Validation of CHIRPS Estimation Rainfall Data Using Numerical Accuracy Test with Precitipation Observation Data

Abstract

Rainfall is the dominant and most studied weather and climate factor in Indonesia. The use of global satellite estimation data is a solution to overcome the constraints of limited observational rainfall data. Although the global data can be extracted according to the desired location, the data still has to be validated with observational data. This study was conducted to validate the CHIRPS rainfall estimation data with observation rainfall data at the Central MKG Region I Medan. Estimation data used is CHIRPS daily estimation data version 2.0 with a spatial resolution of 0.05°, and validator data is daily observation data of Central MKG Region I Medan for the period 2017- 2019. Validation was done by calculating Pearson correlation, accuracy, bias, mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) values, and using simple linear regression to see overestimated or underestimated estimation data on observation data. Results of this study indicated that the CHIRPS data has a low correlation with the observation data. Large MAE and RMSE values of CHIRPS indicated that the average error of CHIRPS is quite large. Estimation of CHIRPS was underestimated to the observation data so that it is not suitable for daily rainfall forecasting in Medan City. High accuracy value of CHIRPS indicated that CHIRPS was able to detect rain events based on a threshold of 1 mm.