Robust Statistical Methods for the Ensemble Kalman Filter

• Activity Number: 446
• Type: Contributed
• Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Colette Smirniotis* and Barbara Ann Bailey
• Companies: San Diego State University and San Diego State University
• Keywords: data assimilation

Abstract

Data assimilation 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. The General Curvilinear Coastal Ocean Model developed at San Diego State University is capable of resolving high-resolution nonlinear processes. Previous studies have found that for small domains on the order of a few kilometers, every observation impacted everystate variable and the assimilation system exhibited sensitivity to observation error variance. Robust methods can be used when observations seem to come from a distribution for which the assumption of normality is suspect. Incorporation of robust statistical methods with the data assimilation framework into the model system can further improve the model accuracy. We present the results of the evaluation of the performance of robust methods for the Ensemble Kalman Filter for data assimilation.