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| First we have to download the data. Here is a [[/download script]] that can be used to retrieve one month of data in a temporary directory called {{{tmp_dir}}}. | First we have to download the data. Here is a [[/download script]] that can be used to retrieve one month of Argo data. |
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| We extract only the surface temperature to reduce the large amount of data. The next script generates an overview of the data and writes the surface temperature in a file [[attachment:lat_lon_T.tab]] which contains latitude, longitude and surface temperature. We can use this file in the following. | We extract only the surface temperature to reduce the large amount of data. This [[/extract script]] generates an overview of the data and writes the surface temperature in a file [[attachment:lat_lon_T.tab]] which contains latitude, longitude and surface temperature. We can use this file in the following. |
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Argo positions of measurements |
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| {{{#!python | Measured temperature profiles (unfiltered data). |
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| import scipy.io as io import glob from pylab import * from mpl_toolkits.basemap import Basemap |
== Tools for 2-dimensional interpolation and gridding == |
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| tmp_dir='/scratch/clisap/seaice/TMP/u242023/ARGO/' | === Python === |
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| === GMT === [[http://gmt.soest.hawaii.edu/| |
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| file_liste=glob.glob(tmp_dir+'*.nc') D={}# Empty dictionary to store selected profiles for f in file_liste:# Loop over all data # Open netcdf data file fid=io.netcdf_file(f,'r') # Read content into variables lat=fid.variables['LATITUDE'][:].copy() lon=fid.variables['LONGITUDE'][:].copy() T=fid.variables['TEMP'][:].copy() P=fid.variables['PRES'][:].copy() T[T>=99999]=nan # Set 99999.0 to "Not a Number" P[P>=99999]=nan (nr_profs,Z)=T.shape # Get dimension for i in range(nr_profs): D[(lon[i],lat[i])]=(T[i,:],P[i,:]) # Close data file fid.close() fid=open('lat_lon_T.tab','w') for k in D.keys():# write position (lat,lon), surface temperature to file fid.write(str(k[0])+'\t'+str(k[1])+'\t'+str(D[k][0][0])+'\n') fid.close() stop # Draw map of positions m = Basemap(projection='ortho',lon_0=-45,lat_0=0,resolution='l') m.bluemarble() for lon,lat in D.keys(): x,y=m(lon,lat) # Coordinate transfer m.plot(x,y,'r.') savefig('argo_position.png',dpi=150) # Plot profiles figure() for k in D.keys(): # print k plot(D[k][0][:],D[k][1][:]) axis([-2,30,2000,0]) xlabel('T') ylabel('P') show() savefig('Argo_plot.png',dpi=75) }}} |
{{{nearneighbor lat_lon_T.tab -Rg -I300m -S300m -N1 -Ggrid.nc}}} |
Contents
2-dimensional interpolation and gridding
2-dimensional interpolation and gridding is a common problem for the representation of measurements on a map. Usually measurements are taken at irregular sample points and not in a regular grid. There are various approaches for the problem and the best solution depends on the data.
In the following we will look at oceanographic parameters that have been measured with the Argo system.
Example data from Argo system
Data are provided at, i.e. ftp://ftp.ifremer.fr//ifremer/argo/
First we have to download the data. Here is a /download script that can be used to retrieve one month of Argo data.
We extract only the surface temperature to reduce the large amount of data. This /extract script generates an overview of the data and writes the surface temperature in a file lat_lon_T.tab which contains latitude, longitude and surface temperature. We can use this file in the following.
Argo positions of measurements
Measured temperature profiles (unfiltered data).
Tools for 2-dimensional interpolation and gridding
Python
GMT
[[http://gmt.soest.hawaii.edu/|
nearneighbor lat_lon_T.tab -Rg -I300m -S300m -N1 -Ggrid.nc