用深度学习方法提升降水数据的时空插值与预报精度
Spatio-temporal DeepKriging in PyTorch: A Supplementary Application to Precipitation Data for Interpolation and Probabilistic Forecasting
- 结合深度学习与克里金法，处理不规则时空数据
- 在欧洲日尺度降水数据上实现高分辨率插值与多步预报
- 开源代码便于气候数据研究者复现与应用
本文针对欧洲降水数据开展详细分析，聚焦时空插值与预报任务。基于PyTorch平台实现了时空深度克里金（STDK）框架，能够有效处理时空不规则性，生成高分辨率插值结果及多步预测。已开发可复现的独立PyTorch模块，分别用于插值（https://github.com/pratiknag/Spatio-temporalDeepKriging-Pytorch.git）与预报（https://github.com/pratiknag/pytorch-convlstm.git），支持对类似气候数据集的广泛应用。通过在日尺度降水观测数据上的广泛评估，验证了该方法在预测性能与鲁棒性方面的有效性。
原文摘要 · Abstract (English)
A detailed analysis of precipitation data over Europe is presented, with a focus on interpolation and forecasting applications. A Spatio-temporal DeepKriging (STDK) framework has been implemented using the PyTorch platform to achieve these objectives. The proposed model is capable of handling spatio-temporal irregularities while generating high-resolution interpolations and multi-step forecasts. Reproducible code modules have been developed as standalone PyTorch implementations for the interpolation\footnote[2]{Interpolation - https://github.com/pratiknag/Spatio-temporalDeepKriging-Pytorch.git} and forecasting\footnote[3]{Forecasting - https://github.com/pratiknag/pytorch-convlstm.git}, facilitating broader application to similar climate datasets. The effectiveness of this approach is demonstrated through extensive evaluation on daily precipitation measurements, highlighting predictive performance and robustness.
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