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Original Research Papers

A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone

Authors:

Luca Delle Monache ,

Atmospheric Science Programme, Earth and Ocean Sciences Department, University of British Columbia, Vancouver, British Columbia, CA; Now at Lawrence Livermore National Laboratory, Livermore, CA, US
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James Wilczak,

Physical Sciences Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, US
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Stuart McKeen,

Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO; Chemical Sciences Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, US
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Georg Grell,

Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO; Global Systems Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, US
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Mariusz Pagowski,

Global Systems Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO; Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, US
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Steven Peckham,

Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO; Global Systems Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, US
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Roland Stull,

Atmospheric Science Programme, Earth and Ocean Sciences Department, University of British Columbia, Vancouver, British Columbia, US
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John McHenry,

Baron Advanced Meteorological Systems, c/o North Carolina State University, Raleigh, NC, US
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Jeffrey McQueen

National Weather Service / National Centers for Environmental Prediction/National Oceanic and Atmospheric Administration, Camp Springs, MD, US
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Abstract

Kalman filtering (KF) is used to estimate systematic errors in surface ozone forecasts. The KF updates its estimate of future ozone-concentration bias using past forecasts and observations. The optimum filter parameter is estimated via sensitivity analysis. KF performance is tested for deterministic, ensemble-averaged and probabilistic forecasts. Eight simulations were run for 56 d during summer 2004 over northeastern USA and southern Canada, with 358 ozone surface stations.

KF improves forecasts of ozone-concentration magnitude (measured by root mean square error) and the ability to predict rare events (measured by the critical success index), for deterministic and ensemble-averaged forecasts. It improves the 24-h maximum ozone-concentration prediction (measured by the unpaired peak prediction accuracy), and improves the linear dependency and timing of forecasted and observed ozone concentration peaks (measured by a lead/lag correlation). KF also improves the predictive skill of probabilistic forecasts of concentration greater than thresholds of 10–50 ppbv, but degrades it for thresholds of 70–90 ppbv. KF reduces probabilistic forecast bias. The combination of KF and ensemble averaging presents a significant improvement for real-time ozone forecasting because KF reduces systematic errors while ensemble-averaging reduces random errors. When combined, they produce the best overall ozone forecast.

How to Cite: Delle Monache, L., Wilczak, J., McKeen, S., Grell, G., Pagowski, M., Peckham, S., Stull, R., McHenry, J. and McQueen, J., 2008. A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone. Tellus B: Chemical and Physical Meteorology, 60(2), pp.238–249. DOI: http://doi.org/10.1111/j.1600-0889.2007.00332.x
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  Published on 01 Jan 2008
 Accepted on 12 Nov 2007            Submitted on 26 Jun 2007

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