32 Effect Analysis of ATMS and CrIS Data Assimilation for Weather Element Forecasts over Tibetan Plateau

Monday, 15 August 2016
Grand Terrace (Monona Terrace Community and Convention Center)
Tong Xue, NUIST, Nanjing, China; and Z. Guan, J. Xu, and M. Shao

The impact of assimilating ATMS and CrIS satellite data to assess model's surface and upper- air weather forecast accuracy over complex terrain of Tibetan Plateau was evaluated by using NOAA's Gridpoint Statistical Interpolation (GSI) data assimilation system and NCAR's Advanced Research WRF ( Weather Research and Forecasting) (ARW-WRF) regional model. Four experiments were designed by (1) a control experiment (CTRL) and three data assimilation (DA) experiments with different data sets, including (2) conventional data only (CONV); (3) a combination of conventional and ATMS satellite data (ATMS); (4) a combination of conventional and CrIS satellite data. The experiments in January and July 2015 and the impacts of the DA on temperature (T), relative humidity (RH) and wind speed (WS) forecasts at the surface and vertical layers in high terrain and low terrain region have been investigated in this study. The results showed that the improvement of the three DA experiments were not universal. 24 h and 48 h forecast of 10-m WS forecasts in January as well as the 2-m RH forecasts in Julycould be modified by assimilating ATMS both in high terrain region while 2-m T prognosis could be rectified in low terrain areas. CRIS showed a good performance in high terrain region of 24 h 2-m T prediction in July. Meanwhile, CRIS can improve the prediction accuracy of high terrain region of 10-m WS both in January and July to some extent. When consider the vertical stratification, the CRIS DA gave a negative contribution in all vertical layers while ATMS DA had different forecast accuracy in different vertical layers and variables. The forecast error in T was typically caused by the systematic error which are controlled largely by the physical representation within the model. In contrast, the inaccuracies in the RH and in WS forecasts are dominated more by nonsystematic errors, derived from the random inadequacies of the initial conditions. Overall the improvement of the ATMS DA showed better than CRIS DA, after the assimilation of the improvement of wind field forecasts was better than forecasts of temperature field and humidity field.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner