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

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

data1 = nc.open_thredds("https://psl.noaa.gov/thredds/dodsC/Datasets/COBE2/sst.mon.mean.nc")
data1.select(timestep = 0)
data1.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

data2 = nc.open_thredds("https://psl.noaa.gov/thredds/dodsC/Datasets/COBE2/sst.mon.mean.nc")
data2.select(timestep = 0)
data2.regrid(data1)

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¶

Please note: recycling weights only works in the dev version, and will be included in v0.3.1, which will be released publicly in March 2021 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.

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

data1 = nc.open_thredds("https://psl.noaa.gov/thredds/dodsC/Datasets/COBE2/sst.mon.mean.nc")
data1.select(timestep = 0)
data1.regrid(data)

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.

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

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