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A new dynamical core, known as the Finite-Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA's Global Forecast System (GFS) and in limited-area models for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), which includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecasting Capability (NAQFC) that will continue to protect human and ecosystem health in the US. Here we describe the development of the FV3GFSv16 coupling with a “state-of-the-science” CMAQ model version 5.3.1. The GFS–CMAQ coupling is made possible by the seminal version of the NOAA-EPA Atmosphere–Chemistry Coupler (NACC), which became a major piece of the next operational NAQFC system (i.e., NACC-CMAQ) on 20 July 2021. NACC-CMAQ has a number of scientific advancements that include satellite-based data acquisition technology to improve land cover and soil characteristics and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ model has significantly different meteorological and chemical predictions compared to the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM2.5 predictions and diurnal patterns, both of which are extended to a 72 h (3 d) forecast with this system.

In September 2020, a global aerosol forecasting model was implemented as an ensemble member of the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Ensemble Forecasting System (GEFS) v12.0.1 (hereafter referred to as “GEFS-Aerosols”). In this study, GEFS-Aerosols simulation results from September 1, 2019 to September 30, 2020 were evaluated using an aerosol budget analysis. These results were compared with results from other global models as well as reanalysis data. From this analysis, the global average lifetimes of black carbon (BC), organic carbon (OC), dust, sea salt, and sulfate are 4.06, 4.29, 4.59, 0.34 and 3.3 days, respectively, with the annual average loads of 0.135, 1.29, 4.52, 6.80 and 0.50 TG. Compared to National Aeronautics and Space Administration (NASA)’s Goddard Earth Observing System-Goddard Chemistry Aerosol and Radiation Transport Model (GEOS4-GOCART), the aerosols in GEFS-Aerosols have a relatively short lifetime because of the faster removal processes in GEFS-Aerosols. Meanwhile, in GEFS-Aerosols, aerosol emissions are the determining factor for the mass and composition of aerosols in the atmosphere. The size (bin) distribution of aerosol emissions is as important as its total emissions, especially in simulations of dust and sea salt. Also most importantly, the strong monthly and interannually variations in natural sources of aerosols in GEFS-Aerosols suggests that improving the accuracy of prognostic concentrations of aerosols is important for applying aerosol feedback to weather and climate predictions.

The latest operational National Air Quality Forecast Capability (NAQFC) has been advanced to use the Community Multiscale Air Quality (CMAQ) model (version 5.3.1) with the CB6r3 (Carbon Bond 6 revision 3) AERO7 (version 7 of the aerosol module) chemical mechanism and is driven by the Finite-Volume Cubed-Sphere (FV3) Global Forecast System, version 16 (GFSv16). This update has been accomplished via the development of the meteorological preprocessor, NOAA-EPA Atmosphere–Chemistry Coupler (NACC), adapted from the existing Meteorology–Chemistry Interface Processor (MCIP). Differing from the typically used Weather Research and Forecasting (WRF) CMAQ system in the air quality research community, the interpolation-based NACC can use various meteorological outputs to drive the CMAQ model (e.g., FV3-GFSv16), even though they are on different grids. In this study, we compare and evaluate GFSv16-CMAQ and WRFv4.0.3-CMAQ using observations over the contiguous United States (CONUS) in summer 2019 that have been verified with surface meteorological and AIRNow observations. During this period, the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign was performed, and we compare the two models with airborne measurements from the NASA DC-8 aircraft. The GFS-CMAQ and WRF-CMAQ systems show similar performance overall with some differences for certain events, species and regions. The GFSv16 meteorology tends to have a stronger diurnal variability in the planetary boundary layer height (higher during daytime and lower at night) than WRF over the US Pacific coast, and it also predicted lower nighttime 10 m winds. In summer 2019, the GFS-CMAQ system showed better surface ozone (O3) than WRF-CMAQ at night over the CONUS domain; however, the models' fine particulate matter (PM2.5) predictions showed mixed verification results: GFS-CMAQ yielded better mean biases but poorer correlations over the Pacific coast. These results indicate that using global GFSv16 meteorology with NACC to directly drive CMAQ via interpolation is feasible and yields reasonable results compared to the commonly used WRF approach.

