Interpolation¶

nctoolkit features built in methods for horizontal and vertical interpolation.

Interpolating to a set of coordinates¶

If you want to regrid a dataset to a specified set of coordinates you can regrid and a pandas dataframe. The first column of the dataframe should be the longitudes and the second should be latitudes. The example below regrids a sea-surface temperature dataset to a single location with longitude -30 and latitude 50.

import nctoolkit as nc
import pandas as pd
ds = nc.open_thredds("https://psl.noaa.gov/thredds/dodsC/Datasets/COBE2/sst.mon.mean.nc")
ds.select(timestep = range(0, 12))
coords = pd.DataFrame({"lon":[-30], "lat":[50]})
ds.regrid(coords)

Interpolating to a regular latlon grid¶

If you want to interpolate to a regular latlon grid, you can use to_latlon. lon and lat specify the minimum and maximum longitudes and latitudes, while res, a 2 variable list specifies the resolution. For example, if we wanted to regrid the globe to 0.5 degree north-south by 1 degree east-west resolution, we could do the following:

ds = nc.open_thredds("https://psl.noaa.gov/thredds/dodsC/Datasets/COBE2/sst.mon.mean.nc")
ds.select(timestep = 0)
ds.to_latlon(lon = [-79.5, 79.5], lat = [0.75, 89.75], res = [1, 0.5])

Interpolating to another dataset’s grid¶

If we are working with two datasets and want to put them on a common grid, we can interpolate one onto the other’s grid. We can illustate this with a dataset of global sea surface temperature. Let’s start by regridding the first timestep in this dataset to a regular latlon grid covering the North Atlantic.

ds1 = nc.open_thredds("https://psl.noaa.gov/thredds/dodsC/Datasets/COBE2/sst.mon.mean.nc")
ds1.select(timestep = 0)
ds1.to_latlon(lon = [-79.5, 79.5], lat = [-0.75, 89.75], res = [1, 0.5])

We can then use this new dataset as the target grid in regrid. So

ds2 = nc.open_thredds("https://psl.noaa.gov/thredds/dodsC/Datasets/COBE2/sst.mon.mean.nc")
ds2.select(timestep = 0)
ds2.regrid(ds1)

This method will also work using netCDF files. So, if you wanted you can also use a path to a netCDF file as the target grid.

How to reuse the weights for regridding¶

Under the hood nctoolkit regrids data by first generating a weights file. There are situations where you will want to be able to re-use these weights. For example, if you are post-processing a large number of files one after the other. To make this easier nctoolkit let’s you recycle the regridding info. This let’s you interpolate using either regrid or to_latlon, but keep the regridding data for future use by regrid.

The example below illustrates this. First, we regrid a global dataset to a regular latlon grid covering the North Atlantic, setting the recycle argument to True.

ds = nc.open_thredds("https://psl.noaa.gov/thredds/dodsC/Datasets/COBE2/sst.mon.mean.nc")
ds.select(timestep = 0)
ds.to_latlon(lon = [-79.5, 79.5], lat = [-0.75, 89.75], res = [1, 0.5], recycle = True)

We can then use the grid from data for regridding:

ds1 = nc.open_thredds("https://psl.noaa.gov/thredds/dodsC/Datasets/COBE2/sst.mon.mean.nc")
ds1.select(timestep = 0)
ds1.regrid(ds)

This, of course, requires that the grids in the datasets are consistent. If you want to access the weights and grid files generated, you can do the following:

These files are deleted either when data is deleted or when the Python session is existed.

Resampling¶

If you want to make data more coarse spatially, just use the resample_grid method. This will, for example, let you select every 2nd grid grid cell in a north-south and east-west direction. This is illustrated in the example below, where a dataset which has spatial resolution of 1 by 1 degrees is coarsened, so that only every 10th cell is selected in a north-south and east-west. In other words it is now a 10 degrees by 10 degrees dataset.

ds = nc.open_thredds("https://psl.noaa.gov/thredds/dodsC/Datasets/COBE2/sst.mon.mean.nc")
ds.select(timestep = 0)
ds.resample_grid(10)

Vertical interpolation¶

We can carry out vertical interpolation using the vertical_interp method. This is particularly useful for oceanic data. This is illustrated below by interpolating ocean temperatures from NOAA’s World Ocean Atlas for January to a depth of 500 metres. The vertical_interp method requires a levels argument, which is sea-depth in this case.

ds = nc.open_thredds("https://data.nodc.noaa.gov/thredds/dodsC/ncei/woa/temperature/A5B7/1.00/woa18_A5B7_t01_01.nc")
ds.select(variables="t_an")
ds.vertical_interp(levels= [500])