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 reformatted TSMP-G2A and COSMO-REA6 precipitation (total precipitation, stratiform precipitation, convective precipitation, and snowfall) over the study area (EU-11 EUROCORDEX, 0.11 degrees) for the years 1995-2017.

2) The actual and predicted spatiotemporal mismatch data for training, validation, and testing periods (1995-2017).

Note: The corrected model-based data is obtained by subtracting the predicted mismatch data (2) from the TSMP-G2A data (1).

References for original TSMP-G2A and COSMO-REA6 data:
TSMP-G2A: http://doi.org/10.17616/R31NJMGR
COSMO-REA6: doi:10.1002/qj.2486, 2015