Articles | Volume 10, issue 7
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

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 24 h and last 24 h 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.

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.