Editorial Type:
Article
Article Type:
Research Article

Evaluation of Precipitation Estimation Accuracy in Reanalyses, Satellite Products, and an Ensemble Method for Regions in Australia and South and East Asia

Jorge L. Peña-Arancibia CSIRO Land and Water, Canberra, Australian Capital Territory, Australia, and Faculty of Earth and Life Sciences, VU University Amsterdam, Amsterdam, Netherlands, and Environmental Monitoring and Modelling Research Group, Department of Geography, King's College London, London, United Kingdom

Search for other papers by Jorge L. Peña-Arancibia in
Current site
Google Scholar
PubMed Close
,
Albert I. J. M. van Dijk CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

Search for other papers by Albert I. J. M. van Dijk in
Current site
Google Scholar
PubMed Close
,
Luigi J. Renzullo CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

Search for other papers by Luigi J. Renzullo in
Current site
Google Scholar
PubMed Close
, and
Mark Mulligan Environmental Monitoring and Modelling Research Group, Department of Geography, King's College London, London, United Kingdom

Search for other papers by Mark Mulligan in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Aug 2013
DOI:
https://doi.org/10.1175/JHM-D-12-0132.1
Page(s):
1323–1333
Restricted access

Abstract

Precipitation estimates from reanalyses and satellite observations are routinely used in hydrologic applications, but their accuracy is seldom systematically evaluated. This study used high-resolution gauge-only daily precipitation analyses for Australia (SILO) and South and East Asia [Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE)] to calculate the daily detection and accuracy metrics for three reanalyses [ECMWF Re-Analysis Interim (ERA-Interim), Japanese 25-yr Reanalysis (JRA-25), and NCEP–Department of Energy (DOE) Global Reanalysis 2] and three satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) 3B42V6, Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)]. A depth-frequency-adjusted ensemble mean of the reanalyses and satellite products was also evaluated. Reanalyses precipitation from ERA-Interim in southern Australia (SAu) and northern Australasia (NAu) showed higher detection performance. JRA-25 had a better performance in South and East Asia (SEA) except for the monsoon period, in which satellite estimates from TRMM and CMORPH outperformed the reanalyses. In terms of accuracy metrics (correlation coefficient, root-mean-square difference, and a precipitation intensity proxy, which is the ratio of monthly precipitation amount to total days with precipitation) and over the three subdomains, the depth-frequency-adjusted ensemble mean generally outperformed or was nearly as good as any of the single members. The results of the ensemble show that additional information is captured from the different precipitation products. This finding suggests that, depending on precipitation regime and location, combining (re)analysis and satellite products can lead to better precipitation estimates and, thus, more accurate hydrological applications than selecting any single product.

Current affiliation: Fenner School of Environment and Society, College of Medicine, Biology and Environment, Australian National University, Canberra, ACT, Australia.

Corresponding author address: Jorge L. Peña-Arancibia, CSIRO Land and Water, GPO 1666, Black Mountain, Canberra ACT 2601, Australia. E-mail: jorge.penaarancibia@csiro.au

Abstract

Precipitation estimates from reanalyses and satellite observations are routinely used in hydrologic applications, but their accuracy is seldom systematically evaluated. This study used high-resolution gauge-only daily precipitation analyses for Australia (SILO) and South and East Asia [Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE)] to calculate the daily detection and accuracy metrics for three reanalyses [ECMWF Re-Analysis Interim (ERA-Interim), Japanese 25-yr Reanalysis (JRA-25), and NCEP–Department of Energy (DOE) Global Reanalysis 2] and three satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) 3B42V6, Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)]. A depth-frequency-adjusted ensemble mean of the reanalyses and satellite products was also evaluated. Reanalyses precipitation from ERA-Interim in southern Australia (SAu) and northern Australasia (NAu) showed higher detection performance. JRA-25 had a better performance in South and East Asia (SEA) except for the monsoon period, in which satellite estimates from TRMM and CMORPH outperformed the reanalyses. In terms of accuracy metrics (correlation coefficient, root-mean-square difference, and a precipitation intensity proxy, which is the ratio of monthly precipitation amount to total days with precipitation) and over the three subdomains, the depth-frequency-adjusted ensemble mean generally outperformed or was nearly as good as any of the single members. The results of the ensemble show that additional information is captured from the different precipitation products. This finding suggests that, depending on precipitation regime and location, combining (re)analysis and satellite products can lead to better precipitation estimates and, thus, more accurate hydrological applications than selecting any single product.

Current affiliation: Fenner School of Environment and Society, College of Medicine, Biology and Environment, Australian National University, Canberra, ACT, Australia.

Corresponding author address: Jorge L. Peña-Arancibia, CSIRO Land and Water, GPO 1666, Black Mountain, Canberra ACT 2601, Australia. E-mail: jorge.penaarancibia@csiro.au
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Adler, R. F., Gu G. , and Huffman G. J. , 2012: Estimating Climatological bias errors for the Global Precipitation Climatology Project (GPCP). J. Appl. Meteor. Climatol., 51, 84–99.

