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Discussion papers
https://doi.org/10.5194/acp-2019-298
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-298
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 16 May 2019

Research article | 16 May 2019

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Chemistry and Physics (ACP) and is expected to appear here in due course.

Machine learning for observation bias correction with application to dust storm data assimilation

Jin Jianbing1, Lin Hai Xiang1, Segers Arjo2, Xie Yu1, and Heemink Arnold1 Jin Jianbing et al.
  • 1Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
  • 2TNO, Department of Climate, Air and Sustainability, Utrecht, the Netherlands

Abstract. Data assimilation algorithms rely on a basic assumption of an unbiased observation error. However, the presence of inconsistent measurements with nontrivial biases or inseparable baselines is unavoidable in practice. Assimilation analysis might diverge from reality, since the data assimilation itself cannot distinguish whether the differences between model simulations and observations are due to the biased observations or model deficiencies. Unfortunately, modeling of observation biases or baselines which show strong spatiotemporal variability is a challenging task. In this study, we report how data-driven machine learning can be used to perform observation bias correction for data assimilation through a real application, which is the dust emission inversion using PM10 observations.

PM10 observations are considered as unbiased, however, a bias correction is necessary if they are used as a proxy for dust during dust storms since they actually represent a sum of dust particles and non-dust aerosols. Two observation bias correction methods have been designed in order to use PM10 measurements as proxy for the dust storm loads under severe dust conditions. The first one is the conventional chemical transport (CTM) model that simulates life cycles of non-dust aerosols. The other one is the machine learning model that describes the relations between the regular PM10 and other air quality measurement. The latter is trained by learning two-year's historical samples. The machine learning based non-dust model is shown to be in better agreements with observations compared to the CTM. The dust emission inversion tests have been performed, either through assimilating the raw measurements, or the bias-corrected dust observations using either the CTM or machine learning model. The emission field, surface dust concentration and forecast skill are evaluated. The worst case is when we directly assimilate the original observations. The forecasts driven by the posterior emission in this case even results in larger errors than the reference prediction. This shows the necessities of bias correction in data assimilation. The best results are obtained when using a machine learning model for bias correction, with the existing measurements used more precisely and the resulting forecasts close to reality.

Jin Jianbing et al.
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Jin Jianbing et al.
Jin Jianbing et al.
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