• Adam, J. C., , Clark E. A. , , Lettenmaier D. P. , , and Wood E. F. , 2006: Correction of global precipitation products for orographic effects. J. Climate, 19, 1538, doi:10.1175/JCLI3604.1.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and et al. , 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, doi:10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ashouri, H., and et al. , 2015: PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull. Amer. Meteor. Soc., 96, 6983, doi:10.1175/BAMS-D-13-00068.1.

    • Search Google Scholar
    • Export Citation
  • Burroughs, W., 2003: Climate: Into the 21st Century. Cambridge University Press, 240 pp.

  • Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 46054630, doi:10.1175/JCLI3884.1.

  • Demargne, J., and et al. , 2014: The science of NOAA’s operational Hydrologic Ensemble Forecast Service. Bull. Amer. Meteor. Soc., 95, 7998, doi:10.1175/BAMS-D-12-00081.1.

    • Search Google Scholar
    • Export Citation
  • Gandin, L. S., 1965: Objective Analysis of Meteorological Fields. Israel Program for Scientific Translations, 242 pp.

  • Hsu, K. L., , Gao X. G. , , Sorooshian S. , , and Gupta H. V. , 1997: Precipitation estimation from remotely sensed information using artificial neural networks. J. Appl. Meteor., 36, 11761190, doi:10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hubacek, K., , Guan D. , , and Barua A. , 2007: Changing lifestyles and consumption patterns in developing countries: A scenario analysis for China and India. Futures, 39, 10841096, doi:10.1016/j.futures.2007.03.010.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and et al. , 1997: The Global Precipitation Climatology Project (GPCP) combined precipitation dataset. Bull. Amer. Meteor. Soc., 78, 520, doi:10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and et al. , 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2, 3650, doi:10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.

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

    • Search Google Scholar
    • Export Citation
  • Klein Tank, A. M. G., , Zwiers F. W. , , and Zhang X. , 2009: Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring. WCDMP 72/WMO-TD 1500, 56 pp. [Available online at www.wmo.int/datastat/documents/WCDMP_72_TD_1500_en_1_1.pdf.]

  • Knapp, K. R., 2008: Scientific data stewardship of International Satellite Cloud Climatology Project B1 global geostationary observations. J. Appl. Remote Sens.,2, 023548, doi:10.1117/1.3043461.

  • Liu, Z. Y., and et al. , 2014: Chinese cave records and the East Asia summer monsoon. Quat. Sci. Rev., 83, 115128, doi:10.1016/j.quascirev.2013.10.021.

    • Search Google Scholar
    • Export Citation
  • Piao, S. L., and et al. , 2010: The impacts of climate change on water resources and agriculture in China. Nature, 467, 4351, doi:10.1038/nature09364.

    • Search Google Scholar
    • Export Citation
  • Rudolf, B., 1993: Management and analysis of precipitation data on a routine basis. Proc. Int. WMO/IAHS/ETH Symp. on Precipitation and Evaporation, Bratislava, Slovakia, Slovak Hydrometeorological Institute, 6976.

  • Rudolf, B., , Hauschild H. , , Rueth W. , , and Schneider U. , 1994: Terrestrial precipitation analysis: Operational method and required density of point measurements. Global Precipitations and Climate Change, M. Desbois and F. Desalmond, Eds., NATO ASI Series I, Vol. 26, Springer, 173–186.

  • Schneider, U., , Fuchs T. , , Meyer-Christoffer A. , , and Rudolf B. , 2008: Global precipitation analysis products of the GPCC. Deutscher Wetterdienst, Offenbach am Main, Germany, 12 pp.

  • Sorooshian, S., , Hsu K.-L. , , Gao X. , , Gupta H. V. , , Imam B. , , and Braithwaite D. , 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 20352046, doi:10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sorooshian, S., and et al. , 2011: Advancing the remote sensing of precipitation. Bull. Amer. Meteor. Soc., 92, 12711272, doi:10.1175/BAMS-D-11-00116.1.