    • Search Google Scholar
    • Export Citation

  • Behrangi, A., Khakbaz B. , Jaw T. C. , AghaKouchak A. , Hsu K. , and Sorooshian S. , 2011: Hydrologic evaluation of satellite precipitation products over a mid-size basin. J. Hydrol., 397, 225–237.

    • Search Google Scholar
    • Export Citation

  • Betts, A. K., Zhao M. , Dirmeyer P. A. , and Beljaars A. C. M. , 2006: Comparison of ERA40 and NCEP/DOE near-surface data sets with other ISLSCP-II data sets. J. Geophys. Res., 111, D22S04, doi:10.1029/2006JD007174.

    • Search Google Scholar
    • Export Citation

  • Betts, A. K., Kohler M. , and Zhang Y. C. , 2009: Comparison of river basin hydrometeorology in ERA-Interim and ERA-40 reanalyses with observations. J. Geophys. Res., 114, D02101, doi:10.1029/2008JD010761.

    • Search Google Scholar
    • Export Citation

  • Bosilovich, M. G., Chen J. Y. , Robertson F. R. , and Adler R. F. , 2008: Evaluation of global precipitation in reanalyses. J. Appl. Meteor. Climatol., 47, 2279–2299.

    • Search Google Scholar
    • Export Citation

  • Bosilovich, M. G., Mocko D. , Roads J. O. , and Ruane A. , 2009: A multimodel analysis for the Coordinated Enhanced Observing Period (CEOP). J. Hydrometeor., 10, 912–934.

    • Search Google Scholar
    • Export Citation

  • Dedong, L., Zhongbo Y. , Zhenchun H. , Chuanguo Y. , and Qin J. , 2007: Groundwater simulation in the Yangtze River basin with a coupled climate-hydrologic model. J. China Univ. Geosci., 18, 155–157.

    • Search Google Scholar
    • Export Citation

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597.

    • Search Google Scholar
    • Export Citation

  • Demaria, E. M. C., Rodriguez D. A. , Ebert E. E. , Salio P. , Su F. , and Valdes J. B. , 2011: Evaluation of mesoscale convective systems in South America using multiple satellite products and an object-based approach. J. Geophys. Res., 116, D08103, doi:10.1029/2010JD015157.

    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., 2001: Ability of a poor man's ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 2461–2480.

    • Search Google Scholar
    • Export Citation

  • 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, 1–47.

    • Search Google Scholar
    • Export Citation

  • Fekete, B. M., Vorosmarty C. J. , Roads J. O. , and Willmott C. J. , 2004: Uncertainties in precipitation and their impacts on runoff estimates. J. Climate, 17, 294–304.

    • Search Google Scholar
    • Export Citation

  • Fernandes, K., Fu R. , and Betts A. K. , 2008: How well does the ERA40 surface water budget compare to observations in the Amazon River basin? J. Geophys. Res., 113, D11117, doi:10.1029/2007JD009220.

    • Search Google Scholar
    • Export Citation

  • Gao, Y. C., and Liu M. F. , 2012: Evaluation of high-resolution satellite precipitation products using rain gauge observations over Tibetan Plateau. Hydrol. Earth Syst. Sci. Discuss., 9, 9503–9532.

    • Search Google Scholar
    • Export Citation

  • Getirana, A. C. V., Espinoza J. C. V. , Ronchail J. , and Rotunno Filho O. C. , 2011: Assessment of different precipitation data-sets and their impacts on the water balance of the Negro River basin. J. Hydrol., 404, 304–322.

    • Search Google Scholar
    • Export Citation

  • Gottschalck, J., Meng J. , Rodell M. , and Houser P. , 2005: Analysis of multiple precipitation products and preliminary assessment of their impact on global land data assimilation system land surface states. J. Hydrometeor., 6, 573–598.

    • 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.

    • Search Google Scholar
    • Export Citation

  • Jeffrey, S. J., Carter J. O. , Moodie K. B. , and Beswick A. R. , 2001: Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model. Software, 16, 309–330.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Kanamitsu, M., Ebisuzaki W. , Woollen J. , Yang S. K. , Hnilo J. J. , Fiorino M. , and Potter G. L. , 2002: NCEP-DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1643.

    • Search Google Scholar
    • Export Citation

  • Khan, S. I., and Coauthors, 2012: Microwave satellite data for hydrologic modeling in ungauged basins. IEEE Geosci. Remote Sens. Lett., 9, 663–667.

    • Search Google Scholar
    • Export Citation

  • Li, J., Gao X. , and Sorooshian S. , 2008: Model performance of downscaling 1999–2004 hydrometeorological fields to the upper Rio Grande Basin using different forcing datasets. J. Hydrometeor., 9, 677–694.

