Editorial Type:
Article
Article Type:
Research Article

Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998

Pingping Xie NOAA/Climate Prediction Center, College Park, Maryland

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Robert Joyce NOAA/Climate Prediction Center, College Park, and Innovim, LLC, Greenbelt, Maryland

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Shaorong Wu NOAA/Climate Prediction Center, College Park, and Innovim, LLC, Greenbelt, Maryland

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Soo-Hyun Yoo NOAA/Climate Prediction Center, College Park, Maryland, and Wyle, Inc., McLean, Virginia

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Yelena Yarosh NOAA/Climate Prediction Center, College Park, Maryland, and Wyle, Inc., McLean, Virginia

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Fengying Sun NOAA/Climate Prediction Center, College Park, and Innovim, LLC, Greenbelt, Maryland

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Roger Lin NOAA/Climate Prediction Center, College Park, and Innovim, LLC, Greenbelt, Maryland

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Print Publication:
01 Jun 2017
DOI:
https://doi.org/10.1175/JHM-D-16-0168.1
Page(s):
1617–1641
Restricted access

Abstract

The Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates are reprocessed and bias corrected on an 8 km × 8 km grid over the globe (60°S–60°N) and in a 30-min temporal resolution for an 18-yr period from January 1998 to the present to form a climate data record (CDR) of high-resolution global precipitation analysis. First, the purely satellite-based CMORPH precipitation estimates (raw CMORPH) are reprocessed. The integration algorithm is fixed and the input level 2 passive microwave (PMW) retrievals of instantaneous precipitation rates are from identical versions throughout the entire data period. Bias correction is then performed for the raw CMORPH through probability density function (PDF) matching against the CPC daily gauge analysis over land and through adjustment against the Global Precipitation Climatology Project (GPCP) pentad merged analysis of precipitation over ocean. The reprocessed, bias-corrected CMORPH exhibits improved performance in representing the magnitude, spatial distribution patterns, and temporal variations of precipitation over the global domain from 60°S to 60°N. Bias in the CMORPH satellite precipitation estimates is almost completely removed over land during warm seasons (May–September), while during cold seasons (October–April) CMORPH tends to underestimate the precipitation due to the less-than-desirable performance of the current-generation PMW retrievals in detecting and quantifying snowfall and cold season rainfall. An intercomparison study indicated that the reprocessed, bias-corrected CMORPH exhibits consistently superior performance than the widely used TRMM 3B42 (TMPA) in representing both daily and 3-hourly precipitation over the contiguous United States and other global regions.

© 2017 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 e-mail: Pingping Xie, pingping.xie@noaa.gov

Abstract

The Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates are reprocessed and bias corrected on an 8 km × 8 km grid over the globe (60°S–60°N) and in a 30-min temporal resolution for an 18-yr period from January 1998 to the present to form a climate data record (CDR) of high-resolution global precipitation analysis. First, the purely satellite-based CMORPH precipitation estimates (raw CMORPH) are reprocessed. The integration algorithm is fixed and the input level 2 passive microwave (PMW) retrievals of instantaneous precipitation rates are from identical versions throughout the entire data period. Bias correction is then performed for the raw CMORPH through probability density function (PDF) matching against the CPC daily gauge analysis over land and through adjustment against the Global Precipitation Climatology Project (GPCP) pentad merged analysis of precipitation over ocean. The reprocessed, bias-corrected CMORPH exhibits improved performance in representing the magnitude, spatial distribution patterns, and temporal variations of precipitation over the global domain from 60°S to 60°N. Bias in the CMORPH satellite precipitation estimates is almost completely removed over land during warm seasons (May–September), while during cold seasons (October–April) CMORPH tends to underestimate the precipitation due to the less-than-desirable performance of the current-generation PMW retrievals in detecting and quantifying snowfall and cold season rainfall. An intercomparison study indicated that the reprocessed, bias-corrected CMORPH exhibits consistently superior performance than the widely used TRMM 3B42 (TMPA) in representing both daily and 3-hourly precipitation over the contiguous United States and other global regions.

© 2017 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 e-mail: Pingping Xie, pingping.xie@noaa.gov
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  • Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 1147–1167, doi:10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Armstrong, R. L., and M. J. Brodzik, 2002: Northern Hemisphere EASE-Grid weekly snow cover and sea ice extent version 2. National Snow and Ice Data Center, CD-ROM.

