scholarly journals 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

2017 ◽  
Author(s):  
Li Pan ◽  
Hyun Cheol Kim ◽  
Pius Lee ◽  
Rick Saylor ◽  
YouHua Tang ◽  
...  
Keyword(s):  
Air Quality ◽  
Evaluation Methods ◽  
Hazard Mapping ◽  
Data Sets ◽  
Observation Data ◽  
Field Campaign ◽  
Smoke Plume ◽  
Fire Emissions ◽  
Air Quality Forecast ◽  
Multiple Data Sets

Abstract. Multiple observation data sets, including Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign are used to evaluate the HMS-BlueSky-SMOKE-CMAQ fire emissions and smoke plume prediction system. A similar configuration is used in the National Air Quality Forecasting Capability (NAQFC). The system was found to capture signatures of most of the observed fire signals. Use of HMS-detected fire hotspots and smoke plume information are valuable for both initiating fire emissions and evaluating model simulations. However, we also found that the current system does not include fire contributions through lateral boundary condition and missed fires that are not associated with visible smoke plumes resulting in significant simulation uncertainties. In this study we focused not only on model evaluation but also on evaluation methods. We discuss how to use observational data correctly to filter out fire signals and synergistic use of multiple data sets together. We also address the limitations of each of the observation data sets and of the evaluation methods.

scholarly journals 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

2020 ◽  
Vol 13 (5) ◽  
pp. 2169-2184
Author(s):  
Li Pan ◽  
HyunCheol Kim ◽  
Pius Lee ◽  
Rick Saylor ◽  
YouHua Tang ◽  
...  
Keyword(s):  
Air Quality ◽  
Evaluation Methods ◽  
Hazard Mapping ◽  
Data Sets ◽  
Observation Data ◽  
Field Campaign ◽  
Smoke Plume ◽  
Fire Emissions ◽  
Air Quality Forecast ◽  
Multiple Data Sets

Abstract. Multiple observation data sets – Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, the Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign – are used to evaluate the HMS–BlueSky–SMOKE (Sparse Matrix Operator Kernel Emission)–CMAQ (Community Multi-scale Air Quality Model) fire emissions and smoke plume prediction system. A similar configuration is used in the US National Air Quality Forecasting Capability (NAQFC). The system was found to capture most of the observed fire signals. Usage of HMS-detected fire hotspots and smoke plume information was valuable for deriving both fire emissions and forecast evaluation. This study also identified that the operational NAQFC did not include fire contributions through lateral boundary conditions, resulting in significant simulation uncertainties. In this study we focused both on system evaluation and evaluation methods. We discussed how to use observational data correctly to retrieve fire signals and synergistically use multiple data sets. We also addressed the limitations of each of the observation data sets and evaluation methods.

scholarly journals 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

2018 ◽  
Author(s):  
Li Pan ◽  
Hyun Cheol Kim ◽  
Pius Lee ◽  
Rick Saylor ◽  
Youhua Tang ◽  
...  
Keyword(s):  
Air Quality ◽  
Evaluation Methods ◽  
Hazard Mapping ◽  
Data Sets ◽  
Observation Data ◽  
Field Campaign ◽  
Smoke Plume ◽  
Fire Emissions ◽  
Air Quality Forecast ◽  
Multiple Data Sets

Abstract. Multiple observation data sets: Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign are used to evaluate the HMS-BlueSky-SMOKE CMAQ fire emissions and smoke plume prediction system. A similar configuration is used in the US National Air Quality Forecasting Capability (NAQFC). The system was found to capture most of the observed fire signals. Usage of HMS-detected fire hotspots and smoke plume information were valuable for both deriving fire emissions and forecast evaluation. This study also helped identified that the operational NAQFC did not include fire contributions through lateral boundary conditions resulting in significant simulation uncertainties. In this study we focused both on system evaluation and evaluation methods. We discussed how to use observational data correctly to filter out fire signals and synergistically use multiple data sets. We also addressed the limitations of each of the observation data sets and evaluation methods.

scholarly journals Importance of transboundary transport of biomass burning emissions to regional air quality in Southeast Asia during a high fire event

2015 ◽  
Vol 15 (1) ◽  
pp. 363-373 ◽  
Author(s):  
B. Aouizerats ◽  
G. R. van der Werf ◽  
R. Balasubramanian ◽  
R. Betha
Keyword(s):  
Air Quality ◽  
Southeast Asia ◽  
Biomass Burning ◽  
Ambient Air ◽  
Basis Set ◽  
Data Sets ◽  
Test Model ◽  
Air Masses ◽  
Fire Emissions ◽  
Regional Air Quality

