Google’s Deepmind AI hurricane tracker was twice as accurate as NWS models

quote:

---

Although Google DeepMind’s Weather Lab only started releasing cyclone track forecasts in June, the company’s AI forecasting service performed exceptionally well. By contrast, the Global Forecast System model, which is operated by the US National Weather Service, is based on traditional physics, and runs on powerful supercomputers, performed abysmally.

---

quote:

---

This chart sums up the track forecast accuracy for all 13 named storms in the Atlantic Basin this season, measuring the mean position error at various hours in the forecast, from 0 to 120 hours (five days). On this chart, the lower a line is, the better a model has performed. The dotted black line shows the average forecast error for official forecasts from the 2022 to 2024 seasons. What jumps out is that the United States’ premier global model, the GFS (denoted here as AVNI), is by far the worst-performing model. Meanwhile, at the bottom of the chart, in maroon, is the Google DeepMind model (GDMI), performing the best at nearly all forecast hours. The difference in errors between the US GFS model and Google’s DeepMind is remarkable. At five days, the Google forecast had an error of 165 nautical miles compared to 360 nautical miles for the GFS model, more than twice as bad. This is the kind of error that causes forecasters to completely disregard one model in favor of another.

---