Abstract The accurate representation of spatio-temporal patterns of precipitation is an essential input for numerous environmental applications. However, the estimation of precipitation patterns derived solely from rain gauges is subject to large uncertainties. We present the Random Forest based MErging Procedure (RF-MEP), which combines information from ground-based measurements, state-of-the-art precipitation products, and topography-related features to improve the representation of the spatio-temporal distribution of precipitation, especially in data-scarce regions. RF-MEP is applied over Chile for 2000—2016, using daily measurements from 258 rain gauges for model training and 111 stations for validation. Two merged datasets were computed: RF-MEP3P (based on PERSIANN-CDR, ERA-Interim, and CHIRPSv2) and RF-MEP5P (which additionally includes CMORPHv1 and TRMM 3B42v7). The performances of the two merged products and those used in their computation were compared against MSWEPv2.2, which is a state-of-the-art global merged product. A validation using ground-based measurements was applied at different temporal scales using both continuous and categorical indices of performance. RF-MEP3P and RF-MEP5P outperformed all the precipitation datasets used in their computation, the products derived using other merging techniques, and generally outperformed MSWEPv2.2. The merged P products showed improvements in the linear correlation, bias, and variability of precipitation at different temporal scales, as well as in the probability of detection, the false alarm ratio, the frequency bias, and the critical success index for different precipitation intensities. RF-MEP performed well even when the training dataset was reduced to 10% of the available rain gauges. Our results suggest that RF-MEP could be successfully applied to any other region and to correct other climatological variables, assuming that ground-based data are available. An R package to implement RF-MEP is freely available online at https://github.com/hzambran/RFmerge .

中文翻译：

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RF-MEP：一种新的随机森林方法，用于合并网格降水产品和地面测量

摘要 降水时空模式的准确表示是众多环境应用的重要输入。然而，仅从雨量计得出的降水模式估计存在很大的不确定性。我们提出了基于随机森林的合并程序 (RF-MEP)，它结合了来自地面测量的信息、最先进的降水产品和与地形相关的特征，以改进降水时空分布的表示，尤其是在数据稀缺地区。RF-MEP 于 2000 年至 2016 年在智利应用，使用来自 258 个雨量计的每日测量值进行模型训练和 111 个站点进行验证。计算了两个合并的数据集：RF-MEP3P（基于 PERSIANN-CDR、ERA-Interim、和 CHIRPSv2）和 RF-MEP5P（另外包括 CMORPHv1 和 TRMM 3B42v7）。将两个合并产品的性能及其计算中使用的性能与 MSWEPv2.2 进行了比较，MSWEPv2.2 是最先进的全球合并产品。使用连续和分类性能指数在不同时间尺度上应用基于地面测量的验证。RF-MEP3P 和 RF-MEP5P 优于其计算中使用的所有降水数据集、使用其他合并技术得出的产品，并且总体上优于 MSWEPv2.2。合并后的 P 产品在不同时间尺度的降水线性相关性、偏差和变异性以及检测概率、误报率、频率偏差、以及不同降水强度的临界成功指数。即使训练数据集减少到可用雨量计的 10%，RF-MEP 也表现良好。我们的结果表明，假设地面数据可用，RF-MEP 可以成功应用于任何其他地区并纠正其他气候变量。实现 RF-MEP 的 R 包可在 https://github.com/hzambran/RFmerge 在线免费获得。