This study quantifies ammonia (NH3) emissions from biomass burning from 2005 to 2015 across the continental US (CONUS) and compares emissions from biomass burning with the US Environmental Protection Agency (EPA) National Emissions Inventory (NEI), the Fire Inventory from the National Center for Atmospheric Research (FINN) and the Global Fire Emissions Database (GFED). A statistical regression model was developed in order to predict NH3 emissions from biomass burning using a combination of fire properties and meteorological data. Satellite data were used to evaluate the annual fire strength and frequency as well as to calculate the total NH3 emissions across the CONUS. The results of this study showed the total fire number has decreased, while the total yearly burn area and the average fire radiative power has increased. The average annual NH3 emissions from biomass burning from this study, on a national scale, were approximately 5.4e8 ± 3.3e8 kg year−1. When comparing the results of this study with other emission inventories, it was found that ammonia emissions estimated by the NEI were approximately a factor of 1.3 lower than what was calculated in this study and a factor of 1.1 lower than what was modeled using the statistical regression model for 2010–2014. The calculated NH3 emissions from biomass burning were a factor of 5.9 and a factor of 13.1 higher than the emissions from FINN and the GFED, respectively. The modeled NH3 emissions from biomass burning were a factor of 5.0 and a factor of 11.1 higher than the emissions from FINN and the GFED, respectively. As the climate continues to change, the pattern (frequency, intensity and magnitude) of fires across the US will also change, leading to changes in NH3 emissions. The statistical regression model developed in this study will allow prediction of NH3 emissions associated with climate change.
A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United St...Published: 22 December 2017 by Copernicus GmbH in Geoscientific Model Development
This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM2.5 observations at 00:00, 06:00, 12:00 and 18:00UTC, and MODIS AOD at 18:00UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM2.5 (particle matter with diameter&lt; 2.5µm) leads to stronger increments in surface PM2.5 compared to its MODIS AOD assimilation at the 550nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00UTC compared to the surface PM2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.
Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple o...Published: 06 November 2017 by Copernicus GmbH in Geoscientific Model Development Discussions
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.
Dynamic Coupling of the NMMB and CMAQ Models Through the U.S. National Unified Operational Prediction Capability (NUOPC)Published: 15 September 2017 by Springer Nature in Complex Systems Modeling and Simulation in Economics and Finance
An earth system modeling framework (ESMF) that enables unprecedented insight into the various aspects of the geophysical sciences of Planet Earth in an integrated and holistic manner is needed to study the physical phenomena of weather and climate. The ESMF concept has recently been promoted and elevated by multiple governmental agencies and institutions in the U.S.A. to unify a standard engineering practice and coding protocol in building geophysical model interfaces towards efficient dynamic coupling of earth models and deployment of earth modeling systems for operational services. This new capability is called the National Unified Operational Prediction Capability (NUOPC) (available at http://www.nws.noaa.gov/nuopc/). This project demonstrates the efficacy of using NUOPC as the software package to efficiently in-line, or 2-way couple at every synchronization time-step, the dust prediction capability of the U.S. National Air Quality Forecasting Capability (NAQFC). The NAQFC in the National Centers for Environmental Prediction (NCEP) operations comprises of an off-line coupled National Weather Service (NWS) North American Mesoscale-model (NAM) and the U.S. EPA Community Air Quality Multiscale Model (CMAQ). The limitation of the off-line coupled NAM-CMAQ is that NAM gives meteorological prediction to CMAQ hourly and uni-directionally. This project attempted a new coupling paradigm allowing NAM and CMAQ communicate with one another per synchronization time-step at roughly 5 min intervals uni-directionally or bi-directionally. In this project, the NUOPC protocol was tightly followed and the in-line NAM-CMAQ ability tested to forecast fine mode particulates concentration with earth-crustal origin. A strong dust storm occurred in the South Western U.S. on May 11 2014 was used as a test case for the NUOPC in-line NAM-CMAQ forecasting capability. The forecast performance for the test case was evaluated against measured surface concentration of fine particulate smaller than 2.5 μm in diameter (PM2.5).
