Editorial Type:
Article
Article Type:
Research Article

Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically Based Retrieval Scheme

Chris Kidd Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, and NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Chris Kidd in
Current site
Google Scholar
PubMed Close
,
Toshihisa Matsui Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, and NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Toshihisa Matsui in
Current site
Google Scholar
PubMed Close
,
Jiundar Chern Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, and NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Jiundar Chern in
Current site
Google Scholar
PubMed Close
,
Karen Mohr NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Karen Mohr in
Current site
Google Scholar
PubMed Close
,
Chris Kummerow Colorado State University, Fort Collins, Colorado

Search for other papers by Chris Kummerow in
Current site
Google Scholar
PubMed Close
, and
Dave Randel Colorado State University, Fort Collins, Colorado

Search for other papers by Dave Randel in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Feb 2016
Collections:
Seventh International Precipitation Working Group (IPWG) Workshop
DOI:
https://doi.org/10.1175/JHM-D-15-0051.1
Page(s):
383–400
Restricted access

Abstract

The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals.

Corresponding author address: Chris Kidd, Code 612.0, NASA Goddard Space Flight Center, Greenbelt, MD 20771. E-mail: chris.kidd@nasa.gov

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

Abstract

The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals.

Corresponding author address: Chris Kidd, Code 612.0, NASA Goddard Space Flight Center, Greenbelt, MD 20771. E-mail: chris.kidd@nasa.gov

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

Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Aires, F., Prigent C. , Bernardo F. , Jiménez C. , Saunders R. , and Brunel P. , 2011: A Tool to Estimate Land-Surface Emissivities at Microwave Frequencies (TELSEM) for use in numerical weather prediction. Quart. J. Roy. Meteor. Soc., 137, 690–699, doi:10.1002/qj.803.

    • Search Google Scholar
    • Export Citation

  • Berg, W., L’Ecuyer T. , and Kummerow C. , 2006: Rainfall climate regimes: The relationship of regional TRMM rainfall biases in the environment. J. Appl. Meteor. Climatol., 45, 434–454, doi:10.1175/JAM2331.1.

    • Search Google Scholar
    • Export Citation

  • Boukabara, S.-A., and Coauthors, 2011: MiRS: An all-weather 1DVAR satellite data assimilation and retrieval system. IEEE Trans. Geosci. Remote Sens., 49, 3249–3272, doi:10.1109/TGRS.2011.2158438.

    • Search Google Scholar
    • Export Citation

  • Casella, D., Panegrossi G. , Sano P. , Milani L. , Petracca M. , and Dietrich S. , 2015: A novel algorithm for detection of precipitation in tropical regions using PMW radiometers. Atmos. Meas. Tech., 8, 1217–1232, doi:10.5194/amt-8-1217-2015.

    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., Janowiak J. E. , and Kidd C. , 2007: Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., 88, 47–64, doi:10.1175/BAMS-88-1-47.

    • Search Google Scholar
    • Export Citation

  • Elsaesser, G. S., and Kummerow C. D. , 2015: The sensitivity of rainfall estimation to error assumptions in a Bayesian passive microwave retrieval algorithm. J. Appl. Meteor. Climatol., 54, 408–422, doi:10.1175/JAMC-D-14-0105.1.

    • 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, 1583–1600, doi:10.1175/1520-0469(1998)055<1583:ASMFPM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 5, 701–722, doi:10.1175/BAMS-D-13-00164.1.

  • Iturbide-Sanchez, F., Boukabara S.-A. , Chen R. , Garrett K. , Grassotti C. , Chen W. , and Weng F. , 2011: Assessment of a variational inversion system for rainfall rate over land and water surfaces. IEEE Trans. Geosci. Remote Sens., 49, 3311–3333, doi:10.1109/TGRS.2011.2119375.

    • Search Google Scholar
    • Export Citation

  • Kidd, C., 1998: On rainfall retrieval using polarization-corrected temperatures. Int. J. Remote Sens., 19, 981–996, doi:10.1080/014311698215829.

    • Search Google Scholar
    • Export Citation

  • Kidd, C., 2014a: GPROF NOAA-18 MHS Level 2A precipitation product. NASA GSFC, accessed 10 November 2015, doi:10.5067/GPM/MHS/NOAA18/GPROF2A.

  • Kidd, C., 2014b: GPROF NOAA-19 MHS Level 2A precipitation product. NASA GSFC, accessed 10 November 2015, doi:10.5067/GPM/MHS/NOAA19/GPROF2A.

  • Kidd, C., 2014c: GPROF EUMETSAT MetOpA MHS Level 2A precipitation product. NASA GSFC, accessed 10 November 2015, doi:10.5067/GPM/MHS/METOPA/GPROF2A.

  • Kidd, C., 2014d: GPROF EUMETSAT MetOpB MHS Level 2A precipitation product. NASA GSFC, accessed 10 November 2015, doi:10.5067/GPM/MHS/METOPB/GPROF2A.

