• Bell, T. L., and P. K. Kundu, 2003: Comparing satellite rainfall estimates with rain gauge data: Optimal strategies suggested by a spectral model. J. Geophys. Res., 108, 4121, https://doi.org/10.1029/2002JD002641.

    • Search Google Scholar
    • Export Citation
  • Bourlès, B., and et al. , 2008: The PIRATA Program. Bull. Amer. Meteor. Soc., 89, 11111126, https://doi.org/10.1175/2008BAMS2462.1.

  • Cook, W. E., and J. S. Greene, 2019: Gridded monthly rainfall estimates derived from historical atoll observations. J. Atmos. Oceanic Technol., 36, 671687, https://doi.org/10.1175/JTECH-D-18-0140.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gehne, M., T. M. Hamill, G. N. Kiladis, and K. E. Trenberth, 2016: Comparison of global precipitation estimates across a range of temporal and spatial scales. J. Climate, 29, 77737795, https://doi.org/10.1175/JCLI-D-15-0618.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and et al. , 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grecu, M., W. S. Olson, S. J. Munchak, S. Ringerud, L. Liao, Z. Haddad, B. L. Kelley, and S. F. McLaughlin, 2016: The GPM combined algorithm. J. Atmos. Oceanic Technol., 33, 22252245, https://doi.org/10.1175/JTECH-D-16-0019.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Greene, J. S., M. Klatt, M. Morrissey, and S. Postawko, 2008: The Comprehensive Pacific Rainfall Database. J. Atmos. Oceanic Technol., 25, 7182, https://doi.org/10.1175/2007JTECHA904.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, Y., K.-L. Hsu, S. Sorooshian, and X. Gao, 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 18341853, https://doi.org/10.1175/JAM2173.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., R. F. Adler, D. T. Bolvin, and E. J. Nelkin, 2010: The TRMM Multi-satellite Precipitation Analysis (TMPA). Satellite Rainfall Applications for Surface Hydrology, F. Hossain and M. Gebremichael, Eds., Springer, 3–22.

    • Crossref
    • Export Citation
  • Huffman, G. J., and et al. , 2019: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Doc., version 6, 34 pp., https://gpm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V06.pdf.

  • Huffman, G. J., and et al. , 2020: Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG). Satellite Precipitation Measurement, V. Levizzani et al., Eds., Vol. 1, Advances Global Change Research, Vol. 67, Springer, 343–353, https://doi.org/10.1007/978-3-030-24568-9_19.

    • Crossref
    • Export Citation
  • Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., P. Xie, and J. E. Janowiak, 2011: Kalman filter based CMORPH. J. Hydrometeor., 12, 15471563, https://doi.org/10.1175/JHM-D-11-022.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khan, S., and V. Maggioni, 2019: Assessment of level-3 gridded Global Precipitation Mission (GPM) products over oceans. Remote Sens., 11, 255, https://doi.org/10.3390/rs11030255.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., 2018: NASA Global Precipitation Measurement (GPM) Precipitation Retrieval and Profiling Scheme (PRPS). Algorithm Theoretical Basis Doc., version 01-02, 16 pp., https://pps.gsfc.nasa.gov/Documents/20180203_SAPHIR-ATBD.pdf.

  • Kidd, C., A. Becker, G. J. Huffman, C. L. Muller, P. Joe, G. Skofronick-Jackson, and D. B. Kirschbaum, 2017: So, how much of the Earth’s surface is covered by rain gauges? Bull. Amer. Meteor. Soc., 98, 6978, https://doi.org/10.1175/BAMS-D-14-00283.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klepp, C., 2015: The oceanic shipboard precipitation measurement network for surface validation—OceanRAIN. Atmos. Res., 163, 7490, https://doi.org/10.1016/j.atmosres.2014.12.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klepp, C., and et al. , 2017: Ocean Rainfall and Ice-Phase Precipitation Measurement Network—OceanRAIN-M. World Data Center for Climate (WDCC) at DKRZ, accessed 13 July 2020, https://doi.org/10.1594/WDCC/OceanRAIN-M.

    • Crossref
    • Export Citation
  • Kucera, P., and C. Klepp, 2017: Validation of High Resolution IMERG Satellite Precipitation over the Global Oceans using OceanRAIN. Geophysical Research Abstracts, Vol. 19, Abstract 11794, https://meetingorganizer.copernicus.org/EGU2017/EGU2017-11794.pdf.

  • Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809817, https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C., 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
  • Legates, D. R., 1987: A Climatology of Global Precipitation. Publications in Climatology, Vol. 40, University of Delaware, 85 pp.

  • McPhaden, M. J., 1995: The Tropical Atmosphere Ocean array is completed. Bull. Amer. Meteor. Soc., 76, 739744, https://doi.org/10.1175/1520-0477-76.5.739.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J., and et al. , 2009: RAMA: The Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction. Bull. Amer. Meteor. Soc., 90, 459480, https://doi.org/10.1175/2008BAMS2608.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrissey, M. L., 1991: Using sparse raingages to test satellite-based rainfall algorithms. J. Geophys. Res., 96, 18 56118 571, https://doi.org/10.1029/91JD01790.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Postawko, S., M. Morrissey, and B. Gibson, 1994: The Schools of the Pacific Rainfall Climate Experiment: Combining research and education. Bull. Amer. Meteor. Soc., 75, 12601266, https://doi.org/10.1175/1520-0477-75.7.1249.

