Bias correction of daily satellite precipitation data using genetic algorithm

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 an...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2018-05, Vol.149 (1), p.12071
Main Authors: Pratama, A W, Buono, A, Hidayat, R, Harsa, H
Format: Article
Language:English
Subjects:
Algorithms
Bias
Blending
Chirp
Environmental hazards
Genetic algorithms
Genetic transformation
Hazards
Hydrologic data
Precipitation
Rainy season
Reduction
Statistics
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Summary: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.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/149/1/012071