  • Ashouri, H., K.-L. Hsu, S. Sorooshian, D. K. Braithwaite, K. R. Knapp, L. D. Cecil, B. R. Nelson, and O. P. Prat, 2015: PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull. Amer. Meteor. Soc., 96, 69–83, doi:10.1175/BAMS-D-13-00068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bates, J. J., and B. R. Barkstrom, 2006: A maturity model for satellite-derived climate data records. 14th Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Amer. Meteor. Soc., P2.11. [Available online at https://ams.confex.com/ams/Annual2006/techprogram/paper_100658.htm.]

  • Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese, 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present. Earth Syst. Sci. Data, 5, 71–99, doi:10.5194/essd-5-71-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bourles, B., and Coauthors, 2008: The PIRATA program: History, accomplishments, and future directions. Bull. Amer. Meteor. Soc., 89, 1111–1125, doi:10.1175/2008BAMS2462.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cosgrove, B. A., and Coauthors, 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108, 8842, doi:10.1029/2002JD003118.

    • 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, 47–64, doi:10.1175/BAMS-88-1-47.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ferraro, R., and Coauthors, 2005: NOAA operational hydrological products derived from the Advanced Microwave Sounding Unit (AMSU). IEEE Trans. Geosci. Remote Sens., 43, 1036–1049, doi:10.1109/TGRS.2004.843249.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Habib, E., A. T. Haile, Y. Tian, and R. J. Joyce, 2012: Evaluation of the high-resolution CMORPH satellite rainfall product using dense rain gauge observations and radar-based estimates. J. Hydrometeor., 13, 1784–1798, doi:10.1175/JHM-D-12-017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Haddad, Z. S., and D. Rosenfeld, 1997: Optimality of empirical Z–R relations. Quart. J. Roy. Meteor. Soc., 123, 1283–1293, doi:10.1002/qj.49712354107.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Haddad, Z. S., E. A. Smith, C. D. Kummerow, T. Iguchi, M. R. Farrar, S. L. Durden, M. Alves, and W. S. Olson, 1997: The TRMM “Day-1” radar/radiometer combined rain-profiling algorithm. J. Meteor. Soc. Japan, 75, 799–809.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Helfrich, S. R., D. McNamara, B. H. Ramsay, T. Baldwin, and T. Kasheta, 2007: Enhancements to, and forthcoming developments to the Interactive Multisensor Snow and Ice Mapping System (IMS). Hydrol. Processes, 21, 1576–1586, doi:10.1002/hyp.6720.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hong, Y., R. F. Adler, A. J. Negri, and G. J. Huffman, 2007: Flood and landslide applications of high-resolution satellite rainfall products. J. Nat. Hazards, 43, 285–294, doi:10.1007/s11069-006-9106-x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hou, A. Y., and Coauthors, 2010: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701–722, doi:10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hsu, K.-L., X. Gao, S. Sorooshian, and V. Gupta, 1997: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks. J. Appl. Meteor., 36, 1176–1190, doi:10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., and D. T. Bolvin, 2014: TRMM and other data precipitation data set documentation. NASA TRMM Doc., 42 pp. [Available online at ftp://precip.gsfc.nasa.gov/pub/trmmdocs/3B42_3B43_doc.pdf.]

  • Huffman, G. J., R. F. Adler, M. Morrissey, D. T. Bolvin, S. Curtis, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multi-satellite observations. J. Hydrometeor., 2, 36–50, doi:10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., R. F. Adler, D. T. Bolvin, G. Gu, E. J. Nelkin, K. P. Bowman, E. F. Stocker, and D. B. Wolff, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38–55, doi:10.1175/JHM560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., and Coauthors, 2011: GPM Day-1 Multi-satellite algorithms (iMERG). 2011 PMM Science Team Meeting, Denver, CO, NASA. [Available online at https://pmm.nasa.gov/meetings/all/2011-pmm-science-team-meeting.]

  • Jaeger, L., 1976: Monatskarten des Niederschiags fur die ganze Erde. Berichte des Deutscher Wetterdienstes, 33 pp.

  • Janowiak, J. E., R. J. Joyce, and Y. Yarosh, 2001: A real-time global half-hourly pixel-resolution IR dataset and its applications. Bull. Amer. Meteor. Soc., 82, 205–217, doi:10.1175/1520-0477(2001)082<0205:ARTGHH>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Joyce, R. J., and P. Xie, 2011: Kalman filter–based CMORPH. J. Hydrometeor., 12, 1547–1563, doi:10.1175/JHM-D-11-022.1.

  • 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, 487–503, doi:10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Leese, J. A., C. S. Novak, and B. B. Clark, 1971: An automated technique for obtaining cloud motion from geosynchronous satellite data using cross correlation. J. Appl. Meteor., 10, 118–132, doi:10.1175/1520-0450(1971)010<0118:AATFOC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Legates, D. R., and C. J. Willmott, 1990: Mean seasonal and spatial variability in gauge corrected global precipitation. Int. J. Climatol., 10, 111–127, doi:10.1002/joc.3370100202.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at http://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.]

