• Anagnostou, E. N., W. F. Krajewski, and J. Smith, 1999: Uncertainty quantification of mean-areal radar-rainfall estimates. J. Atmos. Oceanic Technol., 16, 206215, https://doi.org/10.1175/1520-0426(1999)016<0206:UQOMAR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anagnostou, E. N., V. Maggioni, E. Nikolopoulos, T. Meskele, F. Hossain, and A. Papadopoulos, 2010: Benchmarking high-resolution global satellite rainfall products to radar and rain-gauge rainfall estimates. IEEE Trans. Geosci. Remote Sens., 48, 16671683, https://doi.org/10.1109/TGRS.2009.2034736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ayat, H., J. P. Evans, and A. Behrangi, 2021: How do different sensors impact IMERG precipitation estimates during hurricane days? Remote Sens. Environ., 259, 112417, https://doi.org/10.1016/j.rse.2021.112417.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bowman, K. P., A. B. Phillips, and G. R. North, 2003: Comparison of TRMM rainfall retrievals with rain gauge data from the TAO/TRITON buoy array. Geophys. Res. Lett., 30, 1757, https://doi.org/10.1029/2003GL017552.

    • 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
  • Carr, N., P. E. Kirstetter, J. J. Gourley, and Y. Hong, 2017: Polarimetric signatures of midlatitude warm-rain precipitation events. J. Appl. Meteor. Climatol., 56, 697711, https://doi.org/10.1175/JAMC-D-16-0164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiu, J. C., and G. W. Petty, 2006: Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations. J. Appl. Meteor. Climatol., 45, 10731095, https://doi.org/10.1175/JAM2392.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Conner, M. D., and G. W. Petty, 1998: Validation and intercomparison of SSM/I rain-rate retrieval methods over the continental United States. J. Hydrometeor., 37, 679700, https://doi.org/10.1175/1520-0450(1998)037<0679:VAIOSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Derin, Y., and K. K. Yilmaz, 2014: Evaluation of multiple satellite-based precipitation products over complex topography. J. Hydrometeor., 15, 14981516, https://doi.org/10.1175/JHM-D-13-0191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derin, Y., and Coauthors, 2016: Multi-regional satellite precipitation products evaluation over complex terrain. J. Hydrometeor., 17, 18171836, https://doi.org/10.1175/JHM-D-15-0197.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derin, Y., E. Anagnostou, M. N. Anagnostou, J. Kalogiros, D. Casella, A. C. Marra, G. Panegrossi, and P. Sano, 2018: Passive microwave rainfall error analysis using high-resolution X-band dual-polarization radar observations in complex terrain. IEEE Trans. Geosci. Remote Sens., 56, 25652586, https://doi.org/10.1109/TGRS.2017.2763622.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derin, Y., and Coauthors, 2019: Evaluation of GPM-era global satellite precipitation products over multiple complex terrain regions. Remote Sens., 11, 2936, https://doi.org/10.3390/rs11242936.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., J. E. Janowiak, and C. Kidd, 2007: Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., 88, 4764, https://doi.org/10.1175/BAMS-88-1-47.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gandin, L. S., and A. H. Murphy, 1992: Equitable scores for categorical forecasts. Mon. Wea. Rev., 120, 361370, https://doi.org/10.1175/1520-0493(1992)120<0361:ESSFCF>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gebregiorgis, A., P. E. Kirstetter, Y. Hong, J. J. Gourley, G. J. Huffman, W. A. Petersen, X. Xue, and M. R. Schwaller, 2018: To what extent is the day 1 GPM IMERG satellite precipitation estimate improved as compared to TRMM TMPA-RT? J. Geophys. Res. Atmos., 123, 16941707, https://doi.org/10.1002/2017JD027606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heidke, P., 1926: Berechnung der Erfolges und der Gute der Windstarkevorhersagen im Sturmwarnungdienst (Calculation of the success and goodness of wind strength forecasts in the Storm Warning Service). Geogr. Ann., 8, 301349, https://doi.org/10.1080/20014422.1926.11881138.

    • Search Google Scholar
    • Export Citation
  • Hong, Y., D. Gochis, J. Cheng, K. Hsu, and S. Sorooshian, 2007: Evaluation of PERSIANN-CCS rainfall measurement using the NAME event rain gauge network. J. Hydrometeor., 8, 469482, https://doi.org/10.1175/JHM574.1.

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

  • Jolliffe, I. T., and D. B. Stephenson, 2012: Forecast Verification: A Practitioner’s Guide in Atmospheric Science. 2nd ed. John Wiley and Sons, 296 pp.

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

  • 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., J. Tan, P.-E. Kirstetter, and W. A. Petersen, 2017: Validation of the version 05 level 2 precipitation products from the GPM Core Observatory and constellation satellite sensors. Quart. J. Roy. Meteor. Soc., 144, 313328, https://doi.org/10.1002/qj.3175.

    • Crossref
    • 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, https://doi.org/10.1175/JHM-D-11-0139.1.

