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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2018-19
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Development and technical paper
02 Mar 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
Assimilating Compact Phase Space Retrievals (CPSRs): Comparison with Independent Observations (MOZAIC in situ and IASI Retrievals) and Extension to Assimilation of Truncated Retrieval Profiles
Arthur P. Mizzi1, David P. Edwards1, and Jeffrey L. Anderson2 1National Center for Atmospheric Research, Atmospheric Chemistry Observations and Modelling Laboratory, Boulder, CO 80305, USA
2National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, CO, 80305, USA
Abstract. Assimilation of atmospheric composition retrievals presents computational challenges due to their high data volume and often sparse information density. Assimilation of compact phase space retrievals (CPSRs) meets those challenges and offers a promising alternative to assimilation of raw retrievals at reduced computational cost (Mizzi et al., 2016). This paper compares analysis and forecast results from assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) CPSRs with independent observations. We use MetOp-A/Infrared Atmospheric Sounding Interferometer (IASI) CO retrievals and Measurement of OZone, water vapor, carbon monoxide, and nitrogen oxides by in service AIrbus airCraft (MOZAIC) in situ CO profiles for our independent observation comparisons. Generally, the results confirm that assimilation of MOPITT CPSRs improved the WRF-Chem/DART analysis fit and forecast skill at a reduced computational cost (~ 35 % reduction) when compared to assimilation of raw or quasi-optimal retrievals (QORs). Comparison with the independent observations shows that assimilation of MOPITT CO generally improved the analysis fit and forecast skill in the lower troposphere but degraded it in the upper troposphere. We attribute that degradation to assimilation of MOPITT CO retrievals with a possible bias of ~ 14 % above 300 hPa. To discard the biased retrievals, in this paper we also extend CPSRs to assimilation of truncated retrieval profiles (as opposed to assimilation of full retrieval profiles). Those results show that not assimilating the biased retrievals: (i) resolves the upper tropospheric analysis fit degradation issue, (ii) has commensurate reductions in assimilation computation cost, and (iii) reduces the impact of assimilating the remaining unbiased retrievals because the total information content and vertical sensitivities are changed.
Citation: Mizzi, A. P., Edwards, D. P., and Anderson, J. L.: Assimilating Compact Phase Space Retrievals (CPSRs): Comparison with Independent Observations (MOZAIC in situ and IASI Retrievals) and Extension to Assimilation of Truncated Retrieval Profiles, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-19, in review, 2018.
Arthur P. Mizzi et al.
Arthur P. Mizzi et al.
Arthur P. Mizzi et al.

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Short summary
Accurate air quality forecasts are critical to protection of human health and the environment. One of the most important methods for improving air quality forecast accuracy is incorporation of in situ and remote air quality observations into the forecasting process. The paper shows how ensemble assimilation of air quality observations using the "compact phase space retrieval" (CPSR) algorithm provides significant improvements in the air quality forecasts.
Accurate air quality forecasts are critical to protection of human health and the environment. ...
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