Using observational data of soil moisture from the third Tibetan Plateau Experiment for atmospheric science (TIPEX III), the seasonal and diurnal variations characteristics of soil moisture at different depths of 5–160 cm from seven stations were analyzed, with emphasis on the comparative analysis of the differences of soil moisture between different sites and the differences of the synergistic relationship between soil moisture and temperature. The soil moisture was wet in the southeast and dry in the northwest. The studied sites were Lhari, Biru, Nyainrong, Amdo, Nagqu, Baingoin and Seng-ge Kambab in descending order, according to the soil moisture. The seasonal variation of soil moisture at the different sites showed a significant three-peak structure, which was more obvious in the shallow layer than in the deep layer. The first peak occurred from March to May, which was mainly due to the soil thawing in spring. The other two peaks corresponded to the two rainy seasons in the plateau. Soil moisture was the greatest during this rainy period. The diurnal variations of soil moisture and temperature in Amdo, Nagqu, Nyainrong and Baingoin showed a significant positive correlation in the four seasons. The soil moisture and temperature in Lhari and Biru were significantly positively correlated in winter and spring but negatively correlated in summer and autumn. The profiles of the soil moisture with depth varied greatly at different stations in different seasons. The distribution of soil water content at each observational site did not increase or decrease with depth but showed a certain high aquifer, which might be related to the types of the underlying surface and physical properties of soil. During the summer monsoon period, soil moisture in the shallow layer of 5–10 cm was higher at all observational sites. The spatial distribution of soil moisture in the plateau was more heterogeneous than that in the plain area, and only in the central part of the Tibetan Plateau, the soil moisture varied greatly from site to site. This also indicated that it was unreasonable to only use the soil moisture of several stations to represent the overall soil moisture of the region. The results provided a multi-angle observational basis for the validation of satellite data and parameterization of the numerical model of soil moisture over the Tibetan Plateau.

To promote Tibetan meteorological research, the third Tibetan Plateau (TP) Experiment for atmospheric sciences (TIPEX III) has been carried out over the plateau region since 2014, with near-surface heat fluxes measured at different sites. Using the observational data of near-surface heat fluxes measured at 8 plateau stations in TIPEX III, as well as the ECMWF ERA Interim reanalysis data, the land-atmosphere heat transfers over different regions of TP and their responses to the South Asian summer monsoon (SASM) during active/break periods were investigated. Inhomogeneity was found in the land-atmosphere heat transfers over the plateau, with large differences among plateau stations. During the observation period, the daily averaged total heat transfer (the sum of sensible and latent heat flux) varied from 70.2 to 101.2 Wm⁻² among the 8 plateau stations, with the sensible heat flux from 18.8 to 60.1 Wm⁻² and the latent heat flux from 10.1 to 74.7 Wm⁻². These heat transfers were strongly affected by the SASM evolution, but with strong inhomogeneities over the plateau stations. Overall, the more southern station locations exhibited more SASM impacts. The land-atmosphere heat transfers (the total, sensible and latent heat fluxes) were greatly weakened/strengthened during the SASM active/break period at Namco (southeast plateau), Baingoin (central plateau), Lhari (central plateau), and Nagqu (central plateau), which were closely related to the weakened/strengthened radiation conditions. However, the SASM impacts were quite small or even negligible for the other plateau stations, which complicated our conclusions, and further investigations are still needed.

This article utilized hourly temperature, humidity, pressure and wind speed data from passion fruit meteorological observation stations in three southwestern cities of Fujian Province (Longyan, Sanming, Zhangzhou) from 2020 to 2022, as well as national ground conventional meteorological observation stations. BP neural network and stepwise regression method were applied to construct temperature prediction models for the passion fruit planting bases. The results showed that: (1) The simulation effect of the passion fruit station temperature prediction model based on BP neural network (referred to as BP model) was better than that of the model based on stepwise regression method (referred to as regression model). The average absolute error (MSE) of BP model (2.75–3.42 °C) was smaller than that of regression model (3.32–3.94 °C). (2) For the simulation results of daily temperature changes in the passion fruit station, the difference in hourly average temperature between the BP model predictions (regression model predictions) and observed temperatures at passion fruit station was −4.1–4.4 °C (−6.0–10.2 °C). The BP model showed a daily temperature trend that was closer to the measured values; (3) For the simulation results of high and low temperatures in the passion fruit station, the BP neural network model (regression model) showed a prediction error range of −5.6 °C to 5.2 °C compared to observed temperatures, while the stepwise regression model’s error range was −4.1 °C to 8.8 °C. The BP model’s predicted temperature trend was closer to the measured values. (4) Both models have significant shortcomings in the prediction of high-temperature individual cases and hourly averages, with relatively large errors (generally exceeding 3 °C), especially during the period from 10 to 16 o’clock. The future version needs to be optimized.