Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)

Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)

<p>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 b...

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Main Authors: O. R. Bullock Jr., H. Foroutan, R. C. Gilliam, J. A. Herwehe
Format: Article
Language:English
Published: Copernicus Publications 2018-07-01
Series:Geoscientific Model Development
Online Access:https://www.geosci-model-dev.net/11/2897/2018/gmd-11-2897-2018.pdf
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spelling doaj-88d8c9c05d98459b9e393310e625f2522020-11-25T01:56:01ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032018-07-01112897292210.5194/gmd-11-2897-2018Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)O. R. Bullock Jr.0H. Foroutan1R. C. Gilliam2J. A. Herwehe3Computational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USADepartment of Biomedical Engineering and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USAComputational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USAComputational Exposure Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA<p>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 <q>analysis nudging</q> developed for the Penn State/National Center for Atmospheric Research (NCAR) Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its polygonal Voronoi mesh. Reference fields generated from 1°&thinsp; × &thinsp;1° National Centers for Environmental Prediction (NCEP) FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92–25&thinsp;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&thinsp;m temperature, 2&thinsp;m water vapor mixing ratio, and 10&thinsp;m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.</p>https://www.geosci-model-dev.net/11/2897/2018/gmd-11-2897-2018.pdf
collection DOAJ
language English
format Article
sources DOAJ
author O. R. Bullock Jr.
H. Foroutan
R. C. Gilliam
J. A. Herwehe
spellingShingle O. R. Bullock Jr.
H. Foroutan
R. C. Gilliam
J. A. Herwehe
Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)
Geoscientific Model Development
author_facet O. R. Bullock Jr.
H. Foroutan
R. C. Gilliam
J. A. Herwehe
author_sort O. R. Bullock Jr.
title Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)
title_short Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)
title_full Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)
title_fullStr Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)
title_full_unstemmed Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)
title_sort adding four-dimensional data assimilation by analysis nudging to the model for prediction across scales – atmosphere (version 4.0)
publisher Copernicus Publications
series Geoscientific Model Development
issn 1991-959X
1991-9603
publishDate 2018-07-01
description <p>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 <q>analysis nudging</q> developed for the Penn State/National Center for Atmospheric Research (NCAR) Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its polygonal Voronoi mesh. Reference fields generated from 1°&thinsp; × &thinsp;1° National Centers for Environmental Prediction (NCEP) FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92–25&thinsp;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&thinsp;m temperature, 2&thinsp;m water vapor mixing ratio, and 10&thinsp;m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.</p>
url https://www.geosci-model-dev.net/11/2897/2018/gmd-11-2897-2018.pdf
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