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Development of a Next Generation Air Quality Modeling System
Citation:
Pleim, Jon, David-C Wong, R. Gilliam, J. Herwehe, R. Bullock, G. Pouliot, C. Hogrefe, W. Appel, R. Mathur, AND L. Ran. Development of a Next Generation Air Quality Modeling System. 1st CMAS-Asia-Pacific Conference, Beijing, CHINA, May 21 - 23, 2018.
Impact/Purpose:
A new generation air quality modeling system is being developed at the U.S. EPA to enable modeling of air quality from global to regional to (eventually) local scales. We envision that the system will have three configurations: 1. Global meteorology with seamless mesh refinement and online atmospheric chemistry; 2. Regional (limited area) online meteorology and chemistry; 3. Offline (sequential) regional meteorology and chemistry. A one-dimensional air quality (AQ) component, built from state-of-the-science chemistry and aerosol modules from the Community Multiscale Air Quality (CMAQ) model will be used in all three configurations. For the Global online configuration, the AQ component is coupled to the Model for Prediction Across Scales – Atmosphere (MPAS-A), which is a global meteorological model with seamless mesh refinement developed at the National Center for Atmospheric Research (NCAR). The regional online configurations can be either a regional version of MPAS coupled to the AQ component or a coupled WRF-AQ. The offline regional model will be based on WRF with added components for offline advection of chemical species.
Description:
In the presentation we will describe our modifications to MPAS to improve its suitability for retrospective air quality applications and show evaluations of global and regional meterological simulations. Our modifications include addition of physics schemes that we developed for WRF that are particularly designed for air quality applications: the Pleim surface layer (PSL), the Pleim-Xiu land surface model (PXLSM), and the Asymmetric Convective Model 2 (ACM2) planetary boundary layer scheme. We also added analysis nudging four-dimensional data assimilation (FDDA) to control error growth for long term retrospective simulations. In addition, we updated the KF scheme in MPAS to the latest version which adds subgrid-scale cumulus cloud feedback to the radiation schemes, multiple convective triggers, and a scale-aware convective timescale. We will also show preliminary MPAS-AQ results and evaluations where we have incorporated CMAQ modules for atmospheric chemistry and deposition in MPAS.