As a candidate for the next-generation National Air Quality Forecast Capability (NAQFC), the meteorological forecast from the Global Forecast System with the new Finite Volume Cube-Sphere dynamical core (GFS–FV3) will be applied to drive the chemical evolution of gases and particles described by the Community Multiscale Air Quality modeling system. CMAQv5.0.2, a historical version of CMAQ, has been coupled with the North American Mesoscale Forecast System (NAM) model in the current operational NAQFC. An experimental version of the NAQFC based on the offline-coupled GFS–FV3 version 15 with CMAQv5.0.2 modeling system (GFSv15–CMAQv5.0.2) has been developed by the National Oceanic and Atmospheric Administration (NOAA) to provide real-time air quality forecasts over the contiguous United States (CONUS) since 2018. In this work, comprehensive region-specific, time-specific, and categorical evaluations are conducted for meteorological and chemical forecasts from the offline-coupled GFSv15–CMAQv5.0.2 for the year 2019. The forecast system shows good overall performance in forecasting meteorological variables with the annual mean biases of −0.2 ∘C for temperature at 2 m, 0.4 % for relative humidity at 2 m, and 0.4 m s−1 for wind speed at 10 m compared to the METeorological Aerodrome Reports (METAR) dataset. Larger biases occur in seasonal and monthly mean forecasts, particularly in spring. Although the monthly accumulated precipitation forecasts show generally consistent spatial distributions with those from the remote-sensing and ensemble datasets, moderate-to-large biases exist in hourly precipitation forecasts compared to the Clean Air Status and Trends Network (CASTNET) and METAR. While the forecast system performs well in forecasting ozone (O3) throughout the year and fine particles with a diameter of 2.5 µm or less (PM2.5) for warm months (May–September), it significantly overpredicts annual mean concentrations of PM2.5. This is due mainly to the high predicted concentrations of fine fugitive and coarse-mode particle components. Underpredictions in the southeastern US and California during summer are attributed to missing sources and mechanisms of secondary organic aerosol formation from biogenic volatile organic compounds (VOCs) and semivolatile or intermediate-volatility organic compounds. This work demonstrates the ability of FV3-based GFS in driving the air quality forecasting. It identifies possible underlying causes for systematic region- and time-specific model biases, which will provide a scientific basis for further development of the next-generation NAQFC.

The US National Ambient Air Quality Standard (NAAQS) dictates the limits on atmospheric pollutants, including the tropospheric ozone (O3). Its exceedance of the limit typically happens in the summer because of changes in meteorology, chemistry, and emissions. Since O3 affects pulmonary function and has been linked to higher risks of depression and anxiety diagnoses, it is essential to understand O3 and its precursor nitrogen dioxide (NO2) variations. In July 2021, the Finite Volume Cube-Sphere Dynamic Core in Global Forecasting System (FV3-based GFS) became the new meteorological driver coupled with the Environmental Protection Agency’s Community Multiscale Air Quality System (CMAQ). Together they make up the National Air Quality Forecasting Capability (NAQFC), which provides hourly forecasts of a variety of atmospheric species and meteorological fields. Therefore, it is necessary to evaluate the model’s output relevant to O3 production in an urban environment and investigate potential biases.This study uses integrated remote sensing from a ceilometer, a wind LIDAR, the PANDORA spectrometer, and satellite Sentinel-5, and surface observations to investigate the spatial and temporal variability of NO2, O3, and planetary-boundary-layer height (PBLH) in August 2020 and 2021.At first, surface-level and column NO2 was observed from the co-located in-situ samplers and the PANDORA spectrometers at City College of New York (CCNY) and Queens College (QC) sites in New York City area. Then, the NO2 and O3 temporal variations at the two sites were compared and indicated a strong correlation for O3 and a moderate correlation for NO2. Meanwhile, the TROPOMI observations show spatial variation of tropospheric column NO2.The performance of the model’s product was evaluated using integrated observations and showed to be in good agreement with observations for the surface O3 at the two sites for the month of August 2020. For the surface NO2, the model forecast product generally showed similar diurnal variation but over-predicted the peak values likely related to complex urban emission sources and NO2 vertical mixing in the PBL. The correlation analysis of the model and observation data over weekdays and weekends were conducted that demonstrated the increased emission effects from the vehicular traffic during weekdays. The O3-NO2 titration from the model showed good consistency with the observations. Additionally, O3-NO2 variations from the month of August 2021 were evaluated and compared against the levels in 2020.

