Aim 4: Wildfire smoke impacts
For periods of large-scale wildfire smoke impacts, use available ground-based monitoring data (permanent, temporary-smoke, and low-cost sensors where possible) in combination with the 1-km satellite–derived PM2.5 surfaces and total column AOD products to optimize air dispersion modeling at 1.33-km and 2-km resolutions, resolving PM2.5 concentrations from wildfires.
Wildfire Case 1: We are using the BlueSky smoke modeling framework (Larkin et al., 2009) to resolve primary PM2.5 concentrations at a 2-km resolution from wildland fire for periods when southern California was impacted by smoke. One such period was the Sand Wildfire, which burned over 41,000 acres in the Angeles National Forest sending smoke and ash into the LA Basin on July 23-24, 2016. Near-surface model results will be compared to the 1-km PM2.5 satellite-derived fields from the MAIAC algorithm. We are also simulating total column PM2.5 for comparison with MAIAC AOD. Similarities and differences between near-surface and total column data will be examined as well as the relationship between the satellite-derived PM2.5 data and ground-based instruments during the smoke episode. In Southern California these smoke episodes are highly episodic and often short-lived (1 to several days) but by pin-pointing the Los Angeles area we are taking advantage of the special instrument deployments.
Wildfire Case 2: We are extending this analysis to a second time period in California when wide-spread long-term (weeks-month) smoke impacts occurred. Several options are available, such as July-August 2015 in the Klamath/Six Rivers/Shasta-Trinity National forests with smoke impacts in many small towns extending south to Redding. Other options are periods during wildfires in the Sierras such as the Rim Wildfire (2013, 250K Acres), King Wildfire (2014, 97,000 Acres) or Rough Wildfire (2015, 150K acres). Many temporary PM2.5 monitors were deployed in small towns to identify smoke impacts from the wildfires. Air Resource Advisors deployed to the large wildfires made daily smoke forecasts informing the public about smoke impacts – when the atmosphere was expected to be smoke-free and when smoke was expected to be heavy. The BlueSky smoke modeling framework was used to estimate PM2.5 concentrations at a 1.33-km resolution from satellite hot spot detections, mapped fuel loadings, and fuel consumption and emission algorithms. Near-surface BlueSky results are being compared to the 1-km PM2.5 satellite-derived fields from the MAIAC algorithm. We are also comparing modeled total column PM2.5 with MAIAC AOD. Similarities and differences between near-surface and total column data are being examined as well as the relationship between the 1-km PM2.5 satellite-derived fields and ground-based instruments (temporary and permanent).
We anticipate that these two cases will demonstrate the applicability/utility of the MAIAC products, especially the 1-km surface PM2.5 satellite-derived product, during periods of short-term and long-term wildfire smoke impacts.