New Delhi: Scientists and experts have struggled for millennia to develop reliable techniques for predicting weather patterns. DeepMind, Google’s artificial intelligence division, however, asserts that its new AI model significantly improves upon previous weather-predicting methods.
Google has introduced GraphCast, an AI model that can provide medium-range weather predictions with “unprecedented accuracy,” in the research journal Science. If they could be predicted more quickly and accurately, it would allow us to better prepare for natural catastrophes and maybe save lives.
According to studies published, Google DeepMind’s GraphCast model outperformed the existing gold standard for weather forecasting by both accuracy and speed up to 10 days in the future.
Check out Google DeepMind’s GraphCast AI!
It’s making weather forecasts way faster and more accurate, giving us 10-day predictions in under a minute.
Big step up from the usual methods.
What do you think this means for the future of weather forecasting? pic.twitter.com/s3TrC5tcN6
— Fernando Ocasio (@TechPalsTalk) November 15, 2023
#AI Revolution in weather forecasting.
Google DeepMind’s weather AI can forecast extreme weather faster and more accurately.
In research published in Science today, Google DeepMind’s model, GraphCast, was able to predict weather conditions up to 10 days in advance, more…
— Asad Ali Shah (@Asad_Ashah) November 15, 2023
When compared to the model from the European Centre for Medium-Range Weather Forecasts (ECMWF), GraphCast performed better in more than 90% of over 1,300 test locations. More than 99 per cent of meteorological variables, including precipitation and air temperature, were correctly predicted by GraphCast, whereas the ECMWF’s model was off by a wide margin.
Importantly, GraphCast can provide meteorologists with early warnings of severe circumstances like temperature and cyclone tracks, much before the time when normal models would predict them. According to Remi Lam, a staff research scientist at Google DeepMind, GraphCast properly forecasted Hurricane Lee in Nova Scotia nine days beforehand in September. Only six days before the storm hit Nova Scotia was the location determined by conventional weather predicting models.
Hurricane Lee struck land right where DeepMind’s software, called GraphCast, had predicted more than a week ahead. Lee was at least 10 days out from landfall—eons in forecasting terms— DeepMind had (correctly) made a very specific prognosis of landfall..https://t.co/Q6ybJ09jZj
— Drew Hawkswood (@DrewHawkswood) November 14, 2023
Traditional weather forecasting relies heavily on enormous computer models. Because the simulations include many various meteorological factors, including temperature, precipitation, pressure, wind speed and direction, humidity, and cloud cover, running them consumes a lot of resources and takes a long time.
The machine learning in GraphCast allows for these computations to be completed in under a minute. It makes forecasts based on forty years of weather data instead of equations based on physics. Using graph neural networks, GraphCast divides the planet up into a grid of more than a million points. The model projects conditions like temperature, wind speed and direction, mean sea level pressure, and humidity at each grid point.
Meet DeepMind’s GraphCast: A Leap Forward in Machine Learning-Powered Weather Forecasting
https://t.co/9YkpsI0JKt#ai #itinai #ainews #new #trend pic.twitter.com/cFM6ORfC3A
— Vladimir Ruso PhD (@vlruso) November 15, 2023
Aditya Grover, an associate professor of computer science at UCLA and the creator of ClimaX, a foundation model that facilitates a wide range of weather and climate modelling activities, calls GraphCast a “reckoning moment” for weather prediction.
GraphCast has several limitations, however. According to ECMWF’s director of Earth system modelling Peter Dueben, the organisation is still behind in several areas compared to traditional weather forecasting models, particularly in terms of precipitation. In order to improve their forecasts, meteorologists will still need to employ both traditional and machine-learning algorithms.
