Articles | Volume 10, issue 7
https://doi.org/10.5194/amt-10-2517-2017
https://doi.org/10.5194/amt-10-2517-2017
Research article
 | 
19 Jul 2017
Research article |  | 19 Jul 2017

An assessment of the impact of ATMS and CrIS data assimilation on precipitation prediction over the Tibetan Plateau

Tong Xue, Jianjun Xu, Zhaoyong Guan, Han-Ching Chen, Long S. Chiu, and Min Shao

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Cited articles

Bormann, N., Fouilloux, A., and Bell, W.: Evaluation and assimilation of ATMS data in the ECMWF system, J. Geophys. Res.-Atmos., 118, 12970–12980, https://doi.org/10.1002/2013JD020325, 2013.
Chambon, P., Zhang, S. Q., Hou, A. Y., Zupanski, M., and Cheung, S.: Assessing the impact of preGPM microwave precipitation observations in the Goddard WRF ensemble data assimilation system, Q. J. Roy. Meteor. Soc., 140, 1219–1235, 2014.
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Derber, J. C. and Wu, W. S.: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system, Mon. Weather Rev., 126, 2287–2299, 1998.
Developmental Testbed Center: Gridpoint Statistical Interpolation Advanced User's Guide Version 3.5, 119 pp., available at: http://www.dtcenter.org/com-GSI/users.v3.5/docs/index.php (last access: 14 July 2017), 2016.
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Short summary
In this study, we used diagnostic methods to analyze the impact of data assimilation on the monthly precipitation distribution over the Tibetan Plateau and then focused on one heavy-rainfall case study that occurred from 3 to 6 July 2015. It is conspicuous that the ATMS assimilation showed better performance than the control experiment, conventional assimilation, and CrIS assimilation. Overall, the satellite data assimilation can enhance the WRF-ARW model’s ability to predict precipitation.