WF-UNet: Weather Fusion UNet for Precipitation Nowcasting

TL;DR

This paper introduces WF-UNet, a weather fusion model that improves short-term precipitation nowcasting accuracy by integrating wind speed data with radar images, outperforming existing UNet-based models.

Contribution

The study proposes the Weather Fusion UNet (WF-UNet), a novel model that combines precipitation and wind data for enhanced precipitation nowcasting up to 3 hours ahead.

Findings

WF-UNet reduces MSE by up to 22% compared to baseline models.

Incorporating wind data improves nowcasting accuracy.

The model performs well across six years of European radar data.

Abstract

Designing early warning systems for harsh weather and its effects, such as urban flooding or landslides, requires accurate short-term forecasts (nowcasts) of precipitation. Nowcasting is a significant task with several environmental applications, such as agricultural management or increasing flight safety. In this study, we investigate the use of a UNet core-model and its extension for precipitation nowcasting in western Europe for up to 3 hours ahead. In particular, we propose the Weather Fusion UNet (WF-UNet) model, which utilizes the Core 3D-UNet model and integrates precipitation and wind speed variables as input in the learning process and analyze its influences on the precipitation target task. We have collected six years of precipitation and wind radar images from Jan 2016 to Dec 2021 of 14 European countries, with 1-hour temporal resolution and 31 square km spatial resolution based on the ERA5 dataset, provided by Copernicus, the European Union's Earth observation programme. We compare the proposed WF-UNet model to persistence model as well as other UNet based architectures that are trained only using precipitation radar input data. The obtained results show that WF-UNet outperforms the other examined best-performing architectures by 22%, 8% and 6% lower MSE at a horizon of 1, 2 and 3 hours respectively.