Editorial Type:
Article
Article Type:
Research Article

First-Year Evaluation of GPM Rainfall over the Netherlands: IMERG Day 1 Final Run (V03D)

M. F. Rios Gaona Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, Netherlands

Search for other papers by M. F. Rios Gaona in
Current site
Google Scholar
PubMed Close
,
A. Overeem R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, De Bilt, and Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, Netherlands

Search for other papers by A. Overeem in
Current site
Google Scholar
PubMed Close
,
H. Leijnse R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, De Bilt, Netherlands

Search for other papers by H. Leijnse in
Current site
Google Scholar
PubMed Close
, and
R. Uijlenhoet Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, Netherlands

Search for other papers by R. Uijlenhoet in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Nov 2016
DOI:
https://doi.org/10.1175/JHM-D-16-0087.1
Page(s):
2799–2814
Restricted access

Abstract

The Global Precipitation Measurement (GPM) mission is the successor to the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth for ~17 years. With Core Observatory launched on 27 February 2014, GPM offers global precipitation estimates between 60°N and 60°S at 0.1° × 0.1° resolution every 30 min. Unlike during the TRMM era, the Netherlands is now within the coverage provided by GPM. Here the first year of GPM rainfall retrievals from the 30-min gridded Integrated Multisatellite Retrievals for GPM (IMERG) product Day 1 Final Run (V03D) is assessed. This product is compared against gauge-adjusted radar rainfall maps over the land surface of the Netherlands at 30-min, 24-h, monthly, and yearly scales. These radar rainfall maps are considered to be ground truth. The evaluation of the first year of IMERG operations is done through time series, scatterplots, empirical exceedance probabilities, and various statistical indicators. In general, there is a tendency for IMERG to slightly underestimate (2%) countrywide rainfall depths. Nevertheless, the relative underestimation is small enough to propose IMERG as a reliable source of precipitation data, especially for areas where rain gauge networks or ground-based radars do not offer these types of high-resolution data and availability. The potential of GPM for rainfall estimation in a midlatitude country is confirmed.

Corresponding author address: Manuel F. Rios Gaona, Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, Netherlands. E-mail: manuel.riosgaona@wur.nl

Abstract

The Global Precipitation Measurement (GPM) mission is the successor to the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth for ~17 years. With Core Observatory launched on 27 February 2014, GPM offers global precipitation estimates between 60°N and 60°S at 0.1° × 0.1° resolution every 30 min. Unlike during the TRMM era, the Netherlands is now within the coverage provided by GPM. Here the first year of GPM rainfall retrievals from the 30-min gridded Integrated Multisatellite Retrievals for GPM (IMERG) product Day 1 Final Run (V03D) is assessed. This product is compared against gauge-adjusted radar rainfall maps over the land surface of the Netherlands at 30-min, 24-h, monthly, and yearly scales. These radar rainfall maps are considered to be ground truth. The evaluation of the first year of IMERG operations is done through time series, scatterplots, empirical exceedance probabilities, and various statistical indicators. In general, there is a tendency for IMERG to slightly underestimate (2%) countrywide rainfall depths. Nevertheless, the relative underestimation is small enough to propose IMERG as a reliable source of precipitation data, especially for areas where rain gauge networks or ground-based radars do not offer these types of high-resolution data and availability. The potential of GPM for rainfall estimation in a midlatitude country is confirmed.

Corresponding author address: Manuel F. Rios Gaona, Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, Netherlands. E-mail: manuel.riosgaona@wur.nl
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Bitew, M. M., and Gebremichael M. , 2011: Evaluation of satellite rainfall products through hydrologic simulation in a fully distributed hydrologic model. Water Resour Res, 47, W06526, doi:10.1029/2010WR009917.

    • Search Google Scholar
    • Export Citation

  • Bitew, M. M., Gebremichael M. , Ghebremichael L. T. , and Bayissa Y. A. , 2012: Evaluation of high-resolution satellite rainfall products through streamflow simulation in a hydrological modeling of a small mountainous watershed in Ethiopia. J. Hydrometeor., 13, 338–350, doi:10.1175/2011JHM1292.1.

    • Search Google Scholar
    • Export Citation

  • Dinku, T., Ruiz F. , Connor S. J. , and Ceccato P. , 2010: Validation and intercomparison of satellite rainfall estimates over Colombia. J. Appl. Meteor. Climatol., 49, 1004–1014, doi:10.1175/2009JAMC2260.1.

    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., 2008: Fuzzy verification of high-resolution gridded forecasts: A review and proposed framework. Meteor. Appl., 15, 51–64, doi:10.1002/met.25.