  • Kidd, C., and Huffman G. , 2011: Global precipitation measurement. Meteor. Appl., 18, 334–353, doi:10.1002/met.284.

  • Kidd, C., and Levizzani V. , 2011: Status of satellite precipitation retrievals. Hydrol. Earth Syst. Sci., 15, 1109–1116, doi:10.5194/hess-15-1109-2011.

    • Search Google Scholar
    • Export Citation

  • Kidd, C., Kniveton D. , and Barrett E. C. , 1998: The advantages and disadvantages of statistically derived–empirically calibrated passive microwave algorithms for rainfall estimation. J. Atmos. Sci., 55, 1576–1582, doi:10.1175/1520-0469(1998)055<1576:TAADOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kidd, C., and Hou A. , 2012: Combined satellite–surface precipitation products: Assessment of regional and temporal characteristics and relationships. Geophysical Research Abstracts, Vol. 14, Abstract EGU2012-3459. [Available online at http://meetingorganizer.copernicus.org/EGU2012/EGU2012-3459.pdf.]

  • Kidd, C., Bauer P. , Turk J. , Huffman G. J. , Joyce R. , Hsu K.-L. , and Braithwaite D. , 2012: Intercomparison of high-resolution precipitation products over northwest Europe. J. Hydrometeor., 13, 67–83, doi:10.1175/JHM-D-11-042.1.

    • 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, 1285–1300, doi:10.1175/JHM-D-11-0139.1.

    • Search Google Scholar
    • Export Citation

  • Kummerow, C., 1993: On the accuracy of the Eddington approximation for radiative transfer in the microwave frequencies. J. Geophys. Res., 98, 2757–2765, doi:10.1029/92JD02472.

    • Search Google Scholar
    • Export Citation

  • Kummerow, C., 2014a: GPROF GCOMW1 AMSR2 Level 2A precipitation product. NASA GSFC, accessed 10 November 2015, doi:10.5067/GPM/AMSR/GCOMW1/GPROF2A.

  • Kummerow, C., 2014b: GPROF GPM GMI Level 2A precipitation product. NASA GSFC, accessed 10 November 2015, doi:10.5067/GPM/GMI/GPM/GPROF2A.

  • Kummerow, C., 2014c: GPROF DMSP-F16 SSMIS Level 2A precipitation product. NASA GSFC, accessed 10 November 2015, doi:10.5067/GPM/SSMIS/F16/GPROF2A.

  • Kummerow, C., 2014d: GPROF DMSP-F17 SSMIS Level 2A precipitation product. NASA GSFC, accessed 10 November 2015, doi:10.5067/GPM/SSMIS/F17/GPROF2A.

  • Kummerow, C., 2014e: GPROF DMSP-F18 SSMIS Level 2A precipitation product. NASA GSFC, accessed 10 November 2015, doi:10.5067/GPM/SSMIS/F18/GPROF2A.

  • Kummerow, C., and Giglio L. , 1994: A passive microwave technique for estimating rainfall and vertical structure information from space. Part I: Algorithm description. J. Appl. Meteor., 33, 3–18, doi:10.1175/1520-0450(1994)033<0003:APMTFE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kummerow, C., Olson W. S. , and Giglio L. , 1996: A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors. IEEE Trans. Geosci. Remote Sens., 34, 1213–1232, doi:10.1109/36.536538.

    • 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, 1801–1820, doi:10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kummerow, C., Randel D. L. , Kulie M. , Wang N-Y. , Ferraro R. , Munchak S. J. , and Petkovic V. , 2015: The evolution of the Goddard profiling algorithm to a fully parametric scheme. J. Atmos. Oceanic Technol., doi:10.1175/JTECH-D-15-0039.1, in press.

    • Search Google Scholar
    • Export Citation

  • Lang, S., Tao W.-K. , Chern J.-D. , Wu D. , and Li X. , 2014: Benefits of a fourth ice class in the simulated radar reflectivities of convective systems using a bulk microphysics scheme. J. Atmos. Sci., 71, 3583–3612, doi:10.1175/JAS-D-13-0330.1.

    • Search Google Scholar
    • Export Citation

  • Laviola, S., Levizzani V. , Cattani E. , and Kidd C. , 2013: The 183-WSL fast rain rate retrieval algorithm. Part II: Validation using ground radar measurements. Atmos. Res., 134, 77–86, doi:10.1016/j.atmosres.2013.07.013.

    • Search Google Scholar
    • Export Citation

  • Matsui, T., Zeng X. , Tao W.-K. , Masunaga H. , Olson W. , and Lang S. , 2009: Evaluation of long-term cloud-resolving model simulations using satellite radiance observations and multifrequency satellite simulators. J. Atmos. Oceanic Technol., 26, 1261–1274, doi:10.1175/2008JTECHA1168.1.

