Daily Precipitation Downscaling Using Deep Learning Techniques: The Impact of Missing Value Imputation Methods

The need for high-resolution meteorological data to identify and understand abnormal weather phenomena is increasing as extreme events are predicted to become more intense and destructive over the coming decades. This is particularly the case for extreme precipitation events, which have high spatial variability and observational uncertainty. While dynamical and statistical downscaling models are often used to provide finer resolution data, recent studies have focused on deep learning (DL)-based downscaling techniques due to their ability to extract spatial features from large spatio-temporal data. High-quality and high-resolution target data are crucial for the train of DL-based models, however, most of the available data are based on land observations and, therefore, have significant missing values. Here, we investigate the sensitivity of a DL-based downscaling model to the choice of missing value imputation (MVI) methods to improve the accuracy of DL-based downscaling. We use precipitation data over the European continent (44^∘N-54^∘N×5^∘E-15^∘E) from 2017 to 2020; ERA5 reanalysis data with 0.5^∘ × 0.5^∘ spatial resolution and E-OBS observational gridded data with 0.1^∘ × 0.1^∘ resolution are used as the predictor and target, respectively. To build the DL model, we combine the ResNet and Upsampling features. We find that the DL model describes spatial features of precipitation events better than the simple regridding of the coarser data. Moreover, the model trained with the gap-filled target data using ERA5 regridded values shows improved results compared to that with the gap-filled data by replacing missing values with 0. Our results highlight the importance of MVI methods for the DL-based downscaling and the potential of deep learning for precipitation. Our future work will test a wider range of MVI methods including the method using EM(Expectation-Maximization) algorithm and extending coastal target data, and examine the performance of DL-models focusing on extreme events.