The impact of meteorological observations on weather forecasting varies with
sensor type, location, time, and other environmental factors. Thus,
quantitative analysis of observation impacts is crucial for effective and
efficient development of weather forecasting systems. However, the existing
impact analysis methods are difficult to be widely applied due to their high
dependencies on specific forecasting systems. Also, they cannot provide
observation impacts at multiple spatio-temporal scales, only global impacts of
observation types. To address these issues, we present a novel system called
``CloudNine,'' which allows analysis of individual observations' impacts on
specific predictions based on explainable graph neural networks (XGNNs).
Combining an XGNN-based atmospheric state estimation model with a numerical
weather prediction model, we provide a web application to search for
observations in the 3D space of the Earth system and to visualize the impact of
individual observations on predictions in specific spatial regions and time
periods.

使用可解释的图神经网络（XGNNs）的基于 XGNN 的大气状态估计模型，结合数值天气预报模型，提供了一个名为 “CloudNine” 的新型系统，用于分析个体观测数据对特定预测的影响，并在特定空间区域和时间段可视化观测数据对预测的影响。

云天九：利用可解释图神经网络分析气象观测对天气预报的影响

CloudNine: Analyzing Meteorological Observation Impact on Weather  Prediction Using Explainable Graph Neural Networks

This paper revises previous work and introduces changes in spatio-temporal
scales. The paper presents a model that includes layers A and B with varying
degrees of forgetting and dependence over time. We also model changes in
dependence and forgetting in layers A, A', B, and B' under certain conditions.
In addition, to discuss the formation of opinion clusters that have reinforcing
or obstructive behaviors of forgetting and dependence and are conservative or
brainwashing or detoxifying and less prone to filter bubbling, new clusters C
and D that recommend, obstruct, block, or incite forgetting and dependence over
time are Introduction. This introduction allows us to test hypotheses regarding
the expansion of opinions in two dimensions over time and space, the state of
development of opinion space, and the expansion of public opinion. Challenges
in consensus building will be highlighted, emphasizing the dynamic nature of
opinions and the need to consider factors such as dissent, distrust, and media
influence. The paper proposes an extended framework that incorporates trust,
distrust, and media influence into the consensus building model. We introduce
network analysis using dimerizing as a method to gain deeper insights. In this
context, we discuss network clustering, media influence, and consensus
building. The location and distribution of dimers will be analyzed to gain
insight into the structure and dynamics of the network. Dimertiling has been
applied in various fields other than network analysis, such as physics and
sociology. The paper concludes by emphasizing the importance of diverse
perspectives, network analysis, and influential entities in consensus building.
It also introduces torus-based visualizations that aid in understanding complex
network structures.

通过引入时间和空间上的扩展维度、构建意见聚类、削弱意见遗忘和依赖行为、以及网络分析等方法，本文提出了一个综合框架来加强共识建设，并强调了多样的观点、网络分析和有影响力的实体在其中的重要性。