    • 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., Vol. 206, 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. Quart. J. Roy. Meteor. Soc., 141, 953966, https://doi.org/10.1002/qj.2416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirstetter, P. E., N. Karbalaee, K. Hsu, and Y. Hong, 2018: Probabilistic precipitation rate estimates with space-based infrared sensors. Quart. J. Roy. Meteor. Soc., 144, 191205, https://doi.org/10.1002/qj.3243.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirstetter, P. E., W. A. Petersen, C. D. Kummerow, and D. B. Wolff, 2020: Integrated multi-satellite evaluation for the global precipitation measurement: Impact of precipitation types on spaceborne precipitation estimation. Satellite Precipitation Measurement, V. Levizzani et al., Eds., Advances in Global Change Research, Vol. 69, Springer, 583–608.

    • Crossref
    • Export Citation
  • Kummerow, C. D., 2020: Introduction to passive microwave retrieval methods. Satellite Precipitation Measurement, V. Levizzani et al., Eds., Advances in Global Change Research, Vol. 67, Springer, 123–140.

    • Crossref
    • Export Citation
  • Maggioni, V., P. C. Meyers, and M. D. Robinson, 2016: A review of merged high-resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM) era. J. Hydrometeor., 17, 11011117, https://doi.org/10.1175/JHM-D-15-0190.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maggioni, V., E. I. Nikolopoulos, E. N. Anagnostou, and M. Borga, 2017: Modeling satellite precipitation errors over mountainous terrain: The influence of gauge density, seasonality, and temporal resolution. IEEE Trans. Geosci. Remote Sens., 55, 41304140, https://doi.org/10.1109/TGRS.2017.2688998.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manz, B., S. Paez-Bimos, N. Horna, W. Buytaert, B. Ochoa-Tocachi, W. Lavado-Casimiro, and B. Willems, 2017: Comparative ground validation of IMERG and TMPA at variable spatiotemporal scales in the tropical Andes. J. Hydrometeor., 18, 24692489, https://doi.org/10.1175/JHM-D-16-0277.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCollum, J. R., W. F. Krajewski, R. R. Ferraro, and M. B. Ba, 2002: Evaluation of biases of satellite rainfall estimation algorithms over the continental United States. J. Appl. Meteor., 41, 10651080, https://doi.org/10.1175/1520-0450(2002)041<1065:EOBOSR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mei, Y., E. N. Anagnostou, E. I. Nikolopoulos, and M. Borga, 2014: Error analysis of satellite precipitation products in mountainous basins. J. Hydrometeor., 15, 17781793, https://doi.org/10.1175/JHM-D-13-0194.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrissey, M. L., H. J. Diamond, M. J. McPhaden, H. P. Freitag, and J. S. Greene, 2012: An investigation of the consistency of TAO-TRITON buoy-mounted capacitance rain gauges. J. Atmos. Oceanic Technol., 29, 834845, https://doi.org/10.1175/JTECH-D-11-00171.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NASA, 2017: GPROF2017 Version 1 (used in GPM V5 processing). NASA Algorithm Theoretical Basis Doc., 63 pp., http://rain.atmos.colostate.edu/ATBD/ATBD_GPM_June1_2017.pdf.

  • Neumann, B., A. T. Vafeidis, J. Zimmermann, and R. J. Nicholls, 2015: Future coastal population growth and exposure to sea-level rise and coastal flooding – A global assessment. PLOS ONE, 10, e0118571, https://doi.org/10.1371/journal.pone.0118571.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petty, G. W., and K. Li, 2013: Improved passive microwave retrievals of rain rate over land and ocean. Part II: Validation and intercomparison. J. Atmos. Oceanic Technol., 30, 25092526, https://doi.org/10.1175/JTECH-D-12-00184.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porcacchia, L., P. E. Kirstetter, J. J. Gourley, V. Maggioni, B. L. Cheong, and M. N. Anagnostou, 2017: Toward a polarimetric radar classification scheme for coalescence-dominant precipitation: Application to complex terrain. J. Hydrometeor., 18, 31993215, https://doi.org/10.1175/JHM-D-17-0016.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prigent, C., W. B. Rossow, and E. Matthews, 1997: Microwave land surface emissivities estimated from SSM/I observations. J. Geophys. Res., 102, 21 86721 890, https://doi.org/10.1029/97JD01360.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saharia, M., P. E. Kirstetter, H. Vergara, J. J. Gourley, and Y. Hong, 2017: Characterization of floods in the United States. J. Hydrol., 548, 524535, https://doi.org/10.1016/j.jhydrol.2017.03.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sapiano, M. R. P., and P. A. Arkin, 2009: An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J. Hydrometeor., 10, 149166, https://doi.org/10.1175/2008JHM1052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serra, Y. L., 2018: Precipitation measurements from the Tropical Moored Array: A review and look ahead. Quart. J. Roy. Meteor. Soc., 144, 221234, https://doi.org/10.1002/qj.3287.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serra, Y. L., and M. J. McPhaden, 2003: Multiple time- and space-scale comparisons of ATLAS buoy rain gauge measurements with TRMM satellite precipitation measurements*. J. Appl. Meteor., 42, 10451059, https://doi.org/10.1175/1520-0450(2003)042<1045:MTASCO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serra, Y. L., P. A. A’Hearn, H. P. Freitag, and M. J. McPhaden, 2001: ATLAS self-siphoning rain gauge error estimates. J. Atmos. Oceanic Technol., 18, 19892002, https://doi.org/10.1175/1520-0426(2001)018<1989:ASSRGE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, S. R., and Coauthors, 2009: The data management system for the Shipboard Automated Meteorological and Oceanographic Systems (SAMOS) initiative. Proceedings of the OceanObs’09: Sustained Ocean Observations and Information for Society Conf., Vol. 2, J. Hall, D. E. Harrison, and D. Stammer, Eds., ESA Publication WPP-306, European Space Agency, 10 pp., https://doi.org/10.5270/OceanObs09.cwp.83.