Recent NOAA collaborative efforts supported through the Unified Forecast System Research-to-Operations (UFS-R2O) Project have led to the development of advanced coupled systems to improve aerosol predictions on a global scale. These systems, integrated within the UFS framework, include online-coupled prognostic model components for atmosphere, ocean, sea ice, and waves, and rely upon state-of-the-science interoperable atmospheric physics schemes accessible via the Common Community Physics Package (CCPP) framework. Interoperability has been a key design element for those systems from the start, and thus it has also driven the incorporation of predictive aerosol processes. The approach to aerosol development within the UFS focuses on two primary outcomes: to build the next-generation upgrade to the currently operational Global Ensemble Forecast System (GEFS), and to create a research-oriented platform that allows developing and assessing the latest physical and chemical processes updates. In collaboration with NASA/GMAO, a novel aerosol component (UFS-Aerosols) was developed to succeed GEFS-Aerosols. This UFS component implements NASA’s 2nd generation GOCART model and brings the MAPL infrastructure layer into the UFS framework, enabling tighter collaborations with NASA and ensuring model interoperability across U.S. modeling centers thanks to its NUOPCcompliant interface. It also includes an updated FENGSHA dust scheme along with refinements to surface emissions. Furthermore, a CCPP-compliant implementation of aerosol processes based on GEFS-Aerosols was developed within the UFS framework to support and advance atmospheric chemistry research. This presentation will provide an overview of the architecture of each system as well as results from preliminary evaluations.

NOAA’s National Weather Service (NWS) is on its way to deploy various operational prediction applications using the Unified Forecast System (https://ufscommunity.org/), a community-based coupled, comprehensive Earth modeling system. An aerosol model component developed in a collaboration between the Global Systems Laboratory, Chemical Science Laboratory, the Air Resources Laboratory, and Environmental Modeling Center (GSL, CSL, ARL, EMC) was coupled online with the FV3 Global Forecast System (FV3GFS) using the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software framework. This aerosol prediction system replaced the NEMS GFS Aerosol Component (NGAC) system in the National Center for Environment Prediction (NCEP) production suite in September 2020 as one of the ensemble members of the Global Ensemble Forecast System (GEFS), dubbed GEFS-Aerosols v1. The aerosol component of atmospheric composition in GEFS is based on the Weather Research and Forecasting model (WRF-Chem) that was previously included into FIM-Chem (Zhang et al, 2021). GEFS-Aerosols includes bulk modules from the Goddard Chemistry Aerosol Radiation and Transport model (GOCART). Additionally, the biomass burning plume rise module from High-Resolution Rapid Refresh (HRRR)-Smoke was implemented; the GOCART dust scheme was replaced by the FENGSHA dust scheme (developed by ARL); the Blended Global Biomass Burning Emissions Product (GBBEPx V3) provides biomass burning emission and Fire Radiative Power (FRP) data; and the global anthropogenic emission inventories are derived from the Community Emissions Data System (CEDS). All sub-grid scale transport and deposition is handled inside the atmospheric physics routines, which required consistent implementation of positive definite tracer transport and wet scavenging in the physics parameterizations used by NCEP’s operational Global Forecast System based on FV3 (FV3GFS). This paper describes the details of GEFS-Aerosols model development and evaluation of real-time and retrospective runs using different observations from in situ measurement, satellite and aircraft data. GEFS-Aerosols predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational NGAC system.

A new dynamical core, known as the Finite Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA’s Global Forecast System (GFS) and in limited area models (LAMs) for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), and includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecast Capability (NAQFC) that will continue to protect human and ecosystem health in the U.S. Here we describe the development of the FV3GFSv16 coupling with a “state-of-the-science” CMAQ model version 5.3.1. The GFS-CMAQ coupling is made possible by the seminal version of the NOAA-ARL Atmosphere-Chemistry Coupler (NACC), which became the next operational NAQFC system (i.e., NACC-CMAQ) on July 20, 2021. NACC-CMAQ has a number of scientific advancements that include satellite- based data acquisition technology to improve land cover and soil characteristics, and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ has significantly different meteorological and chemical predictions than the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM2.5 predictions and diurnal patterns, both of which are extended to a 72-hour (3-day) forecast with this system.

