Lecture/tutorial blocks
A 9-10:30
break 10:30-10:45
B 10:45- 12:15
lunch 12:15-1:15
C 1:15 - 2:45
break 2:45 - 3:00
D 3:00 - 4:30
E 4:30 -5:00
Typically each block will be lecturing/ hands on analysis or exrecises
and some discussion/questions
Block E will usually be a short wrap of the day or time for
independent projects.
>>Monday
8:30-9:00 Registration
A
Spatial data: an introduction
What is weather? What is climate?
B Introduction to the R package
Manipulating climate data and analysis tools
C Exploratory data analysis using R for
regional climate
D Matrix "Boot Camp"
Matrices in R
Matrix decompositions for statistics
E Sparse matrices and large data sets.
>>Tuesday
A Estimating curves and surfaces using regression with a penalty
B Spatial data analysis with smoothers
C Spatial Processes
Covariances
Simulating a process
D Spatial process estimators
E What is Kriging?
>> Wednesday
A Basis functions and Kriging
The recipe
What is a spline?
B Estimating parameters for the covariance
the likelihood
the Bayesian connection
C Applying spatial estimates to climate data
D Working with regional climate experiments
Adding covariates
Hierarchical models
E Questions/Discussion