Exercise #6 - Plotting georeferenced gridded data

Objectives

• • Familiarity with plotting gridded data using Basemap

  • Practise in using grib-api and Python tools to plot fields from meteorological datasets

Hello, Basemap

In the first part of this exercise you will use the simple Basemap plotting test program I presented in the course. You will change the location of your plot, the data being plotted, and the projection being used.

First, I have provided you with the Python program file HelloBasemap.py below, and in

/home/kurs/donmor/Files/Exercise06/

Make sure you can run it and get the image presented in class

Then, you should choose a new location try plotting again, and once you have succeeded with that, try a new set of data - choose data that will have some kind of pattern that will allow you to determine that it is plotted correctly.

Finally, try to replot your data with a new projection. Some information on Basemap projections is available at

http://matplotlib.org/basemap/users/mapsetup.html

Plotting Global Forecast System (GFS) model output data

I have provided you with a Python program file, gfsplot.py, and a GFS GRIB2 dataset, gfs.gr2, available at the bottom of this page and in

/home/kurs/donmor/Files/Exercise06/

You should copy this into your own directory and make sure you can generate the plot of 500 mb relative humidity:

This Python program has been customized for relative humidity plots, and your job is to use it to plot other variables. You should try to plot the wind gust field from the GFS input file. You can use grib_ls to find out what parameters are necessary (in particular, shortName, level and type of level).

Once you've succeeded in plotting the wind gust, you will probably notice that your colourbar is set up more for relative humidity. You should change the plotting parameters (like levels, ticklabels and ticks) to better fit the gust data.

Then, create a new title and a new contour scale with appropriate labels for this data.