Multiplatform inversion of the 2013 Rim Fire smoke emissions using regional-scale modeling: important nocturnal fire activity, air quality, and climate impacts

Friday, 19 December 2014: 8:15 AM
Pablo E Saide1, David A Peterson2, Arlindo M da Silva Jr.3, Luke D Ziemba4, Bruce Anderson3, Glenn S Diskin5, Glen W Sachse4, Johnathan W Hair4, Carolyn F Butler6, Marta A Fenn7, Jose L Jimenez8, Pedro Campuzano Jost9, Jack E Dibb10, Robert J Yokelson11, O. Brian Toon12 and Gregory R Carmichael1, (1)The University of Iowa, Iowa City, IA, United States, (2)National Research Council, Ottawa, ON, Canada, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)NASA Langley Research Center, Hampton, VA, United States, (5)NASA Langley Research Ctr, Hampton, VA, United States, (6)Science Systems and Applications, Inc., Lanham, MD, United States, (7)SSAI, Hampton, VA, United States, (8)University of Colorado at Boulder, Boulder, CO, United States, (9)University of Colorado Boulder, Boulder, CO, United States, (10)Univ New Hampshire, Durham, NH, United States, (11)University of Montana, Department of Chemistry, Missoula, MT, United States, (12)University of Colorado at Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, CO, United States
Large wildfire events are increasingly recognized for their adverse effects on air quality and visibility, thus providing motivation for improving smoke emission estimates. The Rim Fire, one of the largest events in California’s history, produced a large smoke plume that was sampled by the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) DC-8 aircraft with a full suite of in-situ and remote sensing measurements on 26-27 August 2013. We developed an inversion methodology which uses the WRF-Chem modeling system to constrain hourly fire emissions, using as initial estimates the NASA Quick Fire Emissions Dataset (QFED). This method differs from the commonly performed top-down estimates that constrain daily (or longer time scale) emissions. The inversion method is able to simultaneously improve the model fit to various SEAC4RS airborne measurements (e.g., organic aerosol, carbon monoxide (CO), aerosol extinction), ground based measurements (e.g., AERONET aerosol optical depth (AOD), CO), and satellite data (MODIS AOD) by modifying fire emissions and utilizing the information content of all these measurements. Preliminary results show that constrained emissions for a 6 day period following the largest fire growth are a factor 2-4 higher than the initial top-down estimates. Moreover, there is a tendency to increase nocturnal emissions by factors sometimes larger than 20, indicating that vigorous fire activity continued during the night. This deviation from a typical diurnal cycle is confirmed using geostationary satellite data. The constrained emissions also have a larger day-to-day variability than the initial emissions and correlate better to daily area burned estimates as observed by airborne infrared measurements (NIROPS). Experiments with the assimilation system show that performing the inversion using only satellite AOD data produces much smaller correction factors than when using all available data, suggesting that top-down emissions estimates for exceptional fire events could be underestimated by current inversion methods. The changes in fire emissions can significantly affect the fire impacts on surface air quality, aerosol loads and its effects on meteorology, highlighting the need of performing these studies.