Accounting for Uncertainties of the TRMM Satellite Estimates

Abstract

Recent advances in the field of remote sensing have led to an increase in available rainfall data on a regional and global scale. Several NASA sponsored satellite missions provide valuable precipitation data. However, the advantages of the data are limited by complications related to the indirect nature of satellite estimates. This study intends to develop a stochastic model for uncertainty analysis of satellite rainfall fields through simulating error fields and imposing them over satellite estimates. In order to examine reliability and performance of the presented model, ensembles of satellite estimates are simulated for a large area across the North and South Carolina. The generated ensembles are then compared with original satellite estimates with respect to statistical properties and spatial dependencies. The results show that the model can be used to describe the uncertainties associated to TRMM multi-satellite precipitation estimates. The presented model is validated using random sub-samples of the observations based on the bootstrap technique. The results indicate that the model performs reasonably well with different numbers of available rain gauges.

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

“…Despite this quite weak performance of all the four satellite rainfall products, they provide spatio-temporal estimate of rainfall not available from gauges. Besides, that estimate can be improved by bias correction (Addor and Seibert 2014;AghaKouchak et al 2009;Müller and Thompson 2013) or by merged/blended improvements (Chappell et al 2013;Li and Shao 2010;Woldemeskel et al 2013) with gauge data as reference. Geophysical and climatological constraints (Jia et al 2011), such as elevation applied in this study, eventually also distance to the sea (Abtew et al 2011;Johansson and Chen 2003) and wind direction (Castro et al 2014), can also improve the performance of satellite rainfall products in contrast to climatology zonation that was not particularly beneficial.…”

Topography and Data Mining Based Methods for Improving Satellite Precipitation in Mountainous Areas of China

“…Although there are potential uncertainties caused by various factors, such as systematic and random errors [1], and difficulties in capturing solid precipitation [2], gauge station rainfall has been widely accepted in terms of both accuracy and effectiveness due to its direct measurement. However, the sparse gauge networks, especially in rough terrains and mountainous areas, hinder the application of gauge rainfall to basins/regional scales [3].…”

“…D is the Rainfall-Elevation Mask (REM) for qualification the ungauged cells precipitation, i.e., the exact closer region in Equation (3). n is the number of satellite ungauged grid cells within the REM having comparable rainfall as gauges, and N is the total number of ungauged grid cells.…”

Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications

“…Therefore, precipitation uncertainty would critically affect the predicted variability in hydrologic simulations. Several validation studies have investigated uncertainties related to satellite rainfall remote sensing over diverse geographic and hydroclimatic regimes (Adler et al, 2001;AghaKouchak et al, 2009;Brown, 2006;Dinku et al, 2007;Ebert et al, 2007;Krajewski et al, 2000;McCollum et al, 2002;Seyyedi et al, 2014a;Su et al, 2008;Tang et al, 2010). These studies have shown that the precision of satellite rainfall products depends on precipitation type (e.g., deep convection vs. shallow convection), as well as terrain and climatological factors Demaria et al, 2011;Turk and Miller, 2005;Seyyedi et al, 2014a).…”