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Brian K. Eder
,
Jerry M. Davis
, and
Peter Bloomfield

Abstract

This paper utilizes a two-stage (average linkage then convergent k means) clustering approach as part of an automated meteorological classification scheme designed to better elucidate the dependence of ozone on meteorology. When applied to 10 years (1981–90) of meteorological data for Birmingham, Alabama, the classification scheme identified seven statistically distinct meteorological regimes, the majority of which exhibited significantly different daily 1-h maximum ozone concentration distributions. Results from this two-stage clustering approach were then used to develop seven “refined” stepwise regression models designed to 1) identify the optimum set of independent meteorological parameters influencing the O3 concentrations within each meteorological cluster, and 2) weigh each independent parameter according to its unique influence within that cluster. Large differences were noted in the number, order, and selection of independent variables found to significantly contribute (α = 0.10) to the variability of O3. When this unique dependence was taken into consideration through the development and subsequent amalgamation of the seven individual regression models, a better parameterization of O3's dependence on meteorology was achieved. This “composite” model exhibited a significantly larger R 2 (0.59) and a smaller rmse (12.80 ppb) when compared to results achieved from an “overall” model (R 2 = 0.53, rmse = 13.85) in which the meteorological data were not clustered.

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Daiwen Kang
,
Rohit Mathur
,
Kenneth Schere
,
Shaocai Yu
, and
Brian Eder

Abstract

Traditional categorical metrics used in model evaluations are “clear cut” measures in that the model’s ability to predict an “exceedance” is defined by a fixed threshold concentration and the metrics are defined by observation–forecast sets that are paired both in space and time. These metrics are informative but limited in evaluating the performance of air quality forecast (AQF) systems because AQF generally examines exceedances on a regional scale rather than a single monitor. New categorical metrics—the weighted success index (WSI), area hit (aH), and area false-alarm ratio (aFAR)—are developed. In the calculation of WSI, credits are given to the observation–forecast pairs within the observed exceedance region (missed forecast) or the forecast exceedance region (false alarm), depending on the distance of the points from the central line (perfect observation–forecast match line or 1:1 line on scatterplot). The aH and aFAR are defined by matching observed and forecast exceedances within an area (i.e., model grid cells) surrounding the observation location. The concept of aH and aFAR resembles the manner in which forecasts are usually issued. In practice, a warning is issued for a region of interest, such as a metropolitan area, if an exceedance is forecast to occur anywhere within the region. The application of these new categorical metrics, which are supplemental to the traditional counterparts (critical success index, hit rate, and false-alarm ratio), to the Eta Model–Community Multiscale Air Quality (CMAQ) forecast system has demonstrated further insight into evaluating the forecasting capability of the system (e.g., the new metrics can provide information about how the AQF system captures the spatial variations of pollutant concentrations).

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Richard D. Cohn
,
Brian K. Eder
,
Sharon K. Leduc
, and
Robin L. Dennis

Abstract

The development of an episode selection and aggregation approach, designed to support distributional estimation for use with the Models-3 Community Multiscale Air Quality (CMAQ) model, is described. The approach utilized cluster analysis of the 700-hPa east–west and north–south wind field components over the time period of 1984–92 to define homogeneous meteorological clusters. Alternative schemes were compared using relative efficiencies and meteorological considerations. An optimal scheme was defined to include 20 clusters (five per season), and a stratified sample of 40 events was selected from the 20 clusters using a systematic sampling technique. The light-extinction coefficient, which provides a measure of visibility, was selected as the primary evaluative parameter for two reasons. First, this parameter can serve as a surrogate for particulate matter with diameter of less than 2.5 μm, for which few observational data exist. Second, of the air quality parameters simulated by CMAQ, this visibility parameter has one of the most spatially and temporally comprehensive observational datasets. Results suggest that the approach reasonably characterizes synoptic-scale flow patterns and leads to strata that explain the variation in extinction coefficient and other parameters (temperature and relative humidity) used in this analysis, and therefore the approach can be used to achieve improved estimates of these parameters relative to estimates obtained using other methods. Moreover, defining seasonally based clusters further improves the ability of the clusters to explain the variation in these parameters and therefore leads to more precise estimates.

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Brian Eder
,
Daiwen Kang
,
S. Trivikrama Rao
,
Rohit Mathur
,
Shaocai Yu
,
Tanya Otte
,
Ken Schere
,
Richard Wayland
,
Scott Jackson
,
Paula Davidson
,
Jeff McQueen
, and
George Bridgers

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 Quality Index (AQI) rather than ambient air concentrations; focuses on the use of categorical-type statistics related to exceedances and nonexceedances; and, most challenging, examines performance, not based on matched grid cells and monitors, but rather over a “local forecast region,” such as an air shed or metropolitan statistical area (MSA). Results from this approach, which is demonstrated for the Charlotte, North Carolina, MSA and subsequently applied to four additional MSAs during the summer of 2007, reveal that the quality of the NAQFC forecasts is generally comparable to forecasts from local agencies. Such findings will hopefully persuade forecasters, whether they are experienced with numerous tools at their disposal or inexperienced with limited resources, to utilize the NAQFC as forecast guidance.

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