Today’s weather forecasts are generated by some of the world’s most sophisticated computers. However, weather forecasts are unpredictable because the climate is a very complex and volatile phenomenon that requires a great amount of money, data, and time to evaluate. Therefore, the future may follow a very different path regarding weather forecasting with AI.  

Weather forecasting has been done in the same way for a few decades. Supercomputers process massive volumes of atmospheric and oceanic data. Forecasting companies aggregate data from ocean buoys and independent weather trackers. This data is then analyzed using models that simulate the physics of fluid dynamics in weather, which takes a significant amount of processing power, hours to finish and a significant amount of money to collect and process. Currently, the joint demand for speed and accuracy in a prediction puts even the most sophisticated weather algorithms to the test. 

Weather monitors in observatories on the land and the waters provide a flood of climate and weather data worldwide. However, it is complex for humans or even standard computer networks to analyze and scan for similarities. That is an issue because it is a waste of time and storage if this cascade of data is unable to be fully analyzed. Since pattern-recognition skills in AI are tailor-made for such jobs, researchers are using ML, Neural Networks and Deep Learning. Enormous quantities of data will be inputted into the algorithms, which can then learn how and when to detect storms that could produce lightning and tornadoes. 

Fine-tuning 

Scientists at NASA’s Jet Propulsion Laboratory have made impressive strides using ML models to better predict a hurricane’s intensity— a task with which existing models have long struggled. In addition, researchers at Michigan State University recently proposed a Deep Learning framework for predicting a hurricane’s trajectory that has proven significantly more accurate than existing forecasting models. 

Recent works on RNN by researchers at Florida International University and Ganzfried research could improve hurricane trajectory forecasts in the near future. However, storm predictions are only part of the challenge of managing natural disasters.  

Communicating accurate, up-to-date information, assessing a storm’s damage and allocating resources effectively is also crucial to a successful response. So, again, this is yet another area where AI can contribute.  

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

IMD uses technologies such as radars and satellite imagery to issue nowcasts. Nowcasts are extreme weather predictions that can occur in the next 3-6 hours. Similarly, Fasal Salah is an app for forecasting weather, including temperature, humidity, wind speed, direction, and rainfall, which can be provided at least ten days in advance. 

Tools underused 

Given the potential of AI tools to revolutionize how we manage natural disasters, these tools remain staggeringly underused. And this needs to change. Every year, India experience loss of life and property due to rain and storm.  

Using AI to remove some guesswork from disaster response efforts, we can ensure that tragedies are far rarer.