    • Search Google Scholar
    • Export Citation

  • Matsui, T., and Coauthors, 2013: GPM satellite simulator over ground validation sites. Bull. Amer. Meteor. Soc., 94, 1653–1660, doi:10.1175/BAMS-D-12-00160.1.

    • Search Google Scholar
    • Export Citation

  • Matsui, T., Tao W.-K. , Munchack S. J. , Grecu M. , and Huffman G. , 2015: Satellite view of quasi-equilibrium states in tropical convection and precipitation microphysics. Geophys. Res. Lett., 42, 1959–1968, doi:10.1002/2015GL063261.

    • Search Google Scholar
    • Export Citation

  • Met Office, 2003: Met Office rain radar data from the NIMROD system. NCAS British Atmospheric Data Centre, accessed 9 April 2015. [Available online at http://catalogue.ceda.ac.uk/uuid/82adec1f896af6169112d09cc1174499.]

  • Mohr, K. I., Tao W.-K. , Chern J.-D. , Kumar S. V. , and Peters-Lidard C. , 2013: The NASA-Goddard Multi-scale Modeling Framework–Land Information System: Global land/atmosphere interaction with resolved convection. Environ. Modell. Software, 39, 103–115, doi:10.1016/j.envsoft.2012.02.023.

    • Search Google Scholar
    • Export Citation

  • Olson, W. S., Bauer P. , Kummerow C. D. , Hong Y. , and Tao W.-K. , 2001: A melting-layer model for passive/active microwave remote sensing applications. Part II: Simulation of TRMM observations. J. Appl. Meteor., 40, 1164–1179, doi:10.1175/1520-0450(2001)040<1164:AMLMFP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Olson, W. S., Yang S. , Stout J. E. , and Grecu M. , 2007: The Goddard profiling algorithm (GPROF): Description and current applications. Measuring Precipitation from Space: EURAINSAT and the Future, V. Levizzani, P. Bauer, and F. J. Turk, Eds., Advances in Global Change Research, Vol. 28, Springer, 179–188, doi:10.1007/978-1-4020-5835-6_14.

  • Robinson, F. J., Sherwood S. C. , Gerstle D. , Liu C. , and Kirshbaum D. J. , 2011: Exploring the land–ocean contrast in convective vigor using islands. J. Atmos. Sci., 68, 602–618, doi:10.1175/2010JAS3558.1.

    • Search Google Scholar
    • Export Citation

  • Sano, P., Panegrossi G. , Casella D. , Di Paola F. , Milani L. , Mugnai A. , Petracca M. , and Dietrich S. , 2015: The Passive Microwave Neural Network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: Description and application to European case studies. Atmos. Meas. Tech., 8, 837–857, doi:10.5194/amt-8-837-2015.

    • Search Google Scholar
    • Export Citation

  • Smith, E. A., Kummerow C. , and Mugnai A. , 1994: The emergence of inversion-type profile algorithm for estimation of precipitation from satellite passive microwave measurements. Remote Sens. Rev., 11, 211–242, doi:10.1080/02757259409532266.

    • Search Google Scholar
    • Export Citation

  • Spencer, R. W., 1986: A satellite passive 37-GHz scattering-based method for measuring oceanic rain rates. J. Climate Appl. Meteor., 25, 754–766, doi:10.1175/1520-0450(1986)025<0754:ASPGSB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Surussavadee, C., and Staelin D. H. , 2008: Global millimeter-wave precipitation retrievals trained with a cloud-resolving numerical weather prediction model, part 1: Retrieval design. IEEE Trans. Geosci. Remote Sens., 46, 99–108, doi:10.1109/TGRS.2007.908302.

    • Search Google Scholar
    • Export Citation

  • Tao, W.-K., and Simpson J. , 1993: Goddard Cumulus Ensemble Model. Part 1: Model description. Terr. Atmos. Ocean. Sci., 4, 35–72.

  • Tao, W.-K., and Coauthors, 2009: A multiscale modeling system: Developments, applications, and critical issues. Bull. Amer. Meteor. Soc., 90, 515–534, doi:10.1175/2008BAMS2542.1.

    • Search Google Scholar
    • Export Citation

  • Tripoli, G. J., 1992: A nonhydrostatic mesoscale model designed to simulate scale interaction. Mon. Wea. Rev., 120, 1342–1359, doi:10.1175/1520-0493(1992)120<1342:ANMMDT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Webb, M., Senior C. , Bony S. , and Morcrette J. J. , 2001: Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models. Climate Dyn., 17, 905–922, doi:10.1007/s003820100157.

    • Search Google Scholar
    • Export Citation

  • Wilheit, T. T., 1979: A model for the microwave emissivity of the ocean’s surface as a function of wind speed. IEEE Trans. Geosci. Electron., 17, 244–249, doi:10.1109/TGE.1979.294653.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 738 326 33
PDF Downloads 296 94 14