气候变化领域集成服务门户(CCPortal): Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model

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DOI	10.1029/2020JD032768
	Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model
	Li Y.; Tong D.Q.; Ngan F.; Cohen M.D.; Stein A.F.; Kondragunta S.; Zhang X.; Ichoku C.; Hyer E.J.; Kahn R.A.
发表日期	2020
ISSN	2169897X
卷号	125 期号:15
英文摘要	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 paper, an ensemble forecast was conducted to predict surface PM2.5 during the 2018 California Camp Fire event using the National Oceanic and Atmospheric Administration (NOAA) HYSPLIT dispersion model at 0.1° horizontal resolution. Different combinations of four satellite-based fire emission data sets (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 1,000 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 10 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. With the help of this technique, the ensemble size was reduced from 112 to 28 members and still produced an ensemble mean that yielded comparable or even better performance to that of the full ensemble. © 2020. The Authors.
英文关键词	air quality; biomass burning; Camp Fire; ensemble forecast; HYSPLIT; satellite
语种	英语
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/185877
作者单位	Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States; Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA, United States; Center for Spatial Science and Systems, George Mason University, Fairfax, VA, United States; NOAA Air Resources Laboratory, College Park, MD, United States; Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, MD, United States; NOAA Satellite Meteorology and Climatology Division, College Park, MD, United States; Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD, United States; Department of Interdisciplinary Studies, College of Arts and Sciences, Howard University, Washington, DC, United States; Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, United States; NASA Goddard Space Flight Center, Greenbelt, MD, United States
推荐引用方式 GB/T 7714	Li Y.,Tong D.Q.,Ngan F.,et al. Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model[J],2020,125(15).
APA	Li Y..,Tong D.Q..,Ngan F..,Cohen M.D..,Stein A.F..,...&Kahn R.A..(2020).Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model.Journal of Geophysical Research: Atmospheres,125(15).
MLA	Li Y.,et al."Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model".Journal of Geophysical Research: Atmospheres 125.15(2020).