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Accurate frost forecast with AI

12.11.2021

Scientists at Meiji University in Kawasaki, Japan have investigated the cause and effect that inevitably leads to freezes when sensor data can be analyzed by algorithms. This improves the accuracy of the forecast.

In the new work, scientists presented methods for computer simulation of frost formation. Among these methods are causal and associative models. They also propose the basis for a hybrid system that can provide a short-term frost forecast within hours and demonstrate how it can be used for longer-term forecasts, such as over the next few days.

There are two types of frost. There is frozen dew (water frost). This occurs when water vapor from the atmosphere condenses as moisture droplets on surfaces such as plants when the temperature falls below the dew point but above the freezing point of water. However, the surface temperature then drops below freezing, causing the dew to solidify.

The second type of hoarfrost is sedimentary hoarfrost (white hoarfrost), which forms when the surface temperature is initially below freezing, and so instead of condensing on the surface and subsequently freezing, water from the atmosphere solidifies on the surface.

Air temperature is usually the main criterion for predicting frost. But the spatial resolution of the data that can be obtained is not always accurate. Moreover, temperature is not the only factor influencing whether frost forms or not. Other parameters such as humidity, wind speed and direction, cloud cover also have an effect. Ultimately, whether a freeze occurs or not can be seen as a combination of factors. Machine learning tools are able to process a large amount of data and, using an algorithm to find a likely answer to a given question, could offer a more timely forecast.

The new computer model can offer a one- to three-hour-per-minute frost forecast to alert anyone who needs advance frost warning.

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