• 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.

    • Search Google Scholar
    • Export Citation
  • Derin, Y., P. E. Kirstetter, and J. J. Gourley, 2021: Evaluation of IMERG satellite precipitation over the land–coast–ocean continuum. Part I: Detection. J. Hydrometeor., 22, 28432859, https://doi.org/10.1175/JHM-D-21-0058.1.

    • Search Google Scholar
    • Export Citation
  • Gebregiorgis, A. S., P. 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.

    • Search Google Scholar
    • Export Citation
  • Gebregiorgis, A. S., and Coauthors, 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.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., 2012: Orographic effects on precipitating clouds. Rev. Geophys., 50, RG1001, https://doi.org/10.1029/2011RG000365.

  • Huffman, G. J., and Coauthors, 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.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2020: Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG). Satellite Precipitation Measurement, V. Levizzani, eds, Advances in Global Change Research, Vol. 67, Springer, 343353, https://doi.org/10.1007/978-3-030-24568-9_19.

    • Search Google Scholar
    • Export Citation
  • Kirshbaum, D. J., B. Adler, N. Kalthoff, C. Barthlott, and S. Serafin, 2018: Moist orographic convection: Physical mechanism and links to surface-exchange process. Atmosphere, 9, 80, https://doi.org/10.3390/atmos9030080.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Kirstetter, P.-E., Y. Hong, J. J. Gourley, Q. Cao, M. Schwaller, and W. Petersen, 2014: A 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, 6179.

    • Search Google Scholar
    • 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.

    • 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.

    • 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 mission: Impact of precipitation types on spaceborne precipitation estimation. Satellite Precipitation Measurement, Vol. 2, V. Levizzani et al., Eds., Advances in Global Change Research, Vol. 69, Springer, 583608, https://doi.org/10.1007/978-3-030-35798-6_7.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., 2020: Introduction to passive microwave retrieval methods. Satellite Precipitation Measurement, Vol. 1, V. Levizzani et al., Eds., Advances in Global Change Research, Vol. 67, Springer, 123140, https://doi.org/10.1007/978-3-030-24568-9_7.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., and L. Giglio, 1994: A passive microwave technique for estimating rainfall and vertical structure information from space. Part I: Algorithm description. J. Appl. Meteor. Climatol., 33, 318, https://doi.org/10.1175/1520-0450(1994)033<0003:APMTFE>2.0.CO;2.

    • 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, https://doi.org/10.1175/1520-0450(2001)040%3C1801:TEOTGP%3E2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nalbantis, I., 2008: Evaluation of a hydrological drought index. Eur. Water, 23, 6777.

  • National Weather Service, 2018: August/September 2017 Hurricane Harvey. NWS Service Assessment, 78 pp., https://www.weather.gov/media/publications/assessments/harvey6-18.pdf.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Purnell, D. J., and D. J. Kirshbaum, 2018: Synoptic control over orographic precipitation distributions during the Olympics Mountains Experiment (OLYMPEX). Mon. Wea. Rev., 146, 10231044, https://doi.org/10.1175/MWR-D-17-0267.1.

    • Search Google Scholar
    • Export Citation
  • Rappaport, E. N., 2014: Fatalities in the United States from Atlantic tropical cyclones: New data and Interpretation. Bull. Amer. Meteor. Soc., 95, 341346, https://doi.org/10.1175/BAMS-D-12-00074.1.

    • Search Google Scholar
    • Export Citation
  • Roe, G. H., 2005: Orographic precipitation. Annu. Rev. Earth Planet. Sci., 33, 645671, https://doi.org/10.1146/annurev.earth.33.092203.122541.

    • Search Google Scholar
    • Export Citation
  • Sui, X., Z. Li, Z. Ma, J. Xu, S. Zhu, and H. Liu, 2020: Ground validation and error sources identification for GPM IMERG product over the southeast coastal regions of China. Remote Sens., 12, 4154, https://doi.org/10.3390/rs12244154.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Tian, F., S. Hou, L. Yang, H. Hu, and A. Hou, 2018: How does the evaluation of the GPM IMERG rainfall product depend on gauge density and rainfall intensity? J. Hydrometeor., 19, 339349, https://doi.org/10.1175/JHM-D-17-0161.1.

    • Search Google Scholar
    • Export Citation
  • Turk, F. J., and Coauthors, 2021: Adapting passive microwave-based precipitation algorithms to variable microwave land surface emissivity to improve precipitation estimation from the GPM constellation. J. Hydrometeor., 22, 17551781, https://doi.org/10.1175/JHM-D-20-0296.1.

    • Search Google Scholar
    • Export Citation
  • Wang, D., X. Wang, L. Liu, D. Wang, H. Huang, and C. Pan, 2019: Evaluation of TMPA 3B42V7, GPM IMERG and CMORPH precipitation estimates in Guangdong Province, China. Int. J. Climatol., 39, 738755, https://doi.org/10.1002/joc.5839.

