Comparison of extreme temperature ensemble forecasts between artificial intelligence models and physics-based numerical weather prediction

Extreme temperature events have significant impacts on human society and economic activities, yet the prediction still involves considerable uncertainties, making the use of ensemble forecasting methods crucial. The Pangu-Weather Global Ensemble Prediction System (PGW-GEPS) was developed by integrating the Pangu-Weather (PGW) with perturbed initial conditions of the China Meteorological Administration Global Ensemble Prediction System (CMA-GEPS). Using the 2022 extreme heat wave event in Zhejiang and the 2024 cold wave event in Inner Mongolia as two cases, the forecasting performances of PGW-GEPS and CMA-GEPS on these two extreme temperature events are evaluated and compared based on multiple assessment metrics. The results indicate that, for both the Zhejiang heat wave and Inner Mongolia cold wave event, PGW-GEPS exhibits forecast accuracy and uncertainty representation capabilities comparable to CMA-GEPS. Both systems effectively capture the increase in 2 m air temperature forecast uncertainty with longer lead times and its subsequent decrease as the forecast initialization approaches the observation period. However, for the Zhejiang heat wave, PGW-GEPS shows deficiencies in forecasting the shear line and exhibits larger forecast errors in the medium range. A comparative analysis of the kinetic energy spectra of these two events further reveals that PGW-GEPS exhibits an attenuation phenomenon below the sub-synoptic scale. In summary, the AI (Artificial Intelligence)-based PGW-GEPS demonstrates its forecasting capability for extreme temperature events. Its forecast accuracy for 3—10 d extreme temperature prediction is comparable to that of CMA-GEPS, while its computational speed is advantageous. However, PGW-GEPS still faces challenges in capturing rapidly evolving meso-micro scale weather systems, and further improvement in forecasting sub-synoptic systems is required. This study provides valuable insights into the application of artificial intelligence models in ensemble forecasting.