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

Jianping Huang I.M. Systems Group, Inc., Rockville, Maryland
National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction/Environmental Modeling Center, College Park, Maryland

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Jeffery McQueen National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction/Environmental Modeling Center, College Park, Maryland

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James Wilczak NOAA/Earth System Research Laboratory, Boulder, Colorado

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Irina Djalalova NOAA/Earth System Research Laboratory, Boulder, Colorado
Cooperative Institute for Research in the Environmental Sciences, University of Colorado Boulder, Boulder, Colorado

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Ivanka Stajner NOAA/National Weather Service/Office of Science and Technology Integration, Silver Spring, Maryland

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Perry Shafran I.M. Systems Group, Inc., Rockville, Maryland
National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction/Environmental Modeling Center, College Park, Maryland

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Dave Allured NOAA/Earth System Research Laboratory, Boulder, Colorado
Cooperative Institute for Research in the Environmental Sciences, University of Colorado Boulder, Boulder, Colorado

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Pius Lee NOAA/Air Resources Laboratory, College Park, Maryland

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Li Pan NOAA/Air Resources Laboratory, College Park, Maryland
Cooperative Institute for Climate and Satellite, University of Maryland, College Park, College Park, Maryland

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Daniel Tong NOAA/Air Resources Laboratory, College Park, Maryland
Cooperative Institute for Climate and Satellite, University of Maryland, College Park, College Park, Maryland

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Ho-Chun Huang I.M. Systems Group, Inc., Rockville, Maryland
National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction/Environmental Modeling Center, College Park, Maryland

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Geoffrey DiMego National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction/Environmental Modeling Center, College Park, Maryland

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Sikchya Upadhayay NOAA/National Weather Service/Office of Science and Technology Integration, Silver Spring, Maryland
Syneren Technologies Corporation, Arlington, Virginia

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Luca Delle Monache National Center for Atmospheric Research/Research Applications Laboratory, Boulder, Colorado

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Abstract

Particulate 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 November 2015. The analog bias correction approach yields substantial improvement in hourly time series and diurnal variation patterns of PM2.5 predictions as well as forecast skill scores. However, two prominent issues appear when the analog ensemble bias correction is applied to the NAQFC for operational forecast guidance. First, day-to-day variability is reduced after using bias correction. Second, the analog bias correction method can be limited in improving PM2.5 predictions for extreme events such as Fourth of July Independence Day firework emissions and wildfire smoke events. The use of additional predictors and longer training periods for analog searches is recommended for future studies.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (http://www.ametsoc.org/PUBSCopyrightPolicy).

Corresponding author e-mail: Jianping Huang, jianping.huang@noaa.gov

Abstract

Particulate 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 November 2015. The analog bias correction approach yields substantial improvement in hourly time series and diurnal variation patterns of PM2.5 predictions as well as forecast skill scores. However, two prominent issues appear when the analog ensemble bias correction is applied to the NAQFC for operational forecast guidance. First, day-to-day variability is reduced after using bias correction. Second, the analog bias correction method can be limited in improving PM2.5 predictions for extreme events such as Fourth of July Independence Day firework emissions and wildfire smoke events. The use of additional predictors and longer training periods for analog searches is recommended for future studies.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (http://www.ametsoc.org/PUBSCopyrightPolicy).

Corresponding author e-mail: Jianping Huang, jianping.huang@noaa.gov
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