• modelisation.pdf

• slideshow.pdf

• exemples.tar.gz

• exnc is a bash script that uses the netCDF operators and allows you to easily extract a subset (lat,lon,levs,time) from the 6-hourly NCEP-R2 yearly files and create a single netCDF file containing your variable.



• exnc2 has the same goal as the above but is written in python and displays a GUI (using Tkinter) : it uses an ascii file (see this file for a template) that describes the data and its location, allowing you not to change the script itself when you update or enhance your NCEP - R2 database.



• fhumcdb is devoted to the calculation of 6-hourly integrated moisture flux, atmospheric water vapour content and divergence of the former from the NCEP - R2 data. It uses GrADS. The specific humidity is derived from relative humidity using standard algorythm. Outputs are GrADS IEEE-LE binary files for each year, and a descriptor file (.ctl) is created, reflecting the structure of the data.

• python-2.2.2.tar.gz: The core python language, install it first !

  		# tar -zxvf python-2.2.2.tar.gz
  		# cd python-2.2.2
  		# ./configure && make && make install
  		

  It installs python executables and libraries in /usr/local/
• Numeric-23.0.tar.gz: The Numeric package gives array support to python. To install it (you must be root):

  		# tar -zxvf Numeric-23.0.tar.gz
  		# cd Numeric-23.0
  		# python setup.py install
  		

• ScientificPython-2.4.1.tar.gz: Konrad Hinsen's Scientific Python module: among numerous functions, it gives an interface to the netCDF library for reading and writing netCDF files

  		# tar -zxvf ScientificPython-2.4.1.tar.gz
  		# cd ScientificPython-2.4.1
  		# python setup.py build
  		# python setup.py install
  		

• PyClimate-1.2.tar.gz: provides tools for univariate and multivariate climate data analysis such as EOFs, SVDs, ...

  		# tar -zxvf PyClimate-1.2.tar.gz
  		# cd PyClimate-1.2
  		# python setup.py install
  		

• pywavelet-0.1.tar.gz. From Arnold Moene at the Wageningen University: it is a wrapping of Torrence and Compo fortran wavelet software: you need f2py to build it.

  First install f2py using the usual commands:

  		# tar -zxvf F2PY-2-latest.tar.gz
  		# cd F2PY-2.35.229-1505
  		# python setup.py install
  		

  Then unpack the pywavelet archive and go to its directory

  		# tar -zxvf pywavelet-0.1.tar.gz
  		# cd pywavelet-0.1
  		

  and build the pywavelet shared object using:

  		# f2py -m pywavelet -c cfftpack.f chisqr.f wavelet.f pywavelet.f
  		

  You should get a pywavelet.so file, that you must copy to /usr/local/lib/python2.2/site-packages. Read the README file in the pywavelet-0.1/ directory to know more on how to use it ...

• date2indOLR.py, date2indCMAP.py, date2indNCEP.py, date2indNCEP_4.py are little python scripts I have written for getting the temporal indices corresponding to a given date, respectively for the OLR dataset from the NOAA, the CMAP (CPC Merged Analyis of Precipitation) dataset on the pentad basis, the daily and 4times daily NCEP/DOE II reanalyses ... to use it copy them somewhere in your path and simply type:

  		# date2indOLR.py 8 1 1991
  		

  returns 8066, i.e. the 8 of January 1991 is the 8066^th day in the daily OLR dataset ...

  		# date2indNCEP_4 18 1 12 1991
  		

  returns 18872, i.e the 1 of December 1991 at 18h UTC is the 18872^th time-step in the 6-hourly NCEP/DOE II reanalysis dataset
  
  You can use it directly from GRADS using variables, take a look at this sample grads script

• how to read GRADS binary files in python ?
  
  It takes me some time to figure out how to read GrADS binary files (as written by the 'set fwrite' statement) into Numeric arrays within python. The problem arise when you try to load a file that has been written on "big endian" machines on a PC (or the reverse).
  Here is a sample python code, suppose you have a GrADS binary file 'gradsfile'
  

  # first import the necessary modules

  	import array
  	import Numeric as N
  	

  # then open the file for read in binary mode and read it into a python array of floats

  	file=open('gradsfile','rb')
  	a=array.array('f',file.read())
  	

  # if the file has been created on a big endian machine and your on a little endian (or the reverse) ; do :

  	var=N.fromstring(a,N.Float32).byteswapped()
  	

  # else (big endian to big endian or little endian to little endian):

  	var=N.fromstring(a,N.Float32)
  	file.close()
  	

wgrib
xgrads
xgribmap
xgribscan
xgxps
xgxtran
xstnmap

# ln -s /usr/local/bin/xgrads /usr/local/bin/grads
# ln -s /usr/local/bin/xgrads /usr/local/bin/gradsnc
# ln -s /usr/local/bin/xgxps /usr/local/bin/gxps

font0.dat
font1.dat
font2.dat
font3.dat
font4.dat
font5.dat
hires
lowres
mres
udunits.dat

# mkdir /usr/local/udunits/
# mkdir /usr/local/udunits/etc
# cp udunits.dat /usr/local/udunits/etc/.

# mkdir /usr/local/lib/grads/
# cp font* *res /usr/local/lib/grads/.

lats4d
lats4d.gs
lats4d.man

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