## Saturday, April 9, 2011

### ## TempLS Ver 2.1 release

This is the first release for a while. The structure is still much like ver 2, and the math basis is the same. The main differences are:
• The code has been internally reorganized. Most variables have been gathered into R-style lists. And more code has been sequestered into functions, to clarify the underlying structure of the program.
• The new work on area-based weighting has been incorporated.
• Other work has been done with weighting. In particular, subsets of stations can be weighted differently relative to each other.
• Hooks have been added so that the adaptions to the Antarctic studies can be included.

The preprocessor which merges and converts data files available on the web to a standard TempLS format is unchanged. Data and inventory files from Ver 2 will still work.

A reminder of how TempLS is used. In an R environment, a jobname is defined, and a .r file with that name is created which defines some variables relevant to the wanted problem. This may have several consecutive runs defined. The variables are variants of supplied defaults.

Then source("TempLSv2.1.r") solves and writes data and jpg files to a directory jobname.

The files you need are on a zip file TempLSv2.1.zip. This 16Mb file contains preprocessed data sets, so you won't need to use the preprocessor. I've had to separate GSOD data; GSOD.zip is another 7 Mb.

If you just want to look at the code and information (everything readable), there is a 170 Kb TempLS2.1_small.zip. A ReadMe.txt file is included. There is also a partly updated manual from V2.0, most of which is still applicable. More details below, and past files on TempLS can be found on the Moyhu index. I'll describe below the things that are new.

#### TempLS internal lists

Being R code, you can of course modify it as you wish. But it's better generally to work through the jobname.r file. To understand the code, you'll want to know about these global lists:
• u - the one most encountered. It is defined in TempLSuserv2.1.r, and includes the variables that I think you're most likely to want to change. Some important ones:
• u$inputstem - for selecting data. eg if ="GHCN", uses files GHCN.data, GHCN.inv • u$name - a string that gets attached to output and filenames - can be varied between runs
• u$num_runs - your file can have several expressions {} and a run could be done with each - num_runs says how many are used • u$station_mask - select from the inventory which stations to include in the analysis. A logical expression based on the names of columns
• u$model - what kind of analysis. 1 for time series, 2 for spatial trend, 3 for spatial period (currently year • u$yr0, u$yr1 analysis endpoints • u$postp() - an output function where you can add requests
• v$d - the full data set from the .data file, v$nd = number rows
• v$i - the inventory table from the .inv file, v$ni = number rows
• s - things created following station selection by u$station_mask • s$o - logical vec (length v$ni) of stations selection • s$ns, s$nm, s$ny - numbers of stations selected, months and years
• s$i - index vec (length s$ns) of stations selection
• s$ib - reverse index vec (length v$ni) of stations selection

If you are requesting area weighting, you can ask for a map of the weights and mesh for a specified month (numbered from start)(u$showmeshmonth=..) #### Weighting (new) Before V2.1, the only weighting was by estimated density based on number of stations in lat/lon cells. Now 4 kinds of weighting are allowed: 1. Uniform weighting 2. Cell weighting (as before) 3. Mesh-based (ie by area) 4. Voronoi (also area) In V2.1 Voronoi is not fully implemented, and mesh-based is preferred. You specify by u$wt_type=1,2 or 3.

You can have two classes of stations that you weight separately. This was created for AVHRR vs land, but would also be useful for SST vs land. You need a mask u$wt_mask to select one subgroup. It's value is a logical expression based on columns of the inventory. Then you need u$wt_type to be a vector, eg c(3,1). That gives the wt_type of the set you've chosen, then the rest. They cannot both be >2.

Then you need to express how one class is weighted relative to the other. This is the wt factor u$wtf, which ranges from 0 to 1. They are scaled so that (sum wts of class 1)/( total sum of weights) = u$wtf.

You can also control smoothing of the weights. The parameter is the characteristic length of smoothing, in km, say u\$wt_len=100. The default is 0, which means no smoothing. Smoothing gives some benefit, but takes a little time.

#### Zip file contents

• ReadMe.txt - summary of the contents
• DataInv.zip - contains the preprocessed data files (eg GHCN.data) and inventory files. With this you won't have to use the preprocessor until you want an update. This file is 16Mb; because complete data exceeds the 20Mb limit here, I've had to separate the GSOD data and inv, which is in GSOD.zip.
• TempLSv2.1.r - the master routine
• TempLSfnsv2.1.r - the collection of functions for the master routine
• TempLSuserv2.1.r - defaults for the user file, defining list u
• TempLSprepv2.1.r - preprocessor file
• TempLSprepinputv2.1.r - some names etc for the preprocessor file
• userfiles.zip - a zip file of user routines for example problems. Most were used in compiling recent Moyhu posts.
• TempLSv2a.pdf - a users guide for v2 which has been updated with new variable names etc for v2.1. It does not have a description of new features yet.
• testall.r - a master file for testing - uses all the supplied example files, creates their directories of results, and makes a file testall.htm in which you can see all the code and output. Handy to check if you have everything. Good to run with R CMD BATCH.
• testall.mht - an output from testall.r that I made recently, stored as IE archive.
• miscR.zip - files like ryan.r (some of Ryan O's graphics routines, called by the user file for Antarctic.r). Also update.r, used by testall.r.