Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales

Understand

Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization.

• Recently, methods for deep generative data assimilation have been proposed which allow for using new input data without retraining the model.
• They could also dramatically accelerate the costly data assimilation process used in operational regional weather models.
• Here, in a central US testbed, we demonstrate the viability of score-based data assimilation in the context of realistically complex km-scale weather.