DeepMind has recently released its research on GraphCast, an AI model for weather forecasting. According to DeepMind, this model is capable of accurately predicting weather patterns for the next 10 days, while also providing fast processing capabilities that surpass current weather forecasting tools.
Traditionally, weather forecasting methods rely on Numerical Weather Prediction, which involves complex equations and time-consuming processes. Additionally, these methods require high computational resources. However, GraphCast takes a different approach. It leverages historical weather data spanning several decades to analyze the correlation between various factors and their impact on global weather patterns. The current dataset used by GraphCast is ERA5, provided by the ECMWF, which covers a 40-year period.
The results of GraphCast’s weather predictions provide data at a resolution of 0.25 degrees latitude and longitude, equivalent to a 28×28 square kilometer grid. This data includes temperature, wind speed and direction, sea level pressure, and humidity. The accuracy of the forecast has been tested and shown to be at a level of 90%.
GraphCast also excels in assessing and predicting disaster situations faster than traditional methods. For example, it can identify areas impacted by hurricanes up to 9 days in advance, compared to the 6-day lead time of conventional warning systems.
TLDR: DeepMind has introduced GraphCast, an AI model capable of accurate weather forecasting up to 10 days in advance. It utilizes historical weather data and outperforms current methods in terms of speed and accuracy. GraphCast has the ability to predict disaster situations earlier, providing a valuable tool for preparation and response.
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