The NEMS GFS Aerosol Component Version 2.0 (NGACv2) for global multispecies aerosol forecast has been developed at the National Centers of Environment Prediction (NCEP) in collaboration with the NESDIS Center for Satellite Applications and Research (STAR), the NASA Goddard Space Flight Center (GSFC), and the University at Albany, State University of New York (SUNYA). This paper describes the continuous development of the NGAC system at NCEP after the initial global dust-only forecast implementation (NGAC version 1.0, NGACv1). With NGACv2, additional sea salt, sulfate, organic carbon, and black carbon aerosol species were included. The smoke emissions are from the NESDIS STAR's Global Biomass Burning Product (GBBEPx), blended from the global biomass burning emission product from a constellation of geostationary satellites (GBBEP-Geo) and GSFC's Quick Fire Emission Data Version 2 from a polar-orbiting sensor (QFED2). This implementation advanced the global aerosol forecast capability and made a step forward toward developing a global aerosol data assimilation system. The aerosol products from this system have been used by many applications such as for regional air quality model lateral boundary conditions, satellite sea surface temperature (SST) physical retrievals, and the global solar insolation estimation. Positive impacts have been seen in these applications.

Significant progress has been made within the last couple of years towards developing online coupled systems aimed at providing more accurate descriptions of atmospheric chemistry processes to improve performance of global aerosol and air quality forecasts. Operating within the U.S. National Weather Service (NWS) research-to-operation initiative to implement the fully-coupled Next Generation Global Prediction System (NGGPS), cooperative development efforts have delivered two integrated online global prediction systems for aerosols (GEFS-Aerosols) and air quality (FV3GFS-AQM). These systems include recent advances in aerosol convective transport and wet deposition processes introduced into the SAS scheme of the National Center for Environmental Prediction’s (NCEP) latest Global Forecast System (GFS) based on the Finite-Volume cubed-sphere dynamical core (FV3). GEFS-Aerosols is slated to become the new control member of the NWS Global Ensemble Forecast System (GEFS). The model features an online-coupled version of the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model with a biomass-burning, plume-rise model and recent advances from NOAA Earth System Research Laboratory (ESRL), along with a state-of-the-art FENGSHA dust scheme from NOAA Air Resource Laboratory (ARL). FV3GFS-AQM incorporates a coupled, single-column adaptation of the U.S. Environmental Protection Agency’s (EPA) Community Multiscale Air Quality (CMAQ) model to improve NOAA’s current National Air Quality Forecast Capability (NAQFC). Both coupled systems’ design and development benefited from the use of the National Unified Operational Prediction Capability (NUOPC) Layer, which provided a common model architecture for interoperable, coupled model components within the framework of NOAA’s Environmental Modeling System (NEMS). Results from each of the described coupled systems will be discussed.

NCEP has implemented the first version of the Finite Volume Cubed Sphere (FV3) dynamic core based Global Forecast System (GFS v15) into operations in June 2019, replacing the spectral model-based GFS. This is the first instantiation of NOAA's Unified Forecast System (UFS), which is being built as a comprehensive coupled Earth system model using modern tools and software infrastructure (e.g., NEMS, NUOPC, and ESMF) to support research and operations. Advancements in model physics and data assimilation are in development using CCPP and JEDI frameworks. Testing and evaluation of UFS are facilitated through the development of unified workflow and METplus capabilities. All these initiatives involve significant engagement with the research community, with emphasis on more efficient and streamlined transition of research advances to operations (R2O).The next major operational upgrade towards UFS is for the Global Ensemble Forecast System (GEFSv12) planned for implementation later this year. Compared to the currently operational GEFS, the next GEFS version 12 incorporates the following advances: the same FV3 based global model and UFS infrastructure as in GFS, higher resolution (~25km), increased membership (31), GFSv15 physics, advanced stochastic physics perturbations (SKEB and SPPT), and 2-tiered SST approach using SST anomalies from CFSv2 as input. For the first time, GEFSv12 will provide ensemble based operational weather predictions for sub-seasonal scales with daily 00z forecasts going out to 35 days. GEFSv12 also comes with 20-year reanalysis, 30-year reforecasts and 3-year retrospective forecasts to support stakeholder needs for calibration and validation. In addition, GEFSv12 absorbs the global wave ensembles and aerosol capabilities (control member only) through one-way coupling, taking major steps towards building a unified system and simplifying NCEP’s production suite.This presentation describes the design and development of GEFSv12 and discusses results from the evaluation of the retrospective and reforecast experiments. Significant improvements were noted in both deterministic and probabilistic forecast metrics for several variables including 500 hPa geopotential height anomaly correction, 850 hPa temperature and winds, near surface variables, precipitation, tropical cyclone tracks and intensity, and modes of variability including MJO and NAO. Substantial improvements were also noted in the performance of wave ensemble and aerosol predictions.This presentation also describes NOAA’s efforts towards accelerating further development of fully coupled UFS consisting of six component models of the Earth system: the FV3 dynamical core for the atmosphere, MOM6 for the ocean, Noah MP for the land surface, GOCART for aerosols, CICE5/CICE6 for sea ice and WW3 for ocean surface waves. Combined with data assimilation advances, an ambitious goal of unifying both the high-resolution deterministic (GFSv17) and probabilistic (GEFSv13) predictions for global medium range and sub-seasonal time scales is planned to significantly advance the global prediction capabilities at NCEP.