    • Crossref
    • Export Citation
  • Stampoulis, D., E. N. Anagnostou, and E. I. Nikolopoulos, 2013: Assessment of high-resolution satellite-based rainfall estimates over the Mediterranean during heavy precipitation events. J. Hydrometeor., 14, 15001514, https://doi.org/10.1175/JHM-D-12-0167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takbiri, Z., A. Ebtehaj, E. Foufoula-Georgiou, P. Kirstetter, and F. J. Turk, 2019: A prognostic nested k-nearest approach for microwave precipitation phase detection over snow cover. J. Hydrometeor., 20, 251274, https://doi.org/10.1175/JHM-D-18-0021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wolff, D. B., and B. L. Fisher, 2009: Comparisons of instantaneous TRMM ground validation and satellite rain-rate estimates at different spatial scales. J. Appl. Meteor. Climatol., 47, 22152237, https://doi.org/10.1175/2008JAMC1875.1.

    • 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
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Evaluation of IMERG Satellite Precipitation over the Land–Coast–Ocean Continuum. Part I: Detection

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  • 1 a Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma
  • | 2 b School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma
  • | 3 c School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • | 4 d NOAA/National Severe Storms Laboratory, Norman, Oklahoma
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Abstract

As a fundamental water flux, quantitative understanding of precipitation is important to understand and manage water systems under a changing climate, especially in transition regions such as the coastal interface between land and ocean. This work aims to assess the uncertainty in precipitation detection over the land–coast–ocean continuum in the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) V06B product. It is examined over three coastal regions of the United States—the West Coast, the Gulf of Mexico, and the East Coast, all of which are characterized by different topographies and precipitation climatologies. Detection capabilities are contrasted over different surfaces (land, coast, and ocean). A novel and integrated approach traces the IMERG detection performance back to its components (passive microwave, infrared, and morphing-based estimates). The analysis is performed by using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference. The best detection performances are reported with PMW estimates (hit rates in the range [25%–39%]), followed by morphing ([20%–34%]), morphing+IR ([17%–27%]) and IR ([11%–16%]) estimates. Precipitation formation mechanisms play an important role, especially in the West Coast where orographic processes challenge detection. Further, precipitation typology is shown to be a strong driver of IMERG detection. Over the ocean, IMERG detection is generally better but suffers from false alarms ([10%–53%]). Overall, IMERG displays nonhomogeneous precipitation detection capabilities tracing back to its components. Results point toward a similar behavior across various land–coast–ocean continuum regions of the CONUS, which suggests that results can be potentially transferred to other coastal regions of the world.

© 2021 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.

This article is included in the IPC12 - 12th International Precipitation Conference special collection.

Corresponding author: Pierre-Emmanuel Kirstetter, pierre.kirstetter@noaa.gov

Abstract

As a fundamental water flux, quantitative understanding of precipitation is important to understand and manage water systems under a changing climate, especially in transition regions such as the coastal interface between land and ocean. This work aims to assess the uncertainty in precipitation detection over the land–coast–ocean continuum in the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) V06B product. It is examined over three coastal regions of the United States—the West Coast, the Gulf of Mexico, and the East Coast, all of which are characterized by different topographies and precipitation climatologies. Detection capabilities are contrasted over different surfaces (land, coast, and ocean). A novel and integrated approach traces the IMERG detection performance back to its components (passive microwave, infrared, and morphing-based estimates). The analysis is performed by using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference. The best detection performances are reported with PMW estimates (hit rates in the range [25%–39%]), followed by morphing ([20%–34%]), morphing+IR ([17%–27%]) and IR ([11%–16%]) estimates. Precipitation formation mechanisms play an important role, especially in the West Coast where orographic processes challenge detection. Further, precipitation typology is shown to be a strong driver of IMERG detection. Over the ocean, IMERG detection is generally better but suffers from false alarms ([10%–53%]). Overall, IMERG displays nonhomogeneous precipitation detection capabilities tracing back to its components. Results point toward a similar behavior across various land–coast–ocean continuum regions of the CONUS, which suggests that results can be potentially transferred to other coastal regions of the world.

© 2021 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.

This article is included in the IPC12 - 12th International Precipitation Conference special collection.

Corresponding author: Pierre-Emmanuel Kirstetter, pierre.kirstetter@noaa.gov
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