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。