首页|遥感降水降尺度高精度校正及不确定性分析方法

遥感降水降尺度高精度校正及不确定性分析方法

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为消除降水场同质部分影响,提升统计降水降尺度结果精度,提出了基于贝叶斯高精度曲面建模(Bayes-HASM)算法的遥感降水降尺度高精度校正方法.该方法通过引入模拟精度更高的高精度曲面建模方法,并结合贝叶斯优化算法,实现了模型参数自动优化选择和高精度降尺度校正,解决了现有降尺度残差校正方法存在的误差和多尺度问题.结果表明:贝叶斯优化使高精度曲面建模的不确定性显著减少;经过Bayes-HASM残差校正后,降尺度结果的散点分布更加接近1∶1线,年、季、月和旬尺度的精度指标均得到了显著的改善,CC和IA指标提高至0.9左右,RMSE下降明显,RB也显著改善.本方法能显著降低模型的不确定性并起到消除降水场同质部分影响的作用,有效提升降水降尺度结果精度.
High-precision correction and uncertainty analysis method for remote sensing precipitation downscaling
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.

digital twin watershedhigh accuracy surface modelingBayesian optimizationstatistical down-scalingremote sensing precipitation data

董甲平、冶运涛、顾晶晶、黄建雄、关昊哲、曹引

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天津大学建筑工程学院,天津 300072

中国水利水电科学研究院水资源研究所,北京 100038

水利部数字孪生流域重点实验室,北京 100038

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

国家自然科学基金面上项目国家重点研发计划项目国家自然科学基金青年项目北京市自然科学基金项目

522790312023YFC3209302-0352309040JQ21029

2024

10.13243/j.cnki.slxb.20230471

水利学报
中国水利学会

水利学报

CSTPCD北大核心
影响因子：1.778
ISSN：0559-9350
年,卷(期)：2024.55(2)
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