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Rainstorms and blizzards are common extreme weather events occurring in the eastern Tibet region. Their complex dynamic and thermodynamic mechanisms present challenges for regional meteorological research and forecasting. Based on station observation data and ERA5 atmospheric reanalysis datasets, a diagnostic analysis of the heavy rain and snow event in eastern Tibet from 24 to 27 October 2022 was conducted. The results indicate that (1) the influence of the cloud systems surrounding the Bay of Bengal storm “Sitrang” was a significant factor contributing to the occurrence of this heavy rain and snow weather. (2) Sustained stability of the southern branch trough and the western Pacific subtropical high favored the establishment and maintenance of the mid-level jet stream ahead of the storm. Storm “Sitrang” transported warm and moist air to eastern Tibet through the southwest mid-level jet stream, providing favorable moisture, dynamic, and thermal conditions for the heavy rain and snow. (3) Most importantly, symmetrical instability generated by the inclined motion of the storm’s warm and moist air emerged as the decisive mechanism driving the occurrence and development of the heavy rain and snow.

At present, sequence-based models have various applications in recommendation systems; these models recommend the interested items of the user according to the user’s behavioral sequence. However, sequence-based models have a limitation of length. When the length of the user’s behavioral sequence exceeds the limitation of the model, the model cannot take advantage of the complete behavioral sequence of the user and cannot know the user’s holistic interests. The accuracy of the model then goes down. Meanwhile, sequence-based models only pay attention to the sequential signals of the data but do not pay attention to the spatial signals of the data, which will also affect the model’s accuracy. This paper proposes a graph sequence-based model called GSRec that combines Graph Convolutional Network (GCN) and Transformer to solve these problems. In the GCN part we designed a reverse-order graph, and in the Transformer part we introduced the user embedding. The reverse-order graph and the user embedding can make the combination of GCN and Transformer more efficient. Experiments on six datasets show that GSRec outperforms the current state-of-the-art (SOTA) models.

To further examine the effectiveness of the ascending and descending sections of the new round-trip drifting sounding system in forecasting high-impact weather such as heavy rainfall, in this study, three assimilation experiments were designed for a rainstorm in eastern China using WRF (Weather Research and Forecast) and WRFDA-3DVAR. Then, the reasons for the improvement in the effects of forecasting rainfall after assimilation were analyzed from various perspectives. The results showed that the assimilation of round-trip drifting sounding data can increase the accuracy of regions of heavy precipitation and improve the effectiveness of rainstorm forecasting. The quality of the wind field improved after the assimilation of the round-trip drifting sounding data, which improved the conditions of moisture transport, resulting in increased humidity in the lower layer, which accumulated more unstable energy for the development of storms. In addition, the enhanced low-level convergence after the assimilation of the new sounding data created a strong upward motion, which was more conducive to triggering heavy rainfall, thus improving the ability to forecast such heavy rainfall.

The plateau vortex is one of the dominant weather systems that modulate summer rainfall in northwestern China, a typical arid/semi-arid area worldwide. Two heavy rainfall cases in the summer of 2012 and 2013, accompanying the northeastward movement of plateau vortexes, are selected here. The role of plateau vortexes in these two cases is explored by diagnosing their moist potential vorticity, helicity, and convective clouds characteristics, using numerous data (ERA-Interim reanalysis, multi-sourced precipitation data, FY-2E satellite images). The plateau vortexes enhanced convective instability in the rainfall area by entraining upper-level dry and cold air, manifested as salient vertical gradients of moist potential vorticity and steep pseudo-equivalent potential temperature isolines. The rainfall occurred eastward relative to the vortex movement track and intensified on the left of cloud black body temperature (TBB) with larger gradients at the middle to late stages of the vortex life span. The generation of moisture helicity is dictated by the water vapor transportation, with the negative value concentrated in the water vapor-rich areas. Notably, the two cases differ in tracks, life spans, water vapor sources, and precipitation amounts, which may be attributed to large-scale circulation background, vertical vortex structure, and moisture condition.

The characteristics of diurnal variation of the surface sensible heat flux (SH) over the Tibetan Plateau (TP) are comprehensively investigated by using the long-term dataset of integrated land–atmosphere interaction observations (2006–2016) on the TP. Results show that the diurnal variation of SH shows obvious seasonal variabilities in terms of amplitude, duration, and peak time. At the Muztagh Ata Westerly Observation and Research Station (MAWORS), the Ngari Desert Observation and Research Station (NADORS), and the Qomolangma Atmospheric and Environmental Observation and Research Station (QOMS), the SH diurnal amplitude is consistently the largest in spring, followed by summer and autumn, and the smallest in winter, with a peak at 15:00. However, for the Southeast Tibet Observation and Research Station (SETORS), the amplitude in winter is rather violent with the peak at 12:00. We find that positive SH at most stations has the longest duration from May to August. Moreover, the peak time fluctuates from month to month, even showing a shift at the QOMS before and after 2015, and the double-peak phenomenon of SH mainly occurs in spring and autumn. Additionally, magnitudes of calculated SH with the conventional heat transfer coefficient (CDH) of 0.004 are about 64–100% larger than those of directly observed SH at the QOMS and the Nam Co Monitoring and Research Station (NAMORS). We here additionally recommend a new CDH values of about 2.24 × 10−3 in spring and 2.78 × 10−3 in summer, respectively, to more accurately calculate the TP SH.