    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., 2010: Neighborhood verification of high resolution precipitation products. Satellite Rainfall Applications for Surface Hydrology, M. Gebremichael and F. Hossain, Eds., Springer, 127–143, doi:10.1007/978-90-481-2915-7_8.

  • Ebert, E. E., Janowiak J. E. , and Kidd C. , 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.

    • Search Google Scholar
    • Export Citation

  • Gebregiorgis, A. S., and Hossain F. , 2014: Estimation of satellite rainfall error variance using readily available geophysical features. IEEE Trans. Geosci. Remote Sens., 52, 288–304, doi:10.1109/TGRS.2013.2238636.

    • Search Google Scholar
    • Export Citation

  • Gebregiorgis, A. S., and Hossain F. , 2015: How well can we estimate error variance of satellite precipitation data around the world? Atmos. Res., 154, 39–59, doi:10.1016/j.atmosres.2014.11.005.

    • Search Google Scholar
    • Export Citation

  • Haan, C. T., 1977: Statistical Methods in Hydrology. Iowa State University Press, 378 pp.

  • Hong, Y., Adler R. F. , Hossain F. , Curtis S. , and Huffman G. J. , 2007a: A first approach to global runoff simulation using satellite rainfall estimation. Water Resour. Res., 43, W08502, doi:10.1029/2006WR005739.

    • Search Google Scholar
    • Export Citation

  • Hong, Y., Adler R. F. , and Huffman G. , 2007b: An experimental global prediction system for rainfall-triggered landslides using satellite remote sensing and geospatial datasets. IEEE Trans. Geosci. Remote Sens., 45, 1671–1680, doi:10.1109/TGRS.2006.888436.

    • Search Google Scholar
    • Export Citation

  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement mission. Bull. Amer. Meteor. Soc., 95, 701–722, doi:10.1175/BAMS-D-13-00164.1.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., and Coauthors, 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.

    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., Bolvin D. T. , Braithwaite D. , Hsu K. , Joyce R. , and Xie P. , 2014: NASA Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Doc., version 4.4, 30 pp. [Available online at https://pps.gsfc.nasa.gov/Documents/IMERG_ATBD_V4.pdf.]

  • Huffman, G. J., Bolvin D. T. , Braithwaite D. , Hsu K. , Joyce R. , Kidd C. , Nelkin E. J. , and Xie P. , 2015a: NASA Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Doc., version 4.5, 30 pp. [Available online at http://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V4.5.pdf.]

  • Huffman, G. J., Bolvin D. T. , and Nelkin E. J. , 2015b: Day 1 IMERG final run release notes. NASA Tech. Doc., 9 pp. [Available online at http://pmm.nasa.gov/sites/default/files/document_files/IMERG_FinalRun_Day1_release_notes.pdf.]

  • Huffman, G. J., Bolvin D. T. , and Nelkin E. J. , 2015c: Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation. NASA/GSFC Code 612 Tech. Doc., 48 pp. [Available online at http://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf.]

  • Joyce, R. J., Janowiak J. E. , Arkin P. A. , and Xie P. , 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.

    • Search Google Scholar
    • Export Citation

  • Khan, S., and Coauthors, 2011: Satellite remote sensing and hydrologic modeling for flood inundation mapping in Lake Victoria basin: Implications for hydrologic prediction in ungauged basins. IEEE Trans. Geosci. Remote Sens., 49, 85–95, doi:10.1109/TGRS.2010.2057513.

    • Search Google Scholar
    • Export Citation

  • Kidd, C., and Huffman G. , 2011: Global Precipitation Measurement. Meteor. Appl., 18, 334–353, doi:10.1002/met.284.

  • Kidd, C., and Levizzani V. , 2011: Status of satellite precipitation retrievals. Hydrol. Earth Syst. Sci., 15, 1109–1116, doi:10.5194/hess-15-1109-2011.

    • Search Google Scholar
    • Export Citation

  • Kidd, C., Levizzani V. , Turk J. , and Ferraro R. , 2009: Satellite precipitation measurements for water resource monitoring. J. Amer. Water Resour. Assoc., 45, 567–579, doi:10.1111/j.1752-1688.2009.00326.x.

    • Search Google Scholar
    • Export Citation

  • Kidd, C., Bauer P. , Turk J. , Huffman G. J. , Joyce R. J. , Hsu K.-L. , and Braithwaite D. , 2012: Intercomparison of high-resolution precipitation products over northwest Europe. J. Hydrometeor., 13, 67–83, doi:10.1175/JHM-D-11-042.1.

    • Search Google Scholar
    • Export Citation

  • Kirschbaum, D., Adler R. F. , Adler D. , Peters-Lidard C. , and Huffman G. , 2012: Global distribution of extreme precipitation and high-impact landslides in 2010 relative to previous years. J. Hydrometeor., 13, 1536–1551, doi:10.1175/JHM-D-12-02.1.

