Bias correction of daily satellite precipitation data using genetic algorithm

4th International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring

农业科学;生态环境科学

Pratama, A.W.^1 ; Buono, A.^1 ; Hidayat, R.^2 ; Harsa, H.^1,3

Department of Computer Science, Faculty of Mathematics and Natural Science, Bogor Agricultural University, Bogor, Indonesia^1

Department of Geophysics and Meteorology, Faculty of Mathematics and Natural Science, Bogor Agricultural University, Bogor, Indonesia^2

Meteorological, Climatological and Geophysics Agency of Indonesia (BMKG), Kemayoran, Jakarta, Indonesia^3

关键词: Bias correction;    Blending process;    Climate hazards;    Precipitation data;    Satellite precipitation;    Statistical moments;    Wet season;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/149/1/012071/pdf
DOI  :  10.1088/1755-1315/149/1/012071

来源: IOP

【 摘 要 】

Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) was producted by blending Satellite-only Climate Hazards Group InfraRed Precipitation (CHIRP) with Stasion observations data. The blending process was aimed to reduce bias of CHIRP. However, Biases of CHIRPS on statistical moment and quantil values were high during wet season over Java Island. This paper presented a bias correction scheme to adjust statistical moment of CHIRP using observation precipitation data. The scheme combined Genetic Algorithm and Nonlinear Power Transformation, the results was evaluated based on different season and different elevation level. The experiment results revealed that the scheme robustly reduced bias on variance around 100% reduction and leaded to reduction of first, and second quantile biases. However, bias on third quantile only reduced during dry months. Based on different level of elevation, the performance of bias correction process is only significantly different on skewness indicators.