Volcanic eruptions could bring a vast amount of sulfur dioxide (SO2) and ash into the air, often imposing substantial impacts on air quality and the ecosystem. Quantifying its impacts, however, is difficult due to the uncertainties in estimating the strength and variations of volcanic emissions. Here we developed and evaluated a new approach to combine satellite SO2 detection and chemical transport modeling to assess the impact of the 2018 Mt. Kilauea eruption on air quality over Hawaii. During the sustained eruption of the Kilauea Volcano in Hawaii's Big Island from May to July 2018, considerable SO2 and PM2.5 enhancements were observed both from the ground and from space. We studied this case using an experimental version of the NOAA National Air Quality Forecast Capability (NAQFC) modeling system. Daily emissions of SO2 and ash were estimated using a combination of SO2 column density retrieved by Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) aboard the Suomi-NPP satellite and the NAQFC model with an inverse emission modeling approach. We found that the volcanic SO2 emission rates peaked at 15,000 mol/s from the Kilauea's East Rift zone and Summit. The formation and transport of volcanic smog, or Vog, was highly dependent upon the vertical distribution of the volcanic emission, controlled by the heat flux of emission sources. We conducted four model simulations with various emission settings, and compared them to satellite data (CALIOP, OMPS and VIIRS) and in-situ measurements. All the runs tended to underpredict the peak values of surface SO2 and PM2.5 (particulate matter smaller than 2.5 μm in diameter). The “No Plume Rise” run underestimated the Vog plume rise and downstream transport. Using fixed emission rate or removing the temporal variations (”3-Day Mean”) led to miss peak Vog effects or inconsistent transport pattern compared to the observations. Therefore, the Base simulation with daily-varying emission and plume rise was used to quantify the air quality effects of the Kilauea eruption. We found that the volcanic eruption elevated surface PM2.5 concentration by 30–40 μg/m3 in the southeast part of the Big Island, with peak values up to 300 μg/m3. The Vog effect on trace gases, such as O3, NOx, and non-methane hydrocarbons, were much weaker (

The NEMS GFS Aerosol component (NGAC) version 2.0 for global multi-species aerosol forecast has been developed at the National Centers of Environment Prediction (NCEP) in collaboration with the NESDIS Center for Satellite Applications and Research (STAR), NASA Goddard Space Flight Center (GSFC), and University at Albany, State University of New York (SUNYA). This paper describes the continuous development of the NGAC system at NCEP after the initial global dust-only forecast implementation (NGAC version 1.0). With version 2, additional sea salt, sulfate, organic carbon and black carbon aerosol species were included. The smoke emissions are from the NESDIS STAR's Global Biomass Burning Product (GBBEPx), blended from the global biomass burning emission product from a constellation of geostationary satellites (GBBEP-Geo) and GSFC's Quick Fire Emission Data Version 2 from a polar orbiting sensor (QFED2). This implementation advanced the global aerosol forecast capability and made a step forward toward developing a global aerosol data assimilation system. The aerosol products from this system have been used by many applications such as for regional air quality model lateral boundary conditions, satellite SST physical retrievals and the global solar insolation estimation. Positive impacts have been seen in these applications.