    • Search Google Scholar
    • Export Citation

  • Miguez-Macho, G., and Fan Y. , 2012: The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands. J. Geophys. Res., 117, D15113, doi:10.1029/2012JD017539.

    • Search Google Scholar
    • Export Citation

  • Nair, S., Srinivasan G. , and Nemani R. , 2009: Evaluation of multi-satellite TRMM derived rainfall estimates over a western state of India. J. Meteor. Soc. Japan, 87, 927–939.

    • Search Google Scholar
    • Export Citation

  • Nesbitt, S. W., Gochis D. J. , and Lang T. J. , 2008: The diurnal cycle of clouds and precipitation along the Sierra Madre Occidental observed during NAME-2004: Implications for warm season precipitation estimation in complex terrain. J. Hydrometeor., 9, 728–743.

    • Search Google Scholar
    • Export Citation

  • Onogi, K., and Coauthors, 2007: The JRA-25 reanalysis. J. Meteor. Soc. Japan, 85, 369–432.

  • Pan, M., Li H. B. , and Wood E. , 2010: Assessing the skill of satellite-based precipitation estimates in hydrologic applications. Water Resour. Res., 46, W09535.

    • Search Google Scholar
    • Export Citation

  • Romilly, T. G., and Gebremichael M. , 2011: Evaluation of satellite rainfall estimates over Ethiopian river basins. Hydrol. Earth Syst. Sci., 15, 1505–1514.

    • Search Google Scholar
    • Export Citation

  • Ruane, A. C., and Roads J. O. , 2007: 6-hour to 1-year variance of five global precipitation sets. Earth Interact., 11 . [Available online at http://EarthInteractions.org.]

    • Search Google Scholar
    • Export Citation

  • Sapiano, M. R. P., and Arkin P. A. , 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

  • Scheel, M. L. M., Rohrer M. , Huggel C. , Villar D. S. , 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, 8545–8586.

    • Search Google Scholar
    • Export Citation

  • Shrestha, M. S., Artan G. A. , Bajracharya S. R. , and Sharma R. R. , 2008: Using satellite-based rainfall estimates for streamflow modelling: Bagmati Basin. J. Flood Risk Manage., 1, 89–99.

    • Search Google Scholar
    • Export Citation

  • 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, 2035–2046.

    • Search Google Scholar
    • Export Citation

  • Stampoulis, D., and Anagnostou E. N. , 2012: Evaluation of global satellite rainfall products over continental Europe. J. Hydrometeor., 13, 588–603.

    • Search Google Scholar
    • Export Citation

  • 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.

    • Search Google Scholar
    • Export Citation

  • Tian, Y. D., 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. D., 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

  • Uppala, S., Simmons A. , Dee D. , Kallberg P. , and Thepaut J. N. , 2007: Atmospheric reanalyses and climate variations. Climate Variability and Extremes During the Past 100 Years, S. Brönnimann et al., Eds., Advances in Global Change Research, Vol. 33, Springer, 103–117.

  • Van Dijk, A. I. J. M., and Renzullo L. J. , 2011: Water resource monitoring systems and the role of satellite observations. Hydrol. Earth Syst. Sci., 15, 39–55.

    • Search Google Scholar
    • Export Citation

  • Vernimmen, R. R. E., Hooijer A. , Mamenun E. , Aldrian, and van Dijk A. I. J. M. , 2012: Evaluation and bias correction of satellite rainfall data for drought monitoring in Indonesia. Hydrol. Earth Syst. Sci., 16, 133–146.

    • Search Google Scholar
    • Export Citation

  • Vila, D., Ferraro R. , and Semunegus H. , 2010: Improved global rainfall retrieval using the Special Sensor Microwave Imager (SSM/I). J. Appl. Meteor. Climatol., 49, 1032–1043.

    • Search Google Scholar
    • Export Citation

  • Voisin, N., Wood A. W. , and Lettenmaier D. P. , 2008: Evaluation of precipitation products for global hydrological prediction. J. Hydrometeor., 9, 388–407.

    • Search Google Scholar
    • Export Citation

  • Wang, B., and LinHo, 2002: Rainy season of the Asian–Pacific summer monsoon. J. Climate, 15, 386–398.

  • Yatagai, A., Kamiguchi K. , Arakawa O. , Hamada A. , Yasutomi N. , and Kitoh A. , 2012: APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Amer. Meteor. Soc., 93, 1401–1415.

    • Search Google Scholar
    • Export Citation

  • Yong, B., Hong Y. , Ren L.-L. , Gourley J. J. , Huffman G. J. , Chen X. , Wang W. , and Khan S. I. , 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
All Time Past Year Past 30 Days
Abstract Views 218 0 0
Full Text Views 300 192 29
PDF Downloads 191 120 6