Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimates over Continental South America

Abstract

This paper describes a comprehensive assessment of a new high-resolution, gauge-satellite-based analysis of daily precipitation over continental South America during 2004. This methodology is based on a combination of additive and multiplicative bias correction schemes to get the lowest bias when compared with the observed values (rain gauges). Intercomparisons and cross-validation tests have been carried out between independent rain gauges and different merging techniques. This validation process was done for the control algorithm [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis real-time algorithm] and five different merging schemes: additive bias correction; ratio bias correction; TRMM Multisatellite Precipitation Analysis, research version; and the combined scheme proposed in this paper. These methodologies were tested for different months belonging to different seasons and for different network densities. All compared, merging schemes produce better results than the control algorithm; however, when finer temporal (daily) and spatial scale (regional networks) gauge datasets are included in the analysis, the improvement is remarkable. The combined scheme consistently presents the best performance among the five techniques tested in this paper. This is also true when a degraded daily gauge network is used instead of a full dataset. This technique appears to be a suitable tool to produce real-time, high-resolution, gauge-and satellite-based analyses of daily precipitation over land in regional domains.

Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013

“…We will discuss the characteristics of the precipitation on the yearly, seasonally, monthly and daily time scales. We use statistical analysis methods, including the linear correlation coefficient (CC), relative bias (BIAS), root mean square error (RMSE), the frequency bias index (FBI), probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI) to compare the differences from pixel to point [31,32]. There is a high correlation between the satellite products and in situ measurements if the CC is greater than 0.7 [33].…”

Comparative performance analysis of climate re-analysis approaches in Angola

“…These results highlight the limitations of re-analysis data to undertake precipitation variability studies over Angola. Previous studies in areas with dense gauge networks or radar coverage suggested that re-analysis exercises perform better in higher latitudes and cooler regions, where precipitation arises from frontal systems, and that convective precipitation, common in lower and warmer latitudes such as in Angola, is better characterized by satellite precipitation (Ebert et al 2007, Sapiano and Arkin 2009, Vila et al 2009). Pombo et al (2015) studied satellite precipitation estimates for Angola and the results generally outperformed the re-analysis estimates at the annual and seasonal levels presented in this paper.…”

Seasonal Variability of Vegetation and Its Relationship to Rainfall and Fire in the Brazilian Tropical Savanna

“…These two datasets (TRMM and observed data) are combined following the approach of Vila et al (2009), which use the Barnes objective analysis (Barnes, 1973;Koch et al, 1983) for data interpolation. This analysis allows the incorporation of observed data in a grid of estimated data and also improves its spatial resolution.…”