Improved High-Resolution Radar-Based Rainfall Estimation

Md. Rashedul Islam, Peter F. Rasmussen
2008 Journal of hydrologic engineering  
The main objective of this paper is to improve the accuracy of radar rainfall estimation by accounting for a storm movement into a radar rainfall accumulation process. The multi-resolution viscous alignment (MVA) technique was used to estimate the velocity of a rain field from two consecutively measured radar images. The analysis used the 10-min radar reflectivity of the Pasicharoen radar and the corresponding 47 rain gauges measurements of 41 rainfall events that occurred in Bangkok during
more » ... Bangkok during 2005-2007. The 28 rainfall events occurring during 2005-2006 were used for calibration, and the 13 rainfall events recorded in 2007 were used for validation. Finer temporal resolutions of radar reflectivity data, taken at 1-9 min intervals, were generated using the MVA technique in order to investigate the optimal temporal resolution of the Pasicharoen radar when the MVA technique was integrated into an hourly radar rainfall estimation algorithm to account for a storm movement within a sampling interval. The results showed that using the generated 5-min MVA reflectivity data for estimating hourly radar rainfall gave the smallest root mean square error (RMSE) between hourly radar rainfall estimates and corresponding rain gauge data when compared to other temporal resolutions of generated MVA reflectivity. Hourly radar rainfall obtained from the proposed algorithm, which integrates the MVA technique into the accumulation approach, was compared with the traditional simple linear interpolation (SLI) technique and conventional method. Using the 5-min generated MVA reflectivity data to estimate hourly radar rainfall can reduce RMSEs between hourly radar and rain gauge rainfall by 10% and 17% for the calibration period, and by 27% and 29% for the validation period when compared to the SLI and conventional methods, respectively.
doi:10.1061/(asce)1084-0699(2008)13:9(910) fatcat:eccxsh6uqvaifnlut53pjfikli