Deep Learning Techniques and Tools for Intelligent Weather Forecasting
Abstract
In this paper, an analysis of deep learning techniques for weather forecasting using statistical downscaling approaches was developed. These are important, since they allow adjusting large-scale climate projections generated by the GCM climate model to more accurate and defined forecasts for specific areas, thus allowing overcoming the limitations of traditional numerical models in the representation of local and small-scale phenomena. Studies implementing Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) were analyzed in order to improve the spatial and temporal resolution of climate data. Both tools and techniques have proven to be effective in projects such as VALUE, which is in charge of evaluating downscaling methods in Europe, and DL4DS, a Python library in charge of applying deep learning algorithms to empirical downscaling of climate data. The main objective of this paper was to analyze the effectiveness of both tools and techniques focused on accuracy, scalability and computational efficiency, providing a complete overview of their use for the improvement of local weather forecasting.
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