Generating high-resolution climate data in the Andes using artificial intelligence: A lightweight alternative to the WRF model

In weather forecasting, generating atmospheric variables for regions with complex topography, such as the Andean regions with peaks reaching 6500 m above sea level, poses significant challenges. Traditional regional climate models often struggle to accurately represent the atmospheric behavior in such areas. Furthermore, the capability to produce high spatio-temporal resolution data (less than 27 km and hourly) is limited to a few institutions globally due to the substantial computational resources required. This study presents the results of atmospheric data generated using a new type of artificial intelligence (AI) models, aimed to reduce the computational cost of generating downscaled climate data using climate regional models like the Weather Research and Forecasting (WRF) model over the Andes. The WRF model was selected for this comparison due to its frequent use in simulating atmospheric variables in the Andes. Our results demonstrate a higher downscaling performance for the four target weather variables studied (temperature, relative humidity, zonal and meridional wind) over coastal, mountain, and jungle regions. Moreover, this AI model offers several advantages, including lower computational costs compared to dynamic models like WRF and continuous improvement potential with additional training data. • We propose an AI model to generate high-resolution climate data. • The model, based on ConvLSTM, predicts temperature, humidity, and wind accurately. • It runs up to 18× faster than WRF with lower memory and storage needs. • It shows promising accuracy across regions with varied topography and climate. • This approach offers an efficient alternative for climate modeling in low-resource areas.