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Abstract: This paper describes the details of the Atmospheric Model Evaluation Tool (AMET) v1.1 created by scientists in the Atmospheric Modeling and Analysis Division (AMAD) of the U.S. Environmental Protection Agency (EPA). AMET was first developed to evaluate the performance of the 5th Generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological model output and was later extended to include capabilities for evaluating output data from the Community Multiscale Air Quality (CMAQ) model as well. AMET is designed to leverage several open-source software packages that are used in combination to 1) pair the modeled and observed values in time and space, 2) store these paired datasets in an easily accessible and searchable database and 3) access and analyze these data using a statistical package. Through this process, AMET is able to provide a convenient method for evaluating meteorological and air quality model predictions. The use of a searchable, relational database allows the entire dataset to be quickly subset into only those data that are of the most interest for the current analysis, a process that is often tedious and time consuming without the use of a database. In addition to common summary statistics (e.g. RMSE, bias, and correlation), several of the many analysis features available in AMET include scatter plots, time series plots, box plot and spatial plots as part of operational model evaluation. Additionally, several unique analysis functions are also available in AMET, and the system provides a framework within which users may extend the current functionality for their own custom analyses. While AMET was designed to work specifically with MM5, WRF and CMAQ model output, it could easily be modified to work with output data from other meteorological and air quality models. [Copyright &y& Elsevier]

Abstract: This paper examines the impact of tripled anthropogenic emissions from China and India over the base level (gaseous species and carbonaceous aerosols for 2000) on air quality over the U.S. using the WRF/Chem (Weather Research and Forecasting – Chemistry) model at 1° resolution. WRF/Chem is a state-of-the-science, fully coupled chemistry and meteorology system suitable for simulating the transport and dispersion of pollutants and their impacts. The analyses in this work were focused on MAM (March, April and May). The simulations indicate an extensive area of elevated pollutant concentrations spanning from the Arabian Sea to the Northern Pacific and to the Northern Atlantic. MAM mean contributions from the tripled Asian emissions over the U.S. are found to be: 6–12 ppbv for CO, 1.0–2.5 ppbv for O3, and 0.6–1.6 μg m−3 for PM2.5 on a daily basis. [Copyright &y& Elsevier]

Abstract: The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA’s North American Mesoscale (NAM) meteorological model with EPA’s Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O3) and experimental fine particular matter (PM2.5) forecasts over the continental United States (CONUS) during 2008. This paper describes the implementation of a real-time Kalman Filter (KF) bias-adjustment technique to improve the accuracy of O3 and PM2.5 forecasts at discrete monitoring locations. The operational surface-level O3 and PM2.5 forecasts from the NAQFC system were post-processed by the KF bias-adjusted technique using near real-time hourly O3 and PM2.5 observations obtained from EPA’s AIRNow measurement network. The KF bias-adjusted forecasts were created daily, providing 24-h hourly bias-adjusted forecasts for O3 and PM2.5 at all AIRNow monitoring sites within the CONUS domain. The bias-adjustment post-processing implemented in this study requires minimal computational cost; requiring less than 10 min of CPU on a single processor Linux machine to generate 24-h hourly bias-adjusted forecasts over the entire CONUS domain. The results show that the real-time KF bias-adjusted forecasts for both O3 and PM2.5 have performed as well as or even better than the previous studies when the same technique was applied to the historical O3 and PM2.5 time series from archived AQF in earlier years. Compared to the raw forecasts, the KF forecasts displayed significant improvement in the daily maximum 8-h O3 and daily mean PM2.5 forecasts in terms of both discrete (i.e., reduced errors, increased correlation coefficients, and index of agreement) and categorical (increased hit rate and decreased false alarm ratio) evaluation metrics at almost all locations during the study period in 2008. [Copyright &y& Elsevier]