Comparison of Ensemble Filters for Data Assimilation

• Activity Number: 91
• Type: Invited
• Date/Time: Sunday, August 9, 2015 : 9:30 PM to 10:15 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Barbara Ann Bailey* and Colette Smirniotis
• Companies: San Diego State University and San Diego State University
• Keywords: data assimliation ; Kalman filter

Abstract

Data assimilation (DA) is the process of combining observations with the output from physics-based numerical models and is used for the purpose of updating and improving forecasts. DA problems are most common in atmospheric and ocean data applications. There has been an increase in the amount of available real time observed ocean and atmospheric data as well as advances in deterministic ocean and atmospheric models, which makes Monte Carlo statistical methods ready for advancing the field of DA. Two commonly used algorithms are the ensemble Kalman filter (EnKF) and the ensemble adjustment Kalman filter (EAKF). This project uses a low-resolution model to compare the performance of the EnKF and the EAKF under different conditions, including varied ensemble size and observation density.