中国农业气象 ›› 2023, Vol. 44 ›› Issue (08): 721-734.doi: 10.3969/j.issn.1000-6362.2023.08.007

• 农业气象信息技术 栏目 • 上一篇    下一篇

基于星地协同的降水数据插值方法及其适用性

徐勇，郭振东，盘钰春，郑志威   

  1. 桂林理工大学测绘地理信息学院，桂林 541006
  • 收稿日期:2022-09-26 出版日期:2023-08-20 发布日期:2023-08-14
  • 作者简介:徐勇，博士，副教授，主要研究方向为气候变化和植被覆盖反演，E-mail：yongxu@glut.edu.cn
  • 基金资助:
    广西自然科学基金项目（2020GXNSFBA297160）；广西科技基地和人才专项（桂科AD21220133）；国家自然科学基金项目（42061059；42161028）；广西空间信息与测绘重点实验室项目（191851016）；桂林理工大学大学生创新创业训练计划项目（202210596388）

Interpolation Method of Satellite-ground Collaborative Precipitation and Its Applicability

XU Yong, GUO Zhen-dong, PAN Yu-chun, ZHENG Zhi-wei   

  1. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
  • Received:2022-09-26 Online:2023-08-20 Published:2023-08-14

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摘要： 以长江中下游地区为研究区，协同地面气象站点降水数据和TRMM以及GPM卫星降水数据，利用六种Anusplin插值模型，基于验证站点实测降水数据对比分析星地协同降水数据插值结果与单一的TRMM和GPM降水数据、TRMM和GPM降尺度降水数据，以及基于地面气象站点插值降水数据的优劣，进而为获取地面气象站稀缺地域的高精度、高分辨率以及优良的空间细节性降水数据提供理论支撑。结果表明：（1）2001−2019年长江中下游地区TRMM（R2=0.81，BIAS=0.06，RMSE=171.1mm）和GPM星地协同插值方案（R2=0.81，BIAS=0.07，RMSE=172.8mm）各模型结果多年平均精度优于地面气象站点（R2=0.66，BIAS=0.02，RMSE=198.66mm）插值方案各模型、TRMM降尺度（R2=0.79，BIAS=0.06，RMSE=174.8mm）和GPM降尺度（R2=0.81，BIAS=0.09，RMSE=192.4mm）多年平均精度。（2）星地协同插值结果在降水数据空间细节表达、图像完整性以及模型稳定性上具有明显的优势，TRMM星地协同插值方案模型五插值效果最优。（3）Anusplin插值模型的变量、样条次数对地面气象站点插值结果精度影响显著，但对星地协同插值结果的影响微弱。（4）降尺度模型受辅助变量影响，会对降尺度结果造成一定的精度损失和图像残缺。

关键词: 长江中下游地区, 星地协同, TRMM, GPM, 降尺度, Anusplin插值模型

Abstract: Changes in precipitation have great impacts on regional terrestrial ecosystems and water cycles. In this study, the middle and lower reaches of the Yangtze River Basin is considered to be the study area. The satellite-ground collaborative precipitation derived from in situ meteorological station, TRMM and GPM from 2001 to 2019 were collected. The interpolation results of satellite-ground collaborative precipitation against six Anusplin interpolation models were compared with the TRMM and GPM precipitation, TRMM and GPM downscaling precipitation, and interpolation precipitation based on the measured precipitation of verification stations. The research result can provide theoretical support for obtaining the precipitation with high accuracy, high resolution and excellent spatial details in the areas with sparse meteorological station. The results show that:（1）both the multi-year average accuracy of the results of the TRMM （R2=0.81, BIAS=0.06, RMSE=171.1mm）and GPM satellite-ground collaborative interpolation models（R2=0.81, BIAS=0.07, RMSE=172.8mm） in the middle and lower reaches of the Yangtze River Basin from 2001 to 2019 were superior to the multi-year average accuracy of interpolation precipitation of in situ meteorological stations（R2=0.66, BIAS=0.02, RMSE=198.66mm）, TRMM downscaling precipitation（R2=0.79, BIAS=0.06, RMSE=174.8mm）, and GPM downscaling precipitation（R2=0.81, BIAS=0.09, RMSE=192.4mm）.（2）The satellite-ground collaborative interpolation precipitation has obvious advantages in the spatial detail expression, image integrity, and model stability. The interpolation result of TRMM satellite-ground collaborative interpolation model 5 has the best accuracy.（3）The variable and spline number of Anusplin interpolation model have a stronger impact on the accuracy of the interpolation result based on in situ meteorological station, but a weaker impact on the interpolation result based on satellite-ground collaborative interpolation precipitation.（4）The result of the downscaling model is closely related to auxiliary variables, which may cause a certain loss of accuracy and image deformity to downscaling precipitation data.

Key words: Satellite-ground collaborative, TRMM, GPM, Downscaling, Anusplin interpolation model

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