Application of a novel deep learning model for precipitation nowcasting

In recent decades, the prediction of precipitation has become a central focus for atmospheric scientists and weather forecasters. In particular, improving the predictability of rapidly forming rainfall events is critical for protecting lives and property. The island of Ischia, located in the Campania region of Italy, has experienced several landslides and flash floods in recent years with catastrophic effects. To mitigate these geohydrological hazards on this island, we propose a method for short-term rainfall forecasting, with "short-term" defined as a time frame up to six hours. Accurate predictions are essential, as they enable timely implementation of protective measures to safeguard the population.

Accurately predicting rainfall is a complex task influenced by numerous factors, including humidity, temperature, pressure, and wind speed. Historically, rainfall nowcasting has primarily relied on numerical weather prediction (NWP) models. However, this approach has notable limitations, such as high computational requirements and significant processing time, which make NWP models less practical for short-term forecasts.

In the past decade, machine learning (ML) models have revolutionized the way complex problems are addressed and solved, offering solutions that are both fast and highly efficient. Within this domain, deep neural networks (DNNs) a subset of ML have become increasingly prevalent for tackling complex problems using large datasets. Among these, U-Net, a specific DNNs architecture, has proven to be one of the most effective and accurate models for prediction tasks when the input data is image-based. However, achieving high accuracy with such models requires careful preprocessing of the dataset to enhance the model’s ability to effectively learn from the data. Additionally, properly tuning the model's hyperparameters is crucial for optimizing its performance.

In this study, we propose an enhanced U-Net model for nowcasting rainfall with a 120-minute lead time. The input data consists of rainfall radar data and rain gauge measurements. Furthermore, the study evaluates the model's performance under different training scenarios, comparing its efficacy when using only rainfall radar data versus an integrated dataset combining radar and rain gauge data. It is worth noting that the model operates in a regression framework, where the labels or outputs are the rain gauge readings with a 120-minute lead time.