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

Abstract

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 assimilationmore » 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.« less

Authors:

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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+ Show Author Affiliations

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

Publication Date:

2016-03-04

Research Org.:

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

OSTI Identifier:

1480715

Grant/Contract Number:  

AC02-05CH11231

Resource Type:

Journal Article: Accepted Manuscript

Journal Name:

Geoscientific Model Development (Online)

Additional Journal Information:

Journal Volume: 9; Journal Issue: 3; Journal ID: ISSN 1991-9603

Publisher:

European Geosciences Union

Country of Publication:

United States

Language:

English

Subject:

58 GEOSCIENCES