Machine Learning application for Satellite Data Assimilation in Space Physics

Abstract

Satellite data assimilation enhances space weather prediction by integrating observational data into models. Traditional methods like Kalman filtering and variational techniques provide a foundation but struggle with high-dimensional, nonlinear, and noisy data. Machine learning (ML) offers adaptive, scalable, and efficient solutions, improving predictive accuracy and real-time analysis. This study explores ML approaches— neural networks, ensemble methods, and reinforcement learning—highlighting their potential in transforming satellite data assimilation while addressing limitations and future research directions.