Atmospheric scientists suggest that AI could be used to make 30-day weather forecasts
A team of atmospheric scientists at the University of Washington has found evidence that weather forecasters may be able to look ahead for up to 30 days when making predictions. In their study, posted on the arXiv preprint server, the group tested Google's GraphCast AI-based weather modeling and predicting system using a technique to improve initial weather conditions to improve its accuracy.
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The researchers conducted tests with GraphCast, an AI weather forecasting model built by Google—it learns via training on 40 years of data from traditional forecasts and satellite imagery. They wondered if improving the accuracy of the initial conditions used to generate a forecast could improve the model's overall accuracy.
The research team compared forecasts made by the model with the most recent state of the atmosphere taken from data used to train the model. They then used miscues made in short-term forecasts as a way to adjust the initial conditions and then applied them to the reanalysis data used to train the model, giving it a more accurate starting point. They then repeated the same exercise more than 1,000 times, each time making the initial conditions more accurate.
The researchers then trained GraphCast using the newly revised data, and found that it improved its 10-day forecasting ability by 86% on average. It also made reasonably accurate predictions up to 33 days into the future.
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