This study uses hourly surface wind direction and wind speed observations from 53 meteorological stations on the Tibetan Plateau (TP) (70–105° E, 25–45° N) between 1995 and 2017 to investigate diurnal variations in the surface wind. The results show large diurnal variations in surface wind on the TP. The minimum wind speed occurs in the morning and the maximum in the afternoon. In all four seasons, the prevailing meridional wind is a southerly, and this is typically evident for more than two-thirds of each day. However, in the mornings during December–February and September–November, this southerly wind is replaced by a northerly, but remains southerly in the afternoon. The TP shows remarkable regional characteristics with respect to diurnal variations in wind speed. In the eastern region, the minimum and maximum daily wind speeds occur about 1 h later than in the west. Among the 53 meteorological stations, 79% observed that it took less time for the minimum speed to rise to the maximum speed than for the maximum to drop to the minimum. The blocking effect of the high surrounding terrain causes the diurnal variations seen in the surface winds at the three stations in the Qaidam Basin to differ significantly from those observed at the other stations elsewhere on the plateau. These Qaidam Basin stations recorded their maximum wind speeds around noon, with the minimum at dusk, which is around 1900 LST. The EOF1 (EOF = empirical orthogonal function) of the hourly wind speed on the TP indicates the key daily circulation feature of the region; i.e., the wind speed is high in the afternoon and low in the morning. The EOF2 reflects the regional differences in the diurnal variations of wind speed on the TP; i.e., the eastern region reaches the daily maximum and minimum wind speeds slightly later than the western region.

A thunderstorm is a weather system that can trigger severe natural disasters, characterized by sudden onset, short duration, and significant damage. Accurate forecasting of thunderstorms has long been a challenging task. Data-driven artificial intelligence (AI) technologies have provided new solutions, yet AI-driven thunderstorm forecasting still lacks high-quality thunderstorm training datasets. Leveraging lightning data from the China Meteorological Administration’s Advanced Direction and Time-of-Arrival Detecting (ADTD) network and the three-dimensional Very Low Frequency/Low Frequency (VLF/LF) lightning location data of the Institute of Electrical Engineering, Chinese Academy of Sciences, we have constructed an AI training dataset for thunderstorms over China (AITDTS) through four sequential procedures: rigorous data quality control, multi-source integration, thunderstorm-prone area labeling, and feature extraction. The AITDTS encompasses 85,071 thunderstorm events and 3,973,171 corresponding gridded samples at 10 min temporal resolution and 1 km × 1 km spatial resolution across China during 2016–2023. Each sample includes location labels, 38 radar-derived physical parameters with a 10-min temporal resolution and 62 environmental parameters with an hourly temporal resolution. We further quantified predictor information gain for thunderstorm forecasting: radar echo top/base heights, composite reflectivity, vertical integrated liquid water content and reflectivity at 10 km showed high information gain. Atmospheric instability, dynamic uplifting, moisture conditions and vertical wind shear at 1 km exhibited moderate information gain. The AITDTS can be directly applied to training and evaluation of AI-driven forecasting models, offering critical data for thunderstorm nowcasting.

This study proposes a coordinated bidirectional meteorological data fusion processing system for controlled data exchange across the intranet, demilitarized zone (DMZ), and Internet. The system adopts a three-layer isolation architecture and integrates Apache MiNiFi, Apache NiFi, and Apache Kafka to coordinate edge preprocessing, DMZ-based fusion processing, asynchronous message buffering, and Internet service publication. Kerberos authentication, access control, and operational monitoring support outbound data-product services and inbound user-request-driven workflows. Operational evaluation at the National Meteorological Science Data Center showed that edge preprocessing reduced cross-domain data volume by an average of 95%; representative bidirectional workflows were completed within minutes; and the average Kafka message-processing success rate over three consecutive months was 99.77%. The system has supported the automated generation and external publication of human comfort index products, while its core mechanisms have been generalized into a reusable software stack for cross-domain scientific data applications. The results indicate that the proposed architecture provides a practical and deployable approach to efficient and controlled bidirectional meteorological data fusion without changing existing network security boundaries.