Updated Screening Procedures for GPROF2010 over Land: Utilization for AMSR-E

Patrick C. Meyers Cooperative Institute for Climate and Satellites, University of Maryland, College Park, College Park, Maryland

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Ralph R. Ferraro Center for Satellite Applications and Research, NOAA/National Environmental Satellite, Data, and Information Service, College Park, Maryland

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Nai-Yu Wang I.M. Systems Group, College Park, Maryland

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Abstract

The Goddard profiling algorithm 2010 (GPROF2010) was revised for the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instrument. The GPROF2010 land algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), which observes slightly different central frequencies than AMSR-E. A linear transfer function was developed to convert AMSR-E brightness temperatures to their corresponding TMI frequency for raining and nonraining instantaneous fields of view (IFOVs) using collocated brightness temperature and TRMM precipitation radar (PR) measurements. Previous versions of the algorithm separated rain from surface ice, snow, and desert using a series of empirical procedures. These occasionally failed to separate raining and nonraining scenes, leading to failed detection and false alarms of rain. The new GPROF2010, version 2 (GPROF2010V2), presented here, prefaced the heritage screening procedures by referencing annual desert and monthly snow climatologies to identify IFOVs where rain retrievals were unreliable. Over a decade of satellite- and ground-based observations from the Interactive Multisensor Snow and Ice Mapping System (IMS) and AMSR-E allowed for the creation of a medium-resolution (0.25° × 0.25°) climatology of monthly snow and ice cover. The scattering signature of rain over ice and snow is not well defined because of complex emissivity signals dependent on snow depth, age, and melting, such that using a static climatology was a more stable approach to defining surface types. GPROF2010V2 was subsequently used for the precipitation environmental data record (EDR) for the AMSR2 sensor aboard the Global Change Observation Mission–Water 1 (GCOM-W1).

Corresponding author address: Patrick Meyers, CICS-MD, University of Maryland, College Park, 5825 University Research Court, Suite 4001, College Park, MD 20740-3823. E-mail: pmeyers@umd.edu

Abstract

The Goddard profiling algorithm 2010 (GPROF2010) was revised for the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instrument. The GPROF2010 land algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), which observes slightly different central frequencies than AMSR-E. A linear transfer function was developed to convert AMSR-E brightness temperatures to their corresponding TMI frequency for raining and nonraining instantaneous fields of view (IFOVs) using collocated brightness temperature and TRMM precipitation radar (PR) measurements. Previous versions of the algorithm separated rain from surface ice, snow, and desert using a series of empirical procedures. These occasionally failed to separate raining and nonraining scenes, leading to failed detection and false alarms of rain. The new GPROF2010, version 2 (GPROF2010V2), presented here, prefaced the heritage screening procedures by referencing annual desert and monthly snow climatologies to identify IFOVs where rain retrievals were unreliable. Over a decade of satellite- and ground-based observations from the Interactive Multisensor Snow and Ice Mapping System (IMS) and AMSR-E allowed for the creation of a medium-resolution (0.25° × 0.25°) climatology of monthly snow and ice cover. The scattering signature of rain over ice and snow is not well defined because of complex emissivity signals dependent on snow depth, age, and melting, such that using a static climatology was a more stable approach to defining surface types. GPROF2010V2 was subsequently used for the precipitation environmental data record (EDR) for the AMSR2 sensor aboard the Global Change Observation Mission–Water 1 (GCOM-W1).

Corresponding author address: Patrick Meyers, CICS-MD, University of Maryland, College Park, 5825 University Research Court, Suite 4001, College Park, MD 20740-3823. E-mail: pmeyers@umd.edu
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  • Adler, R. F., Huffman G. J. , and Keehn P. R. , 1994: Global tropical rain estimates from microwave-adjusted geosynchronous IR data. Remote Sens. Rev., 11, 125152, doi:10.1080/02757259409532262.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and Coauthors, 2003: The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, doi:10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ashcroft, P., and Wentz F. J. , 2006: AMSR-E/Aqua L2A global swath spatially-resampled brightness temperatures, version 2. NASA National Snow and Ice Data Center DAAC. Subset used: Low resolution swath data, accessed 5 October 2011. [Available online at http://nsidc.org/data/docs/daac/ae_l2a_tbs/v2/ae_l2a_tbs.gd.html.]