Abstract. Smoke from biomass and peat burning has a notable impact on ambient air quality and climate in the Southeast Asia (SEA) region. We modeled a large fire-induced haze episode in 2006 stemming mostly from Indonesia using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). We focused on the evolution of the fire plume composition and its interaction with the urbanized area of the city state of Singapore, and on comparisons of modeled and measured aerosol and carbon monoxide (CO) concentrations. Two simulations were run with WRF-Chem using the complex volatility basis set (VBS) scheme to reproduce primary and secondary aerosol evolution and concentration. The first simulation referred to as WRF-FIRE included anthropogenic, biogenic and biomass burning emissions from the Global Fire Emissions Database (GFED3) while the second simulation referred to as WRF-NOFIRE was run without emissions from biomass burning. To test model performance, we used three independent data sets for comparison including airborne measurements of particulate matter (PM) with a diameter of 10 μm or less (PM10) in Singapore, CO measurements in Sumatra, and aerosol optical depth (AOD) column observations from four satellite-based sensors. We found reasonable agreement between the model runs and both ground-based measurements of CO and PM10. The comparison with AOD was less favorable and indicated the model underestimated AOD, although the degree of mismatch varied between different satellite data sets. During our study period, forest and peat fires in Sumatra were the main cause of enhanced aerosol concentrations from regional transport over Singapore. Analysis of the biomass burning plume showed high concentrations of primary organic aerosols (POA) with values up to 600 μg m−3 over the fire locations. The concentration of POA remained quite stable within the plume between the main burning region and Singapore while the secondary organic aerosol (SOA) concentration slightly increased. However, the absolute concentrations of SOA (up to 20 μg m−3) were much lower than those from POA, indicating a minor role of SOA in these biomass burning plumes. Our results show that about 21% of the total mass loading of ambient PM10 during the July–October study period in Singapore was due to biomass and peat burning in Sumatra, but this contribution increased during high burning periods. In total, our model results indicated that during 35 days aerosol concentrations in Singapore were above the threshold of 50 μg m−3 day−1 indicating poor air quality. During 17 days this was due to fires, based on the difference between the simulations with and without fires. Local pollution in combination with recirculation of air masses was probably the main cause of poor air quality during the other 18 days, although fires from Sumatra and probably also from Kalimantan (Indonesian part of the island of Borneo) added to the enhanced PM10 concentrations. The model versus measurement comparisons highlighted that for our study period and region the GFED3 biomass burning aerosol emissions were more in line with observations than found in other studies. This indicates that care should be taken when using AOD to constrain emissions or estimate ground-level air quality. This study also shows the need for relatively high resolution modeling to accurately reproduce the advection of air masses necessary to quantify the impacts and feedbacks on regional air quality.

scholarly journals Assessing boreal forest fire smoke aerosol impacts on U.S. air quality: A case study using multiple data sets

2011 ◽  
Vol 116 (D22) ◽  
pp. n/a-n/a ◽  
Author(s):  
David J. Miller ◽  
Kang Sun ◽  
Mark A. Zondlo ◽  
David Kanter ◽  
Oleg Dubovik ◽  
...  
Keyword(s):  
Air Quality ◽  
Boreal Forest ◽  
Forest Fire ◽  
Data Sets ◽  
Smoke Aerosol ◽  
Multiple Data ◽  
Fire Smoke ◽  
Multiple Data Sets

scholarly journals Connecting smoke plumes to sources using Hazard Mapping System (HMS) smoke and fire location data over North America

2018 ◽  
Vol 18 (3) ◽  
pp. 1745-1761 ◽  
Author(s):  
Steven J. Brey ◽  
Mark Ruminski ◽  
Samuel A. Atwood ◽  
Emily V. Fischer
Keyword(s):  
United States ◽  
Air Quality ◽  
North America ◽  
Fire Severity ◽  
Information Service ◽  
Hazard Mapping ◽  
Fire Emissions ◽  
The North ◽  
Mapping System ◽  
Smoke Plumes

Abstract. Fires represent an air quality challenge because they are large, dynamic and transient sources of particulate matter and ozone precursors. Transported smoke can deteriorate air quality over large regions. Fire severity and frequency are likely to increase in the future, exacerbating an existing problem. Using the National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke data for North America for the period 2007 to 2014, we examine a subset of fires that are confirmed to have produced sufficient smoke to warrant the initiation of a U.S. National Weather Service smoke forecast. We find that gridded HMS-analyzed fires are well correlated (r= 0.84) with emissions from the Global Fire Emissions Inventory Database 4s (GFED4s). We define a new metric, smoke hours, by linking observed smoke plumes to active fires using ensembles of forward trajectories. This work shows that the Southwest, Northwest, and Northwest Territories initiate the most air quality forecasts and produce more smoke than any other North American region by measure of the number of HYSPLIT points analyzed, the duration of those HYSPLIT points, and the total number of smoke hours produced. The average number of days with smoke plumes overhead is largest over the north-central United States. Only Alaska, the Northwest, the Southwest, and Southeast United States regions produce the majority of smoke plumes observed over their own borders. This work moves a new dataset from a daily operational setting to a research context, and it demonstrates how changes to the frequency or intensity of fires in the western United States could impact other regions.