    • Search Google Scholar
    • Export Citation
  • Tong, K., , Su F. G. , , Yang D. Q. , , Zhang L. L. , , and Hao Z. C. , 2014: Tibetan Plateau precipitation as depicted by gauge observations, reanalyses and satellite retrievals. Int. J. Climatol., 34, 265285, doi:10.1002/joc.3682.

    • Search Google Scholar
    • Export Citation
  • Xie, P. P., , Janowiak J. E. , , Arkin P. A. , , Adler R. , , Gruber A. , , Ferraro R. , , Huffman G. J. , , and Curtis S. , 2003: GPCP Pentad precipitation analyses: An experimental dataset based on gauge observations and satellite estimates. J. Climate, 16, 21972214, doi:10.1175/2769.1.

    • Search Google Scholar
    • Export Citation
  • Xie, P. P., , Yatagai A. , , Chen M. Y. , , Hayasaka T. , , Fukushima Y. , , Liu C. M. , , and Yang S. , 2007: A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeor., 8, 607626, doi:10.1175/JHM583.1.

    • Search Google Scholar
    • Export Citation
  • Yatagai, A., , Arakawa O. , , Kamiguchi K. , , Kawamoto H. , , Nodzu M. I. , , and Hamada A. , 2009: A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges. SOLA, 5, 137140, doi:10.2151/sola.2009-035.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 275 275 47
PDF Downloads 253 253 48

Evaluation of the PERSIANN-CDR Daily Rainfall Estimates in Capturing the Behavior of Extreme Precipitation Events over China

View More View Less
  • 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, and Joint Center for Global Change Studies, Beijing, China, and Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
  • | 2 Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
  • | 3 State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
© Get Permissions
Restricted access

Abstract

This study evaluates the performance of a newly developed daily precipitation climate data record, called Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), in capturing the behavior of daily extreme precipitation events in China during the period of 1983–2006. Different extreme precipitation indices, in the three categories of percentile, absolute threshold, and maximum indices, are studied and compared with the same indices from the East Asia (EA) ground-based gridded daily precipitation dataset. The results show that PERSIANN-CDR depicts similar precipitation behavior as the ground-based EA product in terms of capturing the spatial and temporal patterns of daily precipitation extremes, particularly in the eastern China monsoon region, where the intensity and frequency of heavy rainfall events are very high. However, the agreement between the datasets in dry regions such as the Tibetan Plateau in the west and the Taklamakan Desert in the northwest is not strong. An important factor that may have influenced the results is that the ground-based stations from which EA gridded data were produced are very sparse. In the station-rich regions in eastern China, the performance of PERSIANN-CDR is significant. PERSIANN-CDR slightly underestimates the values of extreme heavy precipitation.

Corresponding author address: Chiyuan Miao, College of Global Change and Earth System Science, No. 19, XinJieKouWai St., HaiDian District, Beijing 100875, China. E-mail: miaocy@vip.sina.com

Abstract

This study evaluates the performance of a newly developed daily precipitation climate data record, called Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), in capturing the behavior of daily extreme precipitation events in China during the period of 1983–2006. Different extreme precipitation indices, in the three categories of percentile, absolute threshold, and maximum indices, are studied and compared with the same indices from the East Asia (EA) ground-based gridded daily precipitation dataset. The results show that PERSIANN-CDR depicts similar precipitation behavior as the ground-based EA product in terms of capturing the spatial and temporal patterns of daily precipitation extremes, particularly in the eastern China monsoon region, where the intensity and frequency of heavy rainfall events are very high. However, the agreement between the datasets in dry regions such as the Tibetan Plateau in the west and the Taklamakan Desert in the northwest is not strong. An important factor that may have influenced the results is that the ground-based stations from which EA gridded data were produced are very sparse. In the station-rich regions in eastern China, the performance of PERSIANN-CDR is significant. PERSIANN-CDR slightly underestimates the values of extreme heavy precipitation.

Corresponding author address: Chiyuan Miao, College of Global Change and Earth System Science, No. 19, XinJieKouWai St., HaiDian District, Beijing 100875, China. E-mail: miaocy@vip.sina.com
Save