麻豆淫院

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, 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.

Over the past half-century, weather forecasters have come to believe that a two-week forecast period is the ultimate limit. This is because of the so-called butterfly effect, in which tiny events, such as the wind created by a butterfly's wings, can lead to cascading effects, resulting in greater impacts.

The butterfly effect is a thought experiment, but it is known that random events such as fires, volcanic eruptions and human activity can cause local weather changes. In this new effort, the researchers working in Washington have been testing the possibility of using AI technology to lengthen the forecasting window.

The researchers conducted tests with GraphCast, an AI weather forecasting model built by Google鈥攊t 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.

The researchers acknowledge that much more work is required before AI models can make accurate, long-term predictions, including testing their approach to see how well it works in the real world.

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