It is practical significance to develop a long-term, high-accuracy dataset of the surface heat sources (including surface sensible heat flux (SH) and latent heat flux (LE)) in Qingzang plateau, which can meet the time requirement of climate monitoring and prediction. Among various atmospheric reanalysis data, the NCEP-Ⅱ atmospheric reanalysis data can meet the operational time requirement of climate monitoring and prediction. However, the NCEP-Ⅱ SH and LE calculated based on the classic Monin-Obukhov similarity theory have large errors. Previous studies have developed SH and LE datasets in the Qingzang plateau using the Maximum Entropy Production (MEP) model and the multi-source datasets including satellite remote sensing data and atmospheric reanalysis data. However, these SH and LE datasets are short in time and cannot meet the timeliness requirements. In this paper, using the MEP model and daily average net radiation, surface temperature, and soil moisture data extracted from the NCEP-Ⅱ atmospheric reanalysis dataset, we establish SH and LE in the Qingzang plateau area from 1980 to 2023. We verify the reliability of the new dataset by using the Qingzang plateau intensive observation data and analyze the temporal and spatial variation characteristics of SH and LE. The result shows that the correlation coefficients between monthly mean MEP SH (SH_MEP) and LE (LE_MEP) and the observations are 0.93 and 0.82, respectively, and the root mean square errors (RMSE) are 11.91 and 13.80 W/m², respectively. The results are better than those of the ERA5, ERA-Interim, MERRA-2, JRA-55, and NCEP-Ⅱ atmospheric reanalysis data, and can meet the timeliness and quality needs of climate monitoring and prediction operations. Moreover, for the period 1980—2023, summer SH_MEP exhibits a significant downward trend, with a linear trend of −0.48 W/(m²·10 a), while LE_MEP shows an unsignificant upward trend.

Based on the Rutgers University Global Snow Lab snow cover, the British Meteorological Office Hadley Center sea surface temperature (SST), soil moisture of the European Centre for Medium-Range Weather Forecasts Fifth Generation Reanalysis (ERA-5), the National Centers for Environmental Prediction National Center for Atmospheric Research reanalysis, the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Merged analysis of Precipitation (CMAP) and the Global Precipitation Climatology Project (GPCP) precipitation, effects of the Tibetan Plateau (TP) snow and El Niño-Southern Oscillation (ENSO) as preceding signals on the South China Sea (SCS) summer monsoon and precipitation are analyzed using methods of correlation analysis, composite analysis and regression analysis. Results indicate that the SCS summer monsoon is associated with both the TP snow and ENSO on the interannual scale. When the TP snow is higher in the west and lower in the east in spring, the tropospheric temperature in the west of the TP is abnormally low in the subsequent summer, which generates downdrafts over the plateau that flow outward. Accordingly, there are also descending airflows over the South China Sea in the lower and middle troposphere. In addition, anomalously high SSTs in the east-central equatorial Pacific may cause positive SST and tropospheric temperature anomalies in the Indian Ocean with northeasterly wind anomalies over the Northwest Pacific Ocean, which further strengthens the anticyclonic circulation anomaly over the South China Sea. When the TP snow and ENSO are synergistic, their influences on the SCS summer monsoon are stronger. Their joint effect further strengthens the lower-tropospheric anticyclonic anomaly over the South China Sea in summer, weakens the SCS summer monsoon and decreases precipitation in the South China Sea.

Based on "The Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-Ⅲ)" and the routine meteorological operational sounding data, the European Centre for Medium-Range Weather Forecasts Fifth Generation Reanalysis (ERA-5) data, and the ISCCP cloud cover data during the summers (June, July, and August) of 2013—2015, influences of the east-west difference in the convective boundary layer (CBL) height over the Tibetan Plateau (TP) on synoptic-scale atmospheric circulation are analyzed by statistic and physical diagnosis methods. It is found that the east-west difference in CBL shows an obvious diurnal variation. The CBL is high in the west and low in the east from the noon to the late afternoon, which is mainly attributed to the larger rise of the western CBL height. Corresponding to the large east-west difference in the CBL, temporal change of surface virtual potential temperature displays a "high in the west-low in the east" feature and the change in the western TP (WTP) is larger than that in the eastern TP (ETP). Meanwhile, temperature within the CBL increases in the WTP but slightly decreases in the ETP. Pressure decreases in the CBL but increases in higher levels in the WTP. The anomalous low pressure in the WTP is shallow. At the same time, low-level pressure increases in the ETP. The low-level east-west pressure difference may result in east-west pressure gradient anomaly and anomalous southerly winds over the central TP, which is accompanied by low-level convergence and high-level divergence in the WTP. The shallow low pressure anomaly is favorable for the development of low clouds.

The runoff calculation scheme in the Weather Research and Forecasting (WRF) model is based on an infiltration-excess surface runoff scheme, which likely leads to an overestimation of soil moisture and an underestimation of surface runoff when heavy rainfall occurs in areas with complex terrain. To overcome this defect, we considered the overland flow process on the grid scale of the WRF model for the first time by coupling a two-dimensional diffusion wave equation into the WRF Noah land surface model (LSM), called the WRF_Overland Flow (WRF_OLF) model. The new WRF model was then utilized to simulate the extreme rainfall that occurred during 19–22 July 2021 near the city of Zhengzhou in central China, which led to an extreme flood event. The results showed that the new WRF model simulated well the convergence and accumulation of overland flow in low-lying areas, changing the distributions of surface runoff and soil moisture and thereby influencing the exchanges of heat and water vapor between the surface and the atmosphere. The local change in non-adiabatic heating at the surface contributed to a decrease in surface pressure and then affected the development of the weather systems associated with the heavy rainfall event. Relative to a remarkable underestimation of rainfall in the original WRF simulation, the maximum rainfall intensity and the cumulative rainfall in the simulation with the new WRF configuration increased by 54.7% and 49.5%, respectively, bringing them closer to their observations. Concurrently, the new WRF model increased the skill for flood prediction. The results of this study provide new insights into the mechanisms of interaction between the land surface and the atmosphere and their roles in helping to predict heavy rainfall and associated flooding in areas of complex topography.