Extended Data Fig. 7: Properties of deterministic forecast results.
From: Accurate medium-range global weather forecasting with 3D neural networks
a) Single-model test errors. It shows the test errors (in RMSE) with respect to forecast time using single models (i.e., lead times being 1 h, 3 h, 6 h, and 24 h, respectively). Mind the accumulation of forecast errors as forecast time increases. b) Visualization of the trend of quantiles with respect to lead time. It shows the trend of all the variables displayed in Fig. 2 and the comparisons to operational IFS3 and ERA518. Pangu-Weather often reports lower quantile values because AI-based methods tend to produce smooth forecasts. Here, Z500/T500/Q500/U500/V500 indicates the geopotential, temperature, specific humidity, and u-component and v-component of wind speed at 500 hPa. Z850/T850 indicates the geopotential and temperature at 850 hPa. T2M indicates the 2 m temperature, and U10/V10 indicates the u-component and v-component of 10 m wind speed.
