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Establishing the Suitability of the Model for Prediction Across Scales for Global Retrospective Air Quality Modeling
Author(s) -
Gilliam Robert C.,
Herwehe Jerold A.,
Bullock O. Russell,
Pleim Jonathan E.,
Ran Limei,
Campbell Patrick C.,
Foroutan Hosein
Publication year - 2021
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2020jd033588
Subject(s) - weather research and forecasting model , cmaq , environmental science , meteorology , air quality index , data assimilation , planetary boundary layer , geography , turbulence
The U.S. EPA (United States Environmental Protection Agency) is leveraging recent advances in meteorological modeling to construct an air quality modeling system to allow consistency from global to local scales. The Model for Prediction Across Scales‐Atmosphere (MPAS‐A or MPAS) has been developed by the National Center for Atmospheric Research (NCAR) as a global complement to the Weather Research and Forecasting model (WRF). Patterned after a regional coupled system with WRF, the Community Multiscale Air Quality (CMAQ) modeling system has been coupled within MPAS to explore global‐to‐local chemical transport modeling. Several options were implemented into MPAS for retrospective applications. Nudging‐based data assimilation was added to support continuous simulations of past weather to minimize error growth that exists with a weather forecast configuration. The Pleim‐Xiu land‐surface model, the Asymmetric Convective Model 2 boundary layer scheme, and the Pleim surface layer scheme were added as the preferred options for retrospective air quality applications with WRF. Annual simulations were conducted using this EPA‐enhanced MPAS configuration on two different mesh structures and compared against WRF. MPAS generally compares well with WRF over the conterminous United States. Errors in MPAS surface meteorology are comparable to WRF throughout the year. Precipitation statistics indicate MPAS performs slightly better than WRF. Solar radiation in MPAS is higher than WRF and measurements, suggesting fewer clouds in MPAS than WRF. Upper‐air meteorology is well‐simulated by MPAS, but errors are slightly higher than WRF. These comparisons lend confidence to use MPAS for retrospective air quality modeling and suggest ways it can be further improved in the future.