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

Nearest neighbor gridding with 5 degree grid cell size

nearneighbor lat_lon_T.tab -Rd -I300m -S300m -N1 -Ggrid.nc

Generate color table:

makecpt -Crainbow -T-2/30/1 > g.cpt

grdimage grid.nc -Rg -JG-45/0/4i -Cg.cpt -K  > out.ps

pscoast -Rd -JG-45/0/4i -B15g15 -Dc  -Gblack -P -O >> out.ps

gv out.ps

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

-  ⇤ ← Revision 2 as of 2011-01-17 11:12:55 → 
  Size: 3380
  Editor: anonymous
  Comment:
+   ← Revision 10 as of 2011-01-17 11:57:45 → ⇥
  Size: 1921
  Editor: anonymous
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
 Line 13:
-== Example: ARGO floats ==
+= Example data from Argo system =
 Line 15:
-http://en.wikipedia.org/wiki/Argo_%28oceanography%29
+[[http://en.wikipedia.org/wiki/Argo_%28oceanography%29|Argo observation system]]
 Line 19:
-First we have to download the data. Here is a script that can be used to download 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.
 Line 21:
-{{{#!python
#!/usr/bin/env python
import os,os.path
from ftplib import FTP
+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.
-Line 26:
+Line 23:
-tmp_dir='/scratch/clisap/seaice/TMP/u242023/ARGO/'
-Line 28:
+Line 24:
-ftp_adr='ftp.ifremer.fr'
ftp_dir='/ifremer/argo/geo/atlantic_ocean/2011/01/'
+{{attachment:argo_position.png}}
-Line 31:
+Line 26:
-ftp = FTP(ftp_adr)   # connect to host, default port
ftp.login()          # user anonymous, passwd anonymous@
ftp.cwd(ftp_dir)
+Argo positions of measurements
-Line 35:
+Line 28:
-file_list=ftp.nlst()
+{{attachment:Argo_plot.png}}
-Line 37:
+Line 30:
-for f in file_list:
    urlfile='ftp://'+ftp_adr+ftp_dir+f
    localfile=tmp_dir+f
   
    if not(os.path.exists(localfile)):
        print 'Getting file '+f
         #We use curl instead of ftp.retrbinary for download
        os.system("curl -s -k -o "+localfile+"  "+urlfile)
    else:
        print 'file '+f+' exists'
+Measured temperature profiles (unfiltered data).
-Line 48:
+Line 32:
-ftp.close()
+= Tools for 2-dimensional interpolation and gridding =

== Python ==

== GMT ==

[[http://gmt.soest.hawaii.edu/|GMT]]


Nearest neighbor gridding with 5 degree grid cell size

{{{nearneighbor lat_lon_T.tab -Rd -I300m -S300m -N1 -Ggrid.nc}}}

Generate color table:
{{{
makecpt -Crainbow -T-2/30/1 > g.cpt
-Line 51:
+Line 50:
-The next script generates an overview of the data and writes the surface temperature in a file called {{{lat_lon_T.tab}}} which contains latitude, longitude and surface temperature. [[attachment:lat_lon_T.tab]]
+{{{
grdimage grid.nc -Rg -JG-45/0/4i -Cg.cpt -K  > out.ps
}}}
-Line 53:
+Line 54:
-{{attachment:argo_position.png}}
{{attachment:Argo_plot.png}}
+{{{
pscoast -Rd -JG-45/0/4i -B15g15 -Dc  -Gblack -P -O >> out.ps
}}}
-Line 56:
+Line 58:
-{{{#!python
+{{{
gv out.ps
}}}
-Line 58:
+Line 62:
-import scipy.io as io
import glob
from pylab import *
from mpl_toolkits.basemap import Basemap

tmp_dir='/scratch/clisap/seaice/TMP/u242023/ARGO/'


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)
}}}
+{{attachment:out.png}}