Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-246
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
22 Dec 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
Adding Four Dimensional Data Assimilation by Analysis Nudging to the Model for Prediction Across Scales – Atmosphere (Version 4.0)
Orren Russell Bullock Jr.1, Hosein Foroutan1,2, Robert C. Gilliam1, and Jerold A. Herwehe1 1Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
2Department of Biomedical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
Abstract. The Model for Prediction Across Scales – Atmosphere (MPAS-A) has been modified to allow four dimensional data assimilation (FDDA) by the nudging of temperature, humidity and wind toward target values predefined on the MPAS-A computational mesh. The addition of nudging allows MPAS-A to be used as a global-scale meteorological driver for retrospective air quality modeling. The technique of analysis nudging developed for the Penn State / NCAR Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its unstructured Voronoi mesh. Reference fields generated from 1° × 1° National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92–25 km variable-resolution mesh with refinement centered over the contiguous United States. Test simulations were conducted for January and July 2013 with and without FDDA, and compared to reference fields and near-surface meteorological observations. The results demonstrate that MPAS-A with analysis nudging has high fidelity to the reference data while still maintaining conservation of mass as in the unmodified model. The results also show that application of FDDA constrains model errors relative to 2 m temperature, 2 m water vapor mixing ratio, and 10 m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.

Citation: Bullock Jr., O. R., Foroutan, H., Gilliam, R. C., and Herwehe, J. A.: Adding Four Dimensional Data Assimilation by Analysis Nudging to the Model for Prediction Across Scales – Atmosphere (Version 4.0), Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-246, in review, 2017.
Orren Russell Bullock Jr. et al.
Orren Russell Bullock Jr. et al.

Data sets

Adding Four Dimensional Data Assimilation by Analysis Nudging to the Model for Prediction Across Scales - Atmosphere (MPAS-A) Version 4.0
O. R. Bullock Jr., H. Foroutan, R. C. Gilliam, and J. A. Herwehe
https://doi.org/10.5281/zenodo.1101204

Model code and software

Adding Four Dimensional Data Assimilation by Analysis Nudging to the Model for Prediction Across Scales - Atmosphere (MPAS-A) Version 4.0
O. R. Bullock Jr., H. Foroutan, R. C. Gilliam, and J. A. Herwehe
https://doi.org/10.5281/zenodo.1101204
Orren Russell Bullock Jr. et al.

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Short summary
The U.S. Environmental Protection Agency is developing a new modeling system to investigate air pollution pathways on a global scale. We plan to use the Model for Prediction Across Scales – Atmosphere (MPAS-A) to define the meteorology that affects air pollution transport and fate. In order to do so, MPAS-A must accurately reproduce historical weather conditions. This work demonstrates that our implementation of four dimensional data assimilation by analysis nudging provides that capability.
The U.S. Environmental Protection Agency is developing a new modeling system to investigate air...
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