Ensemble PM_2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model

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/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.

Air quality changes in Ukraine during the April 2020 wildfire event

“…Forward and backward trajectories derived from the HYSPLIT model partially fill the gap in observations and allow defining the areas affected by pollution distribution. Recently, HYSPLIT has already been used for the analysis of wildfires (Chai et al, 2020;Kim et al, 2020;Li et al, 2020) and dust storm episodes (Kalkstein et al, 2020).…”

Dominance of Wildfires Impact on Air Quality Exceedances During the 2020 Record‐Breaking Wildfire Season in the United States

“…Li et al, 2020;Sofiev et al, 2012;Val Martin, et al, 2018), which affect the simulation of surface air pollution due to fires (Y. Li et al, 2020;Xie et al, 2020). Y.…”

“…The α, β, γ, δ values are based on Sofiev et al (2012), and Y. Li et al (2020). For wildfire simulations, the Sofiev scheme is more stable and accurate (Y.…”

“…The Sofiev scheme utilizes fire radiative power (FRP), planetary boundary layer (PBL) height, and the Brunt‐Vaisala frequency in the free troposphere to estimate fire injection height. Both the Sofiev plume rise scheme and the GBBEPx emission products have been shown to perform well during large wildfire events such as the 2018 Camp Fire (Y. Li et al., 2020).…”

“…Both the Sofiev plume rise scheme and the GBBEPx emission products have been shown to perform well during large wildfire events such as the 2018 Camp Fire (Y. Li et al, 2020).…”