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Volume 9, issue 3 | Copyright

Special issue: The community version of the Weather Research and Forecasting...

Geosci. Model Dev., 9, 965-978, 2016
https://doi.org/10.5194/gmd-9-965-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 04 Mar 2016

Development and technical paper | 04 Mar 2016

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

Arthur P. Mizzi1, Avelino F. Arellano Jr.2, David P. Edwards1, Jeffrey L. Anderson3, and Gabriele G. Pfister1 Arthur P. Mizzi et al.
  • 1National Center for Atmospheric Research, Atmospheric Chemistry Observation and Modeling Laboratory, Boulder, CO, USA
  • 2University of Arizona, Department of Hydrology and Atmospheric Science, Tucson, AZ, USA
  • 3National Center for Atmospheric Research, Institute for Applied Mathematics, Boulder, CO, USA

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 compact phase space retrievals 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 (R2). Those CO forecast improvements were significant at the 95 % level.

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.

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This paper introduces (i) WRF-Chem/DART – a state-of-the-art chemical transport/data assimilation system, and (ii) compact phase space retrievals (CPSRs). WRF-Chem/DART is NCAR's regional chemical weather forecasting prototype. Such systems require assimilation of chemical composition observations, such as trace gas retrievals. Retrievals are expensive to assimilate. CPSRs reduce those assimilation costs (~ 35 % for MOPITT CO) without loss in forecast skill by removing redundant information.
This paper introduces (i) WRF-Chem/DART – a state-of-the-art chemical transport/data...
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