Random forest based precipitation nowcasting for Dakar

Approximately 90% of the total precipitation in Senegal is produced by convective storms. The most intense rainfall events are associated with mesoscale convective systems (MCSs), frequently producing high-intensity rainfall that triggers pluvial flooding. Flood vulnerability is particularly high in the Greater Dakar area due to surface sealing and high population exposure. Timely and reliable short-term precipitation forecasts are therefore essential for effective early warning systems and flood risk reduction.

Precipitation nowcasting aims to describe the current atmospheric state and predict weather evolution at short lead times using real-time observations. The quality and availability of input data are key factors determining the nowcasting performance. In this study, three main data sources are employed: (i) in-situ observations from the High-resolution weather observations East of Dakar (DakE) station network, (ii) satellite-based products such as cloud-top temperature (CTT) from EUMETSAT and precipitation estimates from the Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm provided by NASA, and (iii) modeled data from the Weather Research and Forecasting (WRF) model.

The objective of this project is to identify a suitable nowcasting approach while weighing the strengths and limitations of the available data sources. Extrapolation-based methods, such as optical-flow techniques implemented in the pySTEPS library, estimate future precipitation by extrapolating observed patterns under the assumption of steady system evolution. These approaches perform well for large, long-lived convective systems, but they are unable to predict convective initiation, decay, and growth. Their applicability is further limited by the temporal resolution and detection uncertainties of the available satellite-based precipitation products identified in comparisons with station observations.

To address these limitations, a machine-learning-based nowcasting framework is developed, primarily relying on the high-temporal-resolution DakE station data to accurately capture atmospheric boundary conditions. Given the limited time span of data collection and the high predictor dimensionality, a Random Forest model was chosen as a robust approach. To mitigate challenges like zero inflation and the underestimation of extreme events, a two-step model architecture is developed: in a first step, a classification forest (I) is used to determine precipitation occurrence and the duration of the predicted event in the lead time horizon. If precipitation is expected, the model is coupled to a regression forest (II) that returns the rainfall intensity of the detected event. Future work will assess potential performance improvements from incorporating CTT-satellite and WRF-modeled data using feature importance analysis, which can also inform the placement of hypothetical new automatic weather stations.