You are here:
Establishing the Suitability of the Model for Prediction Across Scales for Global Retrospective Air Quality Modeling
Citation:
Gilliam, R., J. Herwehe, R. Bullock, Jon Pleim, L. Ran, P. Campbell, AND H. Foroutan. Establishing the Suitability of the Model for Prediction Across Scales for Global Retrospective Air Quality Modeling. JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES. American Geophysical Union, Washington, DC, 126(10):e2020JD033588, (2021). https://doi.org/10.1029/2020JD033588
Impact/Purpose:
US EPA models air quality of the United States using the CMAQ model. Historically, this modeling was done only over the U.S. Global modeling is becoming more attainable as computer power has increased and modeling systems advanced. This research demonstrates we have a meteorological modeling system that now allows for air quality modeling from global to local scales. The improvements in air quality modeling will directly feed research on the link between air quality and human/ecological health. Specific results indicate the meteorology from the global model that drives the air quality is on par with the meteorology we have been using for the last decade suggesting we are ready to start the air quality simulations and evaluate that part of the modeling system. These results and the model development are useful for an array of partners and the broader modeling community. Air quality modelers and researchers in the academic, governmental and private sectors can all leverage the research here. The model developments that allow for global long-term simulations of past weather with accuracy are applicable to the broader meteorological modeling community that may be interested in a number of issues other than air quality (e.g., climate or hydrology modeling). This work will show that EPA modelers have adapted a next generation meteorology model MPAS for retrospective modeling that is being used to drive global chemical transport models.
Description:
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.
URLs/Downloads:
DOI: Establishing the Suitability of the Model for Prediction Across Scales for Global Retrospective Air Quality Modeling
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JD033588