A123-03
Ensemble PM2.5 Forecasting during the 2018 Camp Fire Event using the HYSPLIT Model and Using TROPOMI to Improve the forecast

Friday, 11 December 2020: 05:38
Virtual
Yunyao Li1, Daniel Tong2, Fong Ngan3,4, Mark Cohen3, Ariel F Stein3, Shobha Kondragunta5, Xiaoyang Zhang6, Charles M Ichoku7, Edward J Hyer8 and Ralph A Kahn9, (1)George Mason University Fairfax, Fairfax, VA, United States, (2)George Mason University Foundation Inc., Columbia, MD, United States, (3)NOAA Air Resources Laboratory, College Park, MD, United States, (4)Cooperative Institute for Climate and Satellites University of Maryland, College Park, MD, United States, (5)NOAA College Park, College Park, MD, United States, (6)Geospatial Sciences Center of Excellence (GSCE), Brookings, SD, United States, (7)NASA Goddard Space Flight Ctr, Greenbelt, MD, United States, (8)Harvard-Smithsonian CFA, Cambridge, MA, United States, (9)NASA/Goddard Space Flight Ctr, Greenbelt, MD, United States
Recorded Presentation
Abstract:
Biomass burning releases a vast amount of aerosols into the atmosphere, often leading to severe air quality and health problems. Prediction of the air quality effects from biomass burning emissions is challenging due to uncertainties in fire emission, plume rise calculation, and other model inputs/processes. Ensemble forecasting is increasingly used to represent model uncertainties. In this research, an ensemble forecast was conducted to predict surface PM2.5 during the 2018 California Camp Fire event using the NOAA HYSPLIT dispersion model at 0.1-degree horizontal resolution. Different combinations of four satellite-based fire emission datasets (FEER, FLAMBE, GBBEPx and GFAS), two plume rise schemes (Briggs and Sofiev), various meteorology inputs and model setup options were used to create the forecast ensemble, for a total of 112 experiments. The performance of each ensemble member and the ensemble mean were evaluated using ground-based observations, with four statistical metrics and an overall rank. The ensemble spread of the 112 members reached 1000 μg/m3, highlighting the large uncertainty in wildfire forecast. The ensemble mean displayed the best performance. Each fire emission product contributed to one or more members among the top ten performers, revealing the forecasting dependence on both the quality of fire emissions data and model representation of emission, transport and removal processes. In addition, an ensemble size reduction technique was introduced. HYSPLIT successfully reproduced the downwind PM2.5 peak of the Camp Fire case in the first week. However, the PM2.5 peak from Nov 15-16 is underestimated when using all kinds of emission dataset. One possible reason is that the emission is underestimated due to the thick smoke. We use TROPOMI total column CO observation and an inverse model to improve emission dataset and reconstruct the TROPOMI missing data under the thick smoke through an linear regression method.
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