Regional climate model animation
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 3-d 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.
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
Where are statisticians in the Earth System?
(PDF) 5.6Mb
An invited talk at JSM08 Denver, August 2008.
Climate Past, Climate Present, Climate Future, A tale
told by a statistician
(PDF) 9Mb
A public lecture given at the 7th World Congress in Probability and
Statistics, Singapore, July 2008.
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.
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.
Do-it-yourself!
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