This study aims to improve the NOAA Operational Dust Forecasting Capability. NOAA has developed and is operating the U.S. Dust Forecasting Capability (DFC) in concert with one of its core missions to build a “Weather Ready Nation”. The current DFC is based on the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler et al. 2010). The NOAA DFC has been in operations since November 2011. DFC gives dust forecast in the form of hourly surface fine particulate (particle small than 2.5 m in diameter (PM2.5)) concentration out to 48 h covering the continental United States (CONUS). It is based on the HYSPLIT simulations made at the National Centers for Environmental Prediction (NCEP) (forecast available at http://airquality.weather.gov). The DFC real-time dust forecast is widely used to help assessing and mitigating dust storm impact on the society and the environment such as on human health (e.g., Valley Fever), air and ground transportation safety, local economy such as estate value depreciation, and climate change. This study leverages the superiority of the High Resolution Rapid Refresh (HRRR) meteorological model. HRRR is a 3 km horizontal resolution regional numerical weather prediction (NWP) model for the CONUS, run operationally at NCEP. HRRR is proposed to provide the meteorology for the DFC. We propose to develop, test, and possibly select among several wind-blown dust emission schemes for the DFC dust-emission modeling. We considered the in-line emission modules in HRRR and the FENGSHA-CMAQ (the U.S. EPA Community Multiscale Air Quality model) windblown-dust module in the operational National Air Quality Forecasting Capability (NAQFC). The FENGSHA-CMAQ version 5.1’s wind-blown dust emission and diffusion module provides the initial wind-blown dust uptake and airborne suspension from the surface by using the surface wind from HRRR, and the HRRR low layer meteorology determines transport and turbulent mixing for the dust. These emission schemes are tested and evaluated over severe dust storms in the Western U.S. on May 11 2014.
Evaluating ammonia (NH3) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in-situ aircraft...Published: 01 August 2017 by Elsevier BV in Atmospheric Environment
3D-Var versus Optimal Interpolation for Aerosol Assimilation: a Case Study over the Contiguous United StatesPublished: 11 July 2017 by Copernicus GmbH in Geoscientific Model Development Discussions
This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. GSI results are compared with those obtained using the optimal interpolation (OI) method (Tang et al., 2015) for July, 2011 over CONUS. Both GSI and OI assimilate surface PM2.5 observations at 00, 06, 12, and 18 UTC, and MODIS AOD at 18 UTC. In the GSI experiments, assimilation of surface PM2.5 leads to stronger increments in surface PM2.5 compared to the MODIS AOD assimilation. In contrast, we find a stronger impact of MODIS AOD on surface aerosols at 18 UTC compared to the surface PM2.5 OI assimilation. The increments resulting from the OI assimilation are spread in 11×11 horizontal grid cells (12&thinsp;km horizontal resolution) while the spatial distribution of GSI increments is controlled by its background error covariances, and the horizontal/vertical length scales. The assimilations of observations using both GSI and OI generally help reduce the prediction biases, and improve correlation between model predictions and observations. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). In this study, OI uses the relatively big model uncertainties, which helps yield better mean biases, but sometimes causes the RMSE increase. We also examine and discuss the sensitivity of the assimilation experiments results to the AOD forward operators
Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and a...Published: 29 June 2017 by Copernicus GmbH in Geoscientific Model Development
In this study, a regional coupled climate–chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting – Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006–2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2m temperature (T2) in this study (with a mean bias of −0.6°C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2. 5 in winter (with a normalized mean bias (NMB) of 6.4% in 2013) and O3 in summer (with an NMB of 18.2% in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate–chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in east Asia (e.g., SWDOWN and T2 decreased by 21.8Wm−2 and 0.45°C, respectively, and most pollutant concentrations increased by 4.8–9.5% in January over China's major cities) were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled...
