit. J. Climate, 22 , 16611681.

    • Search Google Scholar
    • Export Citation
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Special Sensor Microwave Imager (SSM/I) Intersensor Calibration Using a Simultaneous Conical Overpass Technique

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  • 1 NOAA/NESDIS/Center for Satellite Applications and Research, and I. M. Systems Group, Inc., Camp Springs, Maryland
  • | 2 NOAA/NESDIS/Center for Satellite Applications and Research, Camp Springs, Maryland
  • | 3 NOAA/NESDIS/Center for Satellite Applications and Research, and Earth System Science Interdisciplinary Center, Camp Springs, Maryland
  • | 4 NOAA/NESDIS/Center for Satellite Applications and Research, and I. M. Systems Group, Inc., Camp Springs, Maryland
  • | 5 NOAA/NESDIS/Center for Satellite Applications and Research, Camp Springs, Maryland
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Abstract

A new intersensor calibration scheme is developed for the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) to correct its scan-angle-dependent bias, the radar calibration beacon interference on the F-15 satellite, and other intersensor biases. The intersensor bias is characterized by the simultaneous overpass measurements with the F-13 SSM/I as a reference. This sensor data record (SDR) intersensor calibration procedure is routinely running at the National Oceanic and Atmospheric Administration and is now used for reprocessing all SSM/I environmental data records (EDR), including total precipitable water (TPW) and surface precipitation. Results show that this scheme improves the consistency of the monthly SDR’s time series from different SSM/I sensors. Relative to the matched rain products from the Tropical Rainfall Measuring Mission, the bias of SSM/I monthly precipitation is reduced by 12% after intersensor calibration. TPW biases between sensors are reduced by 75% over the global ocean and 20% over the tropical ocean, respectively. The intersensor calibration reduces biases by 20.6%, 15.7%, and 6.5% for oceanic, land, and global precipitation, respectively. The TPW climate trend is 1.59% decade−1 (or 0.34 mm decade−1) for the global ocean and 1.39% decade−1 (or 0.63 mm decade−1) for the tropical ocean, indicating related trends decrease of 38% and 54%, respectively, from the uncalibrated SDRs. Results demonstrate the large impacts of this calibration on the TPW climate trend.

This article included in the International Precipitation Working Group (IPWG) special collection.

* Current affiliation: Naval Research Laboratory, Monterey, California

Corresponding author address: Dr. Song Yang, Naval Research Laboratory, MS 2, 7 Grace Hopper Ave., Monterey, CA 93943. Email: song.yang@nrlmry.navy.mil

Abstract

A new intersensor calibration scheme is developed for the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) to correct its scan-angle-dependent bias, the radar calibration beacon interference on the F-15 satellite, and other intersensor biases. The intersensor bias is characterized by the simultaneous overpass measurements with the F-13 SSM/I as a reference. This sensor data record (SDR) intersensor calibration procedure is routinely running at the National Oceanic and Atmospheric Administration and is now used for reprocessing all SSM/I environmental data records (EDR), including total precipitable water (TPW) and surface precipitation. Results show that this scheme improves the consistency of the monthly SDR’s time series from different SSM/I sensors. Relative to the matched rain products from the Tropical Rainfall Measuring Mission, the bias of SSM/I monthly precipitation is reduced by 12% after intersensor calibration. TPW biases between sensors are reduced by 75% over the global ocean and 20% over the tropical ocean, respectively. The intersensor calibration reduces biases by 20.6%, 15.7%, and 6.5% for oceanic, land, and global precipitation, respectively. The TPW climate trend is 1.59% decade−1 (or 0.34 mm decade−1) for the global ocean and 1.39% decade−1 (or 0.63 mm decade−1) for the tropical ocean, indicating related trends decrease of 38% and 54%, respectively, from the uncalibrated SDRs. Results demonstrate the large impacts of this calibration on the TPW climate trend.

This article included in the International Precipitation Working Group (IPWG) special collection.

* Current affiliation: Naval Research Laboratory, Monterey, California

Corresponding author address: Dr. Song Yang, Naval Research Laboratory, MS 2, 7 Grace Hopper Ave., Monterey, CA 93943. Email: song.yang@nrlmry.navy.mil

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