Observational data from the Global Precipitation Measurement (GPM) Core Observatory during four summers (2014–2017) has been used to investigate deep convection systems (DCSs) over the Tibetan Plateau (TP) and its south slope (SS). The frequency, geographical distribution diurnal variation, and vertical structure of DCSs over the TP and SS are compared among these two regions. The frequency of DCSs over the SS (0.98%) was far higher than over the TP (0.15%), suggesting that stronger DCSs occur to the east and south of the TP. The maximum number of DCS occurred in July and August. A clear diurnal variation in DCS was found over the whole region, DCSs over the TP and SS both have a greatest amplitude in the afternoon. The probability of DCSs from 1200 to 1800 local time (LT) was 76.3% and 44.1% over TP and SS respectively, whereas the probability of DCSs being generated from 2200 (LT) to 0600 on the next day LT was 0.03% and 33.1% over the TP and SS respectively. There was a very low frequency of DCSs over the TP during the night. Five special echo top heights were used to investigate the vertical structure of DCSs. DCSs over the TP were both weaker and smaller than those over the SS.

A subseasonal to seasonal precipitation prediction model was established based on deep learning. q A sensitivity-based method was used to explain the physical basis of deep learning. q The recurrent neural network model has better performance in different terrains.

The eastern Tibetan Plateau has a high elevation, with a cold and dry atmospheric background. The features of the raindrop size distributions (DSD) in this region have notable differences from those in the plains. The general empirical relationships for retrieving parameters of precipitation from radar observations are not applicable in the eastern Tibetan Plateau. In this study, we developed a new method based on optimal estimation theory to retrieve the vertical profiles of N₀ and D_m from a Ka-band zenith-pointing Doppler radar. Validation by a field campaign during the summer of 2024 indicates that the mean bias in the log ₁₀(N₀) and D_m derived from the PARSIVEL² disdrometer and the retrieved values are 0.12 and −0.1 mm respectively, demonstrating the effectiveness of the retrieved DSD parameters in this region. Based on the retrieved vertical profiles of DSD parameters, some unique characteristics are found. The heavy precipitation (the maximum value in the reflectivity profile exceeding 30 dBZ) exhibits a higher particle number concentration above 2 km and larger raindrop size in the bottom of the rainfall on average. The mean values of D_m above 2 km are approximately 0.5 mm, for heavy precipitation, the value increase as the raindrops fall, reaching a peak at around 0.5 km. Precipitation that occurs after the nighttime cooling usually has higher particle concentrations and smaller particle sizes. Based on the above research, empirical relationships for the quantitative precipitation estimates (QPE) and attenuation correction using Ka-band radar in the eastern Tibetan Plateau are established.

The eastern Tibetan Plateau has a high elevation, with a cold and dry atmospheric background. The features of the raindrop size distributions (DSD) in this region have notable differences from those in the plains. The general empirical relationships for retrieving parameters of precipitation from radar observations are not applicable in the eastern Tibetan Plateau. In this study, we developed a new method based on optimal estimation theory to retrieve the vertical profiles of N₀ and D_m from a Ka-band zenith-pointing Doppler radar. Validation by a field campaign during the summer of 2024 indicate that the average bias in the log₁₀(N₀) and D_m derived from the PARSIVEL² disdrometer and the retrieved values is 0.12 and -0.1 mm, demonstrating the effectiveness of the retrieved DSD parameters in this region. Based on the retrieved vertical profiles of DSD parameters, some unique characteristics are found. The heavy precipitation (the maximum value in the reflectivity profile exceeding 30 dBZ) exhibits a higher particle number concentration above 2 km and larger raindrop size in the bottom of the rainfall on average. The mean values of D_m above 2 km are approximately 0.5 mm, for heavy precipitation, the value increase as the raindrops fall, reaching a peak at around 0.5 km. Precipitation that occurs after the nighttime cooling usually has higher particle concentrations and smaller particle sizes. Based on the above research, empirical relationships for the quantitative precipitation estimates (QPE) and attenuation correction using Ka-band radar in the eastern Tibetan Plateau are established.