In this example we’ll demonstrate using geocat-comp’s
climatology_average function to compute daily and monthly climatologies of raw surface temperature data from a 6-hour temporal resolution model.
import cftime import matplotlib.pyplot as plt import xarray as xr import geocat.comp as gc import geocat.datafiles as gdf
Read in data#
We will get the data from the geocat-datafiles package. This package contains example data used in many of the examples for geocat packages.
Then, we use xarray’s open_dataset function to read the data into an xarray dataset, choose a single model from the ensemble run, and extract its surface temperature data into
ds = xr.open_dataset(gdf.get('netcdf_files/atm.20C.hourly6-1990-1995-TS.nc')) ds = ds.isel(member_id=0) # select one model from the ensemble temp = ds.TS # surface temperature data
Look at the Raw Data#
Before we compute the climatologies, let’s take a look at the raw surface temperature data from our selected model run with temporal resolution of 6-hours.
The plot output has adjusted datetimes instead of using integers to denote the day of the year for the time axis. The year for the outputted data is the floor of the median year of the inputted data, which is 1993 in this case. This will be repeated for all subsequent plots in this example.
# Convert datetimes to number of hours since 1990-01-01 00:00:00 # This must be done in order to use the time for the x axis time_num_raw = cftime.date2num(temp.time, 'hours since 1990-01-01 00:00:00') # Plot plt.plot(time_num_raw, temp.data) plt.title('Raw Data') plt.xlabel("Time") tstart = time_num_raw tend = time_num_raw[-1] plt.xlim(tstart, tend) plt.xticks(ticks=range(tstart, tend + 1, 365 * 24), labels=range(1990, 1997));
Calculate daily and monthly climate averages using
Next, we use
geocat.comp.climatology_average to calculate averages across all years in a given dataset.
Note that while daily and monthly climatology averages are demonstrated here, you could also use the frequency keyword argments
season to calculate hourly or seasonal climatology averages.
Let’s start with the daily climatology average. The following plot shows output of the raw data being aggregated using the
climatology_average function with the
freq argument set to ‘day’.
# Compute daily = gc.climatology_average(temp, freq='day') time_num_day = cftime.date2num(daily.time, 'hours since 1990-01-01 00:00:00') # Plot plt.plot(time_num_day, daily.data) plt.title('Daily Climatology') plt.xlabel("Time") plt.xlim(tstart, tend) plt.xticks(ticks=range(tstart, tend + 1, 365 * 24), labels=range(1990, 1997));
Next we’ll compute the monthly climatology averages by using
climatology_average with the
freq argument set to
The data is now grouped by month, which yeilds a smoother curve over the daily-averaged climatology.
# Compute monthly = gc.climatology_average(temp, freq='month') time_num_month = cftime.date2num(monthly.time, 'hours since 1990-01-01 00:00:00') # Plot plt.plot(time_num_month, monthly.data) plt.title('Monthly Climatology') plt.xlabel("Time") plt.xlim(tstart, tend) plt.xticks(ticks=range(tstart, tend + 1, 365 * 24), labels=range(1990, 1997));