|
Size: 2756
Comment:
|
Size: 1921
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 13: | Line 13: |
| == Example data from Argo system == | = Example data from Argo system = |
| Line 21: | Line 21: |
| 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. |
| Line 25: | Line 25: |
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 = |
| Line 34: | Line 34: |
| tmp_dir='/scratch/clisap/seaice/TMP/u242023/ARGO/' | == Python == == GMT == [[http://gmt.soest.hawaii.edu/|GMT]] |
| Line 37: | Line 41: |
| file_liste=glob.glob(tmp_dir+'*.nc') D={}# Empty dictionary to store selected profiles for f in file_liste:# Loop over all data |
Nearest neighbor gridding with 5 degree grid cell size |
| Line 41: | Line 43: |
| {{{nearneighbor lat_lon_T.tab -Rd -I300m -S300m -N1 -Ggrid.nc}}} | |
| Line 42: | Line 45: |
| # Open netcdf data file fid=io.netcdf_file(f,'r') |
Generate color table: {{{ makecpt -Crainbow -T-2/30/1 > g.cpt }}} |
| Line 45: | Line 50: |
| # 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 |
{{{ grdimage grid.nc -Rg -JG-45/0/4i -Cg.cpt -K > out.ps }}} |
| Line 54: | Line 54: |
| (nr_profs,Z)=T.shape # Get dimension | {{{ pscoast -Rd -JG-45/0/4i -B15g15 -Dc -Gblack -P -O >> out.ps }}} |
| Line 56: | Line 58: |
| for i in range(nr_profs): D[(lon[i],lat[i])]=(T[i,:],P[i,:]) |
{{{ gv out.ps }}} |
| Line 59: | Line 62: |
| # 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}} |
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
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
