Observations and simulations have shown that the Tibetan Plateau (TP) plays an important role in the climate and weather of East Asia. Uncertainties in reanalysis data sets have been documented by comparing them with independent rawinsonde data, but the impacts of incorporating this data into a reanalysis data set on the dynamical and thermodynamical features over the TP have not been assessed. Observations from the recent Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX‐III) can be used to evaluate the Eastern TP ERA‐Interim reanalysis data set (ERA). In this study, the Barnes objective analysis scheme is used to incorporate the rawinsonde data into the ERA to acquire the gridded data (OBJ) for examining heat and moisture budgets. It is shown that the OBJ budget‐estimated rainfall intensity is better correlated with the observations than the ERA budget‐estimated rainfall intensity. The ERA overestimates weak rainfall but underestimates heavy rainfall. The incorporation of special rawinsonde into the OBJ impacts the dynamical and thermodynamical characteristics both spatially and temporally. The downward motion and cooling are enhanced over the rainless regions/periods, while the upward motion and heating are enhanced over the rainy regions/periods. The rawinsonde effect is stronger with the increasing rainfall intensity. The differences of spatial distributions and temporal variations in heating between the OBJ and ERA are primarily determined by the vertical advection terms in heat and moisture budgets. The OBJ‐derived large‐scale forcing is essential for cloud‐resolving model simulations of TP cloud systems. The above conclusions can also be applied to the ERA5.
The entrainment rate (λ) is difficult to estimate, and its uncertainties cause a significant error in convection parameterization and precipitation simulation, especially over the Tibetan Plateau, where observations are scarce. The λ over the Tibetan Plateau, and its adjacent regions, is estimated for the first time using five-year satellite data and a reanalysis dataset. The λ and cloud base environmental relative humidity (RH) decrease with an increase in terrain height. Quantitatively, the correlation between λ and RH changes from positive at low terrain heights to negative at high terrain heights, and the underlying mechanisms are here interpreted. When the terrain height is below 1 km, large RH decreases the difference in moist static energy (MSE) between the clouds and the environment and increases λ. When the terrain height is above 1 km, the correlation between λ and RH is related to the difference between MSE turning point and cloud base, because of decreases in specific humidity near the surface with increasing terrain height. These results enhance the theoretical understanding of the factors affecting λ and pave the way for improving the parameterization of λ.
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