Intra-urban Enhancements to Probabilistic Climate Forecasting for the Electric System
Improving weather forecasting methods for management of the electric system.
The project has been completed. The results show mean urban temperature forecasting error was reduced by up to 1.8 degrees C in the San Francisco Bay Area and up to 0.8 degrees C in the Los Angeles region. The magnitudes of intra-urban temperature variations, including effects of heat islands, are similar to or larger than those of the predicted localized impacts of climate change. Intra-urban variability in temperature (within each Energy Commission building climate zone) was found to be larger than the inter-zone differences, sometimes by several times. Therefore, intra-urban variability is important to account for in planning for electric demand and in building energy modeling.
This project developed a methodology for creating probabilistic fine-scale temperature zones in California focusing, initially, on summer conditions in the Los Angeles region and the greater San Francisco Bay Area. This was done for both current and future climates and land-use conditions. The project also reduced forecasting errors and uncertainties by improving the performance of the urban Weather Research and Forecasting model (uWRF). Observational weather data from a dense network of mesonet stations were used in model performance evaluation and validation.
Improved forecasts can result in lowered emissions of GHG and air pollutants, as it reduces the need to procure short-term, flexible generation that is typically from fossil fuel resources. The methodology developed in this proje
This project reduced the mean urban temperature forecasting error significantly. Conservatively assuming an average improvement of 0.5 degrees C in forecasting peak temperatures in Californian cities within the CAISO service terr
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