Deep Learning Approaches for Real‐Time Climate Monitoring and Anomaly Detection in Meteorological Systems

Global climate change has caused climate variability and extreme weather phenomena to be more common, unpredictable, which further explains why real-time monitoring and accurate values of anomalies should be detected. We use the superior deep learning techniques in meteorological systems to enhance climate anomaly detection and monitoring in this chapter. Both the high-dimensional climate data that includes both the spatial and the temporal dependencies are represented by using highly complex Convolutional Neural Networks and the Long Short-Term Memory (LSTM) and the Transformer-based models. The framework is aimed to handle the satellite images, sensor data and stream of past weather data in real-time to foretell the abnormalities in climate such as heat waves, droughts and cyclones with high predictability efficacy. It is a model that makes use of deep perception models, not only to teach what needs to be taught (supervised), but also to obtain information when no labels are known (unsupervised) with solid accuracy concerning the ability to detect the usual characteristics of climatic behavior and abnormal variations. In addition, the paper compares the models in terms of the performance measurement such as RMSE, precision, recall and F1-score. Findings applied to the analysis achieve the realization of deep learning in the field of environmental monitoring, which makes possible timely and data-driven conclusions on environmental forecasting and monitoring required by the decision makers entitled to the early warning systems and disaster risk management.