A new AI-based system is able to predict when it is going to rain.

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Scientists at the Google-owned London lab DeepMind have developed a forecasting system based on artificial intelligence (AI) that, they claim, can tell more accurately than the existing systems if there is any likelihood of rain in the next two hours.

The radar repeatedly fires a beam into the lower atmosphere to track the amount of moisture in the air, which is measured by the relative speed of the signal and how much it is slowed by water vapour. This data is then used by an AI modelling tool in an attempt to pinpoint the timing, location and intensity of precipitation. The hope is that it can improve the accuracy of short-term weather forecasts and particularly the prediction of storms and heavy rain.

That’s because current supercomputer models — used to forecast weather on a larger scale over the next day or week — do not fare so well with shorter two-hour timeframes. They rely heavily on numerical weather prediction (NWP) systems, which use mathematical equations to estimate the chances of rain and other types of weather based on the movement of fluids in the atmosphere.

The report states that “using statistical, economic, and cognitive measures” the system “provides improved forecast quality, forecast consistency, and forecast value, providing fast and accurate short-term predictions at lead times where existing methods struggle”. The report stated that the DeepMind team’s model provided “improved forecast quality, forecast consistency, and forecast value”, and, “using a systematic evaluation by more than 50 expert meteorologists”, was accurate in 89 percent of cases against two existing rain prediction systems.

Though, through meteorologist assessment, the system proved to be good at skillful predictions compared to other solutions, “the prediction of heavy precipitation at long lead times remains difficult for all approaches”. But the scientists hope that their “work will serve as a foundation for new data, code and verification methods — as well as the greater integration of machine learning and environmental science in forecasting larger sets of environmental variables — that makes it possible to both provide competitive verification and operational utility”.

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