Aggregating Data

Overview

Teaching: 0 min
Exercises: 0 min

Questions

• How to I aggregate data over various dimensions?

Objectives

The Oceanic Nino Index ONI is used by the Climate Prediction Center to monitor and predict El Nino and La Nina. It is defined as the 3-month running mean of SST anomalies in the Nino3.4 region. We will use aggregation methods from xarray to calculate this index.

First Steps

Create a new notebook and save it as Aggregating.ipynb

Import the standard set of packages we use:

import xarray as xr
import numpy as np
import cartopy.crs as ccrs
import matplotlib.pyplot as plt

Read in the dataset we wrote in the last notebook.

file='/scratch/kpegion/nino34_1982-2019.oisstv2.nc'
ds=xr.open_dataset(file)
ds

Take the mean over a lat-lon region

ds_nino34_index=ds.mean(dim=['lat','lon'])
ds_nino34_index

Our data now has only a time dimension. Make a plot.

plt.plot(ds_nino34_index['time'],ds_nino34_index['sst'])

Calculate Anomalies

Anomaly means departure from normal (called climatology). We often calculate anomalies for working with climate data. We will spend time in a future class learning about calculating climatology and anomalies using another feature of xarray called groupby. Today, I will show you the steps with little explanation.

ds_climo=ds_nino34_index.groupby('time.month').mean()
ds_anoms=ds_nino34_index.groupby('time.month')-ds_climo
ds_anoms

Plot our data.

plt.plot(ds_anoms['time'],ds_anoms['sst'])

Why do I constantly print and plot the data?

Printing the data so I can see its dimensions after each step provides a check on whether my code did what I intended it to do.

Plotting also gives me a quick look to make sure everything makes sense.

I encourage you to do the same when developing and testing new code!

Rolling (Running Means)

The ONI is calculated using a 3-month running mean. This can be done using the rolling function.

Reading and Learning from Documentation

Read the documentation for the xarray.rolling function. Following their example, make a 3-month running mean of the ds_anoms data.

Solution

ds_3m=ds_anoms.rolling(time=3,center=True).mean().dropna(dim='time') 
ds_3m

Let’s plot our original and 3-month running mean data together

plt.plot(ds_anoms['sst'],color='r')
plt.plot(ds_3m['sst'],color='b')
plt.legend(['orig','smooth'])

Some other aggregation functions

There are a number of other aggregate functions such as: std,min,max,sum, among others.

Using the original dataset in this notebook ds, find and plot the maximum SSTs for each gridpoint over the time dimension.

Solution

ds_max=ds.max(dim='time')
plt.contourf(ds_max['sst'],cmap='Reds')
plt.colorbar()

Using the original dataset in this notebook ds, calculate and plot the standard deviation at each gridpoint.

Solution

ds_std=ds.std(dim='time')
plt.contourf(ds_std['sst'],cmap='RdBu_r')
plt.colorbar()

Key Points