Air Quality Forecast Model Based on Model Products of CMAQ in Fuzhou City, China

2012 ◽  
Vol 518-523 ◽  
pp. 1442-1450
Author(s):  
Bin Bin Chen ◽  
Kai Yang ◽  
Chang Cheng Lin ◽  
Wen Lin ◽  
Hong Wang ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Forecast Model ◽  
Observation Data ◽  
Air Quality Model ◽  
Quality Model ◽  
Forecasting Accuracy ◽  
Air Quality Forecast ◽  
Index Level ◽  
Daily Forecast

Based on the forecast products of Community Multiscale Air Quality Model (CMAQ), observational data of air pollutants and the conventional meteorological observation data of surface from January 2007 to June 2010 in Fuzhou City, Fujian Province, China, the weather systems influencing on Fuzhou City are divided into 7 weather patterns, including continental high, subtropical high, shear, warm sectors convergence, upper trough, typhoon and tropical convergence. According to the forecast method of combination of dynamic and statistic, using multiple linear stepwise regression method, the forecasting models of daily pollutant concentration under different weather systems are established. The confidence level of the equations achieves 0.000 and so the models are statistically significant. Using the models, the prediction effect of the concentration of different air pollutants in Fuzhou City from July to December, 2010 is tested by back substitution. The forecasting accuracy on contamination index level of PM10 reaches to 71.3%, and the forecasting accuracy on SO2和NO2 both reaches to 100%. The comprehensive score of air quality daily forecast in the city comes to 88.8 points on average.

scholarly journals A Harmonized Global Land Evaporation Dataset from Reanalysis Products Covering 1980–2017

2021 ◽  
Author(s):  
Jiao Lu ◽  
Guojie Wang ◽  
Tiexi Chen ◽  
Shijie Li ◽  
Daniel Fiifi Tawia Hagan ◽  
...  
Keyword(s):  
Central Africa ◽  
Model Performance ◽  
Congo Basin ◽  
Data Sets ◽  
Observation Data ◽  
Ensemble Averaging ◽  
Data Merging ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
High Consistency

Abstract. Land evaporation (ET) plays a crucial role in hydrological and energy cycle. However, the widely used numerical products are still subject to great uncertainties due to imperfect model parameterizations and forcing data. Lack of available observed data has further complicated the estimation. Hence, there is an urgency to define the global benchmark land ET for climate-induced hydrology and energy change. In this study, we have used the coefficient of variation (CV) and carefully selected merging regions with high consistency of multiple data sets. Reliability Ensemble Averaging (REA) method has been used to generate a long-term (1980–2017) daily ET product with a spatial resolution of 0.25 degree by merging the selected three data sets, ERA5, GLDAS2 and MERRA2. GLEAM3.2a and flux tower observation data have been selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well under a variety of vegetation cover conditions as the weights were distributed across the east-west direction banding manner, with greater differences near the equator. The merged product also captured well the trend of land evaporation over different areas, showing the significant decreasing trend in Amazon plain in South America and Congo Basin in central Africa, and the increasing trend in the east of North America, west of Europe, south of Asia and north of Oceania. In addition to model performance, REA method also successfully worked for the model convergence showing as an outstanding reference for data merging of other variables. Data can be accessed at https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).

scholarly journals Toward enhanced capability for detecting and predicting dust events in the western United States: the Arizona case study

2015 ◽  
Vol 15 (21) ◽  
pp. 12595-12610 ◽  
Author(s):  
M. Huang ◽  
D. Tong ◽  
P. Lee ◽  
L. Pan ◽  
Y. Tang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Air Quality ◽  
Atmospheric Chemistry ◽  
Human Life ◽  
Stratospheric Ozone ◽  
Data Sets ◽  
Observation Data ◽  
Western Us ◽  
Dust Events ◽  
The Impact

Abstract. Dust aerosols affect human life, ecosystems, atmospheric chemistry and climate in various aspects. Some studies have revealed intensified dust activity in the western US during the past decades despite the weaker dust activity in non-US regions. It is important to extend the historical dust records, to better understand their temporal changes, and to use such information to improve the daily dust forecasting skill as well as the projection of future dust activity under the changing climate. This study develops dust records in Arizona in 2005–2013 using multiple observation data sets, including in situ measurements at the surface Air Quality System (AQS) and Interagency Monitoring of Protected Visual Environments (IMPROVE) sites, and level 2 deep blue aerosol product by the Moderate Resolution Imaging Spectroradiometer. The diurnal and inter-annual variability of identified dust events are shown related to observed weather patterns (e.g., wind and soil moisture) and surface conditions (e.g., land cover type and vegetation conditions), suggesting a potential for use of satellite soil moisture and land products to help interpret and predict dust activity. Backtrajectories computed using NOAA's Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model indicate that the Sonoran and Chihuahuan deserts are important dust source regions during identified dust events in Phoenix, Arizona. Finally, we assess the impact of a recent strong dust event on western US air quality, using various observational and modeling data sets, during a period with a stratospheric ozone intrusion event. The capability of the current US National Air Quality Forecasting Capability (NAQFC) Community Multi-scale Air Quality (CMAQ) modeling system to represent the magnitude and the temporal variability of aerosol concentrations is evaluated for this event. Directions for integrating observations to further improve dust emission modeling in CMAQ are also suggested.

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