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

Argo observation 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

LehreWiki: OpenSource2010/Lesson12 (last edited 2011-01-17 13:34:14 by anonymous)

-  ⇤ ← Revision 5 as of 2011-01-17 11:27:48 → 
  Size: 2756
  Editor: anonymous
  Comment:
+   ← Revision 8 as of 2011-01-17 11:49:48 → ⇥
  Size: 1600
  Editor: anonymous
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
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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
-Line 27:
+Line 30:
-{{{#!python
+Measured temperature profiles (unfiltered data).
-Line 29:
+Line 32:
-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/|
-Line 37:
+Line 39:
-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}}}