The National Air Quality Forecast Capability (NAQFC) project provides the US with operational and experimental real-time ozone predictions using two different versions of the three-dimensional Community Multi-scale Air Quality (CMAQ) modeling system. Routine evaluation using near-real-time AIRNow ozone measurements through 2011 showed better performance of the operational ozone predictions. In this work, quality-controlled and -assured Air Quality System (AQS) ozone and nitrogen dioxide (NO2) observations are used to evaluate the experimental predictions in 2010. It is found that both ozone and NO2 are overestimated over the contiguous US (CONUS), with annual biases of +5.6 and +5.1 ppbv, respectively. The annual root mean square errors (RMSEs) are 15.4 ppbv for ozone and 13.4 ppbv for NO2. For both species the overpredictions are most pronounced in the summer. The locations of the AQS monitoring sites are also utilized to stratify comparisons by the degree of urbanization. Comparisons for six predefined US regions show the highest annual biases for ozone predictions in Southeast (+10.5 ppbv) and for NO2 in the Lower Middle (+8.1 ppbv) and Pacific Coast (+7.1 ppbv) regions. The spatial distributions of the NO2 biases in August show distinctively high values in the Los Angeles, Houston, and New Orleans areas. In addition to the standard statistics metrics, daily maximum eight-hour ozone categorical statistics are calculated using the current US ambient air quality standard (75 ppbv) and another lower threshold (70 ppbv). Using the 75 ppbv standard, the hit rate and proportion of correct over CONUS for the entire year are 0.64 and 0.96, respectively. Summertime biases show distinctive weekly patterns for ozone and NO2. Diurnal comparisons show that ozone overestimation is most severe in the morning, from 07:00 to 10:00 local time. For NO2, the morning predictions agree with the AQS observations reasonably well, but nighttime concentrations are overpredicted by around 100%.

Mature companies have good standards, specifications and maybe even an electrical safety program, but do they know how well these actually work? Verifying how sites perform against the company internal, external standards and processes, usually will involve an element of self-assessment, self-verification and then an external assurance view. The paper will take the reader along the journey the company took of identifying key electrical risks, developing mitigating barriers and bowties, creating a self-verification program and assurance program to determine if these barriers are in place and strong. The key concepts, some details of how these activities were initiated and examples of typical barriers across a downstream petrochemical organisation will be shared to offer the reader a tool for implementation at their sites. The benefit to the reader would be to adopt a similar process to better assure themselves of the integrity of their key electrical risk barriers.

The National Air Quality Forecast Capability (NAQFC) currently provides next-day forecasts of ozone concentrations over the contiguous United States. It was developed collaboratively by NOAA and Environmental Protection Agency (EPA) in order to provide state and local agencies, as well as the general public, air quality forecast guidance. As part of the development process, the NAQFC has been evaluated utilizing strict monitor-to-gridcell matching criteria, and discrete-type statistics of forecast concentrations. While such an evaluation is important to the developers, it is equally, if not more important, to evaluate the performance using the same protocol as the model's intended application. Accordingly, the purpose of this article is to demonstrate the efficacy of the NAQFC from the perspective of a local forecaster, thereby promoting its use. Such an approach has required the development of a new evaluation protocol: one that examines the ability of the NAQFC to forecast values of the EPA's Air Qualit...

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.

This study investigates the impact of model domain extent and the specification of lateral boundary conditions on the forecast quality of air pollution constituents in a specific region of interest. A developmental version of the national Air Quality Forecast System (AQFS) has been used in this study. The AQFS is based on the NWS/NCEP Eta Model (recently renamed the North American Mesoscale Model) coupled with the U.S. Environmental Protection Agency Community Multiscale Air Quality (CMAQ) model. This coupled Eta–CMAQ modeling system provided experimental air quality forecasts for the northeastern region of the United States during the summers of 2003 and 2004. The initial forecast over the northeastern United States was approved for operational deployment in September 2004. The AQFS will provide forecast coverage for the entire United States in the near future. In a continuing program of phased development to extend the geographical coverage of the forecast, the developmental version of AQFS has undergone two domain expansions. Hereinafter, this “developmental” domain-expanded forecast system AQFS will be dubbed AQFS-β. The current study evaluates the performance of AQFS-β for the northeastern United States using three domain sizes. Quantitative comparisons of forecast results with compiled observation data from the U.S. Aerometric Information Retrieval Now (AIRNOW) network were performed for each model domain, and interdomain comparisons were made for the regions of overlap. Several forecast skill score measures have been employed. Based on the categorical statistical metric of the critical success index, the largest domain achieved the highest skill score. This conclusion should catapult the implementation of the largest domain to attain the best forecast performance whenever the operational resource and criteria permit.