    • Search Google Scholar
    • Export Citation
  • Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer, and M. Ziese, 2018a: GPCC Monitoring Product: Near real-time monthly land-surface precipitation from rain-gauges based on SYNOP and CLIMAT data. DWD, accessed 24 July 2020, https://doi.org/10.5676/DWD_GPCC/MP_M_V6_100.

    • Crossref
    • Export Citation
  • Schneider, U.; A. Becker, P. Finger, A. Meyer-Christoffer, and M. Ziese, 2018b: GPCC Full Data Monthly Product version 2018 at 1.0°: Monthly land-surface precipitation from rain-gauges built on GTS-based and historical data. DWD, accessed 24 July 2020, https://doi.org/10.5676/DWD_GPCC/FD_M_V2018_100.

    • Crossref
    • Export Citation
  • Tan, J., G. J. Huffman, D. T. Bolvin, and E. J. Nelkin, 2019: IMERG V06: Changes to the morphing algorithm. J. Atmos. Oceanic Technol., 36, 24712482, https://doi.org/10.1175/JTECH-D-19-0114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, R. C., 1973: An atlas of Pacific islands rainfall. Data Rep. 25, Dept. of Meteorology, University of Hawai‘i at Mānoa, 174 pp.

  • Wu, Q., and Y. Wang, 2019: Comparison of oceanic multisatellite precipitation data from tropical rainfall measurement mission and global precipitation measurement mission datasets with rain gauge data from ocean buoys. J. Atmos. Oceanic Technol., 36, 903920, https://doi.org/10.1175/JTECH-D-18-0152.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Comparison of Monthly IMERG Precipitation Estimates with PACRAIN Atoll Observations

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  • 1 a Laboratory for Atmospheres, NASA GSFC, Greenbelt, Maryland
  • | 2 b Science Systems and Applications, Inc., Lanham, Maryland
  • | 3 c Universities Space Research Association, Columbia, Maryland
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Abstract

Satellite-based precipitation estimates provide valuable information where surface observations are not readily available, especially over the large expanses of the ocean where in situ precipitation observations are very sparse. This study compares monthly precipitation estimates from the Integrated Multisatellite Retrievals for GPM (IMERG) with gauge observations from 37 low-lying atolls from the Pacific Rainfall Database for the period June 2000–August 2020. Over the analysis period, IMERG estimates are slightly higher than the atoll observations by 0.67% with a monthly correlation of 0.68. Seasonally, DJF shows excellent agreement with a near-zero bias, while MAM shows IMERG is low by 4.6%, and JJA is high by 1.2%. SON exhibits the worst performance, with IMERG overestimating by 6.5% compared to the atolls. The seasonal correlations are well contained in the range 0.67–0.72, with the exception of SON at 0.62. Furthermore, SON has the highest RMSE at 4.70 mm day−1, making it the worst season for all metrics. Scatterplots of IMERG versus atolls show IMERG, on average, is generally low for light precipitation accumulations and high for intense precipitation accumulations, with best agreement at intermediate rates. Seasonal variations exist at light and intermediate rate accumulations, but IMERG consistently overestimates at intense precipitation rates. The differences between IMERG and atolls vary over time but do not exhibit any discernable trend or dependence on atoll population. The PACRAIN atoll gauges are not wind-loss corrected, so application of an appropriate adjustment would increase the precipitation amounts compared to IMERG. These results provide useful insight to users as well as valuable information for future improvements to IMERG.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: David T. Bolvin, david.t.bolvin@nasa.gov

Abstract

Satellite-based precipitation estimates provide valuable information where surface observations are not readily available, especially over the large expanses of the ocean where in situ precipitation observations are very sparse. This study compares monthly precipitation estimates from the Integrated Multisatellite Retrievals for GPM (IMERG) with gauge observations from 37 low-lying atolls from the Pacific Rainfall Database for the period June 2000–August 2020. Over the analysis period, IMERG estimates are slightly higher than the atoll observations by 0.67% with a monthly correlation of 0.68. Seasonally, DJF shows excellent agreement with a near-zero bias, while MAM shows IMERG is low by 4.6%, and JJA is high by 1.2%. SON exhibits the worst performance, with IMERG overestimating by 6.5% compared to the atolls. The seasonal correlations are well contained in the range 0.67–0.72, with the exception of SON at 0.62. Furthermore, SON has the highest RMSE at 4.70 mm day−1, making it the worst season for all metrics. Scatterplots of IMERG versus atolls show IMERG, on average, is generally low for light precipitation accumulations and high for intense precipitation accumulations, with best agreement at intermediate rates. Seasonal variations exist at light and intermediate rate accumulations, but IMERG consistently overestimates at intense precipitation rates. The differences between IMERG and atolls vary over time but do not exhibit any discernable trend or dependence on atoll population. The PACRAIN atoll gauges are not wind-loss corrected, so application of an appropriate adjustment would increase the precipitation amounts compared to IMERG. These results provide useful insight to users as well as valuable information for future improvements to IMERG.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: David T. Bolvin, david.t.bolvin@nasa.gov
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