Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign

Pan, Li; Kim, Hyun Cheol; Lee, Pius; Saylor, Rick; Tang, Youhua; Tong, Daniel; Baker, Barry; Kondragunta, Shobha; Xu, Caijun; Ruminski, M.; Chen, Weiwei; McQueen, Jeff; Štajner, Ivanka

doi:10.5194/gmd-13-2169-2020

Cited by 7 publications

(8 citation statements)

References 53 publications

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“…The National Air Quality Forecasting Capability (NAQFC) model is developed by NOAA/Air Resources Laboratory (ARL) and National Centers for Environmental Prediction (NCEP) to provide operational 48 h air quality prediction over the US (Lee et al, 2017;Pan et al, 2020a) (Tong et al, 2015). Wildfire emissions, thermal, and speciation characteristics are determined in near-real time by the US Forest Service X.…”

Section: Naqfcmentioning

confidence: 99%

“…Additionally, multiple regional air quality prediction systems across different regions in North America have also included smoke predictions (Ahmadov et al, 2017;Chen et al, 2019;Herron-Thorpe et al, 2014;Lee et al, 2017;Pavlovic et al, 2016). Effective performance of these air quality forecasting systems has been reported during fire seasons (e.g., Chen et al, 2019;Pan et al, 2020a;Yuchi et al, 2016). However, many of the investigations that evaluate simulations of wildfire smoke are implemented in a retrospective way with the proxies for fire emissions already known, and only a few of them were aimed at demonstrating predictive skill and using different evaluation metrics.…”

Section: Introductionmentioning

confidence: 99%

“…Although advances have been made in a number of forecasting systems, biomass burning emissions and smoke are still within the greatest sources of uncertainties in air quality predictions (Carter et al, 2020;Kaiser et al, 2012;Pan et al, 2020b), relating to many challenges that remain unresolved. Firstly, wildfires occur sporadically in many places; thus the emissions are inherently unpredictable.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

Arab

Ahmadov

et al. 2021

Atmos. Chem. Phys.

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Abstract. Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

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Section: Naqfcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

Arab

Ahmadov

et al. 2021

Atmos. Chem. Phys.

Self Cite

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show abstract

“…The National Air Quality Forecasting Capability (NAQFC) model is developed by NOAA/Air Resources Laboratory (ARL) and National Centers for Environmental Prediction (NCEP) to provide operational 48 h air quality prediction over the US (Lee et al, 2017;Pan et al, 2020a) at 12 km resolution. It is based on the US Environmental Protection Agency (EPA) Community Multi-scale Air Quality (CMAQ) v5.02 (Byun and Schere, 2006) modeling system and is offline-driven by the 12 km North American Model (NAM) Nonhydrostatic Multiscale Model (Janjic and Gall, 2012).…”

Section: Naqfcmentioning

confidence: 99%

Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

Ye¹,

Arab²,

Ahmadov³

et al. 2021

Preprint

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Abstract. Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from twelve state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, the U.S., August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within one day are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by FRP-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019 mainly over the transported smoke plumes, owing to the underestimated emissions on the 7th. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center’s Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper with the day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported one-day-old smoke. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

show abstract

“…The NOAA HMS is a satellite-based fire detection system that includes manual quality control. The detailed wildfire emission process of this system has been described in Pan et al (2020).…”

Section: Model Configuration and Experiments Designmentioning

confidence: 99%

Comparison of chemical lateral boundary conditions for air quality predictions over the contiguous United States during pollutant intrusion events

et al. 2021

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Abstract. The National Air Quality Forecast Capability (NAQFC) operated in the US National Oceanic and Atmospheric Administration (NOAA) provides the operational forecast guidance for ozone and fine particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5) over the contiguous 48 US states (CONUS) using the Community Multi-scale Air Quality (CMAQ) model. The existing NAQFC uses climatological chemical lateral boundary conditions (CLBCs), which cannot capture pollutant intrusion events originating outside of the model domain. In this study, we developed a model framework to use dynamic CLBCs from the Goddard Earth Observing System Model, version 5 (GEOS) to drive NAQFC. A mapping of the GEOS chemical species to CMAQ's CB05–AERO6 (Carbon Bond 5; version 6 of the aerosol module) species was developed. The utilization of the GEOS dynamic CLBCs in NAQFC showed the best overall performance in simulating the surface observations during the Saharan dust intrusion and Canadian wildfire events in summer 2015. The simulated PM2.5 was improved from 0.18 to 0.37, and the mean bias was reduced from −6.74 to −2.96 µg m−3 over CONUS. Although the effect of CLBCs on the PM2.5 correlation was mainly near the inflow boundary, its impact on the background concentrations reached further inside the domain. The CLBCs could affect background ozone concentrations through the inflows of ozone itself and its precursors, such as CO. It was further found that the aerosol optical thickness (AOT) from satellite retrievals correlated well with the column CO and elemental carbon from GEOS. The satellite-derived AOT CLBCs generally improved the model performance for the wildfire intrusion events during a summer 2018 case study and demonstrated how satellite observations of atmospheric composition could be used as an alternative method to capture the air quality effects of intrusions when the CLBCs of global models, such as GEOS CLBCs, are not available.

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Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign

Cited by 7 publications

References 53 publications

Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

Comparison of chemical lateral boundary conditions for air quality predictions over the contiguous United States during pollutant intrusion events

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