The first step in accounting for these imperfections is identifying and masking the corrupted data; the second is filling in the correct or estimated data. Here we present a novel solution utilizing a generative adversarial network (GAN) for inpainting missing data in the radar reflectivity measurements, which has been trained using a synthetic dataset with simulated blockages of correct measurements.
The model is trained to extrapolate from the previous radar images utilizing the information from the uncorrupted neighboring azimuths and elevations to predict accurate data that looks the same to the human eye as the truly measured data. The model has been tested quantitatively on the simulated dataset and qualitatively on Meteopress radar sites. Furthermore, the model transfers well to inpainting data missing for different reasons. Weather radar networks have gaps caused by neighboring radars too far away. We demonstrate the capability of the model to transfer to this setting on a case study simulating gaps in the networks and providing satellite imagery on the input.

