Improving prediction of heavy rainfall with Neural Networks using both observation and Numerical Weather Prediction data

Heavy Precipitation Events (HPE) can cause significant human fatalities and material damages. Therefore, their prediction is crucial but challenging due to the complex processes involved. In this context, artificial intelligence methods have recently been shown to be competitive with state-of-the-art Numerical Weather Prediction (NWP). Our work focuses on improving the prediction of the occurrence of HPE based on Neural Network (NN) models and using both observation and NWP data.

We use the MeteoNet open source database from Meteo-France on northwestern and southeastern France from 2016–2018 including station observations (OBS) and forecasts from the NWP models Arome and Arpege. We train a NN model to predict the occurrence of daily rainfall above a threshold of 10 mm / 24 h at the location of the stations. Our verification metric is the Peirce Skill Score (PSS) with Arome forecasts as a benchmark.

Our results for both northwestern and southeastern regions are 1) the NN model using both OBS and NWP data as inputs has the highest PSS, 2) the NN model using only Arome data as input has higher PSS than the benchmark, 3) the NN model trained only with OBS data has lower PSS than the benchmark, showing the crucial contribution of NWP forecast data at a lead time of 24 h, and 4) due to the rarity of rainfall events meeting the threshold, training the NN model with a weighted loss function significantly increases the PSS.

When extending the results to shorter time scales, we find that the contribution of OBS data to the NN model is dominant at 1–3 h lead times, while including NWP forecast data allows to mitigate the degradation of prediction skill with longer lead time. Finally, the results for northwestern France show slightly lower PSS than for southeastern France due to the different rainfall climatology.