Editorial Type:
Article
Article Type:
Research Article

Evaluation of Satellite-Retrieved Extreme Precipitation over Europe using Gauge Observations

M. Lockhoff Deutscher Wetterdienst, Offenbach, Germany

Search for other papers by M. Lockhoff in
Current site
Google Scholar
PubMed Close
,
O. Zolina Le Laboratoire de Glaciologie et Géophysique de l’Environnement, Grenoble, France, and P. P. Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow, Russia

Search for other papers by O. Zolina in
Current site
Google Scholar
PubMed Close
,
C. Simmer Institute for Meteorology, University of Bonn, Bonn, Germany

Search for other papers by C. Simmer in
Current site
Google Scholar
PubMed Close
, and
J. Schulz European Organisation for the Exploitation of Meteorological Satellites, Darmstadt, Germany

Search for other papers by J. Schulz in
Current site
Google Scholar
PubMed Close
Print Publication:
15 Jan 2014
DOI:
https://doi.org/10.1175/JCLI-D-13-00194.1
Page(s):
607–623
Restricted access

Abstract

Climate change is expected to change precipitation characteristics and particularly the frequency and magnitude of precipitation extremes. Satellite observations form an important part of the observing system necessary to monitor both temporal and spatial patterns of precipitation variability and extremes. As satellite-based precipitation estimates are generally only indirect, however, their reliability has to be verified.

This study evaluates the ability of the satellite-based Global Precipitation Climatology Project One-Degree Daily (GPCP1DD) dataset to reliably reproduce precipitation variability and extremes over Europe compared to the European Daily High-resolution Observational Gridded Dataset (E-OBS). The results show that the two datasets agree reasonably well not only when looking at climatological statistics such as climatological mean, number of wet days (rain rates 1 mm), and mean intensity (i.e., mean over all wet days) but also with respect to their distributions. The results also reveal a pronounced seasonal cycle in the performance of GPCP1DD that is worse in winter and spring. Both deterministic and fuzzy verification methods are used to assess the ability of the GPCP1DD dataset to capture extremes. Fuzzy methods prove to be the better suited evaluation approach for such a highly variable parameter as precipitation because it compensates for slight spatial and temporal displacements. Whereas the deterministic diagnostics confirm previous findings on the deficiencies of satellite products, the “fuzzy” results show that at larger spatiotemporal scales (e.g., 3°/5 days) GPCP1DD has useful skill and is able to reliably represent the spatial and temporal variability of extremes.

Corresponding author address: M. Lockhoff, DWD, Frankfurter Strasse 135, D-63067 Offenbach, Germany. E-mail: maarit.lockhoff@dwd.de

Abstract

Climate change is expected to change precipitation characteristics and particularly the frequency and magnitude of precipitation extremes. Satellite observations form an important part of the observing system necessary to monitor both temporal and spatial patterns of precipitation variability and extremes. As satellite-based precipitation estimates are generally only indirect, however, their reliability has to be verified.

This study evaluates the ability of the satellite-based Global Precipitation Climatology Project One-Degree Daily (GPCP1DD) dataset to reliably reproduce precipitation variability and extremes over Europe compared to the European Daily High-resolution Observational Gridded Dataset (E-OBS). The results show that the two datasets agree reasonably well not only when looking at climatological statistics such as climatological mean, number of wet days (rain rates 1 mm), and mean intensity (i.e., mean over all wet days) but also with respect to their distributions. The results also reveal a pronounced seasonal cycle in the performance of GPCP1DD that is worse in winter and spring. Both deterministic and fuzzy verification methods are used to assess the ability of the GPCP1DD dataset to capture extremes. Fuzzy methods prove to be the better suited evaluation approach for such a highly variable parameter as precipitation because it compensates for slight spatial and temporal displacements. Whereas the deterministic diagnostics confirm previous findings on the deficiencies of satellite products, the “fuzzy” results show that at larger spatiotemporal scales (e.g., 3°/5 days) GPCP1DD has useful skill and is able to reliably represent the spatial and temporal variability of extremes.

Corresponding author address: M. Lockhoff, DWD, Frankfurter Strasse 135, D-63067 Offenbach, Germany. E-mail: maarit.lockhoff@dwd.de
Save
Cover Journal of Climate
Journal of Climate

  • Adler, R. F., H.-Y. M. Yeh, N. Prasad, W.-K. Tao, and J. Simpson, 1991: Microwave simulations of a tropical rainfall system with a three-dimensional cloud model. J. Appl. Meteor., 30, 924–953.

