Blizzard prediction in antarctic meteorological data using deep learning techniques

Abstract

Due to the unpredictable nature of the weather and the complexity of atmospheric movement, extreme weather has always been a significant and challenging meteorological concern. Meteorological problems and the complexity of how the atmosphere moves have made it necessary to find a technological solution. Deep learning techniques can automatically learn and train from vast quantities of data to provide enhanced feature expression. This is frequently used in computer vision, natural language processing, and other domains to enhance the performance of numerous real-time problems. The purpose of this research is to propose a deep learning-based approach for effectively predicting extreme weather events such as blizzards. To recognize weather patterns and forecast blizzards, the proposed deep learning-based method primarily employs RNN with LSTM. Real-time datasets from the Polar Regions were used to test the proposed approach’s accuracy, and tests were conducted to compare it to existing weather forecasting models. The accuracy of the model is 49.60% (univariate) and 55.19% (bivariate) using bivariate attributes of wind speed and air pressure based on the calculated RMSE values such as 0.0023 and 0.0021.