    • 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, 1285–1300, doi:10.1175/JHM-D-11-0139.1.

    • Search Google Scholar
    • Export Citation

  • Kirstetter, P.-E., Hong Y. , Gourley J. J. , Cao Q. , Schwaller M. , and Petersen W. , 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, doi:10.1002/9781118872086.ch4.

  • Kucera, P. A., Ebert E. E. , Turk F. J. , Levizzani V. , Kirschbaum D. , Tapiador F. J. , Loew A. , and Borsche M. , 2013: Precipitation from space: Advancing earth system science. Bull. Amer. Meteor. Soc., 94, 365–375, doi:10.1175/BAMS-D-11-00171.1.

    • Search Google Scholar
    • Export Citation

  • Kummerow, C. D., Ringerud S. , Crook J. , Randel D. , and Berg W. , 2011: An observationally generated a priori database for microwave rainfall retrievals. J. Atmos. Oceanic Technol., 28, 113–130, doi:10.1175/2010JTECHA1468.1.

    • Search Google Scholar
    • Export Citation

  • Marshall, J., Hitschfeld W. , and Gunn K. , 1955: Advances in radar weather. Advances in Geophysics, Vol. 2, Academic Press, 1–56, doi:10.1016/S0065-2687(08)60310-6.

  • NASA, 2014a: Global Precipitation Measurement Core Observatory. 20 pp. [Available online at http://eospso.nasa.gov/sites/default/files/publications/GPMmissionBrochure_508_sm_0.pdf.]

  • NASA, 2014b: NASA rainfall satellite out of fuel, but continues to provide data. Accessed 3 October 2016. [Available online at https://www.nasa.gov/content/goddard/nasa-rainfall-satellite-out-of-fuel-but-continues-to-provide-data.]

  • Nerini, D., Zulkafli Z. , Wang L.-P. , Onof C. , Buytaert W. , Lavado-Casimiro W. , and Guyot J.-L. , 2015: A comparative analysis of TRMM–rain gauge data merging techniques at the daily time scale for distributed rainfall–runoff modeling applications. J. Hydrometeor., 16, 2153–2168, doi:10.1175/JHM-D-14-0197.1.

    • Search Google Scholar
    • Export Citation

  • Nijssen, B., and Lettenmaier D. P. , 2004: Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites. J. Geophys. Res., 109, D02103, doi:10.1029/2003JD003497.

    • Search Google Scholar
    • Export Citation

  • NRC, 2008: Earth Observations from Space: The First 50 Years of Scientific Achievements. The National Academies Press, 144 pp., doi:10.17226/11991.

  • Overeem, A., Buishand T. A. , and Holleman I. , 2009a: Extreme rainfall analysis and estimation of depth–duration–frequency curves using weather radar. Water Resour. Res., 45, W10424, doi:10.1029/2009WR007869.

    • Search Google Scholar
    • Export Citation

  • Overeem, A., Holleman I. , and Buishand A. , 2009b: Derivation of a 10-year radar-based climatology of rainfall. J. Appl. Meteor. Climatol., 48, 1448–1463, doi:10.1175/2009JAMC1954.1.

    • Search Google Scholar
    • Export Citation

  • Overeem, A., Leijnse H. , and Uijlenhoet R. , 2011: Measuring urban rainfall using microwave links from commercial cellular communication networks. Water Resour. Res., 47, W12505, doi:10.1029/2010WR010350.

    • Search Google Scholar
    • Export Citation

  • Rose, C. R., and Chandrasekar V. , 2006: A GPM dual-frequency retrieval algorithm: DSD profile-optimization method. J. Atmos. Oceanic Technol., 23, 1372–1383, doi:10.1175/JTECH1921.1.

    • Search Google Scholar
    • Export Citation

  • Sarachi, S., Hsu K. , and Sorooshian S. , 2015: A statistical model for the uncertainty analysis of satellite precipitation products. J. Hydrometeor., 16, 2101–2117, doi:10.1175/JHM-D-15-0028.1.

    • Search Google Scholar
    • Export Citation

  • Scheel, M. L. M., Rohrer M. , Huggel C. , Santos Villar D. , Silvestre E. , and Huffman G. J. , 2011: Evaluation of TRMM Multi-satellite Precipitation Analysis (TMPA) performance in the central Andes region and its dependency on spatial and temporal resolution. Hydrol. Earth Syst. Sci., 15, 2649–2663, doi:10.5194/hess-15-2649-2011.

