@article{delle monache_wilczak_mckeen_grell_pagowski_peckham_stull_mchenry_mcqueen_2008, title={A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone}, volume={60}, ISSN={["1600-0889"]}, DOI={10.1111/j.1600-0889.2007.00332.x}, abstractNote={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.}, number={2}, journal={TELLUS SERIES B-CHEMICAL AND PHYSICAL METEOROLOGY}, author={Delle Monache, Luca and Wilczak, James and McKeen, Stuart and Grell, Georg and Pagowski, Mariusz and Peckham, Steven and Stull, Roland and Mchenry, John and McQueen, Jeffrey}, year={2008}, month={Apr}, pages={238–249} }
 @article{pagowski_grell_devenyi_peckham_mckeen_gong_delle monache_mchenry_mcqueen_lee_2006, title={Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts}, volume={40}, ISSN={["1352-2310"]}, DOI={10.1016/j.atmosenv.2006.02.006}, abstractNote={Forecasts from seven air quality models and surface ozone data collected over the eastern USA and southern Canada during July and August 2004 provide a unique opportunity to assess benefits of ensemble-based ozone forecasting and devise methods to improve ozone forecasts. In this investigation, past forecasts from the ensemble of models and hourly surface ozone measurements at over 350 sites are used to issue deterministic 24-h forecasts using a method based on dynamic linear regression. Forecasts of hourly ozone concentrations as well as maximum daily 8-h and 1-h averaged concentrations are considered. It is shown that the forecasts issued with the application of this method have reduced bias and root mean square error and better overall performance scores than any of the ensemble members and the ensemble average. Performance of the method is similar to another method based on linear regression described previously by Pagowski et al., but unlike the latter, the current method does not require measurements from multiple monitors since it operates on individual time series. Improvement in the forecasts can be easily implemented and requires minimal computational cost.}, number={18}, journal={ATMOSPHERIC ENVIRONMENT}, author={Pagowski, M and Grell, GA and Devenyi, D and Peckham, SE and McKeen, SA and Gong, W and Delle Monache, L and McHenry, JN and McQueen, J and Lee, P}, year={2006}, month={Jun}, pages={3240–3250} }
 @article{pagowski_grell_mckeen_devenyi_wilczak_bouchet_gong_mchenry_peckham_mcqueen_et al._2005, title={A  simple method to improve ensemble-based ozone forecasts}, volume={32}, number={7}, journal={Geophysical Research Letters}, author={Pagowski, M. and Grell, G. A. and McKeen, S. A. and Devenyi, D. and Wilczak, J. M. and Bouchet, V. and Gong, W. and Mchenry, J. and Peckham, S. and Mcqueen, J. and et al.}, year={2005} }