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

Luca Delle MonacheAtmospheric Science Programme, Earth and Ocean Sciences Department, University of British Columbia, Vancouver, British Columbia, Canada;;Now at Lawrence Livermore National Laboratory, Livermore, CA, USA;Correspondence[email protected]

James WilczakPhysical Sciences Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA;

Stuart MckeenCooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA;;Chemical Sciences Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA;

Georg GrellCooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA;;Global Systems Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA;

Mariusz PagowskiGlobal Systems Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA;;Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA;

Steven PeckhamCooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA;;Global Systems Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA;

Roland StullAtmospheric Science Programme, Earth and Ocean Sciences Department, University of British Columbia, Vancouver, British Columbia, Canada;

John MchenryBaron Advanced Meteorological Systems, c/o North Carolina State University, Raleigh, NC, USA;

Jeffrey McqueenNational Weather Service/ National Centers for Environmental Prediction/National Oceanic and Atmospheric Administration, Camp Springs, MD, USA

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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.

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

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

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