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Seminar talks with supplements
Archive of all talks
Multiresolution Models for Large Spatial Datasets
PDF
For a general sciences audience. National Science Foundation, April 2014.
PDF
Statistics Department, Iowa State University, Ames IA, March 2013.
PDF
Shorter and more tutorial talk at AGU, San Francisco, December, 2012.
KAUST short course on spatial data, inverse
problems, and delaing with large data sets

PDF
Intro to Kriging

PDF
Tapering covariances with Mary Jane Project Example

PDF
LatticeKrig  flexible spatial model for large data

PDF
Linear inverse problem for reconstructing past concentrations of
CO2 from an ice core
Lectures given at KAUST, Saudi Arabia, March 2014
Uncertain Climate, Uncertain Weather
PDF
Statistics Department, University of Toronto, March 2013.
2013 Year
of Statistics Public Lecture Series
Ten Lectures on Statistics and Climate
PDF
CMBS Lecture series, University of Washington, Seattle, August, 2012.
Regional Climate: Design and Analysis of Computer
Experiments?
PDF
AGU Fall Meetings, San Francisco, December, 2010.
Beyond the hockey stick: indirect methods of
paleoclimate reconstruction PDF
AGU Fall Meetings, San Francisco, December, 2010.
Some friendly talks about climate change and statistics

Climate Past, Climate Present, Climate Future, A tale
told by a statistician
My evolving public lecture about how statistics is related to
understanding climate and cliamte change.
SAMSI Public Lecture February 2011, Durham, NC. (PDF)
7th World Congress in Probability and
Statistics, Singapore, July 2008. (PDF)

Where are statisticians in the Earth System?
(PDF) 5.6Mb
An invited talk at JSM08 Denver, August 2008.

Climate past, present and future
(PDF)
An invited talk at West Point for background on
math/climate projects and curriculum. .
Four Lectures on Statistical methods applied to
Climate Science
These lectures were given at the
41st Winter Conference in Statistics at Storhogna, Vemdalen,
Sweden March 711, 2010. The focus of these lectures in on the
statistical analysis of the output from climate model simulations.

Climate Processes and Models 7.3Mb

The World of Large Spatial Data 8.9Mb

Regional Climate Experiments and Statistics 4.3Mb

Observing Weather  Modeling Climate 1.7Mb
Two lectures on the relationship between splines and
Kriging.

Smoothing Data and Spline PDF

Spatial Process Estimates PDF
Most recently given at the University of British Columbia, CA,
October 2010. This material is very similar to the fields short
course.
Demo from lecture. See
Short course CD .
Statistical Analysis of Regional Climate Models
(PDF) 6.5Mb
An introduction to global and regional climate models for
stats folks and some statistical ideas using functional ANOVA and
density estimation.
Oslo, February 2010.
Two lectures on the relationship between splines and
Kriging.

Smoothing Data and Spline PDF

Spatial Process Estimates PDF
Most recently given at the University of British Columbia, CA,
October 2010. This material is very similar to the fields short
course.
Demo from lecture. See
Short course CD .
Regional Climate

Statistical Analysis of Regional Climate Models
(PDF) 6.5Mb
An introduction to global and regional climate models for
stats folks and some statistical ideas using functional ANOVA and
density estimation.
Oslo, February 2010.
 Regional climate models, spatial data and extremes
(PDF) 8.1Mb
EURANDOM workshop on climate modeling and extremes, May 2009
CIB workshop on spatiotemporal extremes, EPFL, lausanne, CH
November, 2009
 A Climate Model Animation (mp4) 2.3Mb
This is a striking animation of
the simulated clouds by a high resolution atmospheric model.
These runs use the NCAR Community Atmospheric Model (CAM) as part of a
Breakthrough Science Project using dedicated supercomputer time at
NCAR. CAM is a spectral model with this experiemnt being conducted
at T341 resolution  translating into a grid of
approximately 1/3 of a degree (40km) at the equator.
Scientific description and
model data sources
Credits:
 Science:
James J. Hack (ESSL),
Julie M. Caron (ESSL),
John E. Truesdale (ESSL)
 Visualization:
Tim Scheitlin (CISL)
 Post Production:
Ryan McVeigh (CISL)
A Regional climate model animation from NARCCAP
An animation ncepjan2000.mp4 (6.4Mb) of one of the NARCCAP regional climate models being
forced by the global (and lower resolution) NCEP reanalysis field over the
month of January 2000 (courtesy of Stephan Sain). See also
NARCCAP for information
of the sources of these data and model output.
The NCEP reanalysis is a gridded data product for the atmosphere
that uses a weather forecast model with observations to give a
physically balanced estimate of the physical variables of the
atmopsphere on a 3d grid. In this animiation the surface
precipitation is indicated for NCEP although the actual variables
driving (known as forcing) the region model in the interior are
moisture, momentum and energy fluxes. This model particular regional
model (ECPC) is unusual for a regional model in that it not only
uses the fluxes at the domain boundaries but also nudges its
interior values to the NCEP field. The finer resolution and more
detailed structure in the regional model is considered to be a more
accurate simulation of weather processes such as rainfall. The
cliamte predicted by the model is found by simulating a long period
of weather and averaging the values over time. A numerical
simulation where a regional model is forced by observed weather,
such as the NCEP reanalysis, is a test of how well the model can
reproduced detailed climate under observed conditions. To determine
possible changes in climate for the future the NCEP forcings are
replaced by the fields from a global climate system that uses future
scenarios of greenhouse emissions and other changes to simulate a
different set of set of large scale conditions.
 Regional climate models, spatial data and extremes
(PDF) 8.1Mb
EURANDOM workshop on climate modeling and extremes, May 2009
CIB workshop on spatiotemporal extremes, EPFL, lausanne, CH
November, 2009
A Regional climate model animation from
NARCCAP
Large Spatial data sets
 The world of large spatial data (PDF) 8.3Mb
JSM 2009, Washington, DC, August 2009
75th Anniversary of the Iowa State University Statistics, June 2009

