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
2020-08-04 01:35 61.6 67.2 50.6 4.9 286 6.9 305 24.952 0.0 0.00
2020-08-04 01:30 61.5 67.6 50.7 7.4 305 8.9 292 24.952 0.0 0.00
2020-08-04 01:25 61.7 66.9 50.6 7.6 290 9.3 284 24.953 0.0 0.00
2020-08-04 01:20 61.9 66.6 50.6 6.7 303 8.7 299 24.957 0.0 0.00
2020-08-04 01:15 62.3 66.6 51.1 6.1 317 7.8 296 24.954 0.0 0.00
2020-08-04 01:10 62.4 66.6 51.1 6.3 294 7.8 300 24.953 0.0 0.00
2020-08-04 01:05 61.8 67.6 51.0 8.4 305 9.1 310 24.954 0.0 0.00
2020-08-04 01:00 61.0 69.0 50.8 8.8 297 9.6 294 24.958 0.0 0.00
2020-08-04 00:55 61.0 67.7 50.3 6.2 305 8.0 305 24.952 0.0 0.00
2020-08-04 00:50 60.9 68.4 50.5 6.0 317 6.4 313 24.957 0.0 0.00
2020-08-04 00:45 62.6 64.2 50.3 4.5 313 5.5 314 24.964 0.0 0.00
2020-08-04 00:40 63.0 64.1 50.6 2.5 319 3.2 319 24.970 0.0 0.00
2020-08-04 00:35 62.7 63.8 50.3 1.2 297 1.8 296 24.974 0.0 0.00
2020-08-04 00:30 62.6 65.5 50.9 3.1 296 3.8 303 24.976 0.0 0.00
2020-08-04 00:25 62.4 64.3 50.2 3.9 305 4.3 306 24.975 0.0 0.00
2020-08-04 00:20 62.3 65.0 50.4 2.6 305 3.2 306 24.976 0.0 0.00
2020-08-04 00:15 62.3 65.4 50.6 1.9 304 2.6 304 24.975 0.0 0.00
2020-08-04 00:10 62.2 65.1 50.3 4.0 304 4.9 317 24.971 0.0 0.00
2020-08-04 00:05 61.6 66.1 50.2 5.5 317 6.0 317 24.971 0.0 0.00
2020-08-04 00:00 61.1 65.4 49.4 5.4 317 5.9 317 24.972 0.0 0.00
2020-08-03 23:55 61.1 67.8 50.4 4.5 318 5.6 318 24.969 0.0 0.00
2020-08-03 23:50 61.8 66.1 50.4 3.5 285 3.9 282 24.967 0.0 0.00
2020-08-03 23:45 61.5 66.4 50.2 3.5 298 4.1 298 24.971 0.0 0.00
2020-08-03 23:40 62.6 64.3 50.4 3.4 346 4.1 346 24.974 0.0 0.00
2020-08-03 23:35 63.3 62.9 50.4 4.5 346 5.1 348 24.976 0.0 0.00
2020-08-03 23:30 64.2 61.5 50.7 3.4 330 3.8 330 24.978 0.0 0.00
2020-08-03 23:25 65.0 60.3 50.9 3.1 323 4.3 327 24.979 0.0 0.00
2020-08-03 23:20 65.7 58.2 50.6 0.6 36 1.1 36 24.980 0.0 0.00
2020-08-03 23:15 65.5 59.0 50.8 0.7 36 1.9 91 24.982 0.0 0.00
2020-08-03 23:10 66.5 58.5 51.5 0.8 91 2.0 91 24.983 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:

970-491-8241

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 112
About Me:

Dr. Steven Fletcher FRMetS 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).  In the October of 2017 Dr. Fletcher was elected to Fellow of the Royal Meteorological Society.  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.

Between 2013 and 2015 Dr. Fletcher supervised Dr. Anton Kliewer as a postdoctoral fellow on the first of Dr. Fletcher’s NSF awards, to look at detecting non-Gaussian behavior of moisture variables.  The award also lead to the first ever mixed-Gaussian-lognormal temperature and mixing ratio microwave brightness temperature retrieval system.  This work also lead to discovering a sensitivity to the initial conditions of the Newton-Rhapson solver which is some form of Newton Fractal.

In 2017 Dr. Fletcher was successful in obtaining a second NSF award to investigate the impacts of non-Gaussian behavior on data assimilation systems.  As part of this award a new postdoc was hired at CIRA, Dr. Michael Goodliff who is overseeing machine learning techniques to detect changes in probability density distributions with different dynamical systems.

In 2017 and 2019 Dr. Fletcher published two text books:

Data Assimilation for the Geosciences: From Theory to Applications 
and
Semi-Lagrangian Advection Methods and their Applications in Geoscience both with Elsevier.

Currently Dr. Fletcher is working with Drs. Goodliff, Kliewer and Jones, along with Mr. Forsythe on the NSF project where they maintain a website that has non-Gaussian based retrievals running in near real time of the west coast of the USA.  Dr. Fletcher is also helping to organize the 8th International Symposium on Data Assimilation in Fort Collins at Canvas Stadium on the Colorado State University’s main campus on June 8th to June 12 2020.

Past Work

NSF: Establishing links between atmospheric dynamics and non-Gaussian distribution and quantifying their effects on numerical weather prediction

July 2017 -

This NSF award is looking at the impacts of non-Gaussian dynamics have on data assimilation systems, and different machine learning techniques to detect a change of probability density functions due to a change in the dynamics.

NRL: Regional, Seasonal and Large Dynamical Scale Based Covariance and Humidity Control Variable Transform implementation in NAVDAS-AR.

March 2015 - March 2020

This project examines the updating of the background error covariance matrix in the US Navy operational numerical weather prediction.  As part of this work a NMC based method will be coded to develop a method to update the covariance matrix when there are operational upgrades to the 4DVAR system.  The second part of this work will be to introduce a normalized version of the relative humidity to couple the moisture field increment with the virtual temperature and pressure increments.