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.

doi:10.1029/2020JD032768

i

Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model

2020
By Li, Y. ; Tong, D. Q. ; Ngan, F. ; ...

Select the Download button to view the document

Please click the download button to view the document.

Details

Journal Title:

Journal of Geophysical Research: Atmospheres
Personal Author:

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.
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; ARL (Air Resources Laboratory) ; CPO (Climate Program Office) ; OWAQ (Office of Weather and Air Quality) ; NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CISESS (Cooperative Institute for Satellite Earth System Studies)
Description:

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.
Keywords:

Air Quality Wildfires Air Quality
Source:

Journal of Geophysical Research: Atmospheres, 125(15)
DOI:

https://doi.org/10.1029/2020JD032768
Document Type:

Journal Article
Funding:

Grant no. NA16SEC4810006
Rights Information:

CC BY-NC-ND
Compliance:

Submitted
Main Document Checksum:

urn:sha256:731860367a262555c2ca81abb8501a70e40da8bd607762eaa62817ac776c70e4
Download URL:

https://repository.library.noaa.gov/view/noaa/27389/noaa_27389_DS1.pdf
File Type:

[PDF - 11.53 MB ]

HYSPLIT

Dataset

HYSPLIT is a complete system for computing simple air parcel trajectories, as well as complex transport, dispersion, chemical transformation, and depo ...

United States. National Oceanic and Atmospheric Administration. Office of Oceanic and Atmospheric Research ...

ON THIS PAGE

Details Datasets

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.