Improving a WRF-Based High-Impact Weather Forecast System for a Northern California Power Utility

• Published in: Atmosphere Vol. 15; no. 10; p. 1244
• Main Authors: Carpenter, Richard L., Gowan, Taylor A., Lillo, Samuel P., Strenfel, Scott J., Eiserloh, Arthur. J., Duffey, Evan J., Qu, Xin, Capps, Scott B., Liu, Rui, Zhuang, Wei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2024
• Subjects: Configuration management; Customer services; Data assimilation; Decision making; Diablo winds; Electric utilities; Ensemble forecasting; ensemble weather forecasting; Evaluation; Heat; Land use; Machine learning; Meteorological research; Numerical weather forecasting; power utility; Radiation; Real time; Vegetation; Weather; Weather forecasting; Weather Research and Forecasting (WRF) model; wildfire weather; Wildfires; Wind; Winter; United States; California; Northern California; Southern California; United States > US; Nevada
• DOI: 10.3390/atmos15101244
• EISSN: 2073-4433

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to forecast conditions in Northern and Central California for critical decision-making such as proactively de-energizing selected circuits within the power grid. WRF forecasts are routinely produced on a 2 km grid, and the results are used as input to wildfire fuel moisture, fire probability, wildfire spread, and outage probability models. This forecast system produces skillful real-time forecasts while achieving an optimal blend of model resolution and ensemble size appropriate for today’s computational resources afforded to utilities. Numerous experiments were performed with different model settings, grid spacing, and ensemble configuration to develop an operational forecast system optimized for skill and cost. Dry biases were reduced by leveraging a new irrigation scheme, while wind skill was improved through a novel approach involving the selection of Global Ensemble Forecast System (GEFS) members used to drive WRF. We hope that findings in this study can help other utilities (especially those with similar weather impacts) improve their own forecast system.