Bias correction of radar and satellite rainfall estimates and increasing its accuracy using modified merging

Abstract

Bias correction in the weather radar and the tropical rainfall measuring mission (TRMM) rainfall estimates are used to improve its accuracy. This correction is usually done separately for both radar and TRMM. Even though the corrections are done separately, the results of these corrections can be further improved using the merging. Among the methods of merging, modified local bias, mean field bias and conditional merging may be suitable methods used to correct rainfall estimates from remote sensing surrounding in the Makassar Strait. The aim of this research corrects radar and TRMM rainfall estimates, then combining them to obtain more accurate rainfall estimates. The performance will be validated using correlation, root mean square error (RMSE) and mean absolute error (MAE). The result shows that modified mean field bias (Mod_MFB) and local bias (LB) can increase accuracy, mainly RMSE and MAE but not in correlation. However, conditional merging (CM) and modified LB can improve accuracy by increasing correlation and decrease RMSE and MAE. The modification of CM, LB modification and original estimation of remote sensing successively are the order of the best methods. Moreover, merging three data types is not automatically better than merging the two types of data. However, combination 3 types of data offer the stability of accuracy.