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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/amt-2017-31
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
27 Feb 2017
Review status
A revision of this discussion paper was accepted for the journal Atmospheric Measurement Techniques (AMT) and is expected to appear here in due course.
An Assessment of the Impact of ATMS and CrIS Data Assimilation on Precipitation Prediction over the Tibetan Plateau
Tong Xue1,2, Jianjun Xu2,3, Zhaoyong Guan4, Long S. Chiu5, Han-Ching Chen6, and Min Shao3 1Key Laboratory of China Education Ministry for Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
2Guangdong Ocean University, Zhanjiang, China
3GENRI, College of Science, George Mason University, Fairfax, Virginia, USA
4Nanjing University of Information Science and Technology, Nanjing, China
5AOES, College of Science, George Mason University, Fairfax, Virginia, USA
6Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
Abstract. Using the National Oceanic and Atmospheric Administration’s Gridpoint Statistical Interpolation data assimilation system and the National Center for Atmospheric Research’s Advanced Research Weather Research and Forecasting (WRF-ARW) regional model, the impact of assimilating advanced technology microwave sounder (ATMS) and cross-track infrared sounder (CrIS) satellite data on precipitation prediction over the Tibetan Plateau in July 2015 was evaluated. Four experiments were designed: a control experiment and three data assimilation experiments with different data sets injected: conventional data only, a combination of conventional and ATMS satellite data, and a combination of conventional and CrIS satellite data. The results showed that the monthly mean of precipitation is shifted northward in the simulations and shows an orographic bias described as an overestimation in the upwind of the mountains and an underestimation in the south of the rainbelt. The rain shadow mainly influenced prediction of the quantity of precipitation, although the main rainfall pattern was well simulated. For the first 24-hourand last 24-hour accumulated daily precipitation, the model generally overestimated the amount of precipitation, but it was underestimated in the heavy rainfall periods of 3–6, 13–16, and 22–25 July. The observed water vapor conveyance from the southeastern Tibetan Plateau was larger than in the model simulations, which induced inaccuracies in the forecast of heavy rain on 3–6 July. The data assimilation experiments, particularly the ATMS assimilation, were closer to the observations for the heavy rainfall process than the control. Overall, the satellite data assimilation can enhance the WRF-ARW model’s ability to predict the spatial and temporal pattern of precipitation in July 2015 although the model capability exists a significant limitation in the complex terrain area.

Citation: Xue, T., Xu, J., Guan, Z., Chiu, L. S., Chen, H.-C., and Shao, M.: An Assessment of the Impact of ATMS and CrIS Data Assimilation on Precipitation Prediction over the Tibetan Plateau, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2017-31, in review, 2017.
Tong Xue et al.
Tong Xue et al.

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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 control experiment, conventional assimilation , and CrIS assimilation. Overall, the satellite data assimilation can enhance the WRF-ARW model’s ability to predict the precipitation.
In this study, we used diagnostic methods to analyze the impact of data assimilation on the...
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