摘要
[目的]精准可靠的逐日卫星降水产品(Satellite Precipitation Product,SPP)是气象和水文研究中不可或缺的基础数据源。然而,现有逐日SPP存在局部降水细节捕捉不足、部分地区误差较大等问题,导致降水空间刻画不清、预测精度不高。[方法]基于此,本研究提出一种精准捕捉降水空间异质性的加权堆叠遥感日降水产品质量提升方法(Spatial Heterogeneity Weight Stacking,SHW-Stacking)。该方法首先耦合特征重要性与非线性相关性进行自适应特征优选,然后利用高斯混合聚类获得描述日降水空间异质性的同质子区,最后构建加权堆叠机器学习模型融合SPP与站点数据以获得高质量降水产品。[结果]选取中国区域2016—2020年逐日IMERG降水数据为研究对象,将SHW-Stacking与基于传统分区的类别型梯度提升(TC-Catboost)、5种经典机器学习方法(Catboost、lightGBM、XGBoost、RF、ELM)和原始SPP进行比较。结果表明,SHW-Stacking能够准确再现实测降水空间分布,其多时间尺度性能均表现最优,在日、季和年尺度上MAE分别至少降低4.6%、3.1%和2.7%,KGE分别至少提高3.4%、1.9%和2.0%,且在降水强度大于1 mm/day时对降水事件的捕捉能力明显优于其他对比方法。此外,聚类分区因素贡献度仅次于IMERG,其作为首要和次要影响因子的天数占比达39.6%和33.9%,凸显空间异质性对降水融合的关键作用。[结论]SHW-Stacking能够有效捕捉局部降水细节,精确刻画降水空间分布,为精细化降水数据生产提供了一种切实可行的思路和方法。
[Objectives]Accurate and reliable daily Satellite Precipitation Products(SPPs)are essential data sources for meteorological and hydrological research.However,existing daily SPPs often fail to capture local precipitation details accurately and exhibit substantial errors in certain regions,resulting in unclear precipitation spatial characterization and low prediction accuracy.[Methods]To address these limitations,this study proposes SHW-Stacking,a weighted stacking method designed to enhance the quality of daily SPPs by accurately capturing spatial heterogeneity in precipitation.The method first integrates feature importance and nonlinear correlations for adaptive feature selection.It then employs Gaussian mixture clustering to delineate homogeneous subregions that reflect spatial heterogeneity of daily precipitation.Finally,a weighted stacking machine learning model fuses SPP data with gauge observations to generate high-accuracy precipitation estimates.[Results]Using daily IMERG precipitation data from China between 2016 and 2020,SHW-Stacking was rigorously compared against a traditionally partitioned categorical gradient boosting model(TC-CatBoost),five classical machine learning algorithms(CatBoost,LightGBM,XGBoost,RF,and ELM),and the original SPP.Results show that SHW-Stacking consistently outperforms all benchmarks across multiple temporal scales,accurately reconstructing the spatial distribution of observed precipitation.Specifically,it reduces the Mean Absolute Error(MAE)by at least 4.6%,3.1%,and 2.7%at daily,seasonal,and annual scales,respectively,while improving the Kling-Gupta Efficiency(KGE)by a minimum of 3.4%,1.9%,and 2.0%at the corresponding scales.Notably,SHW-Stacking demonstrates superior performance in capturing precipitation events exceeding 1 mm/day.Furthermore,clustering-based spatial partitioning emerged as the second most influential factor after IMERG data,ranking as the top and second-most significant contributor in 39.6%and 33.9%of cases,respectively.This highlights the critical role of spatial heterogeneity characterization in precipitation fusion.[Conclusions]In summary,SHW-Stacking effectively captures local precipitation details and accurately characterizes spatial precipitation distribution,providing a promising approach for refined precipitation data production.
作者
杨淑凡
李艳艳
王兴杰
杨子明
徐联中
洪壮壮
潘宏铭
陈传法
YANG Shufan;LI Yanyan;WANG Xingjie;YANG Ziming;XU Lianzhong;HONG Zhuangzhuang;PAN Hongming;CHEN Chuanfa(College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao 266590,China)
出处
《地球信息科学学报》
北大核心
2025年第5期1179-1194,共16页
Journal of Geo-information Science
基金
国家自然科学基金项目(42271438)
山东省自然科学基金(ZR2024MD040)。
关键词
逐日遥感降水
IMERG
点面融合
空间异质性
自适应
加权堆叠
机器学习
质量提升
daily remote sensing precipitation
IMERG
point surface fusion
spatial heterogeneity
self-adaptation
weighted stacking
machine learning
quality improvement
作者简介
杨淑凡(1999-),男,山东潍坊人,硕士生,主要从事多源遥感降水产品质量提升。E-mail:yangshufancn@163.com;通信作者:李艳艳(1987-),女,山东潍坊人,博士,副教授,主要从事空间数据质量提升研究。E-mail:yylee@sdust.edu.cn。
