Optimizing WRF Spectral Nudging to Improve Heatwave Forecasts: A Case Study of the Sichuan Electricity Grid

Accurate forecasting of heatwaves is critical for ensuring the safe operation of electricity grids. Focusing on the complex terrain of Sichuan, China, this study investigates the optimization of spectral nudging parameters within the Weather Research and Forecasting (WRF) model to improve predictions of heatwave events. To overcome the subjectivity inherent in the traditional selection of the spectral nudging cutoff wavenumber, we propose an objective method based on power-spectrum energy diagnostics of the background field. This method determines an optimal domain-specific cutoff wavenumber. A series of sensitivity experiments were designed for a significant heatwave event that affected the Sichuan electricity grid in August 2019. These experiments evaluated the impact of different spectral nudging configurations, which considered varying domain sizes and forecast lead times, on correcting large-scale circulation drift and enhancing near-surface air temperature forecasts. The results demonstrate the following: (1) For a smaller domain or a longer forecast lead time, spectral nudging effectively compensates for circulation drift induced by weakening lateral boundary constraints, significantly improving the forecast of heatwave intensity and spatial extent, representing a compensatory effect. (2) For a larger domain that already adequately resolves large-scale circulation evolution, spectral nudging can over-constrain the model’s internal dynamical processes, thereby degrading forecast performance, an outcome termed the over-constraint effect. (3) The proposed energy-threshold method provides an objective, physics-based strategy for identifying dominant large-scale waves and optimizing the spectral nudging cutoff wavenumber. This work offers practical insights for the operational application of spectral nudging over complex terrain to advance extreme temperature forecasting.