Hybrid RF–ConvLSTM Approach for Rainfall Estimation from MSG Data over Northern Algeria

This study introduces a novel approach to 3-hourly and daily precipitation estimation over northern Algeria. The novel approach benefits from the classification capabilities of Random Forest (RF) and the predictive power of Convolutional Long Short-Term Memory (ConvLSTM) regression, with multi-temporal observations from the SEVIRI radiometer onboard the Meteosat Second Generation (MSG) satellite. The approach is a two-stage process: A Random Forest classifier is first used to provide a probabilistic characterization of precipitation occurrence and rainfall regimes. The ConvLSTM model then applies spatio-temporal regression to estimate rainfall intensities by analyzing multi-channel temporal sequences. The hybrid model produces spatially and temporally consistent precipitation fields by taking advantage of the spatio-temporal correlations of meteorological events, with the aim of obtaining accurate 3-hourly and daily rainfall accumulations for Northern Algeria. Results show a dramatic improvement over the reference RF-based technique, with correlation coefficients reaching 0.89 for 3-hourly accumulations and 0.91 for daily rainfall.