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    中国逐日气象因素的空间插值方法R代码实现

    Implementation of R script on spatial interpolation method for daily meteorological factor data in China

      摘要
    • 摘要:
      目的 本研究旨在利用R代码, 通过案例分析实现将气象因素站点数据插值为栅格数据, 为气象因素个体暴露水平及其健康风险评估提供数据整理的方法。
      方法 收集整理2010年1月1日至2021年12月31日, 中国2 419个国家级地面气象站的逐日气象数据序列。借助R代码应用反距离加权法(inverse distance weighting, IDW)进行空间插值。
      结果 当插值算法参数p取2、N取10~15时, 可获得最佳的插值精度。同时, 利用furrr包进行并行运算, 开启8个CPU核心可实现最优的插值效率。
      结论 该R代码可有效实现将气象因素站点数据插值为栅格数据。

       

      Abstract:
      Objective To interpolate meteorological factor data into gridded data through case analysis using R script, and to provide a data collation method for individual exposure levels to meteorological factors and health risk assessment.
      Methods Daily meteorological data sequences were collected from 2 419 national ground-based meteorological stations in China from January 1, 2010 to December 31, 2021. The spatial interpolation was performed by inverse distance weighting with R script.
      Results The best interpolation precision was achieved when the interpolation algorithm parameters p was set to 2 and N was set between 10 and 15. Meanwhile, the optimal interpolation efficiency was realized by utilizing the "furrr" package to perform parallel computation with 8 CPU cores.
      Conclusion Meteorological factor data could be effectively interpolated into gridded data with R script.

       

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