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Preparing the Model for Prediction Across Scales (MPAS) for global retrospective air quality modeling
Citation:
Gilliam, R., J. Herwehe, Jon Pleim, H. Foroutan, AND L. Ran. Preparing the Model for Prediction Across Scales (MPAS) for global retrospective air quality modeling. 18th Annual WRF Users' Workshop, Boulder, CO, June 12 - 16, 2017.
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:
The US EPA has a plan to leverage recent advances in meteorological modeling to develop a "Next-Generation" air quality modeling system that will allow consistent modeling of problems from global to local scale. The meteorological model of choice is the Model for Prediction Across Scales (MPAS) that has been developed by the National Center for Atmospheric Research in recent years. While CMAQ developers have been working on a method to couple CMAQ components to MPAS for full global chemical transport modeling, a team of weather modelers has been preparing MPAS for accurate meteorological simulations. This includes four dimensional data assimilation that allows for long simulations of past weather with no growth in error. We have also added the Pleim-Xiu land-surface model (P-X LSM), Asymmetric Convective Model 2 (ACM2) and Pleim surface layer, which are key components in the current meteorological model WRF. Other testing will be presented including blending regional and global analyses for better soil nudging, using fractional landuse inputs, sub-grid scale convective model options and whether or not finer grids improve the global modeling. Our current WRF model is being used as a measure of progress and in most cases MPAS errors have been reduced to a level that near our regional 12 km modeling.
