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Project Title: Collaborative Research: CMG-Ensemble Data Assimilation for Nonlinear and Nondifferentiable Problems in Geosciences. This is a collaborative effort between Colorado State University (CSU) and Florida State University (FSU).
Principal Investigators: Dr Milija Zupanski (CSU) and Prof I. Michael Navon (FSU)
Graduate Students, Postdoctoral and other Investigators: Jeff Steward (FSU), Biljana Orescanin (CSU Atmospheric Science), Dr. Sangmi Pallickara (CSU Computer Science), Dr. Man Zhang (CIRA/CSU), Dr. Karina Apodaca (CIRA/CSU)
Sponsor: NSF-Collaboration in Mathematical Geosciences
Project duration: 1 September 2009 - 31 August 2013
Funded amount (CSU): $399,056
Goals and objectives:
- Evaluate nondifferentiable minimization methods suitable for ensemble data assimilation (EnsDA),
- Examine the value of hybrid EnsDA methods for nonlinear and nondifferentiable applications,
- Develop and evaluate a nonlinear/nondifferentiable EnsDA method designed to quantify uncertainty in realistic high-dimensional geosciences applications.
Summary:
Among most challenging problems remaining for EnsDA applications in geosciences is the problem of highly nonlinear and nondifferentiable processes and observations. Examples of such processes are cloud, aerosol and precipitation processes, as well as remote sensing (e.g., satellite and radar) observations. Unfortunately, a typical EnsDA analysis equation is linear, being based on the Kalman filter equations, which fundamentally prevents EnsDA from extracting maximum information from such observations, and ultimately limits its applicability to outstanding geosciences problems such as hurricane prediction and high resolution climate simulation. In order to overcome the limitation of existing EnsDA methodologies in application to highly nonlinear and nondifferentiable geoscience problems, we will develop a new strategy based on including nondifferentiable minimization algorithm in EnsDA.
Educational Impact:
Several graduate research assistants and postdoctoral researchers who are contributing in this project, as well as the students attending a graduate course taught at FSU by Prof. Navon will be exposed to state-of-the-art data assimilation and to advanced minimization techniques.
Selected Accomplishments:
