Intra-urban Enhancements to Probabilistic Climate Forecasting for the Electric System

Improving weather forecasting methods for management of the electric system.

Altostratus, Inc.

Recipient

Martinez, CA

Recipient Location

3rd

Senate District

14th

Assembly District

beenhere

$193,075

Amount Spent

closed

Completed

Project Status

Project Result

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.

The Issue

Probabilistic decadal, seasonal, and short-term climate forecasts for the electric system are typically produced with climate models at relatively coarse resolutions (e.g., 3-10 km) and using observations from sparse networks of meteorological stations. These forecasts and observational analyses do not explicitly take into account the fine-scale intra-urban variations in climate. Intra-urban variations in temperature average 1-4 degrees C in California and can be as large as 10 degrees C. It is important to explicitly account for them in the seasonal, decadal, and short-term forecasts of the electric system that serve as a basis for planning by the Energy Commission and the utilities.

Project Innovation

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.

Project Benefits

This project added fine-resolution, intra-urban climate detail to coarse-scale, regional-level probabilistic or deterministic forecasting, thus allowing for more accurate, area-specific characterizations and forecasts for the electricity system and better apportionment of electricity generation.

Environmental & Public Health

Environmental Sustainability

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

Greater Reliability

Reliability

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

Key Project Members

Project Member

Haider Taha

Principal Investigator

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