  In many scenarios, it is necessary to monitor a complex system via a
time-series of observations and determine when anomalous exogenous events have
occurred so that relevant actions can be taken. Determining whether current
observations are abnormal is challenging. It requires learning an extrapolative
probabilistic model of the dynamics from historical data, and using a limited
number of current observations to make a classification. We leverage recent
advances in long-term probabilistic forecasting, namely {\em Deep Probabilistic
Koopman}, to build a general method for classifying anomalies in
multi-dimensional time-series data. We also show how to utilize models with
domain knowledge of the dynamics to reduce type I and type II error. We
demonstrate our proposed method on the important real-world task of global
atmospheric pollution monitoring, integrating it with NASA's Global Earth
System Model. The system successfully detects localized anomalies in air
quality due to events such as COVID-19 lockdowns and wildfires.
