Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Richard L Carpenter, Jr
* ,
Taylor A Gowan
,
Samuel P Lillo
,
Scott J Strenfel
,
Arthur J Eiserloh
,
Evan J Duffey
,
Xin Qu
,
Scott B Capps
,
Rui Liu
,
Wei Zhuang
Version 1 : Received: 31 August 2024 / Approved: 2 September 2024 / Online: 2 September 2024 (10:45:14 CEST)

How to cite: Carpenter, Jr, R. L.; Gowan, T. A.; Lillo, S. P.; Strenfel, S. J.; Eiserloh, A. J.; Duffey, E. J.; Qu, X.; Capps, S. B.; Liu, R.; Zhuang, W. Improving a WRF-based High-Impact Weather Forecast System for a Northern California Power Utility. Preprints 2024, 2024090093. https://doi.org/10.20944/preprints202409.0093.v1 Carpenter, Jr, R. L.; Gowan, T. A.; Lillo, S. P.; Strenfel, S. J.; Eiserloh, A. J.; Duffey, E. J.; Qu, X.; Capps, S. B.; Liu, R.; Zhuang, W. Improving a WRF-based High-Impact Weather Forecast System for a Northern California Power Utility. Preprints 2024, 2024090093. https://doi.org/10.20944/preprints202409.0093.v1

Abstract

We describe a 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 operationally forecast conditions in Northern and Central California. It was established in 2014 and has been periodically improved. In the current configuration, WRF forecasts are routinely performed multiple times each day on a 2-km grid, and the results are used as input to wildfire fuel moisture, fire probability, and wildfire spread models. Components of the system include a stochastically perturbed ensemble; an annually updated reanalysis covering more than 30 years; and validation against PG&E’s extensive mesonet and other observing networks. We describe the development and validation of the next operational edition. Configurations tested include irrigation triggered by crop growing season and an ensemble-based approach with intelligently sub-selected Global Ensemble Forecast System (GEFS) members. The latter approach allows for enhanced uncertainty sampling in WRF forecasts and can greatly improve WRF forecasts when calibrated by observations.

Keywords

Weather Research and Forecasting (WRF) model; wildfire weather; Diablo winds; ensemble weather forecasting; power utility

Subject

Environmental and Earth Sciences, Atmospheric Science and Meteorology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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