# Method for Meteorological Radiosonde Data Completion Based on Hierarchy-Aware Spatiotemporal Graph Neural Network

> This article introduces an innovative VHT-GNN model that achieves high-quality missing value completion for radiosonde meteorological data by constructing a three-dimensional graph structure (vertical, horizontal, temporal), combined with hierarchy-aware normalization and edge conditional gating mechanisms.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-12T19:45:46.000Z
- 最近活动: 2026-06-12T19:48:00.613Z
- 热度: 160.0
- 关键词: 图神经网络, 气象数据, 数据补全, 探空观测, 时空建模, 机器学习, 深度学习, 天气预测
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-alsnakty-radiosonde-gnn
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-alsnakty-radiosonde-gnn
- Markdown 来源: floors_fallback

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## Introduction: VHT-GNN Model—An Innovative Solution for Meteorological Radiosonde Data Completion

Meteorological radiosonde data is an important foundation for weather forecasting and climate research, but it often has missing values due to equipment failures, communication interruptions, etc. The Hierarchy-Aware Spatiotemporal Graph Neural Network (VHT-GNN) introduced in this article achieves high-quality missing value completion and significantly improves accuracy by constructing a three-dimensional graph structure (vertical, horizontal, temporal), combined with hierarchy-aware normalization and edge conditional gating mechanisms.

## Background and Challenges: Pain Points of Missing Radiosonde Data and Limitations of Traditional Methods

Radiosonde data covers multiple pressure layers from the ground to an altitude of 30 km, which is crucial for numerical weather prediction, climate monitoring, and extreme weather early warning. However, missing data is widespread. Traditional methods such as Inverse Distance Weighting (IDW) interpolation and linear interpolation struggle to capture complex spatiotemporal correlations; deep learning methods like LSTM and CNN ignore the physical characteristics of the atmospheric vertical structure, leading to limited completion effects.

## Core Innovation: Three-Dimensional Graph Structure Modeling (Vertical-Horizontal-Temporal)

VHT-GNN represents radiosonde observations as a multi-relational graph, defining three types of edges: vertical edges connecting adjacent pressure layers in the same profile (utilizing physical constraints like hydrostatic equilibrium), horizontal edges connecting different stations at the same pressure layer (utilizing spatial continuity), and temporal edges connecting consecutive time steps of the same station-pressure layer (utilizing temporal inertia), thus capturing the inherent structural characteristics of atmospheric data.

## Model Architecture: Technical Details of Hierarchy-Aware and Gating Mechanisms

VHT-GNN workflow: Input projection → Vertical/Horizontal/Temporal gated convolution → Adaptive fusion → Masked temporal attention → Output projection. Key technologies: Hierarchy-aware normalization (independent standardization for each pressure layer), edge conditional gating (dynamically adjusting information transfer intensity), adaptive fusion (data-driven weighting), and masked temporal attention (avoiding interference from missing information).

## Experimental Validation: Performance Advantages of VHT-GNN on the IGRA Dataset

Validated on the IGRA dataset (14 pressure layers, 6 variables), comparing statistical methods (IDW, linear interpolation), deep learning methods (LSTM, CNN), graph neural networks (GraphSAGE, etc.), and ablation models. The results show that VHT-GNN significantly outperforms baselines in accuracy and physical consistency (e.g., hydrostatic equilibrium). The higher the missing rate, the more obvious the advantage, and the stability is good.

## Application Value: Practical Scenarios for Weather Forecasting and Climate Research

1. Numerical weather prediction: Completing data improves the integrity of the initial field and enhances forecasting skills; 2. Climate change reconstruction: Repairing missing historical data to support trend analysis; 3. Extreme weather early warning: Completing interrupted observations under severe weather to provide decision support for forecasters.

## Code Guide: Project Structure and Quick Run Examples

The project is divided into data collection (format conversion, merging, analysis) and model implementation (data loading, model definition, training, physical validation) modules. Run examples: cd vht_stgnn; python M99_MAIN.py --model vht_gnn --seed 42 123 456 789 2024; python M98_CompareResults.py to summarize and compare.

## Summary and Outlook: Potential of Combining Domain Knowledge with Deep Learning

Core contributions of VHT-GNN: Three-dimensional graph modeling framework, hierarchy-aware normalization and edge conditional gating, physical consistency check. Future directions: Extending to satellite data completion, joint modeling of multi-source observations, end-to-end physical constraint learning.