Learn how to implement the Ensemble Kalman filter for data assimilation, with step-by-step mathematical details code
Many real-world dynamical systems are chaotic, where small changes in initial conditions lead to significant differences in subsequent states. This phenomenon, also known as the butterfly effect, makes it difficult for programmed physical models to accurately predict system behavior. Data assimilation addresses this problem by integrating observations into the estimation of the model state. It is commonly applied to time series prediction problems, especially in physical system models such as weather forecasting. Ensemble Kalman Filter (EnKF) is a widely used algorithm in data assimilation with elegant theory and simple implementation, gaining popularity from science to industry.
This post serves as a tutorial on EnKF. It will introduce the basic mathematics of EnKF, provide step-by-step code, and show practical implementation using a toy…