  • Cecil, D. J., 2009: Passive microwave brightness temperatures as proxies for hailstorms. J. Appl. Meteor. Climatol., 48, 12811286, doi:10.1175/2009JAMC2125.1.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., Grody N. C. , and Marks G. F. , 1994a: Effects of surface conditions on rain identification using the SSM/I. Remote Sens. Rev., 11, 195209, doi:10.1080/02757259409532265.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., and Coauthors, 1994b: Microwave measurements produce global climatic, hydrologic data. Eos, Trans. Amer. Geophys. Union, 75, 337343, doi:10.1029/94EO00988.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., Smith E. A. , Berg W. , and Huffman G. J. , 1998: A screening methodology for passive microwave precipitation retrieval algorithms. J. Atmos. Sci., 55, 15831600, doi:10.1175/1520-0469(1998)055<1583:ASMFPM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., and Coauthors, 2013: An evaluation of microwave land surface emissivities over the continental United States to benefit GPM-era precipitation algorithms. IEEE Trans. Geosci. Remote Sens., 51, 378398, doi:10.1109/TGRS.2012.2199121.

    • Search Google Scholar
    • Export Citation
  • Gopalan, K., Wang N.-Y. , Ferraro R. R. , and Liu C. , 2010: Status of the TRMM 2A12 land precipitation algorithm. J. Atmos. Oceanic Technol., 27, 13431354, doi:10.1175/2010JTECHA1454.1.

    • Search Google Scholar
    • Export Citation
  • Grody, N. C., 1991: Classification of snow cover and precipitation using the Special Sensor Microwave Imager. J. Geophys. Res., 96, 74237435, doi:10.1029/91JD00045.

    • Search Google Scholar
    • Export Citation
  • Grody, N. C., and Basist A. , 1996: Global identification of snow cover using SSM/I measurements. IEEE Trans. Geosci. Remote Sens., 34, 237249, doi:10.1109/36.481908.

    • Search Google Scholar
    • Export Citation
  • Hong, Y., Adler R. , and Huffman G. , 2007: Use of satellite remote sensing data in the mapping of global landslide susceptibility. J. Nat. Hazards, 43, 245256, doi:10.1007/s11069-006-9104-z.

    • Search Google Scholar
    • Export Citation
  • Houghton, J. T., 1979: The future role of observations from meteorological satellites. Quart. J. Roy. Meteor. Soc., 105, 123, doi:10.1002/qj.49710544302.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., Adler R. F. , Bolvin D. T. , and Gu G. , 2009: Improving the global precipitation record: GPCP Version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.

    • Search Google Scholar
    • Export Citation
  • Janowiak, J., 2007: Validation of rainfall algorithms at the NOAA Climate Prediction Center. Measuring Precipitation from Space: EURAINSAT and the Future, V. Levizzani, P. Bauer, and F. Joseph, Eds., Advances in Global Change Research, Vol. 28, Springer Netherlands, 393–401.

  • Kirschbaum, D. B., Adler R. , Hong Y. , and Lerner-Lam A. L. , 2009: Evaluation of a satellite-based landslide algorithm using global landslide inventories. Nat. Hazards Earth Syst. Sci., 9, 673686, doi:10.5194/nhess-9-673-2009.

    • Search Google Scholar
    • Export Citation
  • Kirstetter, P.-E., and Coauthors, 2012: Toward a framework for systematic error modeling of spaceborne precipitation radar with NOAA/NSSL ground radar–based National Mosaic QPE. J. Hydrometeor., 13, 12851300, doi:10.1175/JHM-D-11-0139.1.

    • Search Google Scholar
    • Export Citation
  • Kirstetter, P.-E., Hong Y. , Gourley J. J. , Schwaller M. , Petersen W. , and Zhang J. , 2013: Comparison of TRMM 2A25 products, version 6 and version 7, with NOAA/NSSL ground radar–based National Mosaic QPE. J. Hydrometeor., 14, 661669, doi:10.1175/JHM-D-12-030.1.

    • Search Google Scholar
    • Export Citation
  • Kucera, P. A., Ebert E. E. , Turk F. J. , Levizzani V. , Kirschbaum D. , Tapiador F. J. , Loew A. , and Borsche M. , 2013: Precipitation from space: Advancing Earth system science. Bull. Amer. Meteor. Soc., 94, 365375, doi:10.1175/BAMS-D-11-00171.1.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., and Coauthors, 2001: The evolution of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40, 18011820, doi:10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., Ringerud S. , Crook J. , Randel D. , and Berg W. , 2011: An observationally generated a priori database for microwave rainfall retrievals. J. Atmos. Oceanic Technol., 28, 113130, doi:10.1175/2010JTECHA1468.1.

