Contents

Exercise

Exercise

Text Data

  • Load the data from ../../data/nao_station_monthly.txt. Mask every missing value. Therefore you will need the following parameters of the function np.genfromtxt:

    • skipt_header

    • missing_values

    • usemask

  • Restructure the loaded data as arrays. Create a time vector (1D-array) called time and a 1D data vector called nao. Use the code below to plot the NAO time series.

plt.plot(time, nao)
plt.xlabel('year')
plt.ylabel('nao')
plt.title('station based monthly NAO index');

NetCDF data

  • Open the dataset ../../data/CRUTEM.4.6.0.0.anomalies.nc

  • Inspect the content of the attributes dimensions and variables. How long are the dimensions longitude and time? Which unit and data type does the variable temperature_anomaly have and on which dimensions is it defined?

  • Read the last time step of the variable temperature_anomaly to the memory.

  • Compute the globally averaged temperature anomaly. The data is given in spherical coordinates. Therefore single data points need to be weighted with the cosine of the latitude in radians:

    \[t_{global} = \frac{1}{N M}\sum_{j=1}^M\sum_{i=1}^{N} t_{anom}(j, i) \cos(\theta(j))\]

    Try to use the broadcasting functionality and the mean function from numpy! Use the code below to plot the time series of globally averaged temperature anomaly.

# create a plotable time axis
# encode numerical time representation into datetime information
# comprehensible by matplotlib
time = nc.num2date(
    ds.variables['time'][:],
    units=ds.variables['time'].units,
    calendar=ds.variables['time'].calendar,
    only_use_cftime_datetimes=False,
    only_use_python_datetimes=True
)

plt.plot(time, t_global);

  • Write the computed global mean temperature anomaly time series from above with all necessary metadata as a NetCDF data set. Include as many information from the source data set as possible.

    Hint: To overwrite an existing data set, the argument clobber=True is needed for the creation of the data set.

previous

Data Input and Output

next

Data Visualization