Summary
The article discusses the importance of weather and climate forecasting, particularly focusing on precipitation forecasting as a mechanism to mitigate the societal and economic impacts caused by extreme weather events like floods. It highlights that floods resulting from heavy rainfall can lead to substantial economic losses and human fatalities, especially in developing regions such as India and Latin America. The text provides statistical insights into the financial and social repercussions of flooding, noting that significant losses occurred globally, with floods accounting for nearly half of the natural disasters reported in certain regions.
The article reviews the physical processes governing precipitation formation and the complex atmospheric dynamics that drive weather patterns. Using dynamical models, including numerical weather prediction (NWP) and Earth System Models (ESMs), these processes can be simulated to improve forecasting accuracy. Recent advancements in artificial intelligence (AI) and machine learning (ML) provide promising methodologies for enhancing precipitation forecasts. The review identifies various ML models, such as artificial neural networks (ANNs), support vector machines (SVMs), and decision trees, that have been applied to predict rainfall effectively.
The challenges in precipitation prediction underscore issues like the scarcity of high-quality training data for ML models, especially for extreme weather events. Additionally, the article notes the complexities arising from temporal scales and varying correlations between climate variables, which impact forecasting accuracy.
Overall, the article stresses the continuing need for improved forecasting models and data availability to enhance preparedness for extreme weather events, thus reducing the associated economic and humanitarian toll. The integration of AI techniques into existing forecasting frameworks is highlighted as a key area for future research and development.
