Strengthening storm forecasts with AI algorithms

Today’s weather forecasts are generated by some of the most sophisticated computers in the world. However, weather forecasts are unpredictable because climate is a very complex and volatile phenomenon that requires a lot of money, data and time to assess. Therefore, the future could follow a very different path when it comes to weather forecasting with AI.

Weather forecasting has been done the same way for decades. Supercomputers process massive volumes of atmospheric and ocean data. Forecast companies aggregate data from ocean buoys and independent weather trackers. This data is then analyzed using models that simulate the physics of fluid dynamics over time, which requires significant processing power, hours to complete, and a significant amount of money to collect and process. Currently, the joint requirement for speed and accuracy in a forecast challenges even the most sophisticated weather algorithms.

Weather monitors at land and water observatories provide a flood of climate and weather data around the world. However, it is complex for humans or even standard computer networks to analyze and search for similarities. This is a problem because it is a waste of time and storage if this data cascade cannot be fully analyzed. Since pattern recognition skills in AI are tailor-made for such jobs, researchers use ML, neural networks, and deep learning. Huge amounts of data will be fed into the algorithms, which can then learn how and when to detect thunderstorms that could produce lightning and tornadoes.

Fine Tuning

Scientists at NASA’s Jet Propulsion Laboratory have made impressive progress using ML models to better predict a hurricane’s intensity, a task that existing models have long struggled with. Additionally, researchers at Michigan State University recently proposed a deep learning framework for predicting the path of a hurricane that was found to be significantly more accurate than existing forecasting models.

Recent work on RNN by researchers at Florida International University and Ganzfried’s research may improve hurricane track predictions in the near future. However, storm forecasting is only part of the challenge of managing natural disasters.

Communicating accurate and up-to-date information, assessing storm damage, and allocating resources efficiently are also essential for a successful response. So again, this is yet another area where AI can help.

The AI ​​box developed by Remark AI can integrate with existing cameras to identify severe wind or flood damage and alert the relevant authorities.

IMD uses technologies such as radar and satellite imagery to issue nowcasts. Nowcasts are extreme weather forecasts that may occur within the next 3 to 6 hours. Similarly, Fasal Salah is a weather forecast app including temperature, humidity, wind speed, direction and rainfall which can be provided at least ten days in advance.

Underutilized Tools

Given the potential of AI tools to revolutionize the way we deal with natural disasters, these tools remain incredibly underutilized. And that must change. Every year, India suffers loss of life and property due to rain and storms.

By using AI to take some of the guesswork out of disaster response efforts, we can ensure that tragedies are much rarer.

Sharon D. Cole