2 citations · 4 across the 4 of their papers we have counts for
4 papers
Discovering Spatial Correlations of Earth Observations for weather forecasting by using Graph Structure Learning
Hyeon-Ju Jeon, Jeon-Ho Kang, In-Hyuk Kwon +1
This study aims to improve the accuracy of weather predictions by discovering spatial correlations between Earth observations and atmospheric states. Existing numerical weather pre…
Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation
Hyeon-Ju Jeon, Jeon-Ho Kang, In-Hyuk Kwon +1
This paper investigates the impact of observations on atmospheric state estimation in weather forecasting systems using graph neural networks (GNNs) and explainability methods. We…
CloudNine: Analyzing Meteorological Observation Impact on Weather Prediction Using Explainable Graph Neural Networks
Hyeon-Ju Jeon, Jeon-Ho Kang, In-Hyuk Kwon +1
The impact of meteorological observations on weather forecasting varies with sensor type, location, time, and other environmental factors. Thus, quantitative analysis of observatio…
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
Van Thuy Hoang, O-Joun Lee
Graph representation learning (GRL) methods, such as graph neural networks and graph transformer models, have been successfully used to analyze graph-structured data, mainly focusi…