You are here:
Ensemble and Bias-Correction Techniques for Air-Quality Model Forecasts of Surface O3 and PM2.5 during the TEXAQS-II Experiment of 2006
Citation:
Djalalova, I., J. M. Wilczak, S. McKeen, G. Grell, S. Peckham, M. Pagowski, L. DelleMonache, J. McQueen, P. Lee, Y. Tang, J. MCHENRY, W. Gong, V. Bouchet, AND R. MATHUR. Ensemble and Bias-Correction Techniques for Air-Quality Model Forecasts of Surface O3 and PM2.5 during the TEXAQS-II Experiment of 2006. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 44(4):455-467, (2010).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories. Evaluation of individual model and ensemble forecasts of surface ozone and particulate matter (PM) was performed using data from 119 EPA AIRNow ozone sites and 38 PM sites during a 5O-day period in August and September of 2006. From the original set of models, two new bias-corrected model data sets were built, either by applying a simple running mean average to the past 7 days of data or by a Kalman-Filter approach. From the original and two bias-corrected data sets, three ensembles were created by a simple averaging of the seven models. For further improvements three additional weighted model ensembles were created, where individual model weights were calculated using the singular value decomposition method. All six of the ensembles are compared to the individual models and to each other in terms of root mean square error, correlation, and contingency and probabilistic statistics. In most cases, each of the ensembles show improved skill compared to the best of the individual models. The overall best ensemble technique was found to be the combination of Kalman-Filtering and weighted averaging. PM2.5 aerosol ensembles demonstrated significant improvement gains, mostly because the original model's skill was very low.
URLs/Downloads:
Ensemble and Bias-Correction Techniques for Air-Quality Model Forecasts of Surface O3 and PM2.5 during the TEXAQS-II Experiment of 2006 (PDF, NA pp, 6360 KB, about PDF)Atmospheric Environment
