A123-01
Forecasting smoke pollution from the 2018 Camp Fire using HRRR-Smoke

Friday, 11 December 2020: 05:30
Virtual
Fotini K Chow, University of California Berkeley, Berkeley, CA, United States, Katelyn Yu, University of California, Berkeley, Berkeley, CA, United States, Ravan Ahmadov, NOAA ESRL/CSL, Boulder, CO, United States, Alexander Young, University of California, Berkeley, Berkeley, United States, Eric James, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, Ivan Andras Csiszar, NOAA/NESDIS, College Park, MD, United States, Marina Tsidulko, IMSG (NOAA/NESDIS/STAR), College Park, MD, United States and Gabriel Pereira, Federal University of São João Del Rei, São João Del Rei, Brazil
Recorded Presentation
Abstract:
Smoke from the 2018 Camp Fire in California blanketed a large part of California for two weeks, creating air quality in the “unhealthy” range for millions of people. The NOAA HRRR-Smoke model was operating in real time at the time, with satellite detection of fire radiative power and a scalar transport module to predict smoke dispersion. Here, output from the HRRR-Smoke model is compared to surface observations of PM2.5 from EPA and Purple Air sensors. This is the first time HRRR-Smoke has been evaluated with extensive surface air quality observations at a regional level where complex terrain effects come into play as well. The model is evaluated in particular during the first phase of the fire on Nov. 8-9, 2018 and then on Nov. 15-16, 2018 when regional air quality worsened further. The HRRR-Smoke model at 3 km resolution was able to capture the strong downslope winds which fed the Camp Fire and the down-valley winds along Central Valley which pushed the smoke toward the Bay Area. HRRR also captured the bifurcation of the plume at low levels (down-valley winds) and upper levels (east winds). Overall, HRRR-Smoke captured the strong increases in PM2.5 measured during the two week period reasonably well. The fire was initialized a few hours late in the HRRR model due to delays in satellite detection, which accounts for a delay in the initial arrival of the smoke plume to the San Francisco Bay Area. During the second week, HRRR-Smoke was able to capture the intensification of PM2.5 values (259 AQI was measured in San Francisco) due to a high pressure system and subsidence that kept smoke trapped close to the surface. The intensity of this smoke event makes it an excellent test case for HRRR-Smoke in comparing PM2.5 levels which were so high that the usual anthropogenic sources became insignificant. The HRRR-Smoke model has the potential to become a useful operational tool to provide improved smoke forecasts for wildfire and prescribed burn events.
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