The National Air Quality Forecasting Capability (NAQFC) upgraded its modeling system that provides developmental numerical predictions of particulate matter smaller than 2.5 μm in diameter (PM2.5) in January 2015. The issuance of PM2.5 forecast guidance has become more punctual and reliable because developmental PM2.5 predictions are provided from the same system that produces operational ozone predictions on the National Centers for Environmental Prediction (NCEP) supercomputers. There were three major upgrades in January 2015: 1) incorporation of real-time intermittent sources for particles emitted from wildfires and windblown dust originating within the NAQFC domain, 2) suppression of fugitive dust emissions from snow- and/or ice-covered terrain, and 3) a shorter life cycle for organic nitrate in the gaseous-phase chemical mechanism. In May 2015 a further upgrade for emission sources was included using the U.S. Environmental Protection Agency’s (EPA) 2011 National Emission Inventory (NEI). Emissions for ocean-going ships and on-road mobile sources will continue to rely on NEI 2005. Incremental tests and evaluations of these upgrades were performed over multiple seasons. They were verified against the EPA’s AIRNow surface monitoring network for air pollutants. Impacts of the three upgrades on the prediction of surface PM2.5 concentrations show large regional variability: the inclusion of windblown dust emissions in May 2014 improved PM2.5 predictions over the western states and the suppression of fugitive dust in January 2015 reduced PM2.5 bias by 52%, from 6.5 to 3.1 μg m−3 against a monthly average of 9.4 μg m−3 for the north-central United States.
Multi-year Downscaling Application of Online Coupled WRFCMAQ over East Asia for Regional Climate and Air Quality Modelin...Published: 07 December 2016 by Copernicus GmbH in Geoscientific Model Development Discussions
In this study, a regional coupled climate-chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional online coupled Weather Research and Forecasting &ndash; Community Multiscale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over East Asia for a multiyear climatological application during 2006&ndash;2010 driven with CESM downscaling data under Representative Concentration Pathway 4.5 (RCP 4.5) as well as a short-term air quality application in representative months in 2013 driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the online coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2-m temperature (T2) in this study compared with the Coupled Model Inter-comparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2.5 in winter and O3 in summer in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2.5 concentrations in China, WRF-CMAQ was able to capture the high PM2.5 concentrations in urban areas. In general, the online coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate-chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in East Asia were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled models over East Asia for regional climate and air quality modeling and to study the important climate-chemistry interactions.
Impact of the 2008 Global Recession on air quality over the United States: Implications for surface ozone levels from ch...Published: 05 September 2016 by Wiley in Geophysical Research Letters
Satellite and ground observations detected large variability in nitrogen oxides (NOx) during the 2008 economic recession, but the impact of the recession on air quality has not been quantified. This study combines observed NOx trends and a regional chemical transport model to quantify the impact of the recession on surface ozone (O3) levels over the continental United States. The impact is quantified by simulating O3 concentrations under two emission scenarios: business‐as‐usual (BAU) and recession. In the BAU case, the emission projection from the Cross‐State Air Pollution Rule is used to estimate the “would‐be” NOx emission level in 2011. In the recession case, the actual NO2 trends observed from Air Quality System ground monitors and the Ozone Monitoring Instrument on the Aura satellite are used to obtain “realistic” changes in NOx emissions. The model prediction with the recession effect agrees better with ground O3 observations over time and space than the prediction with the BAU emission. The results show that the recession caused a 1–2 ppbv decrease in surface O3 concentration over the eastern United States, a slight increase (0.5–1 ppbv) over the Rocky Mountain region, and mixed changes in the Pacific West. The gain in air quality benefits during the recession, however, could be quickly offset by the much slower emission reduction rate during the post‐recession period.
Evaluating ammonia (NH3) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in situ aircraft...Published: 01 September 2016 by Elsevier BV in Atmospheric Environment