A minimum spanning tree (MST) rank histogram (RH) is a multidimensional ensemble reliability verification tool. The construction of debiased, decorrelated, and covariance-homogenized MST RHs is described. Experiments using Euclidean L2, variance, and Mahalanobis norms imply that, unless the number of ensemble members is less than or equal to the number of dimensions being verified, the Mahalanobis norm transforms the problem into a space where ensemble imperfections are most readily identified. Short-Range Ensemble Forecast Mahalanobis-normed MST RHs for a cluster of northeastern U.S. cities show that forecasts of the temperature–humidity index are the most reliable of those considered, followed by mean sea level pressure, 2-m temperature, and 10-m wind speed forecasts. MST RHs of a Southwest city cluster illustrate that 2-m temperature forecasts are the most reliable weather component in this region, followed by mean sea level pressure, 10-m wind speed, and the temperature–humidity index. Forecast reliabilities of the Southwest city cluster are generally less reliable than those of the Northeast cluster.

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.

The U.S. Weather Research Program convenes expert working groups on a one-time basis to identify critical research needs in various problem areas. The most recent expert working group was charged to “identify and delineate critical meteorological research issues related to the prediction of air quality.” In this context, “prediction” is denoted as “forecasting” and includes the depiction and communication of the present chemical state of the atmosphere, extrapolation or nowcasting, and numerical prediction and chemical evolution on time scales up to several days. Emphasis is on the meteorological aspects of air quality. The problem of air quality forecasting is different in many ways from the problem of weather forecasting. The latter typically is focused on prediction of severe, adverse weather conditions, while the meteorology of adverse air quality conditions frequently is associated with benign weather. Boundary layer structure and wind direction are perhaps the two most poorly determined met...

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.

The height of the Planetary Boundary Layer (PBL) is an important quantity for certain applications such as dispersion modeling. A dedicated two-dimensional PBL height analysis has been developed as an additional component of NCEP’s Real-Time Mesoscale Analysis. As for other meteorological analysis applications, the quality of the output is dependent on the quality of the input, including the observation. Here we assess the quality and potential for use in the PBL height analysis of a series of candidate observations, including Radiosonde Observations (RAOBS), Aircraft Communications Addressing and Reporting System (ACARS), Cooperative Agency Profilers (CAP), COSMIC satellite Radio Occultation and NWS Next-Rad radar reflectivities. The quality is assessed both by physical plausibility of the measurements and by comparison of the observations and the resulting analysis with independent observations not used in the analysis.

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.

[1] This study examines meteorological conditions associated with regional surface ozone using data collected during the summer Second Texas Air Quality Experiment, and the ability of the Nonhydrostatic Mesoscale Model–Community Multi-scale Air Quality Model (NMM-CMAQ) and the Weather Research and Forecast (WRF) model coupled with Chemistry (WRF-Chem) models to simulate the observed meteorology and surface ozone. The surface ozone data consist of 118 sites that are part of the U.S. Environmental Protection Agency Aerometric Information Retrieval Now (AIRNow) network, while the meteorological data came from a network of eleven 915-MHz wind profilers with RASS temperatures and supporting surface meteorological stations. High and low 8-h maximum ozone occurrences most frequently develop as regional events, with similar ozone concentration patterns across all of east Texas, allowing for a separate analysis of high- and low-ozone day conditions. The ability of the NMM-CMAQ and WRF-Chem models to simulate the meteorologically distinct high- and low-ozone events is analyzed. Histograms of surface ozone show that both the NMM-CMAQ and WRF-Chem models underpredict the full range found in the observations. For low ozone values, the analysis indicates that the models have a positive bias because of too large of an ozone inflow boundary condition value over the Gulf of Mexico. In contrast, the models have a negative bias for very high ozone values that occur mostly in Houston and Dallas, which suggests that the urban emissions and/or chemistry is misrepresented in the models.