Steven Fletcher received his B.Sc. in Mathematics and Statistics from the University of Reading in the United Kingdom (1998), his M.Sc. in Numerical Solutions to Differential Equations again from the University of Reading (1999) and his Ph.D. in Data Assimilation also from the Mathematics department at the University of Reading (2004). His areas of interest are numerical weather prediction and modeling, data assimilation, control theory, calculus of variation, control variable transforms and preconditioning for large scale nonlinear inverse problems and non-Gaussian probability models for data assimilation. From 2004 to 2006, he worked as a post doctoral fellow at the Cooperative Institute for Research in the Atmosphere (CIRA) under a National Science Foundation grant into the use of control theory in Numerical weather prediction. During this time he published the first non-Gaussian three-dimensional variational data assimilation system for lognormally distributed observational errors. Besides this he also published the first paper to combine Gaussian and lognormal errors together to allow for the simultaneous assimilation of different observational error types. He has also worked with the Maximum Likelihood Ensemble Filter (MLEF) with Dr. Milija Zupanski and Prof. Mike Navon of Florida State University exploring the size of the ensemble and the dynamics in the shallow water equations. From 2006 to the present Dr. Fletcher has been a research scientist at the Center for Geosciences/Atmospheric Research (CG/AR) in CIRA. During this time he has been working on applying the hybrid assimilation system to four dimensions as well as demonstrating the impacts of wrong assumptions in data assimilation with simpler models, as well as detecting important dynamical properties of the MLEF with respect to Lyapunov and Bred vectors. He has also worked with the DA group at the National Center for Atmospheric Research on their preconditioner for the Weather, Research and Forecasting model’s 4D VAR system for the US Air Force.

 Zupanski, M., D. Zupanski, S. J. Fletcher, M. DeMaria and R. Dumais 2008: Ensemble data assimilation with the Weather, Research and Forecasting (WRF) model: The hurricane Katrina case. Submitted to J. Geophys. Res.

 

Fletcher, S. J. and M. Zupanski, 2008: A study of ensemble size and shallow water dynamics with the Maximum Likelihood Ensemble Fitler, Tellus, 60A, 348—360.

 

Fletcher, S. J. and M. Zupanski, 2007: Implications and impacts of transforming lognormal variables into normal variables in VAR. Meteorologische Zeitschrift, 16, 755—765.

 

Uzunoglu, B., S. J. Fletcher, M. Zupanski and I. M. Navon, 2007: Adaptive ensemble reduction and inflation. Quart. J. Roy. Meteor. Soc. 133, 1281—1294.

 

Fletcher, S. J. and M. Zupanski, 2006a: A hybrid multivariate normal and lognormal distribution for data assimilation. Atmos. Sci. Lett., 7, 43—46.

 

Fletcher, S. J. and M. Zupanski, 2006b: A data assimilation method for lognormal distributed observational errors. Quart. J. Roy. Meteor. Soc., 132, 2505—2519.

 

Zupanski, M., S. J. Fletcher, I. M. Navon, B. Uzunoglu, R. P. Heikes, D. A. Randall, T. D. Ringler and D. Daescu, 2006: Initiation of ensemble data assimilation. Tellus, 58A, 159—170.