Highlights•In situ aircraft measurements carried out in July and August of 2014 suggest that the CMAQ model used in the National Air Quality Forecast System underestimated the NH3 concentration in Northeastern Colorado by a factor of 2.7 (NMB = −63%).•Ground-level monitors also produced a similar result. Average satellite-retrieved NH3 levels also exceeded model predictions by a factor of 1.5–4.2 (NMB = −33 to −76%).•The underestimation of NH3 vapor was not accompanied by a comparable underestimation of particulate NH4+, which is further controlled by factors including acid availability, removal rate, and gas-particle partition.•Seasonal patterns measured at an AMoN site in the region suggest that the underestimation of NH3 is not due to the seasonal allocation of emissions, but to the overall annual emissions estimate. AbstractThe U.S. National Oceanic and Atmospheric Administration (NOAA) is responsible for forecasting elevated levels of air pollution within the National Air Quality Forecast Capability (NAQFC). The current research uses measurements gathered in the DISCOVER-AQ Colorado field campaign and the concurrent Front Range Air Pollution and Photochemistry Experiment (FRAPPE) to test performance of the NAQFC CMAQ modeling framework for predicting NH3. The DISCOVER-AQ and FRAPPE field campaigns were carried out in July and August 2014 in Northeast Colorado. Model predictions are compared with measurements of NH3 gas concentrations and the NH4+ component of fine particulate matter concentrations measured directly by the aircraft in flight. We also compare CMAQ predictions with NH3 measurements from ground-based monitors within the DISCOVER-AQ Colorado geographic domain, and from the Tropospheric Emission Spectrometer (TES) on the Aura satellite.In situ aircraft measurements carried out in July and August of 2014 suggest that the NAQFC CMAQ model underestimated the NH3 concentration in Northeastern Colorado by a factor of ∼2.7 (NMB = −63%). Ground-level monitors also produced a similar result. Average satellite-retrieved NH3 levels also exceeded model predictions by a factor of 1.5–4.2 (NMB = −33 to −76%). The underestimation of NH3 was not accompanied by an underestimation of particulate NH4+, which is further controlled by factors including acid availability, removal rate, and gas-particle partition. The average measured concentration of NH4+ was close to the average predication (NMB = +18%).Seasonal patterns measured at an AMoN site in the region suggest that the underestimation of NH3 is not due to the seasonal allocation of emissions, but to the overall annual emissions estimate. The underestimation of NH3 varied across the study domain, with the largest differences occurring in a region of intensive agriculture near Greeley, Colorado, and in the vicinity of Denver. The NAQFC modeling framework did not include a recently developed bidirectional flux algorithm for NH3, which has shown to considerably improve NH3 modeling in agricultural regions. The bidirectional flux algorithm, however, is not expected to obtain the magnitude of this increase sufficient to overcome the underestimation of NH3 found in this study. Our results suggest that further improvement of the emission inventories and modeling approaches are required to reduce the bias in NAQFC NH3 modeling predictions.
Impact of Wildfires on Atmospheric Ammonia Concentrations in the US: Coupling Satellite and Ground Based MeasurementsPublished: 15 July 2016 by MDPI AG in The 1st International Electronic Conference on Atmospheric Sciences
<p>Gaseous ammonia plays a crucial role in the earth’s atmosphere. Major sources of atmospheric ammonia include agriculture and fires. As the climate continues to change, the pattern of fires across the US will also change, leading to changes in ammonia emissions. This study examines four major science questions using satellite and <em>in-situ</em> data from 2010–2014: (1) How have concentrations of ammonia changed across the US? (2) How have the strength and frequency of fires changed? (3) How has this change in fires impacted ammonia emissions? (4) How does the US EPA NEI compare with the calculated emissions? Satellite and <em>in-situ</em> data were used to evaluate the annual concentrations of ammonia and to calculate the total ammonia emissions across the continental US. The results of this study showed that ammonia concentrations have slightly increased over the five-year period. The total fire number and the average fire radiative power have decreased, while the total yearly burn area has increased. The calculated ammonia emissions from fires on a national scale show an increasing trend and when compared with the US EPA NEI for ammonia emissions from fires, annual ammonia emissions are, on average, a factor of 0.49 higher than the NEI.</p>
The implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for global dust forecasting at NOAA/NCEPPublished: 20 May 2016 by Copernicus GmbH in Geoscientific Model Development
The NOAA National Centers for Environmental Prediction (NCEP) implemented the NOAA Environmental Modeling System (NEMS) Global Forecast System (GFS) Aerosol Component (NGAC) for global dust forecasting in collaboration with NASA Goddard Space Flight Center (GSFC). NGAC Version 1.0 has been providing 5-day dust forecasts at 1° × 1° resolution on a global scale, once per day at 00:00 Coordinated Universal Time (UTC), since September 2012. This is the first global system capable of interactive atmosphere aerosol forecasting at NCEP. The implementation of NGAC V1.0 reflects an effective and efficient transitioning of NASA research advances to NCEP operations, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders, as well as to allow the effects of aerosols on weather forecasts and climate prediction to be considered.
