收稿：2024-08-13，
纸质出版：2026-05-07
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湖南省地处亚热带季风湿润气候区，水汽活动剧烈，强对流、暴雨等灾害性天气频发。精确监测高动态大气水汽场对该区域的极端天气预警具有重要意义，但这通常受制于传统水汽层析技术在时间分辨率上的不足。针对此问题，本文提出了一种高动态节点参数化水汽层析两步法，该方法在节点参数化模型基础上，通过引入随迭代过程自适应更新的水汽垂直变化参数，优化了层析方程的设计矩阵。其核心“两步法”包含：第一步，在30分钟长时段内重构水汽场的线性变化趋势项；第二步，基于第一步的建模残差，进一步反演5分钟短时段内的高频偏离项，从而实现高时空分辨率的精确重构。基于2022年6月湖南省123个全球导航卫星系统GNSS（Global Navigation Satellite System）站的实测数据实验表明，与传统体素基模型（体素内水汽含量均匀分布且不变）、节点参数化模型（湿折射率由所在体素的八个节点经空间内插求取）和线性时变节点参数化模型（湿折射率随时间呈线性变化）相比，本文提出的高动态节点参数化水汽层析两步法在精度上表现出显著优势，其外符合精度分别提升了35%、29%和26%。本文方法为亚热带等水汽高动态变化区域提供了有效的高分辨率三维水汽场监测手段。
Atmospheric water vapor is a crucial component of the troposphere
playing a decisive role in the global energy budget and the formation of severe weather events. Hunan province
located in a subtropical monsoon humid climate zone with complex topography
frequently experiences extreme weather disasters such as severe convection and rainstorms driven by intense water vapor activity. Precisely monitoring the high-dynamic three-dimensional (3D) water vapor field is of great significance for early warning of these disasters. However
Global Navigation Satellite System (GNSS) water vapor tomography
a primary remote sensing technique
faces significant challenges. Traditional voxel-based models assume uniform water vapor distribution within a grid
failing to describe continuous spatial variations. Moreover
limited by station density
these methods often require long time windows (e.g.
30 minutes) to accumulate observations
resulting in low temporal resolution that misses the rapid evolution of water vapor during extreme weather. This study aims to develop a novel tomography framework to reconstruct water vapor fields with high spatiotemporal resolution (e.g.
5 minutes) in high-dynamic environments.
A High-dynamic Node-parameterized Water Vapor Tomography Two-step Method (HNT-TSM) is proposed. Unlike discrete voxel models
this method employs a node-based parameterization strategy where the wet refractivity at any spatial point is determined by the interpolation of eight surrounding node parameters
ensuring spatial continuity. To address the ill-posed problem in high-resolution retrieval
the method incorporates an adaptive vertical constraint
introducing a vertical variation parameter that updates automatically during iteration to optimize the design matrix. The core reconstruction follows a "two-step" framework: (1) a 30-minute window is used to reconstruct the linear variation trend (background field) to ensure model stability; (2) based on the modeling residuals from the first step
a residual tomography model inverts high-frequency deviation terms within short 5-minute intervals. This approach effectively separates stable background signals from rapid dynamic fluctuations
implemented using the Algebraic Reconstruction Technique (ART).
The method was validated using GNSS data from 123 stations in the Hunan Continuously Operating Reference Stations (HNCORS) network during June 2022. Four schemes were designed for comparison: Scheme 1 (traditional voxel-based model with uniform distribution)
Scheme 2 (standard node-parameterized model)
Scheme 3 (linear time-varying node-parameterized model)
and Scheme 4 (the proposed HNT-TSM). Evaluation using independent Slant Wet Delays (SWD) and ERA5 reanalysis demonstrated that HNT-TSM significantly outperforms the benchmarks. Specifically
in terms of external validation accuracy
the proposed method improved by 35%
29%
and 26% compared to Scheme 1
Scheme 2
and Scheme 3
respectively. Furthermore
in a heavy rainstorm case study on June 19
the 5-minute resolution products generated by HNT-TSM successfully captured rapid water vapor convergence and dissipation processes missed by low-resolution models
showing high consistency with the precipitation distribution.
The HNT-TSM effectively resolves the conflict between temporal resolution and model stability in GNSS tomography. By integrating node parameterization with a two-step reconstruction strategy
it achieves high-precision 3D monitoring at a 5-minute level. This method demonstrates significant advantages in regions with complex terrain and active water vapor changes
such as the subtropical monsoon region. The resulting high-spatiotemporal-resolution products provide robust data for analyzing extreme weather mechanisms and hold great potential for data assimilation in numerical weather prediction systems.
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