H200-0022
Spatial and Temporal downscaling of TRMM Precipitation with Novel Algorithms
Spatial and Temporal downscaling of TRMM Precipitation with Novel Algorithms
Wednesday, 16 December 2020
Poster
Abstract:
Tropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets
over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the
precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of
original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM
precipitation products. This study contributes two precipitation downscaling algorithms by exploring the
nonstationarity relations between precipitation and various environment factors [daytime surface temperature
(LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude,
and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of
precipitation is achieved with the geographically weighted regression model (GWR) and the backwardpropagation
artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects
the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling.
The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate
monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and
upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed
downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within
Hubei Province from 0.258 (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled
datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through
2010. This paper’s results demonstrate the bias correction is necessary before downscaling. The highresolution
precipitation datasets obtained with the proposed downscaling model with GWR relying on the
NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate
downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.
over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the
precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of
original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM
precipitation products. This study contributes two precipitation downscaling algorithms by exploring the
nonstationarity relations between precipitation and various environment factors [daytime surface temperature
(LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude,
and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of
precipitation is achieved with the geographically weighted regression model (GWR) and the backwardpropagation
artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects
the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling.
The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate
monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and
upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed
downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within
Hubei Province from 0.258 (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled
datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through
2010. This paper’s results demonstrate the bias correction is necessary before downscaling. The highresolution
precipitation datasets obtained with the proposed downscaling model with GWR relying on the
NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate
downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.