AI-Powered Weather Forecasting Promises Game-Changer for North East India

By Sunil Sonkar
2 Min Read
AI-Powered Weather Forecasting Promises Game-Changer for North East India

A groundbreaking development is underway as artificial intelligence (AI) and deep learning promise to transform the landscape of weather forecasting in North East India. Recent research conducted by a team of experts from the Indian Institute of Technology (IIT) in Bhubaneswar unveils the potential for these advanced technologies to significantly enhance the accuracy of predicting heavy rainfall events in the region’s intricate terrains.

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The integration of AI and deep learning into Weather Research and Forecasting (WRF) models holds the key to a revolutionary shift in weather prediction. This development is set to offer a higher level of precision in forecasting heavy rainfall in a region known for its complex topographies.

The study, titled “Improvement in district scale heavy rainfall prediction over complex terrain of North East India using deep learning,” emphasizes that combining high-resolution dynamical and AI models in WRF is crucial for better early warnings and disaster preparedness in an area that frequently faces extreme weather events. This development is a game-changer for the North East, which grapples with annual climate-related challenges.

The study, featured in the pages of IEEE Transactions on Geo-science and Remote Sensing, highlights the significant advancements AI deep learning offers. It showcases an impressive level of accuracy with a remarkable 77.9% precision in predicting various types of rainfall. This breakthrough demonstrates the potential for AI deep learning to greatly outperform conventional methods in the realm of rainfall forecasting.

One of the researchers involved in the study pointed out that the ongoing challenges in accurately predicting heavy rainfall events. This AI-based approach offers a major breakthrough by forecasting heavy rainfall events with impressive precision. It is a significant step toward minimizing the impact of severe weather events in this vulnerable region.

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