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Discussion papers | Copyright
https://doi.org/10.5194/acp-2018-78
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Apr 2018

Research article | 10 Apr 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

The impact of data assimilation on the prediction of Asian desert dust using an operational 4D-Var system

Angela Benedetti1, Francesca Di Giuseppe1, Luke Jones1, Vincent-Henri Peuch1, Samuel Rémy2, and Xiaoye Zhang3,4 Angela Benedetti et al.
  • 1European Centre for Medium Range Weather Forecast (ECMWF), Reading, UK
  • 2Institut Pierre-Simon Laplace, CNRS / Sorbonne Université, Paris, France
  • 3State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, China
  • 4Center for Excellence in Regional Atmospheric Environment, INE, CAS

Abstract. Asian Dust is a seasonal meteorological phenomenon which affects East Asia, and has severe consequences on the air quality of China, North and South Korea and Japan. Despite the continental extent, the prediction of severe episodes and the anticipation of their consequences is challenging. Three one-year experiments were run to assess the skill of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring Asian dust and understand its relative contribution to air quality over China. Data used were the MODIS Dark Target and the Deep Blue Aerosol Optical Depth. In particular the experiments aimed at understanding the added value of data assimilation runs over a model run without any aerosol data. The year 2013 was chosen as representative for the availability of independent Aerosol Optical Depth (AOD) data from two established ground-based networks (AERONET and CARSNET), which could be used to evaluate experiments. Particulate Matter (PM) data from the China Environmental Protection Agency (CEPA) were also used in the evaluation. Results show that the assimilation of satellite AOD data is beneficial to predict the extent and magnitude of desert-dust events and to improve the forecast of such events. The availability of observations from the MODIS Deep Blue algorithm over bright surfaces is an asset, allowing for a better localization of the sources and definition of the dust events. In general both experiments constrained by data assimilation perform better that the unconstrained experiment, generally showing smaller mean normalized bias and fractional gross error with respect to the independent verification datasets. The impact of the assimilated satellite observations is larger at analysis time, but lasts well into the forecast. While assimilation is not a substitute for model development and characterization of the emission sources, results indicate that it can play a big role in delivering improved forecasts of Asian Dust.

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Angela Benedetti et al.
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