Implementation of deep learning algorithms in the sub-hourly rainfall fields estimation from remote sensors and rainfall gauge information in the tropical Andes

One of the latent difficulties in the fields of climatology, meteorology, and hydrology is the scarce rainfall information available due to the limited or nonexistent instrumentation of river basins, especially in developing countries where the establishment and maintenance of equipment entail high costs relative to the available budget. Hence, the importance of generating alternatives that seek to improve spatial precipitation estimation has been increasing, given the advances in the implementation of computational algorithms that involve Machine Learning techniques. In this study, a multitask convolutional neural network was implemented, composed of an encoder-decoder architecture (U-Net), which simultaneously estimates the probability of rain through a classification model and the precipitation rate through a regression model at a spatial resolution of 2 km² and a temporal resolution of 10 minutes. The input modalities included data from rain gauge stations, weather radar, and satellite information (GOES 16). For model training,  validation, and testing, a dataset was consolidated with 3 months of information (February to April 2021) with a distribution of 70/15/15 percent, covering the effective coverage range of the Munchique weather radar located in the Andean region of Colombia. The obtained results show a Probability of Detection (POD) of 0.59 and a False Alarm Rate (FAR) of 0.39. Regarding precipitation rate estimation, it is assessed with a Root Mean  Square Error (RMSE) of 1.13 mm/10min. This research highlights the significant capability of deep learning algorithms in reconstructing and reproducing the spatial pattern of rainfall in tropical regions with limited instrumentation. However, there is a need to continue strengthening climatological monitoring networks to achieve significant spatial representativeness, thereby reducing potential biases in model estimations.