weather-data.rst

Toy weather data

Here is an example of how to easily manipulate a toy weather dataset using xarray and other recommended Python libraries:

Shared setup:

_code/weather_data_setup.py

python

fpath = "examples/_code/weather_data_setup.py" with open(fpath) as f: code = compile(f.read(), fpath, 'exec') exec(code)

Examine a dataset with pandas and seaborn

python

ds

df = ds.to_dataframe()

df.head()

df.describe()

@savefig examples_tmin_tmax_plot.png ds.mean(dim='location').to_dataframe().plot()

python

@savefig examples_pairplot.png sns.pairplot(df.reset_index(), vars=ds.data_vars)

Probability of freeze by calendar month

python

freeze = (ds['tmin'] <= 0).groupby('time.month').mean('time') freeze

@savefig examples_freeze_prob.png freeze.to_pandas().plot()

Monthly averaging

python

monthly_avg = ds.resample(time='1MS').mean()

@savefig examples_tmin_tmax_plot_mean.png monthly_avg.sel(location='IA').to_dataframe().plot(style='s-')

Note that MS here refers to Month-Start; M labels Month-End (the last day of the month).

Calculate monthly anomalies

In climatology, "anomalies" refer to the difference between observations and typical weather for a particular season. Unlike observations, anomalies should not show any seasonal cycle.

python

climatology = ds.groupby('time.month').mean('time') anomalies = ds.groupby('time.month') - climatology

@savefig examples_anomalies_plot.png anomalies.mean('location').to_dataframe()[['tmin', 'tmax']].plot()

Calculate standardized monthly anomalies

You can create standardized anomalies where the difference between the observations and the climatological monthly mean is divided by the climatological standard deviation.

python

climatology_mean = ds.groupby('time.month').mean('time') climatology_std = ds.groupby('time.month').std('time') stand_anomalies = xr.apply_ufunc( lambda x, m, s: (x - m) / s, ds.groupby('time.month'), climatology_mean, climatology_std)

@savefig examples_standardized_anomalies_plot.png stand_anomalies.mean('location').to_dataframe()[['tmin', 'tmax']].plot()

Fill missing values with climatology

The :py~xarray.Dataset.fillna method on grouped objects lets you easily fill missing values by group:

python

# throw away the first half of every month some_missing = ds.tmin.sel(time=ds['time.day'] > 15).reindex_like(ds) filled = some_missing.groupby('time.month').fillna(climatology.tmin)

both = xr.Dataset({'some_missing': some_missing, 'filled': filled}) both

df = both.sel(time='2000').mean('location').reset_coords(drop=True).to_dataframe()

@savefig examples_filled.png df[['filled', 'some_missing']].plot()