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

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 depthfrequency-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.

Using cloud ice flux to parametrise large-scale lightning

“…The time series of precipitation rate over total land performs well whilst during the 2007-2011 period there is an over-estimation by approximately 0.4 mm day −1 over total ocean when compared to GPCP. Many independent studies have looked at precipitation compared to observations and other reanalysis sets (Kim et al, 2013;Peña Arancibia et al, 2013;Pfeifroth et al, 2013;Zhang et al, 2013;Lin et al, 2014). Generally, ERA-Interim was found to perform well.…”

Calibration of Spatially Distributed Hydrological Processes and Model Parameters in SWAT Using Remote Sensing Data and an Auto-Calibration Procedure: A Case Study in a Vietnamese River Basin

“…Water 2018, 10, x 6 of 21 precipitation product was suggested as a reduction of system-specific and random errors [65]. TRMM data, in situ measurements and other atmospheric and climatology models were assimilated in [66] to create an ensemble precipitation product Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS, University of California Santa Barbara -Climate Hazards Group, Santa Barbara, California, U.S.) with a superior resolution at 0.05° × 0.05°.…”

A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble

“…From the model outputs we used (i) SWE depth, (ii) total soil moisture storage over a soil depth that varies between models and (iii) generated streamflow, calculated as the sum of surface runoff and subsurface drainage. In addition to GLDAS, we used global water balance estimates generated by the W3RA (WorldWide Water Resources Assessment) model ( Van Dijk et al, 2013) in the configuration used in the Asia-Pacific Water Monitor (http://www.wenfo.org/apwm/). For 2003, the model was forced with the "Princeton" merged precipitation, down-welling short-wave radiation, minimum and maximum daily temperature and air pressure data produced by Sheffield et al (2006).…”

“…River water storage was estimated by propagating runoff fields from each of the five models through a global routing scheme. In a previous study, we compared these runoff fields with streamflow records from 6192 small (<10 000 km 2 ) catchments worldwide and found that observed runoff was 1.28 to 1.77 times greater than predicted by the different models ( Van Dijk et al, 2013). The respective ratios were used to uniformly bias-correct the runoff fields.…”

“…More accurate global water balance estimates are needed, to better understand interactions between the global climate system and water cycle (Sheffield et al, 2012), the causes of observed sea level rise (Boening et al, 2012;Fasullo et al, 2013;Cazenave et al, 2009;Leuliette and Miller, 2009) and human impacts on water resources (Wada et al, 2010(Wada et al, , 2013, and to improve hydrological models (van Dijk et al, 2011) and initialise water resources forecasts ( Van Dijk et al, 2013). The current generation of global hydrological models have large uncertainties arising from a combination of data deficiencies (e.g.…”