Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model. Issue 15 (28th July 2020)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 15(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-28
• Subjects: biomass burning -- satellite -- air quality -- Camp Fire -- ensemble forecast -- HYSPLIT; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2020JD032768
• Languages: English
• ISSNs: 2169-897X

Abstract

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 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/m 3, 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. Key Points: The difference between satellite fire emission data sets is large, which reaches a factor of 10 Ensemble forecast with different emission data sets, meteorology, and plume rise schemes can reduce forecast uncertainty The new ensemble size reduction technique can save calculation time while improving the performance of the ensemble forecast