    • Search Google Scholar
    • Export Citation
  • Wang, J., W. A. Petersen, and D. B. Wolff, 2021: Validation of satellite-based precipitation products from TRMM to GPM. Remote Sens., 13, 1745, https://doi.org/10.3390/rs13091745.

    • Search Google Scholar
    • Export Citation
  • Wang, N.-Y., C. Liu, R. Ferraro, D. Wolff, E. Zipser, and C. Kummerow, 2009: TRMM 2A12 land precipitation product-status and future plans. J. Meteor. Soc. Japan, 87A, 237253, https://doi.org/10.2151/jmsj.87A.237.

    • Search Google Scholar
    • Export Citation
  • Wolff, D. B., and B. L. Fisher, 2008: 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.

    • Search Google Scholar
    • Export Citation
  • Xu, W., S. A. Rutledge, and W. Zhang, 2017: Relationships between total lightning, deep convection, and tropical cyclone intensity change. J. Geophys. Res. Atmos., 122, 70477063, https://doi.org/10.1002/2017JD027072.

    • Search Google Scholar
    • Export Citation
  • Yilmaz, K. K., T. S. Hogue, K. L. Hsu, S. Sorooshian, H. V. Gupta, and T. Wagener, 2005: Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting. J. Hydrometeor., 6, 497517, https://doi.org/10.1175/JHM431.1.

    • Search Google Scholar
    • Export Citation
  • Young, G. S., and T. D. Sikora, 2003: Mesoscale stratocumulus bands caused by Gulf Stream meanders. Mon. Wea. Rev., 131, 21772191, https://doi.org/10.1175/1520-0493(2003)131<2177:MSBCBG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., Y. Qi, C. Langston, and B. Kaney, 2011: Radar quality index (RQI) – A combined measure of beam blockage and VPR effects in a national network. IAHS Publ., 351, 388393.

    • Search Google Scholar
    • Export Citation
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Evaluation of IMERG Satellite Precipitation over the Land–Coast–Ocean Continuum. Part II: Quantification

Yagmur DerinaAdvanced Radar Research Centerand School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma

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Pierre-Emmanuel KirstetteraAdvanced Radar Research Centerand School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma
bSchool of Meteorology, University of Oklahoma, Norman, Oklahoma
cNOAA/National Severe Storms Laboratory, Norman, Oklahoma

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Noah BraueraAdvanced Radar Research Centerand School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, Oklahoma
bSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Jonathan J. GourleycNOAA/National Severe Storms Laboratory, Norman, Oklahoma

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Jianxin WangdScience System Applications, Inc., Lanham, Maryland
eNASA Goddard Space Flight Center, Greenbelt, Maryland

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Abstract

To understand and manage water systems under a changing climate and meet an increasing demand for water, a quantitative understanding of precipitation is most important in coastal regions. The capabilities of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B product for precipitation quantification are 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. A novel uncertainty analysis of IMERG is proposed that considers environmental and physical parameters such as elevation and distance to the coastline. The IMERG performance is traced back to its components, i.e., passive microwave (PMW), infrared (IR), and morphing-based estimates. The analysis is performed using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference at the native resolution of IMERG of 30 min and 0.1°. IMERG Final (IM-F) quantification performance heavily depends on the respective contribution of PMW, IR, and morph components. IM-F and its components overestimate the contribution of light rainfall (<1 mm h−1) and underestimate the contribution of high rainfall rates (>10 mm h−1) to the total rainfall volume. Strong regional dependencies are highlighted, especially over the West Coast, where the proximity of complex terrain to the coastline challenges precipitation estimates. Other major drivers are the distance from the coastline, elevation, and precipitation types, especially over the land and coast surface types, that highlight the impact of precipitation regimes.

© 2022 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 12th International Precipitation Conference (IPC12) Special Collection.

This article is included in the Global Precipitation Measurement (GPM): Science and Applications Special Collection.

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

Abstract

To understand and manage water systems under a changing climate and meet an increasing demand for water, a quantitative understanding of precipitation is most important in coastal regions. The capabilities of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B product for precipitation quantification are 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. A novel uncertainty analysis of IMERG is proposed that considers environmental and physical parameters such as elevation and distance to the coastline. The IMERG performance is traced back to its components, i.e., passive microwave (PMW), infrared (IR), and morphing-based estimates. The analysis is performed using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference at the native resolution of IMERG of 30 min and 0.1°. IMERG Final (IM-F) quantification performance heavily depends on the respective contribution of PMW, IR, and morph components. IM-F and its components overestimate the contribution of light rainfall (<1 mm h−1) and underestimate the contribution of high rainfall rates (>10 mm h−1) to the total rainfall volume. Strong regional dependencies are highlighted, especially over the West Coast, where the proximity of complex terrain to the coastline challenges precipitation estimates. Other major drivers are the distance from the coastline, elevation, and precipitation types, especially over the land and coast surface types, that highlight the impact of precipitation regimes.

© 2022 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 12th International Precipitation Conference (IPC12) Special Collection.

This article is included in the Global Precipitation Measurement (GPM): Science and Applications Special Collection.

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