Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based...

Area of Science:

• Dynamical Systems and Chaos Theory
• Machine Learning Applications
• Data Assimilation Techniques

Background:

• Forecasting chaotic dynamical systems is challenging due to inherent sensitivity to initial conditions and model imperfections.
• Existing data-driven methods often require complete state measurements for training, limiting their applicability.
• Hybrid approaches combining machine learning with knowledge-based models show promise for correcting model deficiencies.

Purpose of the Study:

• To develop a method for data-assisted forecasting of chaotic systems using partial measurements.
• To integrate data assimilation with machine learning to overcome limitations of existing forecasting techniques.
• To improve the predictive accuracy of imperfect knowledge-based models in chaotic systems.

Main Methods:

• Employed a hybrid approach combining data assimilation and machine learning.
• Utilized the Ensemble Transform Kalman Filter for assimilating synthetic data.
• Demonstrated the technique on the Lorenz 1963 and Kuramoto-Sivashinsky systems with simulated model errors.

Main Results:

• Successfully trained a machine-learning model using partial state measurements.
• Showed that the hybrid approach improves predictions from imperfect knowledge-based models.
• Validated the method's effectiveness on two distinct chaotic systems.

Conclusions:

• Combining data assimilation with machine learning enables accurate forecasting of chaotic systems with partial data.
• This approach relaxes the requirement for complete state measurements, broadening the applicability of data-driven forecasting.
• The developed technique offers a powerful tool for improving predictions in complex dynamical systems like the atmosphere and ocean.