Urban Precipitation Estimation Using GOES-16 Infrared Observations and Random Forest Models

Accurate precipitation estimation is fundamental for the analysis of hydrological processes, especially in urban areas with limited rain-gauge networks. The objective of this study was to develop two models based on the Random Forest (RF) algorithm for the detection of rainy and non-rainy days and for the estimation of daily precipitation during the wet season in the city of Culiacán, Sinaloa, Mexico. For this purpose, in situ meteorological station data and variables derived from images from the GOES-16 geostationary satellite were used, employing only spectral bands available 24 hours a day, specifically bands 7, 9, 13, 14, and 15. As part of the preprocessing stage, a parallax correction and a temporal adjustment were performed to harmonize the different data sources. Additionally, a prior classification of the days under analysis was implemented to reduce the radiometric heterogeneity of the training dataset. According to the main results, the rainfall detection model showed satisfactory performance, with an accuracy of 88%, a sensitivity of 86%, and a specificity of 89%, indicating an adequate ability to identify the presence and absence of precipitation. In turn, the precipitation estimation model achieved a correlation coefficient (R) of 0.74, a mean absolute error (MAE) of 6.59 mm, and an RMSE of 14.26 mm, demonstrating a good capacity to capture temporal variability, although with a tendency to overestimate intense events. The variable importance analysis showed that infrared bands 13 (10.3 μm) and 14 (11.2 μm) dominate the estimation in most groups, while the band 7 (3.9 μm) band becomes more relevant in events associated with microphysical processes. In conclusion, the integration of GOES-16 data and machine learning models have shown to be a viable alternative for complementing precipitation information in urban areas with scarce rain-gauge instrumentation; however, its application to other regions or periods requires model retraining.