Spatial data integrated into numerical model for analysis meteorological and local climate in mountains of Cuba

Abstract

Local-scale weather and climate modeling in mountains has been relevant for the study of agroforestry ecosystems and their future evolution. The study aimed to integrate a set of high-resolution spatial data to the WRF numerical meteorological prediction and research model, suitable for meteorological and local climate analysis in the mountains of Cuba. In the study, priority spatial data were defined as those referring to the relief, the land mask as the ocean-soil interface, the type of cover and type of soil. The national or global high-resolution spatial databases of the best quality and most appropriate to the characteristics of the model were selected. The databases integrated into the WRF had spatial resolution between approximately 0.95 and 0.03 km in Ecuador. The increase and updating of spatial data strengthened the model's capacity for the analysis of the geographic factors that shape the local climate, promoting better performance in the mountains. The research results could contribute to meteorological and climate monitoring and forecasting at a local scale for the mountains of Cuba. Likewise, studies and risk management associated with forest fires, severe hydrometeorological events, climate variability, climate change, and other dangers related to the climate system that have affected Cuban agroforestry ecosystems would benefit.