Data for: Deep Learning of Model- and Reanalysis- Based Precipitation and Pressure Mismatches over Europe

This study focuses on using UNet Convolutional Neural Networks to predict the spatiotemporal mismatches (errors) between TSMP-G2A model-based and COSMO-REA6 reanalysis-based precipitation and surface pressure over Europe. The following data are provided in this dataset: 1) The remapped and NetCDF-merged TSMP-G2A and COSMO-REA6 precipitation and surface pressure over the study area (EU-11 EUROCORDEX, ~0.11 degrees) for the years 1995-2017. Files: COSMO-REA6_PREPROCESSED.zip and TSMP_PREPROCESSED.zip 2) The actual and predicted spatiotemporal mismatch data for training, validation, and testing periods (1995-2017). Files: MISMATCH_ACTUAL.zip and MISMATCH_PREDICTED.zip<br> References for original TSMP-G2A and COSMO-REA6 data:<br> TSMP-G2A: http://doi.org/10.17616/R31NJMGR<br> COSMO-REA6: doi:10.1002/qj.2486, 2015本研究聚焦于采用U型卷积神经网络（UNet），预测欧洲区域内基于TSMP-G2A模型与COSMO-REA6再分析资料得到的降水与地面气压之间的时空偏差（误差）。本数据集包含以下数据： 1) 针对研究区域EU-11欧洲协同区域气候降尺度实验（EUROCORDEX，分辨率约0.11度），包含1995-2017年经过重映射与网络公共数据格式（NetCDF）合并的TSMP-G2A与COSMO-REA6降水及地面气压数据，对应文件为COSMO-REA6_PREPROCESSED.zip与TSMP_PREPROCESSED.zip。 2) 包含1995-2017年训练、验证与测试时段的实际与预测时空偏差数据，对应文件为MISMATCH_ACTUAL.zip与MISMATCH_PREDICTED.zip。 原始TSMP-G2A与COSMO-REA6数据的参考文献如下： TSMP-G2A：http://doi.org/10.17616/R31NJMGR COSMO-REA6：doi:10.1002/qj.2486, 2015