Publication Date:
2023-06-02
Description:
The Tibetan Plateau (TP) contains the largest amount of snow outside the polar regions and is the source of many major rivers in Asia. An accurate long-range (i.e. seasonal) meteorological forecast is of great importance for this region. SEAS5 provides global long-range meteorological forecasts including over the TP. However, SEAS5 assimilating IMS snow data only below 1500m altitude may affect its forecast skill for this region. To investigate the impacts of snow assimilation and dynamical downscaling on temperature and precipitation forecasts, twin ensemble reforecasts initialized with and without snow assimilation above 1500m altitude over the TP for spring and summer 2018 are conducted, and are further downscaled by WRF. The results show that the reforecasts without snow assimilation underestimate daily temperature over the TP, which are consistently improved after snow assimilation in the spring. However, the positive biases between the precipitation reforecasts and satellite observations worsen in the east TP. Compared to the experiment without snow assimilation, the snow assimilation experiment significantly increases temperature and precipitation for the east TP and around the longitude 95ºE. The higher temperature after snow assimilation, in particular the cold bias reduction after initialization, can be attributed to the effects of a more realistic, decreased snowpack, providing favourable conditions for generating more precipitation. Overall, snow assimilation can improve seasonal forecasts through the interaction between land surface and atmosphere. Moreover, the convection-permitting dynamical downscaling further decreases the biases of seasonal forecasts, as more accurate physical processes can be resolved at this scale.
Language:
English
Type:
info:eu-repo/semantics/conferenceObject
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