To eliminate the influence of homogeneous parts in precipitation fields and enhance the accuracy of sta-tistical precipitation downscaling results,a high-precision correction method is proposed for remote sensing pre-cipitation downscaling based on the Bayesian High-Accuracy Surface Modeling(Bayes-HASM)algorithm.This method introduces a higher-precision surface modeling approach and combines it with Bayesian optimization algo-rithms to achieve automatic optimization of model parameters and high-precision downscaling correction.It ad-dresses the errors and multi-scale issues present in existing downscaling residual correction methods.The results indicate that Bayesian optimization significantly reduces the uncertainty of high-precision surface modeling;after Bayes-HASM residual correction,the scatter distribution of the downscaled results is closer to the 1∶1 line.The accuracy indicators at the annual,seasonal,monthly,and ten-day scales have been significantly improved,with CC and IA indicators reaching around 0.9,RMSE has significantly decreased,and RB significantly reduced.The above results indicate that this method can significantly reduce the model's uncertainty and effectively eliminate the impact of homogeneous parts of the precipitation field,thereby improving the accuracy of the downscaled precip-itation results.
关键词
数字孪生流域/高精度曲面建模/贝叶斯优化/统计降尺度/遥感降水
Key words
digital twin watershed/high accuracy surface modeling/Bayesian optimization/statistical down-scaling/remote sensing precipitation data
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