A Calibration Method Of GPM Precipitation Based On Genetic Programming

Accurately predicting temporal-spatial distributions of precipitation has important and direct impacts on agriculture,animal husbandry,grazing,and energy production and is also closely related to disasters,such as typhoons,floods,droughts and debris flows.Influenced by topography,temperatureand other factors,precipitation varies greatly in time and space.Therefore,accurate estimations of the precipitation in various regions of the China are still facing great challenges.In recent years,with the evaluation of satellite precipitation products,satellite precipitation products have been widely utilized in a variety of operational and research applications.However,uncertainties still exist in the accuracy of the high-resolution satellite precipitation products in representing the spatial and temporal variations of precipitation,which requires examined for the further researches on the construction algorithms for calibration.At present,most calibration and evaluation of satellite precipitation are based on monthly and annual scales,and are rarely analyzed on daily scale precipitation.Because the daily scale precipitation data is large and the data deviation is large,the research is more complicated.In this paper,based on the relationship among daily scale precipitation of Global Precipitation Measurement(GPM)satellite products and gauge-observed,topography,elevation,temperature,seasonal variability,and vegetation type,we calibrate and verify the daily scale precipitation of GPM by Genetic Programming(GP)from 2015 to 2016.Results indicate that the GP has a good skill in calibrating GPM satellite daily scale precipitation over wet regions and for warm season of China.After calibration,the correlation coefficient(CC)is increased by 14%.Taking into account the vegetation regionalization,China is divided into 8 vegetation areas,and the accuracy of calibrated GPM satellite daily scale precipitation data has been significantly improved.With high temperature and abundant precipitation,the CC of Tropical monsoon rain forest regionalization and Subtropical broadleaf evergreen forest regionalization has increased 10-20%.In conclusion,the research results not only show that GP has a good ability to calibrate China's GPM satellite daily scale precipitation data,but also further improve the calibration effect on considering the impact of the season and vegetation type on GPM satellite precipitation products.