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Kenneth L. Schere and Carlie J. Coats

Abstract

Three-dimensional, regional scale (≈1000 km) air-quality simulation models require hourly inputs of U and V wind components for each vertical layer of the model and for each grid cell in the horizontal. The standard North American meteorological observation network is used to derive the wind-field inputs for the U.S. Environmental Protection Agency's Regional Oxidant Model (ROM) and other regional models. While a fairly dense surface network with hourly observations exists, upper-air data are obtained only twice per day at monitoring sites typically separated by distances of 300–500 km. Using these data to derive the more spatially and temporally resolved gridded wind fields needed by the ROM introduces uncertainties and errors into the model. We present a method of developing gridded wind fields for the ROM that accounts for these nondeterministic features. The method produces a family of potential gridded wind fields that allows for the stochastic nature of the interpolation process. Examples of the derived wind fields are given for the northeastern United States. Potential differences between wind fields, in terms of their effects on air-quality modeling, are inferred from following multiday flow trajectories using various members of the wind-field family. After 72 h of travel time, a trajectory spread of 100–200 km was not uncommon. The sensitivity of results to the density of surface observational data is also presented. Results of the sensitivity analysis showed that, as more stations were eliminated from the analysis, the calculated flow followed the domain mean flow more closely, showing fewer local influences.

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Kiran Alapaty, Donald T. Olerud Jr., Kenneth L. Schere, and Adel F. Hanna

Abstract

Objective analysis and diagnostic methods are used to provide hourly meteorological fields to many air quality simulation models. The viability of using predictions from the Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model version 4 (MM4) together with four-dimensional data assimilation, technique to provide meteorological information to the U.S. EPA Regional Oxidant Model (ROM) was studied. Two numerical simulations were performed for eight days using the ROM for a domain covering the eastern United States. In the first case, diagnostically analyzed data were used to provide meteorological conditions, while in the second case the MM4's prognostic data were used. Comparisons of processed diagnostic and prognostic meteorological data indicated differences in dynamical, thermodynamical, and other derived variables. Uncertainties and forecast errors present in the predicted vertical temperature profiles led to estimation of lower mixed-layer heights (∼ 30%–50%) and a smaller diurnal range of atmospheric temperatures (∼ 2 K) compared with those obtained from the diagnostic data. Comparison of area-averaged horizontal winds for four subdomains indicated minor differences (∼ 1–2 m s−1). These differences systematically affected the estimation of other derived meteorological parameters, such as friction velocity and sensible heat flux. Processed emission data also showed some differences (∼ 1–5 ppb h−1) that resulted from the differing characteristics of the diagnostic and prognostic meteorological data.

Comparison of predicted concentrations of primary (emitted) chemical species such as NOx and reactive organic gases in the two numerical simulations indicated higher values (1–5 and 1–25 ppb, respectively) when the prognostic meteorological data were used. This result was consistent with the lower estimated values of the ROM's layer 1 and layer 2 heights (planetary boundary layer) with the prognostic meteorology. However, comparison of predicted ozone concentrations did not indicate similar features. Area averages of predicted concentrations of ozone for four subdomains indicated both increases and decreases (+1 5 to −10 ppb) over the area averages predicted by the ROM using diagnostic meteorological data. These results indicate that the prediction of trace gas concentrations and the nonlinearity in the model's chemistry are sensitive to the type of meteorological input used.

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Tanya L. Otte, George Pouliot, Jonathan E. Pleim, Jeffrey O. Young, Kenneth L. Schere, David C. Wong, Pius C. S. Lee, Marina Tsidulko, Jeffery T. McQueen, Paula Davidson, Rohit Mathur, Hui-Ya Chuang, Geoff DiMego, and Nelson L. Seaman

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. Preoperational testing to support the 2003 and 2004 O3 forecast seasons showed that the AQF system provided valuable guidance that could be used in the air quality forecast process. The AQF system will be expanded over the next several years to include a nationwide domain, a capability for forecasting fine particle pollution, and a longer forecast period. State and local agencies will now issue air quality forecasts that are based, in part, on guidance from the AQF system. This note describes the process and software components used to link the Eta Model and CMAQ for the national AQF system, discusses several technical and logistical issues that were considered, and provides examples of O3 forecasts from the AQF system.

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