    • Search Google Scholar
    • Export Citation
  • Morrissey, M. L., Maliekal J. A. , Greene J. S. , and Wang J. , 1995: The uncertainty of simple spatial averages using rain gauge networks. Water Resour. Res., 31, 20112017, doi:10.1029/95WR01232.

    • Search Google Scholar
    • Export Citation
  • Munchak, S. J., and Skofronick-Jackson G. , 2013: Evaluation of precipitation detection over various surfaces from passive microwave imagers and sounders. Atmos. Res., 131, 8194, doi:10.1016/j.atmosres.2012.10.011.

    • Search Google Scholar
    • Export Citation
  • National Ice Center, 2008: IMS daily Northern Hemisphere snow and ice analysis at 1 km, 4 km, and 24 km resolutions. National Snow and Ice Data Center. Subset used: 24 km, accessed 27 February 2012, doi:10.7265/N52R3PMC.

  • Petty, G. W., and Li K. , 2013: Improved passive microwave retrievals of rain rate over land and ocean. Part I: Algorithm description. J. Atmos. Oceanic Technol., 30, 24932508, doi:10.1175/JTECH-D-12-00144.1.

    • Search Google Scholar
    • Export Citation
  • Ramsay, B. H., 1998: The interactive multisensor snow and ice mapping system. Hydrol. Processes, 12, 15371546, doi:10.1002/(SICI)1099-1085(199808/09)12:10/11<1537::AID-HYP679>3.0.CO;2-A.

    • Search Google Scholar
    • Export Citation
  • Sapiano, M., Janowiak J. E. , Shi W. , Higgins R. W. , and Silva V. B. S. , 2010: Regional evaluation through independent precipitation measurements: USA. Satellite Rainfall Applications for Surface Hydrology, M. Gebremichael and F. Hossain, Eds., Springer Science, 169–191.

  • Schneider, U., Becker A. , Finger P. , Meyer-Christoffer A. , Rudolf B. , and Ziese M. , 2011: GPCC full data reanalysis version 6.0 (at 0.5°, 1.0°, 2.5°): Monthly land-surface precipitation from rain-gauges built on GTS-based and historic data. Global Precipitation Climatology Centre. Subset used: 2.5° grid resolution, accessed 12 November 2013, doi:10.5676/DWD_GPCC/FD_M_V6_250.

  • Sudradjat, A., Wang N.-Y. , Gopalan K. , and Ferraro R. R. , 2011: Prototyping a generic, unified land surface classification and screening methodology for GPM-era microwave land precipitation retrieval algorithms. J. Appl. Meteor. Climatol., 50, 12001211, doi:10.1175/2010JAMC2572.1.

    • Search Google Scholar
    • Export Citation
  • Tang, L., Tian Y. , and Lin X. , 2014: Validation of precipitation retrievals over land from satellite-based passive microwave sensors. J. Geophys. Res. Atmos., 119, 45464567, doi:10.1002/2013JA018955.

    • Search Google Scholar
    • Export Citation
  • Tedesco, M., Kelly R. , Foster J. L. , and Chang A. T. C. , 2004: AMSR-E/Aqua daily L3 global snow water equivalent EASE-Grids, version 2. NASA National Snow and Ice Data Center DAAC. Subset used Monthly SWE, accessed 23 April 2012. [Available online at http://nsidc.org/data/docs/daac/ae_swe_ease-grids.gd.html.]

  • Wilber, A. C., Kratz D. P. , and Gupta S. K. , 1999: Surface emissivity maps for use in satellite retrievals of longwave radiation. NASA Tech. Publ. NASA/TP-1999-209362, 35 pp. [Available online at https://eosweb.larc.nasa.gov/sites/default/files/project/calipso/Wilber.NASATchNote99.pdf.]

  • Wilheit, T. T., Theon J. S. , Shenk W. E. , Allison L. J. , and Rodgers E. B. , 1976: Meteorological interpretations of the images from the Nimbus 5 electrically scanned microwave radiometer. J. Appl. Meteor., 15, 166172, doi:10.1175/1520-0450(1976)015<0166:MIOTIF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 13211338, doi:10.1175/2011BAMS-D-11-00047.1.

    • Search Google Scholar
    • Export Citation
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