    • Search Google Scholar
    • Export Citation

  • Adler, R. F., C. Kidd, G. Petty, M. Morissey, and H. Goodman, 2001: Intercomparison of global precipitation products: The third Precipitation Intercomparison Project (PIP-3). Bull. Amer. Meteor. Soc., 82, 1377–1396.

    • Search Google Scholar
    • Export Citation

  • Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 1147–1167.

    • Search Google Scholar
    • Export Citation

  • AghaKouchak, A., A. Behrangi, S. Sorooshian, K. Hsu, and E. Amitai, 2011: Evaluation of satellite-retrieved extreme precipitation rates across the central United States. J. Geophys. Res., 116, D02115, doi:10.1029/2010JD014741.

    • Search Google Scholar
    • Export Citation

  • Allan, R. P., and B. J. Soden, 2008: Atmospheric warming and the amplification of precipitation extremes. Science, 32, 1481–1494.

  • Arkin, P. A., and B. N. Meisner, 1987: The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982–84. Mon. Wea. Rev., 115, 51–74.

    • Search Google Scholar
    • Export Citation

  • Bauer, P., 2001: Over-ocean rainfall retrieval from multisensor data of the tropical rainfall measuring mission. Part I: Design and evaluation of inversion databases. J. Atmos. Oceanic Technol., 18, 1315–1330.

    • Search Google Scholar
    • Export Citation

  • Bolvin, D. T., R. F. Adler, G. J. Huffman, E. J. Nelkin, and J. P. Poutiainen, 2009: Comparison of GPCP monthly and daily precipitation estimates with high-latitude gauge observations. J. Appl. Meteor. Climatol., 48, 1843–1857.

    • Search Google Scholar
    • Export Citation

  • Brienen, S., A. Kapala, H. Mächel, and C. Simmer, 2013: Regional centennial precipitation variability over Germany from extended observation records. Int. J. Climatol., 33, 2167–2184.

    • Search Google Scholar
    • Export Citation

  • Chambon, P., I. Jobard, R. Roca, and N. Viltard, 2013: An investigation of the error budget of tropical rainfall accumulation derived from merged passive microwave and infrared satellite measurements. Quart. J. Roy. Meteor. Soc., 139, 879–893, doi:10.1002/qj.1907.

    • 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.

    • 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.

    • Search Google Scholar
    • Export Citation

  • Eyre, J., and W. P. Menzel, 1989: Retrieval of cloud parameters from satellite sounder data: A simulation study. J. Appl. Meteor., 28, 267–275.

    • Search Google Scholar
    • Export Citation

  • Ferraro, R. R., 1997: Special sensor microwave imager derived global rainfall estimates for climatological applications. J. Geophys. Res., 102 (D14), 16 715–16 735.

    • Search Google Scholar
    • Export Citation

  • Ferraro, R. R., E. A. Smith, W. Berg, and G. J. Huffman, 1998: A screening methodology for passive microwave precipitation retrieval algorithms. J. Atmos. Sci., 55, 1583–1600.

    • Search Google Scholar
    • Export Citation

  • Groisman, P. Ya., R. W. Knight, D. R. Easterling, T. R. Karl, G. C. Hegerl, and V. N. Razuvaev, 2005: Trends in intense precipitation in the climate record. J. Climate, 18, 1326–1350.

    • Search Google Scholar
    • Export Citation

  • Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones, and M. New, 2008: A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.

    • Search Google Scholar
    • Export Citation

  • Hofstra, N., M. New, and C. McSweeney, 2010: The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data. Climate Dyn., 35, 841–858.

    • Search Google Scholar
    • Export Citation

  • 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 multisatellite observations. J. Hydrometeor., 2, 36–50.

    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., 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 scale. J. Hydrometeor., 8, 38–55.

    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the global precipitation record: GPCP version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.

    • Search Google Scholar
    • Export Citation

  • Huuskonen, A., 2006: EUMETNET OPERA: Operational Programme for the Exchange of Weather Radar information. Proc. ERAD, 3, 371–373.

  • 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.

