Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system

Persistent Link:
http://hdl.handle.net/10150/616998
Title:
Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system
Author:
Mizzi, Arthur P.; Arellano Jr., Avelino F.; Edwards, David P.; Anderson, Jeffrey L.; Pfister, Gabriele G.
Affiliation:
Univ Arizona, Dept Hydrol & Atmospher Sci
Issue Date:
2016-03-04
Publisher:
COPERNICUS GESELLSCHAFT MBH
Citation:
Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system 2016, 9 (3):965 Geoscientific Model Development
Journal:
Geoscientific Model Development
Rights:
© Author(s) 2016. CC Attribution 3.0 License.
Collection Information:
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
Abstract:
This paper introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of <i>compact phase space retrievals</i> of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination (<i>R</i><sup>2</sup>). Those CO forecast improvements were significant at the 95 % level.<br><br> Trace gas retrieval data sets contain (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilation of compact phase space retrievals (CPSRs). CPSRs are obtained by preprocessing retrieval data sets with an algorithm that (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by  ∼  35 %, while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.
ISSN:
1991-9603
DOI:
10.5194/gmd-9-965-2016
Version:
Final published version
Sponsors:
National Science Foundation (NSF); NASA [NNX11A110G, NNX10AH45G]
Additional Links:
http://www.geosci-model-dev.net/9/965/2016/

Full metadata record

DC FieldValue Language
dc.contributor.authorMizzi, Arthur P.en
dc.contributor.authorArellano Jr., Avelino F.en
dc.contributor.authorEdwards, David P.en
dc.contributor.authorAnderson, Jeffrey L.en
dc.contributor.authorPfister, Gabriele G.en
dc.date.accessioned2016-07-15T01:05:18Z-
dc.date.available2016-07-15T01:05:18Z-
dc.date.issued2016-03-04-
dc.identifier.citationAssimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system 2016, 9 (3):965 Geoscientific Model Developmenten
dc.identifier.issn1991-9603-
dc.identifier.doi10.5194/gmd-9-965-2016-
dc.identifier.urihttp://hdl.handle.net/10150/616998-
dc.description.abstractThis paper introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of <i>compact phase space retrievals</i> of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination (<i>R</i><sup>2</sup>). Those CO forecast improvements were significant at the 95 % level.<br><br> Trace gas retrieval data sets contain (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilation of compact phase space retrievals (CPSRs). CPSRs are obtained by preprocessing retrieval data sets with an algorithm that (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by  ∼  35 %, while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.en
dc.description.sponsorshipNational Science Foundation (NSF); NASA [NNX11A110G, NNX10AH45G]en
dc.language.isoenen
dc.publisherCOPERNICUS GESELLSCHAFT MBHen
dc.relation.urlhttp://www.geosci-model-dev.net/9/965/2016/en
dc.rights© Author(s) 2016. CC Attribution 3.0 License.en
dc.titleAssimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation systemen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Hydrol & Atmospher Scien
dc.identifier.journalGeoscientific Model Developmenten
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
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