Masking

Overview

Teaching: 0 min
Exercises: 0 min

Questions

• How to I maskout land or ocean?

Objectives

Sometimes we want to maskout data based on a land/sea mask that is provided.

First Steps

Create a new notebook and save it as Masking.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 these two datasets:

data_file='/shared/obs/gridded/OISSTv2/monthly/sst.mnmean.nc'
ds_data=xr.open_dataset(data_file)
ds_data

This is the original data file we read in.

mask_file='/shared/obs/gridded/OISSTv2/lmask/lsmask.nc'
ds_mask=xr.open_dataset(mask_file)
ds_mask

Let’s look at this file. It contains a lat-lon grid that is the same as our SST data and a single time. The data are 0’s and 1’s. Plot it.

plt.contourf(ds_mask['mask'])

What does this error mean? The data has a single time dimension so it appears as 3-D to contourf. We can drop single dimensions using squeeze

ds_mask=ds_mask.squeeze()
plt.contourf(ds_mask['mask'])

The latitudes are upside down. Let’s fix that like we did before. In this case, we will fix it for the mask and the data.

ds_mask=ds_mask.reindex(lat=list(reversed(ds_mask['lat'])))
ds_data=ds_data.reindex(lat=list(reversed(ds_data['lat'])))
plt.contourf(ds_mask['mask'])
plt.colorbar()

That’s better! We can see that the mask data are 1 for ocean and 0 for land. We can use xarray.where to mask the data to only over the ocean.

da_ocean=ds_data['sst'].mean('time').where(ds_mask['mask'].squeeze()==1)
da_ocean
plt.contourf(da_ocean,cmap='coolwarm')
plt.colorbar()

Key Points