CloudNine: Analyzing Meteorological Observation Impact on Weather
  Prediction Using Explainable Graph Neural Networks
Hyeon-Ju Jeon Jeon-Ho Kang In-Hyuk Kwon O-Joun Lee
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.