    • Search Google Scholar
    • Export Citation

  • Schneider, U., Becker A. , Finger P. , Meyer-Christoffer A. , Rudolf B. , and Ziese M. , 2015a: GPCC full data reanalysis version 7.0 at 0.5°: Monthly land-surface precipitation from rain-gauges built on GTS-based and historic data. DWD, accessed 3 October 2016, doi:10.5676/DWD_GPCC/FD_M_V7_05.

  • Schneider, U., Becker A. , Finger P. , Meyer-Christoffer A. , and Ziese M. , 2015b: GPCC monitoring product: Near real-time monthly land-surface precipitation from rain-gauges based on SYNOP and CLIMAT data. Subset: 1.0° grid resolution. DWD, accessed 3 October 2016, doi:10.5676/DWD_GPCC/MP_M_V5_100.

  • Skofronick-Jackson, G., Petersen W. A. , Hou A. Y. , Stocher E. F. , Kaye J. , and Kakar R. , 2013: Global Precipitation Measurement (GPM) science implementation plan. NASA Tech. Rep., 162 pp. [Available online at https://pmm.nasa.gov/sites/default/files/document_files/GPM%20Science%20Implementation%20Plan%20-%20April%202013.pdf.]

  • Su, F., Hong Y. , and Lettenmaier D. P. , 2008: Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and its utility in hydrologic prediction in the La Plata basin. J. Hydrometeor., 9, 622–640, doi:10.1175/2007JHM944.1.

    • Search Google Scholar
    • Export Citation

  • Tang, G., Zeng Z. , Long D. , Guo X. , Yong B. , Zhang W. , and Hong Y. , 2015: Statistical and hydrological comparisons between TRMM and GPM level-3 products over a midlatitude basin: Is Day-1 IMERG a good successor for TMPA 3B42V7? J. Hydrometeor., 17, 121–137, doi:10.1175/JHM-D-15-0059.1.

    • Search Google Scholar
    • Export Citation

  • Tapiador, F. J., and Coauthors, 2012: Global Precipitation Measurement: Methods, datasets and applications. Atmos. Res., 104–105, 70–97, doi:10.1016/j.atmosres.2011.10.021.

    • Search Google Scholar
    • Export Citation

  • Tian, Y., and Peters-Lidard C. D. , 2010: A global map of uncertainties in satellite-based precipitation measurements. Geophys. Res. Lett., 37, L24407, doi:10.1029/2010GL046008.

    • 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.

    • Search Google Scholar
    • Export Citation

  • Tobin, K. J., and Bennett M. E. , 2010: Adjusting satellite precipitation data to facilitate hydrologic modeling. J. Hydrometeor., 11, 966–978, doi:10.1175/2010JHM1206.1.

    • Search Google Scholar
    • Export Citation

  • Vergara, H., Hong Y. , Gourley J. J. , Anagnostou E. N. , Maggioni V. , Stampoulis D. , and Kirstetter P.-E. , 2014: Effects of resolution of satellite-based rainfall estimates on hydrologic modeling skill at different scales. J. Hydrometeor., 15, 593–613, doi:10.1175/JHM-D-12-0113.1.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Wentz, F. J., 2015: A 17-yr climate record of environmental parameters derived from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. J. Climate, 28, 6882–6902, doi:10.1175/JCLI-D-15-0155.1.

    • Search Google Scholar
    • Export Citation

  • Wilks, D., 2011: Forecast verification. Statistical Methods in the Atmospheric Sciences, 3rd ed., International Geophysics, Vol. 100, Academic Press, 301–394, doi:10.1016/B978-0-12-385022-5.00008-7.

  • Wu, H., Adler R. F. , Hong Y. , Tian Y. , and Policelli F. , 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.

    • Search Google Scholar
    • Export Citation

  • Xue, X., Hong Y. , Limaye A. S. , Gourley J. J. , Huffman G. J. , Khan S. I. , Dorji C. , and Chen S. , 2013: Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? J. Hydrol., 499, 91–99, doi:10.1016/j.jhydrol.2013.06.042.

    • Search Google Scholar
    • Export Citation

  • Yang, S., and Nesbitt S. W. , 2014: Statistical properties of precipitation as observed by the TRMM Precipitation Radar. Geophys. Res. Lett., 41, 5636–5643, doi:10.1002/2014GL060683.

    • Search Google Scholar
    • Export Citation

  • Yong, B., Liu D. , Gourley J. J. , Tian Y. , Huffman G. J. , Ren L. , and Hong Y. , 2015: Global view of real-time TRMM Multisatellite Precipitation Analysis: Implications for its successor Global Precipitation Measurement mission. Bull. Amer. Meteor. Soc., 96, 283–296, doi:10.1175/BAMS-D-14-00017.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1065 355 15
PDF Downloads 594 145 10