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

Journal metrics

Journal metrics

  • IF value: 3.248 IF 3.248
  • IF 5-year value: 3.650 IF 5-year 3.650
  • CiteScore value: 3.37 CiteScore 3.37
  • SNIP value: 1.253 SNIP 1.253
  • SJR value: 1.869 SJR 1.869
  • IPP value: 3.29 IPP 3.29
  • h5-index value: 47 h5-index 47
  • Scimago H index value: 60 Scimago H index 60
Volume 10, issue 7 | Copyright
Atmos. Meas. Tech., 10, 2517-2531, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

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 Xue1,2,4,5, Jianjun Xu2,3, Zhaoyong Guan1, Han-Ching Chen5,6, Long S. Chiu5, and Min Shao7 Tong Xue et al.
  • 1Key Laboratory of China Education Ministry for Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
  • 2Guangdong Ocean University, Zhanjiang, China
  • 3State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China
  • 4China Meteorological Administration Training Centre, Beijing, China
  • 5AOES, College of Science, George Mason University, Fairfax, Virginia, USA
  • 6Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
  • 7GENRI, College of Science, George Mason University, Fairfax, Virginia, USA

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 showed an orographic bias described as an overestimation upwind of the mountains and an underestimation in the south of the rain belt. The rain shadow mainly influenced prediction of the quantity of precipitation, although the main rainfall pattern was well simulated. For the first 24h and last 24h of accumulated daily precipitation, the model generally overestimated the amount of precipitation, but it was underestimated in the heavy-rainfall periods of 3–5, 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 3–5 July. The data assimilation experiments, particularly the ATMS assimilation, were closer to the observations for the heavy-rainfall process than the control. Overall, based on the experiments in July 2015, the satellite data assimilation improved to some extent the prediction of the precipitation pattern over the Tibetan Plateau, although the simulation of the rain belt without data assimilation shows the regional shifting.

Publications Copernicus
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.
In this study, we used diagnostic methods to analyze the impact of data assimilation on the...