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

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Albert I. J. M. van Dijk CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

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Luigi J. Renzullo CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

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Mark Mulligan Environmental Monitoring and Modelling Research Group, Department of Geography, King's College London, London, United Kingdom

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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
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