Spatial statistics, black diamonds and the fields
package
(PDF) 9.6Mb
University of Wyoming, November 2008
See the
Mary Jane Project
for the R code and data sets. There is also a
MJ project
poster on
this
topic from JSM 2006.
What can statistics tell us about the uncertainty of past
climate? (PDF) 5.6Mb
American Public Health Association, San Diego October 2008
The uncertain hockey stick: a statistical reconstruction of
past
temperatures
(PDF) 9Mb
North Symposium, TAMU, June 2009
ETH seminar, Zurich, CH, November 2009
The uncertain hockey stick: a statistical reconstruction of
past
temperatures
(PDF) 9Mb
North Symposium, TAMU, June 2009
ETH seminar, Zurich, CH, November 2009
About the Santa ensemble:
The Santa images are taken from an article:
Jason Salavon (2004). Artist Project: 100 Special Moments, Cabinet
15, pp 77 81.
Is part of a series of art projects,
100 Special Moments by Jason Salavon. His completed work is Kids with
Santa , a composite Santa image that I understand to be a
weighted sum of mean and median ensemble pixel values. I was surprised
that Salavon's web page does not explicitly show the ensemble or give
the details of his process.
The construction, even though it is fairly
complicated, is not referenced and I interpret this to mean that the
process of construction and the underlying sample of 100 images is not
important to enjoying the work. But it does
raise a caution about taking my illustration in the talk too literally. It
is not clear how Salavon choose the 100 Santa images, what digital
preprocessing he might have done to align images, adjust color levels,
and other manipulations. So the 100 Santa's may not be the kind of
representative and random ensemble one would strive for in a statistical context.
The 100 images leading to the santa estimate of the "central tendancy"
are reproduced in the article by Salavon in Cabinet Magazine,
Issue 15.
A framework to understand the asymptotic
properties of Kriging and splines
(PDF) 2.7Mb
Korean Statistical Society, Seoul, November 2006
U Wyoming and U Colorado, November 2008
Colorado State, November 2010
Technical Report: (PDF)
Robust splines and robust wavelets
(PDF) 1.7M
University Wisconsin NCAR, September 2006
SEE:
The Role of Pseudo Data for Robust Smoothing with
Application to Wavelet Regression
(In review).
The Ensemble Kalman Filter: The Movie
(PDF)
3.9M
University of Florida, January 2007
University Wisconsin, October 2006
NCAR Advanced study Program, NCAR November, 2005
Univ. Washington Statistics, November 2006
The movie:
(Quicktime) 32M
(First two plots are ensemble mean and sd fields, remaining 6 plots are
the first 6 members. The animation brings in each observation sequentially
having sorted by location latitude and updates the ensemble. The result of
each update is the conditional distribution of the full ozone fields given
the observations brought in so far. The first
frame are the ensembles draw from an ozone "summer climatology".)
The calculations for a large ensemble approximate those needed to update
the conditional multivariate normal distribution but instead of keeping
track of the conditional covariances and mean the information is
propagated by through the discrete distribution represented by the
ensemble. Any mean of covariance is determined by the sample statistics
based on the ensemble members. The R code available below makes this
calculations explicit.
Doityourself!
To construct the frames for this movie, transfer the files README.txt
fun.R setup.R from the directory EKFmovie.
Follow the instructions in the
README.txt file. These animations may
seem complicated but there are really just a few tricks (like everything
else I do!). Basically a for loop to write out each frame as a separate
jpeg image. A key step is that I use Quicktime on a mac to
assemble the
image sequence into a quick time movie, surprisingly this is just a few
clicks. NOTE: These functions require the R statistical environment and
the
fields package.
Statistics, data assimilation and estimating sources of
carbon.
MSRI Summer Graduate Workshop on Data Assimilation for the Carbon Cycle,
MSRI, Berkeley California. July 17  21