Bias-corrected ensemble and probabilistic forecasts of surface ozone over eastern North America during the summer of 2004

[1] A multimodel ensemble air quality forecasting system was created as part of the New England Air Quality Study (NEAQS-2004) during the summer of 2004. Seven different models were used, with their own meteorology, emissions, and chemical mechanisms. In addition, one model was run at two different horizontal grid resolutions, providing a total of eight members for the ensemble. Model forecasts of surface ozone were verified at 342 sites from the EPA's AIRNOW observational network, over a 56 day period in July and August 2004. Because significant biases were found for each of the models, a simple 7-day running mean bias correction technique was implemented. The 7-day bias correction is found to improve the forecast skill of all of the individual models and to work nearly equally well over the entire range of observed ozone values. Also, bias-corrected model skill is found to increase with the length of the bias correction training period, but the increase is gradual, with most of the improvement occurring with only a 1 or 2 day bias correction. Analysis of the ensemble forecasts demonstrates that for a variety of skill measures the ensemble usually has greater skill than each of the individual models, and the ensemble of the bias-corrected models has the highest skill of all. In addition to the higher skill levels, the ensemble also provides potentially useful probabilistic information on the ozone forecasts, which is evaluated using several different techniques.

[1]  Hal Westberg,et al.  A biogenic hydrocarbon emission inventory for the U.S.A. using a simple forest canopy model , 1993 .

[2]  Jung-Hun Woo,et al.  Impacts of aerosols and clouds on photolysis frequencies and photochemistry during TRACE-P: 2. Three-dimensional study using a regional chemical transport model , 2003 .

[3]  K. Droegemeier,et al.  Objective Verification of the SAMEX ’98 Ensemble Forecasts , 2001 .

[4]  D. Byun.  Science algorithms of the EPA Models-3 community multi-scale air quality (CMAQ) modeling system , 1999 .

[5]  Thomas T. Warner,et al.  Ensemble Simulations with Coupled Atmospheric Dynamic and Dispersion Models: Illustrating Uncertainties in Dosage Simulations , 2002 .

[6]  J. Swets.  The Relative Operating Characteristic in Psychology , 1973, Science.

[7]  Daniel S. Wilks,et al.  Statistical Methods in the Atmospheric Sciences: An Introduction , 1995 .

[8]  Georg A. Grell,et al.  Fully coupled “online” chemistry within the WRF model , 2005 .

[9]  J. Milford,et al.  The effect of acetyl peroxy-peroxy radical reactions on peroxyacetyl nitrate and ozone concentrations , 1995 .

[10]  William P L Carter,et al.  Documentation of the SAPRC-99 chemical mechanism for VOC reactivity assessment. Volume 2. , 2000 .

[11]  M. Sofiev,et al.  Ensemble dispersion forecasting—Part I: concept, approach and indicators , 2004 .

[12]  Walter F. Dabberdt,et al.  Uncertainty, ensembles and air quality dispersion modeling: applications and challenges , 2000 .

[13]  W. Stockwell,et al.  The second generation regional acid deposition model chemical mechanism for regional air quality modeling , 1990 .

[14]  Rohit Mathur,et al.  Assessment of an ensemble of seven real-time ozone forecasts over eastern North America during the summer of 2004 , 2005 .

[15]  T. Hamill,et al.  Evaluation of Eta-RSM Ensemble Probabilistic Precipitation Forecasts , 1998 .

[16]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[17]  Luca Delle Monache,et al.  Ozone ensemble forecasts: 1. A new ensemble design , 2006 .

[18]  T. Hamill.  Interpretation of Rank Histograms for Verifying Ensemble Forecasts , 2001 .

[19]  Roland R. Draxler,et al.  Evaluation of an ensemble dispersion calculation , 2003 .

[20]  E. Atlas,et al.  Observations of Ozone Formation in Power Plant Plumes and Implications for Ozone Control Strategies , 2001, Science.

[21]  David G. Streets,et al.  Regional‐scale chemical transport modeling in support of the analysis of observations obtained during the TRACE‐P experiment , 2003 .

[22]  David J. Stensrud,et al.  Short-Range Ensemble Predictions of 2-m Temperature and Dewpoint Temperature over New England , 2003 .

[23]  Gerhard Wotawa,et al.  Ozone production from Canadian wildfires during June and July of 1995 , 2002 .

[24]  E. Grimit,et al.  Initial Results of a Mesoscale Short-Range Ensemble Forecasting System over the Pacific Northwest , 2002 .

[25]  Anne Grete Straume,et al.  A More Extensive Investigation of the Use of Ensemble Forecasts for Dispersion Model Evaluation , 2001 .

[26]  Renate Hagedorn,et al.  Predictability of Weather and Climate: List of contributors , 2006 .

[27]  William F. Ryan,et al.  A Real-Time Eulerian Photochemical Model Forecast System: Overview and Initial Ozone Forecast Performance in the Northeast U.S. Corridor , 2004 .

[28]  Christopher K. Wikle,et al.  Atmospheric Modeling, Data Assimilation, and Predictability , 2005, Technometrics.

[29]  Torben Mikkelsen,et al.  Ensemble dispersion forecasting—Part II: application and evaluation , 2004 .

[30]  Youhua Tang,et al.  A simple method to improve ensemble‐based ozone forecasts , 2005 .

[31]  Harold E. Brooks,et al.  Using Ensembles for Short-Range Forecasting , 1999 .

[32]  A. Dalcher,et al.  Forecasting forecast skill , 1987 .

[33]  C. Ziehmann.  Comparison of a single-model EPS with a multi-model ensemble consisting of a few operational models , 2000 .