Dr. Steven Fletcher
Dr. Steven Fletcher
Research Scientist III
Office: CIRA RM 12, Fort Collins
Phone: 970-491-8376
Fax: 970-491-8241
Email:
Mailing Address:
Dr. Steven J. Fletcher
Cooperative Institute for Research in the Atmosphere
Colorado State University
1375 Campus Delivery
Fort Collins, CO 80523-1375
Biography
Steven Fletcher received his B.Sc.(HONS) 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), (Advisor: Prof. Nancy Nichols) and his Ph.D. in Data Assimilation also from the Mathematics department at the University of Reading (2004), (Advisors: Prof. Nancy Nichols and Prof. Ian Roulstone, Met Office/U. Surrey). 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 2010 Dr. Fletcher was a research scientist at the Center for Geosciences/Atmospheric Research (CG/AR) in CIRA. During this time he developed a Bayesian based probability model for 4DVAR to enable any distribution to be used. In particular he derived the 4DVAR equivalent for the mixed lognormal-Gaussian distribution.  As part of the review process for the Fletcher (2010) Tellus paper Dr. Fletcher derived and coded into the Lorenz 1963 model, the equations for a mixed distribution constant model error term.  Also during this time Dr. Fletcher worked on 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.
 
 
During 2008 to 2011,  Dr. Fletcher worked on techniques associated with how to assimilate both binary MODIS 500m snow cover observations with coarse AMSR-E 25km snow water equivalence into the snow evolution model SnowModel.  There were four different schemes developed from simple nudging to a rule based iterative direct insertion technique as well as combination of the two to optimize the data available.  This work was tested over two different spacial domains; the first in south-eastern Colorado and the second over a region of the tri-state are of Colorado, Wyoming and Nebraska, during the winter season of 2006-2007.  This was work done in collaboration with Drs. Glen Liston, Christopher Hiemstra and Steven Miller.
 
In 2012 Dr. Fletcher became PI on three projects at CIRA; the first is associated with the Naval Research Laboratory in Monterey, California, working on the NAVDAS-AR system, the second is with NOAA/NESDIS investigating the impact of the new hybrid 3DVAR GSI system compared to the static, but logarithmic transform based, MIRS retrieval system, the third project is a NSF grant to investigate the impacts of non-Gaussian errors in Gaussian based variational data assimilation systems.
Recent Work

Dr. Fletcher is investigating different approaches to improve the background error covariance matrix in the NAVDAS-AR system as well as investigating possible new choices for moisture representation in the same system.  He is also investigating the differences in both the analysis and the first guess from the NOAA/NCEP data assimilation system compared to the NOAA/NESDIS temperature-humidity retrieval system for the hurricane Sandy test case.

 

Dr. Fletcher's newest project is an NSF grant to investigate the impacts of using Gaussian and logarithmic transform approaches compared to a lognormal based Bayesian approach firstly in the CIRA 1-Dimensional Optimal Estimator (C1DOE) but eventually this work will be extended to the WRF-GSI system and then combinations of both.  Also as part of the award Dr. Fletcher is developing the incremental 4DVAR for geometric errors and an associated Bayesian quality control measure.  As part of this project Dr. Fletcher will be joined by a postdoctoral fellow in the Spring of 2013.  This project is also in collaboration with Dr. Andrew Jones and Mr. John Forsythe.

Selected Publications

Fletcher, S.J., G. E. Liston, C. A. Hiemstra and S. D. Miller, 2012: Assimilating MODIS and AMSR-E Snow observations in a Snow Evolution Model. J. Hydrometeor. 13, 1475--1492.

Guillot, E. M., T. H. Vonder Haar, J. M. Forsythe and S. J. Fletcher, 2012: Evaluating satellite based cloud persistance and displacment nowcasting techniques over complex terrain. Weather and Forecasting, 27, 502--514.

Fletcher, S. J. 2010: Mixed lognormal-Gaussian four-dimensional data assimilation. Tellus, 62A, 266-287.

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.
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