Applications of machine learning in climate science: Weather prediction and environmental monitoring

Abstract

The application of machine learning (ML) in climate science is rapidly changing how weather and the environment are measured and predicted. While operating optimally, conventional climate models bear the issues related to the non-linear nature of many natural systems and the presence of high-dimensional data. Here, ML offers a solution to these limitations as it allows a system to assess the dataset's patterns, improve the prediction's reliability, and lower the cost of computations. Neural networks, support vector machines (SVMs), ensemble models, and reinforcement learning enabled short-term and precise long-term predictions of weather and its quantification, including hurricanes, droughts, and floods. Other areas that benefit from ML include monitoring the environment by tracking forest loss, air quality, the water temperature of the ocean, and the level of hazardous emissions into the atmosphere. Extension with the Numerical Weather Prediction models enhances the accuracy of the forecast. There are problems with data quality, the computational expense required, and proper interpretability, but there are solutions involving resource sustainability, renewable energy predictions, and ecological surveys. This paper demonstrates how ML can revolutionize climate science and facilitate climate change mitigation and adaptation worldwide.