The Performance and Issues of a Regional Chemical Transport Model During Discover-AQ 2014 Aircraft Measurements Over Col...Published: 11 February 2016 by Springer Nature in Complex Systems Modeling and Simulation in Economics and Finance
Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for J...Published: 19 June 2015 by Informa UK Limited in Journal of the Air & Waste Management Association
We employed an optimal interpolation (OI) method to assimilate AIRNow ozone/PM2.5 and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth (AOD) data into the Community Multi-scale Air Quality (CMAQ) model to improve the ozone and total aerosol concentration for the CMAQ simulation over the contiguous United States (CONUS). AIRNow data assimilation was applied to the boundary layer, and MODIS AOD data were used to adjust total column aerosol. Four OI cases were designed to examine the effects of uncertainty setting and assimilation time; two of these cases used uncertainties that varied in time and location, or "dynamic uncertainties." More frequent assimilation and higher model uncertainties pushed the modeled results closer to the observation. Our comparison over a 24-hr period showed that ozone and PM2.5 mean biases could be reduced from 2.54 ppbV to 1.06 ppbV and from -7.14 µg/m³ to -0.11 µg/m³, respectively, over CONUS, while their correlations were also improved. Comparison to DISCOVER-AQ 2011 aircraft measurement showed that surface ozone assimilation applied to the CMAQ simulation improves regional low-altitude (below 2 km) ozone simulation.
Evaluating the Vertical Distribution of Ozone and Its Relationship to Pollution Events in Air Quality Models Using Satel...Published: 28 March 2014 by Springer Nature in Complex Systems Modeling and Simulation in Economics and Finance
Most regional scale models that are used for air quality forecasts and ozone source attribution do not adequately capture the distribution of ozone in the mid- and upper troposphere, but it is unclear how this shortcoming relates to their ability to simulate surface ozone. We combine ozone profile data from the NASA Earth Observing System (EOS) Tropospheric Emission Spectrometer (TES) and a new joint product from TES and the Ozone Monitoring Instrument along with ozonesonde measurements and EPA AirNow ground station ozone data to examine air quality events during August 2006 in the Community Multi-Scale Air Quality (CMAQ) and National Air Quality Forecast Capability (NAQFC) models. We present both aggregated statistics and case-study analyses with the goal of assessing the relationship between the models’ ability to reproduce surface air quality events and their ability to capture the vertical distribution of ozone. We find that the models lack the mid-tropospheric ozone variability seen in TES and the ozonesonde data, and discuss future work to determine the conditions under which this variability appears to be important for surface air quality.
Incremental Development of Air Quality Forecasting System with Off-Line/On-Line Capability: Coupling CMAQ to NCEP Nation...Published: 17 June 2011 by Springer Nature in NATO Science for Peace and Security Series C: Environmental Security
The National Air Quality Forecast Capability (NAQFC) is based on the EPA Community Multiscale Air Quality (CMAQ) model driven by meteorological data from the NOAA North American Mesoscale (NAM) Non-hydrostatic Meso-scale Model (NMM). Currently, NMM meteorological data on Arakawa E-grid are interpolated on a CMAQ’s Arakawa C-grid using the processors PRODGEN and PREMAQ to handle map-projection transform, vertical layer collapsing, and other emission and meteorological data feed issues. The FY11 pre-implementation version of NAM has undergone significant changes in the vertical layering, horizontal grid projection and improved science components for its FY11 upcoming major upgrade release. This provides an opportunity to improve the coupling methodology between NMM and CMAQ that reduces uncertainties both in the meteorological and emission inputs for the off-line air quality modeling and helps development of on-line NMM-CMAQ version. Three major tasks are needed to achieve a tighter coupling between them: (1) Adapt to NAM’s vertical hybrid pressure and grid structure; (2) Change CMAQ to use the same rotated latitude longitude B staggered horizontal grid structure as NAM, (3) Modify emission model to provide generic inputs for the B staggered grid and hybrid vertical structure of NAM. The first task achieves consistent matching of dynamics between the two systems, despite the possible necessity of layer-collapsing to fit within operational time-lines. The second task removes unnecessary interpolation of meteorology data for air quality simulations. The third task involves modification of the U.S. EPA Sparse Matrix Object Kernel Emission (SMOKE) model to handle the staggered B grid. At this time only the first of these three steps has been accomplished, and the test result from this test focusing on the selected test period has been compared to that produced by the operational NAQFC. Further work with all these three modifications concurrently in place is underway.
