Integrated nowcasting of convective precipitation with Transformer-based models

Precipitation nowcasting remains a challenging topic in weather prediction, particularly in the initial stages of convective activity. Ground-based weather radar observations have generally been used to estimate the motion vectors of precipitation fields, and have a pivotal role in precipitation nowcasting. However, during convection initiation, such data hold limited information, which hampers prediction performance. Nevertheless, different data streams, such as lightning activity and geostationary satellite infrared channels, have demonstrated skill in the early detection of convective activity. Therefore, there is a need to integrate data from various domains to improve nowcasting of convective precipitation, and data-driven approaches offer robust solutions for integrating large volumes of data and extracting the information therein. 

Here, we present a Transformer-based precipitation nowcasting model that integrates data from various sources. To train the model, we created a dataset by harmonising space- and ground-based observations with precipitation reanalysis and convective information data from the Integrated Nowcasting through Comprehensive Analysis (INCA) model over the spatial domain of INCA. Space-based observations include four infrared channels of the Meteosat Second Generation, while ground-based observations comprise lightning and weather radar data with 5-minute temporal resolution and a spatial resolution varying from 1 to 8 kilometres depending on the data source. The data is sampled over 5 years of observations from convective seasons to target convective precipitation events, which are particularly challenging for prediction. Trained on this dataset, our model can nowcast precipitation over the INCA domain for a lead time of 90 minutes. While the model reproduces the shape and location of the fields, the performance in reproducing the structure of the precipitation fields is limited at longer lead times, resulting in blurred predictions.

We will present the model, analyse its performance, present case studies, compare it against the operational INCA predictions, and provide insights through model interpretation experiments. We will also elaborate on the details of developing the dataset, a critical but often underrated step for enhancing model performance. The model offers a novel approach to integrated nowcasting of convective precipitation and motivates further studies with a data-driven perspective.