Optimizing snow property forecasts over the tibetan plateau through hybrid assimilation of satellite precipitation and water vapor radiances using WRF model configured with Noah-MP

The Eastern region of the Qinghai-Tibet Plateau (EQTP) A regional numerical weather prediction and data assimilation system is constructed to investigate the impact of assimilating Global Precipitation Measurement (GPM) precipitation and Himawari-8/Advanced Himawari Imager (AHI) water vapor radiance using Weather Research and forecast (WRF) model and Four-dimensional variational assimilation (4Dvar) method on snow properties predictions. The predictions were compared with some reference datasets, including MODIS、VIIRS、GLDAS and ERA5-land. DA_G&A showed a significant increase in deep snow area (SD >15 cm), and a decrease in shallow snow area (SD<5 cm). Comparing with some reference datasets, the predictions exhibit good physical consistency between snow parameters and fine temporal-spatial resolution. The forecasts are found to be reliable and reasonable. However, Noah-MP coupled in WRF tends to overestimate SCF and SAL, which is largely attributed to the limitations of the associated parameterization schemes. These findings highlight the assimilation of atmospheric data can improve the forecasting of snow properties. However, in Noah-MP, there remains significant uncertainty in the snow-related parameterization schemes and initial conditions. • Snow forecasts are performed with multiple DA strategies using WRF and 4Dvar system. • Multi-source reference datasets are utilized for comparative analysis to verify the reliability and rationality of the predictions. • The scheme of snowpack in Noah-MP contains substantial uncertainty, which leads to overestimation in snow property predictions.