2020
DOI: 10.1029/2020jd032554
|View full text |Cite
|
Sign up to set email alerts
|

A Novel Ensemble Design for Probabilistic Predictions of Fine Particulate Matter Over the Contiguous United States (CONUS)

Abstract: This study examines the benefit of using a dynamical ensemble for 48 hr deterministic and probabilistic predictions of near‐surface fine particulate matter (PM2.5) over the contiguous United States (CONUS). Our ensemble design captures three key sources of uncertainties in PM2.5 modeling including meteorology, emissions, and secondary organic aerosol (SOA) formation. Twenty‐four ensemble members were simulated using the Community Multiscale Air Quality (CMAQ) model during January, April, July, and October 2016… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
4
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
3

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(4 citation statements)
references
References 98 publications
0
4
0
Order By: Relevance
“…In addition, diurnal and day‐to‐day variations of wildfire behavior due to fuel aridity and availability, fire weather, fire containment activities and combustion stage can limit model forecasting performance during large wildfires (Saide et al., 2015). Besides, a variety of input data sets, such as meteorological fields and chemical transports (Garcia‐Menendez et al., 2013; F. Li et al., 2019; Y. Li et al., 2020) and plume rise schemes (Briggs, 1969; Freitas et al., 2007; Y. Li et al., 2023; Paugam et al., 2016; Sofiev et al., 2012; Stein et al., 2009; Vernon et al., 2018; Zhu et al., 2018), are implemented differently in each model and can also impact the AOD and PM 2.5 forecasting performance (Delle Monache & Stull, 2003; Kumar et al., 2020).…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…In addition, diurnal and day‐to‐day variations of wildfire behavior due to fuel aridity and availability, fire weather, fire containment activities and combustion stage can limit model forecasting performance during large wildfires (Saide et al., 2015). Besides, a variety of input data sets, such as meteorological fields and chemical transports (Garcia‐Menendez et al., 2013; F. Li et al., 2019; Y. Li et al., 2020) and plume rise schemes (Briggs, 1969; Freitas et al., 2007; Y. Li et al., 2023; Paugam et al., 2016; Sofiev et al., 2012; Stein et al., 2009; Vernon et al., 2018; Zhu et al., 2018), are implemented differently in each model and can also impact the AOD and PM 2.5 forecasting performance (Delle Monache & Stull, 2003; Kumar et al., 2020).…”
Section: Resultsmentioning
confidence: 99%
“…Li et al, 2020) and plume rise schemes (Briggs, 1969;Freitas et al, 2007;Y. Li et al, 2023;Paugam et al, 2016;Sofiev et al, 2012;Stein et al, 2009;Vernon et al, 2018;Zhu et al, 2018), are implemented differently in each model and can also impact the AOD and PM 2.5 forecasting performance (Delle Monache & Stull, 2003;Kumar et al, 2020).…”
Section: Discussion Of Ensemble Forecast Performancementioning
confidence: 99%
See 1 more Smart Citation
“…For both species, the correlation coefficient of the median is higher than the correlation coefficient derived from each of the nine models. The ensemble approach can be improved by attributing a weight to each model that is determined by an optimization procedure like a least‐square error minimization of the ensemble forecasts during a training period covering a few weeks (Pagowski et al., 2005) or via ensemble‐calibration using the variance‐deficit and model output statistics methods (Kumar, Alessandrini, et al., 2020). Large computational costs of a multi‐model ensemble can make their implementation in operations challenging, but this problem can be partially addressed using an analog‐based method of generating air quality ensemble (Delle Monache et al., 2020).…”
Section: Chemical Weathermentioning
confidence: 99%