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Two Decades of WRF/CMAQ simulations over the continental United States: New approaches for performing dynamic model evaluation and determining confidence limits for ozone exceedances
Citation:
Astitha, M., H. Luo, S. Rao, C. Hogrefe, R. Mathur, AND N. Kumar. Two Decades of WRF/CMAQ simulations over the continental United States: New approaches for performing dynamic model evaluation and determining confidence limits for ozone exceedances. 2016 CMAS Conference, Chapel Hill, NC, October 24 - 26, 2016.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
Confidence in the application of models for forecasting and regulatory assessments is furthered by conducting four types of model evaluation: operational, dynamic, diagnostic, and probabilistic. Operational model evaluation alone does not reveal the confidence limits that can be associated with modeled air quality concentrations. This paper presents novel approaches for performing dynamic model evaluation and for evaluating the confidence limits of ozone exceedances using the WRF/CMAQ model simulations over the continental United States for the period from 1990 to 2010. The methodology presented here entails spectral decomposition of ozone time series using the KZ filter to assess the variations in the strengths of the synoptic (i.e., weather-induced variation) and baseline (i.e., long-term variation attributable to emissions, policy, and trends) forcings embedded in the modeled and observed concentrations. A method is presented where the future year observations are estimated based on the changes in the concentrations predicted by the model applied to the current year observations. The proposed method can provide confidence limits for ozone exceedances for a given emission reduction scenario. We present and discuss these new approaches to identify the strengths of the model in representing the changes in simulated O3 air quality over the 21-year period.
