Christman Field Latest Observations
Date Time
MST
Temp
°F
RH
%
DewPt
°F
Wind
mph
Dir
°
Gust
mph
Dir
°
Press
in Hg
Solar
W/m^2
Prec
in
2019-11-12 06:30 10.3 90.3 8.0 3.2 114 4.2 133 24.996 0.8 0.00
2019-11-12 06:25 11.0 89.2 8.4 2.7 144 4.2 151 24.998 0.3 0.00
2019-11-12 06:20 11.0 91.4 9.0 4.6 151 6.0 173 24.995 0.0 0.00
2019-11-12 06:15 10.5 90.1 8.2 5.3 173 6.3 173 24.992 0.0 0.00
2019-11-12 06:10 10.7 91.2 8.6 5.2 164 6.5 161 24.993 0.0 0.00
2019-11-12 06:05 10.0 90.4 7.7 3.8 158 5.9 160 24.991 0.0 0.00
2019-11-12 06:00 9.8 88.6 7.2 0.7 166 3.2 166 24.991 0.0 0.00
2019-11-12 05:55 10.1 89.3 7.6 0.3 240 1.7 333 24.992 0.0 0.00
2019-11-12 05:50 11.6 86.3 8.3 0.7 352 2.1 352 24.993 0.0 0.00
2019-11-12 05:45 13.2 88.8 10.5 0.5 352 1.5 352 24.994 0.0 0.00
2019-11-12 05:40 13.0 92.0 11.1 0.5 352 1.6 351 24.995 0.0 0.00
2019-11-12 05:35 11.9 92.8 10.2 1.1 351 2.5 352 24.995 0.0 0.00
2019-11-12 05:30 9.8 91.6 7.9 3.1 352 4.5 340 24.996 0.0 0.00
2019-11-12 05:25 8.8 89.4 6.3 3.2 340 3.8 348 24.996 0.0 0.00
2019-11-12 05:20 11.5 84.2 7.6 3.2 5 4.2 5 24.999 0.0 0.00
2019-11-12 05:15 12.1 92.6 10.3 0.3 323 2.1 322 24.999 0.0 0.00
2019-11-12 05:10 10.8 93.8 9.4 0.2 236 1.8 236 25.002 0.0 0.00
2019-11-12 05:05 10.5 93.0 8.9 0.5 290 1.1 290 25.006 0.0 0.00
2019-11-12 05:00 9.2 91.8 7.3 1.2 290 2.0 290 25.012 0.0 0.00
2019-11-12 04:55 8.9 90.4 6.6 2.2 290 4.6 296 25.015 0.0 0.00
2019-11-12 04:50 8.2 92.4 6.5 1.3 295 4.9 299 25.017 0.0 0.00
2019-11-12 04:45 6.3 89.2 3.8 0.5 168 1.7 168 25.020 0.0 0.00
2019-11-12 04:40 7.5 87.4 4.5 1.1 168 2.0 168 25.021 0.0 0.00
2019-11-12 04:35 8.1 88.4 5.3 1.9 105 3.5 25 25.023 0.0 0.00
2019-11-12 04:30 9.4 87.4 6.4 1.2 25 3.0 25 25.024 0.0 0.00
2019-11-12 04:25 10.2 91.8 8.3 0.7 171 1.6 171 25.021 0.0 0.00
2019-11-12 04:20 9.2 90.9 7.1 0.5 171 1.6 171 25.023 0.0 0.00
2019-11-12 04:15 9.3 90.9 7.2 1.2 170 3.4 170 25.032 0.0 0.00
2019-11-12 04:10 7.8 92.7 6.1 1.3 231 3.2 231 25.037 0.0 0.00
2019-11-12 04:05 7.2 88.6 4.6 1.2 231 3.2 231 25.040 0.0 0.00
CIRA

Cooperative Institute for Research in the Atmosphere

Dr. Steven Fletcher

Job Title:
Research Scientist/Scholar III
Phone Number:

970-491-8376

Fax Number:

970-49-8241

Mailing Addresss:
Dr. Steven J. Fletcher

Cooperative Institute for Research in the Atmosphere

Colorado State University

1375 Campus Delivery

Fort Collins, CO 80523-1375
Office Location:
CIRA Room 12
About Me:

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.
 
Between 2012 and 2014 Dr. Fletcher worked with the data assimilation scientist at the Naval Research Laboratory in Monterey, CA, on the Navy’s numerical weather prediction system investigating updating the background error covariance model as well as non-Gaussian possibilities inside of the observation space based DA system.  Also during this period Dr. Fletcher was awarded an NSF grant to investigate the non-Gaussian effects inside of Gaussian data assimilation systems.  Part of this research lead to the first ever non-additive incremental based 3D and 4D VAR systems.
Fletcher, S. J. and A. S. Jones, 2014: Multiplicative and Additive Incremental Variational Data Assimilation for Mixed Lognormal-Gaussian Errors. Mon. Wea. Rev., 142, 2521–2544.
 
Dr. Fletcher has organized a session at the Annual Fall Meeting of the American Geophysical Union (AGU) since 2010 on different aspects of DA, data fusion, predictability and uncertainty quantification in the Nonlinear Geophysics (NG) Focus Group of AGU.  Since 2013 Dr. Fletcher has been part of the program committee of the AGU Annual Fall Meeting representing Nonlinear Geophysics where he oversees the scientific program for NG for the meeting in San Francisco.
 
Currently Dr. Fletcher is still working on the NSF project supervising the postdoctoral fellow – Dr. Anton Kliewer.  He is also working on a new project with NRL in Monterey with NAVGEM as well as teaching as part of the data assimilation internship program at CIRA – Variational DA theory.

Past Work

Project # 1

Wednesday, March 12, 2014

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.

Project # 2

Monday, March 24, 2014

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

    Publications

    Implementing non-Gaussian background error statistics for cloud-related control variable in the hybrid GSI for improved convective-scale assimilation and prediction

    Published Date: 2017
    Published By: Conference

    Comparison of Gaussian, logarithmic transform and mixed Gaussian–log‐normal distribution based 1DVAR microwave temperature–water‐vapour mixing ratio retrievals

    Published Date: 2017
    Published By: Royal Meteorological Society