  • McPhaden, M. J., and Coauthors, 1998: The Tropical Ocean–Global Atmosphere (TOGA) observing system: A decade of progress. J. Geophys. Res., 103, 14 169–14 240, doi:10.1029/97JC02906.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • McPhaden, M. J., and Coauthors, 2009: RAMA: The Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction. Bull. Amer. Meteor. Soc., 90, 459–480, doi:10.1175/2008BAMS2608.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mega, T., T. Ushio, T. Kubota, M. Kachi, K. Aonashi, and S. Shige, 2014: Gauge adjusted global satellite mapping of precipitation (GSMaP_Gauge). XXXIth URSI General Assembly and Scientific Symp., Beijing, China, IEEE, 1–4, doi:10.1109/URSIGASS.2014.6929683.

    • Crossref
    • Export Citation

  • Meng, H., R. F. Ferraro, and B. Yan, 2011: Satellite remote sensing of snowfall rate. 2011 PMM Science Team Meeting, Denver, CO, NASA. [Available online at https://pmm.nasa.gov/meetings/all/2011-pmm-science-team-meeting.]

  • Meng, J., R. Yang, H. Wei, M. Ek, G. Gayno, P. Xie, and K. Mitchell, 2012: The land surface analysis in the NCEP Climate Forecast System Reanalysis. J. Hydrometeor., 13, 1621–1630, doi:10.1175/JHM-D-11-090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Morrissey, M. L., M. A. Shafer, S. E. Postawko, and B. Gibson, 1995: The Pacific rain gauge rainfall database. Water Resour. Res., 31, 2111–2113, doi:10.1029/95WR01233.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Nelson, B. R., O. P. Prat, D.-J. Seo, and E. Harib, 2016: Assessment and implications of NCEP stage IV quantitative precipitation estimates for product intercomparisons. Wea. Forecasting, 31, 371–394, doi:10.1175/WAF-D-14-00112.1.

    • 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, 149–166, doi:10.1175/2008JHM1052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer, M. Ziese, and B. Rudolf, 2014: GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol., 115, 15–40, doi:10.1007/s00704-013-0860-x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Seo, D.-J., and J. P. Breidenbach, 2002: Real-time correction of spatially nonuniform bias in radar rainfall data using rain-gauge measurements. J. Hydrometeor., 3, 93–111, doi:10.1175/1525-7541(2002)003<0093:RTCOSN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sevruk, B., 1982: Methods of correction for systematic error in point precipitation measurement for operational use. Operational Hydrology Rep. 589, WMO, 91 pp.

  • Simpson, J., R. F. Adler, and G. R. North, 1988: A proposed Tropical Rainfall Measuring Mission (TRMM) satellite. Bull. Amer. Meteor. Soc., 69, 278–295, doi:10.1175/1520-0477(1988)069<0278:APTRMM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tang, L., Y. Tian, and X. Lin, 2014: Validation of precipitation retrievals over land from satellite-based passive microwave sensors. J. Geophys. Res. Atmos., 119, 4546–4567, doi:10.1002/2013JD020933.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tian, Y., and Coauthors, 2009: Component analysis of errors in satellite-based precipitation estimates. J. Geophys. Res., 114, D24101, doi:10.1029/2009JD011949.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Turk, F. J., E. E. Ebert, B.-J. Sohn, H.-J. Oh, V. Levizzani, E. A. Smith, and R. Ferraro, 2003: Validation of an operational global precipitation analysis at short time scales. 12th Conf. on Satellite Meteorology and Oceanography, Long Beach, CA, Amer. Meteor. Soc., JP1.2. [Available online at https://ams.confex.com/ams/annual2003/techprogram/paper_56865.htm.]

  • Turk, F. J., S. Miller, and C. Castello, 2010: A dynamic global cloud layer for virtual globes. Int. J. Remote Sens., 31, 1897–1914, doi:10.1080/01431160902926657.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ushio, T., and Coauthors, 2009: A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. J. Meteor. Soc. Japan, 87A, 137–151, doi:10.2151/jmsj.87A.137.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ushio, T., and Coauthors, 2013: Global Satellite Mapping of Precipitation (GSMaP) project. Int. Water Technol. J., 3, 192–196.