    • Search Google Scholar
    • Export Citation

  • Kidd, C., 2001: Satellite rainfall climatology: A review. Int. J. Climatol., 21, 1041–1066.

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

  • Kidd, C., P. Bauer, J. Turk, G. J. Huffman, R. Joyce, K.-L. Hsu, and D. Braithwaite, 2012: Inter-comparison of high-resolution precipitation products over northwest Europe. J. Hydrometeor., 13, 67–83.

    • Search Google Scholar
    • Export Citation

  • Klein Tank, A. M. G., and G. P. Können, 2003: Trends in indices of daily temperature and precipitation extremes in Europe, 1946–99. J. Climate, 16, 3665–3680.

    • Search Google Scholar
    • Export Citation

  • Lensky, I. M., and D. Rosenfeld, 2006: The time-space exchangeability of satellite retrieved relations between cloud top temperature and particle effective radius. Atmos. Chem. Phys., 6, 2887–2894.

    • Search Google Scholar
    • Export Citation

  • Levizzani, V., R. Amorati, and F. Meneguzzo, 2002: A review of satellite based rainfall estimation methods. European Commission Project MUSIC Rep. EVK1-CT-20000-00058, 70 pp.

  • Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 78–97.

    • Search Google Scholar
    • Export Citation

  • Roebeling, R. A., and I. Holleman, 2009: SEVIRI rainfall retrieval and validation using weather radar observations. J. Geophys. Res., 114, D21202, doi:10.1029/2009JD012102.

    • Search Google Scholar
    • Export Citation

  • Roebeling, R. A., E. L. A. Wolters, J. F. Meirink, and H. Leijnse, 2012: Triple collocation of summer precipitation retrievals from SEVIRI over Europe with gridded rain gauge and weather radar data. J. Hydrometeor., 13, 1552–1566.

    • Search Google Scholar
    • Export Citation

  • Sapiano, M., and P. Arkin, 2009: An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J. Hydrometeor., 10, 149–166.

    • Search Google Scholar
    • Export Citation

  • Sorooshian, S., K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 2035–2046.

    • Search Google Scholar
    • Export Citation

  • Susskind, J., and J. Pfaendtner, 1989: Impact of interactive physical retrievals of NWP. Report on the Joint ECMWF/EUMETSAT Workshop on the Use of Satellite Data in Operational Weather Prediction: 1989–1993, T. Hollingsworth, Ed., Vol. 1, ECMWF, 245–270.

  • Susskind, J., P. Piraino, L. Rokke, T. Iredell, and A. Mehta, 1997: Characteristics of the TOVS Pathfinder Path A dataset. Bull. Amer. Meteor. Soc., 78, 1449–1472.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., 1999: Conceptual framework for changes of extremes of the hydrological cycle with climate change. Climatic Change, 42, 327–339.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 1205–1217.

    • Search Google Scholar
    • Export Citation

  • Turk, F. J., P. Arkin, E. E. Ebert, and M. R. P. Sapiano, 2008: Evaluating high-resolution precipitation products. Bull. Amer. Meteor. Soc., 89, 1911–1916.

    • Search Google Scholar
    • Export Citation

  • Viltard, N., C. Burlaud, and C. Kummerow, 2006: Rain retrieval from TMI brightness temperature measurements using a TRMM PR–based database. J. Appl. Meteor. Climatol., 45, 455–466.

    • Search Google Scholar
    • Export Citation

  • Wilks, D., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 648 pp.

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

    • Search Google Scholar
    • Export Citation

  • Zolina, O., C. Simmer, K. Belyalev, A. Kapala, and S. Gulev, 2009: Improving estimates of heavy and extreme precipitation using daily records from European rain gauges. J. Hydrometeor., 10, 701–716.

    • Search Google Scholar
    • Export Citation

  • Zolina, O., C. Simmer, S. Gulev, and S. Kollet, 2010: Changing structure of European precipitation: Longer wet periods leading to more abundant rainfalls. Geophys. Res. Lett., 37, L06704, doi:10.1029/2010GL042468.

    • Search Google Scholar
    • Export Citation

  • Zolina, O., C. Simmer, K. Belyaev, S. K. Gulev, and P. Koltermann, 2013: Changes in the duration of European wet and dry spells during the last 60 years. J. Climate, 26, 2022–2047.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 645 181 66
PDF Downloads 392 93 0