Jeffery T. McQueen

Linking the Eta Model with the Community Multiscale Air Quality (CMAQ) Modeling System to Build a National Air Quality Forecasting System

Abstract NOAA and the U.S. Environmental Protection Agency (EPA) have developed a national air quality forecasting (AQF) system that is based on numerical models for meteorology, emissions, and chemistry. The AQF system generates gridded model forecasts of ground-level ozone (O3) that can help air quality forecasters to predict and alert the public of the onset, severity, and duration of poor air quality conditions. Although AQF efforts have existed in metropolitan centers for many years, this AQF system provides a national numerical guidance product and the first-ever air quality forecasts for many (predominantly rural) areas of the United States. The AQF system is currently based on NCEP’s Eta Model and the EPA’s Community Multiscale Air Quality (CMAQ) modeling system. The AQF system, which was implemented into operations at the National Weather Service in September of 2004, currently generates twice-daily forecasts of O3 for the northeastern United States at 12-km horizontal grid spacing. Preoperationa...

Description and Verification of the NOAA Smoke Forecasting System: The 2007 Fire Season

Abstract An overview of the National Oceanic and Atmospheric Administration’s (NOAA) current operational Smoke Forecasting System (SFS) is presented. This system is intended as guidance to air quality forecasters and the public for fine particulate matter (≤2.5 μm) emitted from large wildfires and agricultural burning, which can elevate particulate concentrations to unhealthful levels. The SFS uses National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS), which is based on satellite imagery, to establish the locations and extents of the fires. The particulate matter emission rate is computed using the emission processing portion of the U.S. Forest Service’s BlueSky Framework, which includes a fuel-type database, as well as consumption and emissions models. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model is used to calculate the transport, dispersion, and deposition of the emitted particulate matter. The model evaluation is carried out...

Influence of grid size and terrain resolution on wind field predictions from an operational mesoscale model

Abstract One of the activities of the National Oceanic and Atmospheric Administrations Air Resources Laboratory is to predict the consequences of atmospheric releases of radioactivity and other potentially harmful materials. This paper describes the application of the Regional Atmospheric Modeling System (RAMS) to support air quality forecasting. The utility of using RAMS for real time prediction of local-scale flows and for detailed postevent analysis is examined for a Nuclear Regulatory Commission exercise at the Susquehanna nuclear power plant in Pennsylvania. During the exercise (10 December 1992) a strong East Coast low pressure system created complex interactions between the regional-scale and local topographical features of the Susquehanna River valley. Results from a series of sensitivity experiments indicated significant topographical forcing and vertical de-coupling although the synoptic forcing was quite strong in this relatively wide and shallow valley. The best agreement between the RAMS pre...

An evaluation of air pollutant exposures due to the 1991 Kuwait oil fires using a Lagrangian model

Abstract A Lagrangian model was adapted to simulate the transport, dispersion, and deposition of pollutants from the Kuwait oil fires. Modifications to the model permitted radiative effects of the smoke plume to modify the pollutants vertical mixing. Calculated SO 2 (sulfur dioxide) air concentrations were compared with the observations from several intensive aircraft measurement compaigns as well as longer-term ground-based measurements. Model sensitivity tests and comparison to the aircraft measurements confirmed (1) the magnitude of the tabulated emission rates for SO 2 and carbon soot; (2) the most appropriate value for the smokes specific extinction coefficient was about 4 m 2 g −1 ; (3) that the model was sensitive to the vertical mixing in the first 100 km downwind from the fires; (4) that the SO 2 conversion rate was about 6% h −1 ; and (5) although there were large variations in the height of the initial smoke plume and ground-level concentrations were most sensitive to that height, an average value of 1500 m a.g.l. (above ground level) provided reasonable model predictions. Six ground-level sampling locations, all along the Arabian Gulf Coast, were used for model evaluation. Although the measurements and model calculations were in qualitative agreement, the highest space- and time-paired correlation coefficient was only 0.40. The monitoring stations were located in industrial areas, requiring the subtraction of a background concentration of anywhere from 5 to 34 μg m −3 , which at some stations was larger than the contribution from the oil fires smoke. The coastal location and lack of correlation between some of the sites suggested that mesoscale flow features not properly represented in the coarse meteorological data used in the computations may have influenced the smoke transport.

NAQFC Developmental Forecast Guidance for Fine Particulate Matter (PM2.5)

AbstractThe 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). Emiss...

The implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for global dust forecasting at NOAA/NCEP

The NOAA National Centers for Environmental Prediction (NCEP) implemented NEMS 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.

