Impacts of snow assimilation on seasonal snow and meteorological forecasts for the Tibetan Plateau
- 1State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
- 2Hubei Key Laboratory of Water System Science for Sponge City Construction, Wuhan University, Wuhan, China
- 3NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway
- 4NILU – Norwegian Institute for Air Research, Kjeller, Norway
- 5Institute of Heavy Rain, China Meteorological Administration (CMA), Wuhan, China
- 6ECWMF, Reading, UK
Abstract. The Tibetan Plateau (TP) contains the largest amount of snow outside the polar regions and is the headwater of many major rivers in Asia. An accurate long-range (i.e., seasonal) meteorological forecast is of great importance for this region. The fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (Seasonal System 5, in short SEAS5), is able to provide long-range meteorological forecast for the TP. However, the SEAS5 is produced without assimilating the Interactive Multisensor Snow and Ice Mapping System (IMS) snow data above 1500 m (hence over the TP in particular) which may affect the forecasting ability of SEAS5 over the region. To investigate the impacts of snow assimilation on the forecasting of snow, temperature and precipitation, twin ensemble reforecasts with and without snow assimilation above 1500 m over the TP were conducted for the spring and summer 2018. Significant changes occur in the springtime. Without snow assimilation, the reforecasts overestimate the snow cover and snow depth while underestimate daily temperature over the TP. Compared to satellite-based estimates, the precipitation reforecasts perform better in the west TP (WTP) than in the east TP (ETP). With the snow assimilation, the reforecasts of snow cover, snow depth and temperature are consistently improved in the TP in the spring. However, the positive bias between the precipitation reforecasts and satellite observations worsens in the ETP. Compared to the experiment with no snow assimilation, the snow assimilation experiment significantly increases temperature and precipitation for the ETP and around the longitude 95 °E. The higher temperature after snow assimilation, in particular the cold bias reduction after initialization, can be attributed to the combined effects of a more realistic, decreased snowpack and of wind changes, providing favourable conditions for generating more precipitation. Overall, the snow assimilation can improve the seasonal forecasts by affecting the surface energy budget.
Wei Li et al.
Status: open (until 18 Jul 2022)
Wei Li et al.
Wei Li et al.
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