Evaluation of Global Precipitation Measurement Rainfall Estimates against Three Dense Gauge Networks

Jackson Tan Universities Space Research Association, Columbia, and NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Jackson Tan in
Current site
Google Scholar
PubMed
Close
,
Walter A. Petersen Earth Science Office, NASA Marshall Space Flight Center, Huntsville, Alabama

Search for other papers by Walter A. Petersen in
Current site
Google Scholar
PubMed
Close
,
Gottfried Kirchengast Wegener Center for Climate and Global Change, and Institute for Geophysics, Astrophysics and Meteorology, Institute of Physics, University of Graz, Graz, Austria

Search for other papers by Gottfried Kirchengast in
Current site
Google Scholar
PubMed
Close
,
David C. Goodrich Southwest Watershed Research Center, Agricultural Research Service, USDA, Tucson, Arizona

Search for other papers by David C. Goodrich in
Current site
Google Scholar
PubMed
Close
, and
David B. Wolff Earth Sciences Field Support Office, NASA Wallops Flight Facility, Wallops Island, Virginia

Search for other papers by David B. Wolff in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5–15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-17-0174.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Global Precipitation Measurement (GPM) special collection.

Corresponding author: Jackson Tan, jackson.tan@nasa.gov

Abstract

Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5–15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-17-0174.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Global Precipitation Measurement (GPM) special collection.

Corresponding author: Jackson Tan, jackson.tan@nasa.gov

Supplementary Materials

    • Supplemental Materials (PDF 574.16 KB)
Save
  • Aires, F., C. Prigent, F. Bernardo, C. Jiménez, R. Saunders, and P. Brunel, 2011: A Tool to Estimate Land-Surface Emissivities at Microwave frequencies (TELSEM) for use in numerical weather prediction. Quart. J. Roy. Meteor. Soc., 137, 690699, https://doi.org/10.1002/qj.803.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Amitai, E., C. L. Unkrich, D. C. Goodrich, E. Habib, and B. Thill, 2012: Assessing satellite-based rainfall estimates in semiarid watersheds using the USDA-ARS Walnut Gulch gauge network and TRMM PR. J. Hydrometeor., 13, 15791588, https://doi.org/10.1175/JHM-D-12-016.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carr, N., and Coauthors, 2015: The influence of surface and precipitation characteristics on TRMM Microwave Imager rainfall retrieval uncertainty. J. Hydrometeor., 16, 15961614, https://doi.org/10.1175/JHM-D-14-0194.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garcia, M., C. D. Peters-Lidard, and D. C. Goodrich, 2008: Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States. Water Resour. Res., 44, W05S13, https://doi.org/10.1029/2006WR005788.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gebregiorgis, A. S., P.-E. Kirstetter, Y. E. Hong, N. J. Carr, J. J. Gourley, W. Petersen, and Y. Zheng, 2017: Understanding overland multisensor satellite precipitation error in TMPA-RT products. J. Hydrometeor., 18, 285306, https://doi.org/10.1175/JHM-D-15-0207.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goodrich, D. C., T. O. Keefer, C. L. Unkrich, M. H. Nichols, H. B. Osborn, J. J. Stone, and J. R. Smith, 2008: Long-term precipitation database, Walnut Gulch Experimental Watershed, Arizona, United States. Water Resour. Res., 44, W05S04, https://doi.org/10.1029/2006WR005782.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamada, A., and Y. N. Takayabu, 2016: Improvements in detection of light precipitation with the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR). J. Atmos. Oceanic Technol., 33, 653667, https://doi.org/10.1175/JTECH-D-15-0097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hiebl, J., and C. Frei, 2018: Daily precipitation grids for Austria since 1961—Development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling. Theor. Appl. Climatol., https://doi.org/10.1007/s00704-017-2093-x, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2017: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Doc., version 4.6, 28 pp., https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_ATBD_V4.6.pdf.

  • Iguchi, T., S. Seto, R. Meneghini, N. Yoshida, J. Awaka, M. Le, V. Chandrasekar, and T. Kubota, 2010: GPM/DPR level-2. Algorithm Theoretical Basis Doc., 72 pp., https://pmm.nasa.gov/sites/default/files/document_files/ATBD_GPM_DPR_n3_dec15.pdf.

  • Kann, A., I. Meirold-Mautner, F. Schmid, G. Kirchengast, J. Fuchsberger, V. Meyer, L. Tüchler, and B. Bica, 2015: Evaluation of high-resolution precipitation analyses using a dense station network. Hydrol. Earth Syst. Sci., 19, 15471559, https://doi.org/10.5194/hess-19-1547-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., T. Matsui, J. Chern, K. Mohr, C. Kummerow, and D. Randel, 2016: Global precipitation estimates from cross-track passive microwave observations using a physically based retrieval scheme. J. Hydrometeor., 17, 383400, https://doi.org/10.1175/JHM-D-15-0051.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., J. Tan, P.-E. Kirstetter, and W. A. Petersen, 2018: Validation of the version 05 level 2 precipitation products from the GPM Core Observatory and constellation satellite sensors. Quart. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.3175, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirchengast, G., T. Kabas, A. Leuprecht, C. Bichler, and H. Truhetz, 2014: WegenerNet: A pioneering high-resolution network for monitoring weather and climate. Bull. Amer. Meteor. Soc., 95, 227242, https://doi.org/10.1175/BAMS-D-11-00161.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirstetter, P.-E., Y. Hong, J. J. Gourley, Q. Cao, M. Schwaller, and W. Petersen, 2014: Research framework to bridge from the Global Precipitation Measurement Mission core satellite to the constellation sensors using ground-radar-based National Mosaic QPE. Remote Sensing of the Terrestrial Water Cycle, Geophys. Monogr., Amer. Geophys. Union, 61–79, https://doi.org/10.1002/9781118872086.ch4.

    • Crossref
    • Export Citation
  • Kirstetter, P.-E., Y. Hong, J. J. Gourley, M. Schwaller, W. Petersen, and Q. Cao, 2015: Impact of sub-pixel rainfall variability on spaceborne precipitation estimation: Evaluating the TRMM 2A25 product: Impact of sub-pixel rainfall variability on TRMM 2A25. Quart. J. Roy. Meteor. Soc., 141, 953966, https://doi.org/10.1002/qj.2416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., D. L. Randel, M. Kulie, N.-Y. Wang, R. Ferraro, S. J. Munchak, and V. Petkovic, 2015: The evolution of the Goddard profiling algorithm to a fully parametric scheme. J. Atmos. Oceanic Technol., 32, 22652280, https://doi.org/10.1175/JTECH-D-15-0039.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menne, M. J., I. Durre, R. S. Vose, B. E. Gleason, and T. G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily database. J. Atmos. Oceanic Technol., 29, 897910, https://doi.org/10.1175/JTECH-D-11-00103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O, S., U. Foelsche, G. Kirchengast, J. Fuchsberger, J. Tan, and W. A. Petersen, 2017: Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria. Hydrol. Earth Syst. Sci., 21, 65596572, https://doi.org/10.5194/hess-21-6559-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O, S., U. Foelsche, G. Kirchengast, and J. Fuchsberger, 2018: Validation and correction of rainfall data from the WegenerNet high density network in southeast Austria. J. Hydrol., 556, 11101122, https://doi.org/10.1016/j.jhydrol.2016.11.049.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petković, V., and C. D. Kummerow, 2015: Performance of the GPM passive microwave retrieval in the Balkan flood event of 2014. J. Hydrometeor., 16, 25012518, https://doi.org/10.1175/JHM-D-15-0018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petković, V., and C. D. Kummerow, 2017: Understanding the sources of satellite passive microwave rainfall retrieval systematic errors over land. J. Appl. Meteor. Climatol., 56, 597614, https://doi.org/10.1175/JAMC-D-16-0174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romanov, P., G. Gutman, and I. Csiszar, 2000: Automated monitoring of snow cover over North America with multispectral satellite data. J. Appl. Meteor., 39, 18661880, https://doi.org/10.1175/1520-0450(2000)039<1866:AMOSCO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skofronick-Jackson, G., and Coauthors, 2017: The Global Precipitation Measurement (GPM) mission for science and society. Bull. Amer. Meteor. Soc., 98, 16791695, https://doi.org/10.1175/BAMS-D-15-00306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, J., W. A. Petersen, and A. Tokay, 2016: A novel approach to identify sources of errors in IMERG for GPM ground validation. J. Hydrometeor., 17, 24772491, https://doi.org/10.1175/JHM-D-16-0079.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, J., W. A. Petersen, P.-E. Kirstetter, and Y. Tian, 2017: Performance of IMERG as a function of spatiotemporal scale. J. Hydrometeor., 18, 307319, https://doi.org/10.1175/JHM-D-16-0174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., and W. F. Krajewski, 2007: Evaluation of the research version TMPA three-hourly 0.25° × 0.25° rainfall estimates over Oklahoma. Geophys. Res. Lett., 34, L05402, https://doi.org/10.1029/2006GL029147.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. International Geophysics Series, Vol. 100, Academic Press, 704 pp.

  • 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, https://doi.org/10.1175/2008JAMC2127.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621638, https://doi.org/10.1175/BAMS-D-14-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 845 182 11
PDF Downloads 677 106 7