Discrepancies in grid structure, dynamics and physics packages in the offline coupled NWS/NCEP NAM meteorological model with the U.S. Environmental Protection Agency Community Multiscale Air Quality (CMAQ) model can give rise to inconsistencies. This study investigates the use of three vertical mixing schemes to drive chemistry tracers in the National Air Quality Forecast Capability (NAQFC). The three schemes evaluated in this study represent various degrees of coupling to improve the commonality in turbulence parameterization between the meteorological and chemistry models. The methods tested include: (1) using NAM predicted TKE-based planetary boundary height, h, as the prime parameter to derive CMAQ vertical diffusivity; (2) using the NAM mixed layer depth to determine h and then proceeding as in (1); and (3) using NAM predicted vertical diffusivity directly to parameterize turbulence mixing within CMAQ. A two week period with elevated surface O3 concentrations during the summer 2006 has been selected to test these schemes in a sensitivity study. The study results are verified and evaluated using the EPA AIRNow monitoring network and other ozonesonde data. The third method is preferred a priori as it represents the tightest coupling option studied in this work for turbulent mixing processes between the meteorological and air quality models. It was found to accurately reproduce the upper bounds of turbulent mixing and provide the best agreement between predicted h and ozonesonde observed relative humidity profile inferred h for sites investigated in this study. However, this did not translate into the best agreement in surface O3 concentrations. Overall verification results during the test period of two weeks in August 2006, did not show superiority of this method over the other 2 methods in all regions of the continental U.S. Further efforts in model improvement for the parameterizations of turbulent mixing and other surface O3 forecast related processes are warranted.
The impact of chemical lateral boundary conditions on CMAQ predictions of tropospheric ozone over the continental United...Published: 10 September 2008 by Springer Nature in Environmental Fluid Mechanics
A sensitivity study is performed to examine the impact of lateral boundary conditions (LBCs) on the NOAA-EPA operational Air Quality Forecast Guidance over continental USA. We examined six LBCS: the fixed profile LBC, three global LBCs, and two ozonesonde LBCs for summer 2006. The simulated results from these six runs are compared to IONS ozonesonde and surface ozone measurements from August 1 to 5, 2006. The choice of LBCs can affect the ozone prediction throughout the domain, and mainly influence the predictions in upper altitude or near inflow boundaries, such as the US west coast and the northern border. Statistical results shows that the use of global model predictions for LBCs could improve the correlation coefficients of surface ozone prediction over the US west coast, but could also increase the ozone mean bias in most regions of the domain depending on global models. In this study, the use of the MOZART (Model for Ozone And Related chemical Tracers) prediction for CMAQ (Community Multiscale Air Quality) LBC shows a better surface ozone prediction than that with fixed LBC, especially over the US west coast. The LBCs derived from ozonesonde measurements yielded better O3 correlations in the upper troposphere.
Influence of lateral and top boundary conditions on regional air quality prediction: A multiscale study coupling regiona...Published: 25 April 2007 by American Geophysical Union (AGU) in Journal of Geophysical Research
 The sensitivity of regional air quality model to various lateral and top boundary conditions is studied at 2 scales: a 60 km domain covering the whole USA and a 12 km domain over northeastern USA. Three global models (MOZART‐NCAR, MOZART‐GFDL and RAQMS) are used to drive the STEM‐2K3 regional model with time‐varied lateral and top boundary conditions (BCs). The regional simulations with different global BCs are examined using ICARTT aircraft measurements performed in the summer of 2004, and the simulations are shown to be sensitive to the boundary conditions from the global models, especially for relatively long‐lived species, like CO and O3. Differences in the mean CO concentrations from three different global‐model boundary conditions are as large as 40 ppbv, and the effects of the BCs on CO are shown to be important throughout the troposphere, even near surface. Top boundary conditions show strong effect on O3 predictions above 4 km. Over certain model grids, the model's sensitivity to BCs is found to depend not only on the distance from the domain's top and lateral boundaries, downwind/upwind situation, but also on regional emissions and species properties. The near‐surface prediction over polluted area is usually not as sensitive to the variation of BCs, but to the magnitude of their background concentrations. We also test the sensitivity of model to temporal and spatial variations of the BCs by comparing the simulations with time‐varied BCs to the corresponding simulations with time‐mean and profile BCs. Removing the time variation of BCs leads to a significant bias on the variation prediction and sometime causes the bias in predicted mean values. The effect of model resolution on the BC sensitivity is also studied.