Three-Dimensional GNSS Water Vapor Tomography for Object-Based Precipitation Precursor Identification in Complex Terrain

Conventional water vapor–precipitation studies rely on column-integrated or single-site observations, which cannot capture the three-dimensional (3D) structure of moisture aggregation and its role in precipitation triggering in complex terrain where convective, frontal, and orographic rainfall coexist. Using Ningxia Continuously Operating Reference Station (NXCORS) Global Navigation Satellite System (GNSS) observations (2018–2021), we develop a 3D water vapor tomography model (5 km × 6 km × 1 km computational grid; 5 min retrieval interval), validated against independent radiosonde and ERA5 data (r = 0.953/0.881; RMSE = 1.998/1.949 g m−3). We propose 3D Water Vapor Preferential Zones (WVPZs) and an object-based framework using stratified dual-percentile thresholds (99%/97%) and 3D connected-component tracking to identify, track, and match WVPZs with rainfall regions under temporal (≤6 h prior) and spatial (≤100 km centroid distance) constraints. Across 16 events, WVPZs were present in 94.97% of 5 min time steps. In 416 matched pairs, WVPZs typically formed 2–6 h before rainfall, with a mean precursor time lag of 231.5 min at onset. Mean centroid distances were 52–55 km (mean minimum centroid distance: 39.4 km). By rainfall type, convective rainfall had the shortest precursor time lag (220.6 min) but largest distance (55.7 km); frontal rainfall showed the tightest coupling (49.1 km); orographic rainfall had the longest precursor time lag (254.0 min). Sample-derived precursor-lag and spatial-proximity ranges are 3–5 h and 30–60 km. This framework shifts moisture diagnosis from continuous fields to discrete, trackable water vapor objects, providing a quantitative 3D indicator for characterizing pre-precipitation moisture organization and supporting precipitation monitoring in complex terrain.