AI and ML Bring Revolution to the Weather Sector

As in numerous other scientific disciplines, the spread of methods such as artificial intelligence and machine learning holds considerable potential for weather forecasting.

FREMONT, CA: Current weather forecasts are based on the type of complicated computations, and they are more accurate. In recent decades, significant advancements in research, data, and computers have permitted a silent revolution in numerical weather forecasting. For instance, a two-day forecast of heavy rainfall is as accurate as a same-day forecast. There are still significant obstacles. It is difficult to predict thunderstorms that produce tornadoes, big hail, or significant rainfall. And then there is chaos, frequently referred to as the "butterfly effect," the notion that slight changes in complicated processes render the weather less predictable.

Applying machine learning to high-impact weather forecasts is some of the possibilities. But while these tools give up new opportunities for more accurate projections, seasoned professionals handle many aspects of the work better. AI and machine learning will enable human forecasters to work more effectively, spending less time creating regular forecasts and more time communicating forecasts' implications and repercussions to the public. The rigorous collaboration between scientists, forecasters, and forecast users is the most effective means of achieving these objectives and fostering confidence in computer-generated weather forecasts.

Predictions based on historical storm data: Forecasters rely primarily on numerical weather prediction models. These models employ observations of the current atmosphere condition from weather stations, balloons, and satellites to solve equations regulating air circulation. These models are exceptional in predicting most weather systems, but smaller weather events are more challenging to anticipate. Seasoned forecasters are exceptionally adept at synthesizing the vast amounts of daily weather data they must examine, but their memories and bandwidth are limited. Artificial intelligence and machine learning can assist in overcoming a number of these obstacles. Forecasters use these techniques in various ways, including predicting severe weather that models cannot deliver.

The value of human expertise: There are other grounds for prudence. In contrast to numerical weather prediction models, machine learning-based forecasting systems are not restricted by the physical rules that regulate the atmosphere. So, it is feasible that they could provide unrealistic outcomes, such as anticipating temperature extremes that exceed natural limits. Uncertain is their performance during exceedingly unexpected or unprecedented weather events. Reliance on AI techniques can pose ethical difficulties. For instance, locales with relatively limited weather observations to train a machine learning system may not reap the same forecasting benefits as other regions.