Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm
Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm
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DOI：
10.1155/2014/472741
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发表时间：
2014-01-01
影响因子：
2.9
通讯作者：
Bernhardt, Matthias
中科院分区：
文献类型：
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作者：
Gao, Lu;Schulz, Karsten;Bernhardt, Matthias
降水是陆面模式的一个重要输入参数，因为它控制着各种各样的环境过程。然而，在复杂地形下，通常稀疏的气象网络无法提供许多应用所需的信息。因此，有必要降低当地降水量。为此，一种新的机器学习方法，LASSO算法（最小绝对收缩和选择算子），用于解决ERA-Interim预测降水数据（0.25度网格）和点尺度气象观测之间的差异。LASSO进行了测试和验证对其他三个降尺度方法，当地强度缩放（LOCI），分位数映射（QM），逐步回归（逐步）在50个气象站，位于阿尔卑斯山中部的高山地区。降尺度过程分两步实现。首先，对干湿天进行分类，并建立了以湿天出现为条件的降水量模型。与其他三种降尺度方法相比，LASSO在降水发生和降水量预测方面表现出最好的平均性能。此外，LASSO可以减少某些站点的错误，当使用LOCI和QM时没有看到改善。这项研究证明，LASSO是一个合理的替代其他统计方法方面的降水数据的降尺度。
Precipitation is an essential input parameter for land surface models because it controls a large variety of environmental processes. However, the commonly sparse meteorological networks in complex terrains are unable to provide the information needed for many applications. Therefore, downscaling local precipitation is necessary. To this end, a new machine learning method, LASSO algorithm (least absolute shrinkage and selection operator), is used to address the disparity between ERA-Interim forecast precipitation data (0.25 degrees grid) and point-scale meteorological observations. LASSO was tested and validated against other three downscaling methods, local intensity scaling (LOCI), quantile-mapping (QM), and stepwise regression (Stepwise) at 50 meteorological stations, located in the high mountainous region of the central Alps. The downscaling procedure is implemented in two steps. Firstly, the dry or wet days are classified and the precipitation amounts conditional on the occurrence of wet days are modeled subsequently. Compared to other three downscaling methods, LASSO shows the best performances in precipitation occurrence and precipitation amount prediction on average. Furthermore, LASSO could reduce the error for certain sites, where no improvement could be seen when LOCI and QM were used. This study proves that LASSO is a reasonable alternative to other statistical methods with respect to the downscaling of precipitation data.