We investigate the applicability of deep learning methods for reconstructing daily weather data. Inspired by video inpainting, we propose a novel method, WeRec3D, which utilizes a three-dimensional convolutional neural network. Our approach was developed iteratively by evaluating six modeling improvement techniques.
The resulting method reduces the validation error to 48% compared to the baseline.
Additionally, we demonstrate the impact of the spatial distribution of observations on reconstruction accuracy and propose a potential integration with the analogue resampling method.
WeRec3D is trained and validated in a self-supervised manner using ERA5’s surface temperature and pressure data over Europe.
On a hold-out set from 1950 to 1954, the validation results in an MAE of 1.11 °C and 199 Pa.
As a case study, we reconstruct the 1807 heat wave and validate it using a leave-one-out method in space.
Compared to the original data, the reconstructed time series exhibit a correlation of at least 0.91, with a maximum normalized RMSE and standard deviation delta of 0.58 and 0.51 respectively.
To the best of our knowledge, this is the first study to investigate weather reconstruction using deep learning algorithms, proposing video inpainting as a novel approach for reconstructing missing weather information.