  • 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, 237–253, doi:10.2151/jmsj.87A.237.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, N.-Y., K. Gepalan, and R. F. Ferraro, 2011: Developing winter precipitation algorithm over land from satellite observations and C3VP field campaign. 2011 PMM Science Team Meeting, Denver, CO, NASA. [Available online at https://pmm.nasa.gov/meetings/all/2011-pmm-science-team-meeting.]

  • Wang, W., and P. Xie, 2007: A multiplatform-merged (MPM) SST analysis. J. Climate, 20, 1662–1679, doi:10.1175/JCLI4097.1.

  • Wolff, D. B., D. A. Marks, E. Amitai, D. S. Silberstein, B. L. Fisher, A. Tokay, J. Wang, and J. L. Pippitt, 2005: Ground validation for the Tropical Rainfall Measuring Mission (TRMM). J. Atmos. Oceanic Technol., 22, 365–380, doi:10.1175/JTECH1700.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wu, H., R. F. Adler, Y. Hong, Y. Tian, and F. Policelli, 2012: Evaluation of global flood detection using satellite-based rainfall and a hydrologic model. J. Hydrometeor, 13, 1268–1284, doi:10.1175/JHM-D-11-087.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xie, P., and P. A. Arkin, 1995: An intercomparison of gauge observations and satellite estimates of monthly precipitation. J. Appl. Meteor., 34, 1143–1160, doi:10.1175/1520-0450(1995)034<1143:AIOGOA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analyses based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539–2558, doi:10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xie, P., and A.-Y. Xiong, 2011: A conceptual model for constructing high-resolution gauge–satellite merged precipitation analyses. J. Geophys. Res., 116, D21106, doi:10.1029/2011JD016118.

    • Search Google Scholar
    • Export Citation

  • Xie, P., and R. Joyce, 2014: Integrating information from satellite observations and numerical models for improved global precipitation analyses. Remote Sensing of the Terrestrial Water Cycle, Geophys. Monogr., Vol. 206, Amer. Geophys. Union, 43–59.

    • Crossref
    • Export Citation

  • Xie, P., J. E. Janowiak, P. A. Arkin, R. Adler, A. Gruber, R. Ferraro, G. J. Huffman, and S. Curtis, 2003: GPCP pentad precipitation analyses: An experimental data set based on gauge observations and satellite estimates. J. Climate, 16, 2197–2214, doi:10.1175/2769.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xie, P., M. Chen, A. Yatagai, T. Hayasaka, Y. Fukushima, and S. Yang, 2007: A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeor., 8, 607–626, doi:10.1175/JHM583.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xie, P., M. Chen, and W. Shi, 2010: CPC Unified gauge-based analysis of global daily precipitation. 24th Conf. on Hydrology, Atlanta, GA, Amer. Meteor. Soc., 2.3A. [Available online at https://ams.confex.com/ams/90annual/webprogram/Paper163676.html.]

  • Xie, P., S.-H. Yoo, W. Wang, A. Kumar, L. Zhang, and W. Ebisuzaki, 2012: Seasonal, interannual, intraseasonal and diurnal variations of global precipitation depicted in the reanalyses and observations. 4th Int. Conf. on Reanalysis, Silver Spring, MD, WCRP, UA-50. [Available online at https://www.wcrp-climate.org/ICR4/posters/Xie_Seasonal_Interannual.pdf.]

  • Xie, P., and Coauthors, 2014: An in situ-satellite blended analysis of global sea surface salinity. J. Geophys. Res. Oceans, 119, 6140–6160, doi:10.1002/2014JC010046.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xie, P., R. Joyce, and S. Wu, 2016: Recent progress on the second generation CMORPH. 2016 PMM Science Team Meeting, Houston, TX, NASA. [Available online at https://pmm.nasa.gov/meetings/all/2016-pmm-science-team-meeting.]

  • Xu, B., S.-H. Yoo, and P. Xie, 2010: Quantifying error in the CMORPH satellite precipitation estimates. 2010 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, Abstract H14A-06.

  • Xu, B., P. Xie, M. Xu, L. Jiang, C. Shi, and R. You, 2015: A validation of passive microwave rain-rate retrievals from the Chinese FengYun-3B satellite. J. Hydrometeor., 16, 1886–1905, doi:10.1175/JHM-D-14-0143.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Yong, B., Y. Hong, L.-L. Ren, J. J. Gourley, G. J. Huffman, X. Chen, W. Wang, and S. I. Khan, 2012: Assessment of evolving TRMM-based multisatellite real-time precipitation estimation methods and their impacts on hydrologic prediction in a high latitude basin. J. Geophys. Res., 117, D09108, doi:10.1029/2011JD017069.

    • Search Google Scholar
    • Export Citation
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