Sub-seasonal daily precipitation forecasting based on Long Short-Term Memory (LSTM) models

Water managers need accurate rainfall forecasts for a wide spectrum of applications, ranging from water resources evaluation and allocation, to flood and drought predictions. In the past years, several frameworks based on Artificial Intelligence have been developed to improve the traditional Numerical Weather Prediction (NWP) forecasts, thanks to their ability of learning from past data, unravelling hidden relationships among variables and handle large amounts of inputs. Among these approaches, Long Short-Term Memory (LSTM) models emerged for their ability to predict sequence data, and have been successfully used for rainfall and flow forecasting, mainly with short lead-times. In this study, we explore three different multi-variate LSTM-based models, i.e. vanilla LSTM, stacked LSTM and bidirectional LSTM, to forecast daily precipitation for the upcoming 30 days in the area of Rhine Delta, the Netherlands. We use both local atmospheric and global climate variables from the ERA-5 reanalysis dataset to predict rainfall, and we introduce a fuzzy index for the models to account for seasonality effects. The framework is developed within the H2020 project CLImate INTelligence (CLINT), and its outcomes have the potential to improve forecasting precipitation deficit in the study area.