New Categorical Metrics for Air Quality Model Evaluation

Daiwen Kang Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic and Atmospheric Administration, Research Triangle Park, North Carolina

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Rohit Mathur Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic and Atmospheric Administration, and National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina

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Kenneth Schere Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic and Atmospheric Administration, and National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina

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Shaocai Yu Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic and Atmospheric Administration, Research Triangle Park, North Carolina

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Brian Eder Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic and Atmospheric Administration, and National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina

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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).

* Additional affiliation: Science and Technology Corporation, Hampton, Virginia

Corresponding author address: Daiwen Kang, Atmospheric Modeling Division, U.S. EPA, Mail Drop E243-03, Research Triangle Park, NC 27711. Email: kang.daiwen@epa.gov

This article included in the NOAA/EPA Golden Jubilee special collection.

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).

* Additional affiliation: Science and Technology Corporation, Hampton, Virginia

Corresponding author address: Daiwen Kang, Atmospheric Modeling Division, U.S. EPA, Mail Drop E243-03, Research Triangle Park, NC 27711. Email: kang.daiwen@epa.gov

This article included in the NOAA/EPA Golden Jubilee special collection.

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