Consistent Robust and Recursive Estimation of Atmospheric Motion Vectors From Satellite Images

Atmospheric motion vectors (AMVs) estimation helps in better understanding of atmospheric dynamics and also plays a key role in weather forecasting. It has been a challenging task because of the nonrigid motion of clouds and cyclones. In this paper, a modified Weighted Ensemble Transform Kalman Filter-based data assimilation technique is proposed for accurate flow vector estimation at each pixel directly from satellite generated infrared images of clouds/cyclones. This method provides clear visualization of both local and global motion with spatial and temporal consistencies very efficiently even in the case of splitting and merging of clouds or over long tracks. One of the key abilities of proposed method is in forecasting applications and also for generating motion vectors in the absence of data in real scenarios, even without the usage of the existing complex weather models. Estimated AMVs are validated using state-of-the-art European Centre for Medium-range Weather Forecasting (ECMWF) analysis data, and cyclone tracks are validated using the Indian Meteorological Department (IMD) best track data. The results obtained demonstrate the efficacy of proposed method over other existing methods.