US National Air Quality Forecast Capability: Expanding Coverage to Include Particulate Matter

The US National Air Quality Forecast Capability (NAQFC), developed by the National Oceanic and Atmospheric Administration (NOAA) in partnership with the Environmental Protection Agency (EPA), currently provides next-day operational predictions for ground level ozone and smoke for 50 US states. Ozone predictions are produced with the Community Multiscale Air Quality (CMAQ) model driven by NOAA’s operational North American Mesoscale weather forecast Model (NAM); routine verification is conducted with monitoring data compiled by the EPA. Smoke prediction relies on satellite detections of smoke sources, US Forest Service emission estimates, with transport and dispersion simulated by the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model driven by NAM; routine verification is conducted with satellite observations of smoke. Quantitative predictions of fine particulate matter (PM2.5) are in development. Inventory based simulations using CMAQ with aerosol modules show seasonal biases: overestimating in wintertime and underestimating in summertime. Current testing focuses on including intermittent aerosol sources directly emitted by wildfires and dust storms within the forecast domain; longer-range transport of dust is incorporated through lateral boundary conditions. For example, simulations of trans-Atlantic transport of Saharan dust, injected into the prediction domain, contribute enhanced surface PM2.5 concentrations in the southern US, as observed in surface monitoring. Simulated PM2.5 concentrations are being evaluated with speciated observations in order to improve seasonal biases in predictions. Research on assimilating PM2.5 surface observations shows potential to improve predictions. Furthermore, analysis of discrepancies between observations and model predictions that are produced during assimilation can provide insight on impacts of proposed improvements to PM2.5 predictions.

Improving NOAA NAQFC PM2.5 Predictions with a Bias Correction Approach

AbstractParticulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) is a critical air pollutant with important impacts on human health. It is essential to provide accurate air quality forecasts to alert people to avoid or reduce exposure to high ambient levels of PM2.5. The NOAA National Air Quality Forecasting Capability (NAQFC) provides numerical forecast guidance of surface PM2.5 for the United States. However, the NAQFC forecast guidance for PM2.5 has exhibited substantial seasonal biases, with overpredictions in winter and underpredictions in summer. To reduce these biases, an analog ensemble bias correction approach is being integrated into the NAQFC to improve experimental PM2.5 predictions over the contiguous United States. Bias correction configurations with varying lengths of training periods (i.e., the time period over which searches for weather or air quality scenario analogs are made) and differing ensemble member size are evaluated for July, August, September, and No...

Update on NOAA’s Operational Air Quality Predictions

NOAA provides operational predictions of ozone and wildfire smoke for the United States (U.S.) and predictions of airborne dust over the contiguous 48 states. Predictions are produced beyond midnight of the following day at 12 km spatial and hourly temporal resolution and are available at http://airquality.weather.gov/. Ozone predictions and testing of fine particulate matter (PM2.5) predictions combine the NOAA National Centers for Environmental Prediction (NCEP) operational North American Mesoscale (NAM) weather predictions with inventory based emission estimates from the EPA and chemical processes within the Community Multiscale Air Quality (CMAQ) model. Predictions of smoke and dust from dust storms use the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Verification of ozone and developmental aerosol predictions relies on AIRNow compilation of observations from surface monitors. Verification of smoke and dust predictions uses satellite retrievals of smoke and dust.

Linking the ETA Model with the Community Multiscale Air Quality (CMAQ) Modeling System: Ozone Boundary Conditions

Until the recent decade, air quality forecasts have been largely based on statistical modeling techniques. There have been significant improvements and innovations made to these statistically based air quality forecast models during past years (Ryan et al., 2000). Forecast fidelity has improved considerably using these methods. Nonetheless, being non-physically-based models, the performance of these models can vary dramatically, both spatially and temporally. Recent strides in computational technology and the increasing speed of supercomputers, combined with scientific improvements in meteorological and air quality models has spurred the development of operational numerical air quality prediction models (e.g., Vaughn et al., 2004, McHenry et al., 2004). In 2003, NOAA and the U.S. Environmental Protection Agency (EPA) signed a memorandum of agreement to work collaboratively on the development of a national air quality forecast capability. Shortly afterwards, a joint team of scientists from the two agencies developed and evaluated a prototype surface ozone concentration forecast capability for the Eastern U.S. (Davidson et al., 2004). The National Weather Service (NWS) / National Centers for Environmental Prediction (NCEP) ETA model (Black, 1994, Rogers et al., 1996, and Ferrier et al., 2003) with 12-km