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

Journal Article · Fri Mar 04 00:00:00 EST 2016 · Geoscientific Model Development (Online)

DOI:https://doi.org/10.5194/gmd-9-965-2016· OSTI ID:1480715

Mizzi, Arthur P. ^[1]; Arellano, Jr., Avelino F. ^[2]; Edwards, David P. ^[1]; Anderson, Jeffrey L. ^[1]; Pfister, Gabriele G. ^[1]

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1. National Center for Atmospheric Research, Boulder, CO (United States)
2. Univ. of Arizona, Tucson, AZ (United States)

This study 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 ($R^2$). 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.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1480715
• Journal Information: Geoscientific Model Development (Online), Journal Name: Geoscientific Model Development (Online) Journal Issue: 3 Vol. 9; ISSN 1991-9603
• Publisher: European Geosciences Union
• Country of Publication: United States
• Language: English

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