Horizontal Regridding

Horizontal Regridding#

Updated: 04/15/24 [xcdat v0.7.0]

Related APIs:

xarray.Dataset.regridder.horizontal

Other Resources#

xESMF documentation

Overview#

We’ll cover horizontal regridding using the xESMF and Regrid2 tools as well as various methods supported by xESMF.

It should be noted that Regrid2 treats the grid cells as being flat.

The data used in this example can be found through the Earth System Grid Federation (ESGF) search portal.

Notebook Kernel Setup#

Users can install their own instance of xcdat and follow these examples using their own environment (e.g., with VS Code, Jupyter, Spyder, iPython) or enable xcdat with existing JupyterHub instances.

First, create the conda environment:

conda create -n xcdat_notebook_0.7.0 -c conda-forge xcdat=0.7.0 xesmf matplotlib ipython ipykernel cartopy nc-time-axis gsw-xarray jupyter

Then install the kernel from the xcdat_notebook_0.7.0 environment using ipykernel and name the kernel with the display name (e.g., xcdat_notebook_0.7.0):

python -m ipykernel install --user --name xcdat_notebook_0.7.0 --display-name xcdat_notebook_0.7.0

Then to select the kernel xcdat_notebook_0.7.0 in Jupyter to use this kernel.

[1]:

# %matplotlib inline

import os
import sys

os.environ["ESMFMKFILE"] = sys.prefix + "/lib/esmf.mk"  # TODO remove after esmf>=8.5
import xesmf

[2]:

import matplotlib.pyplot as plt
import xarray as xr
import xcdat

2. Create the output grid#

Related API: xcdat.create_gaussian_grid()

In this example, we will generate a gaussian grid with 32 latitudes to regrid our input data to.

Alternatively a grid can be loaded from a file, e.g.

grid_urlpath = "http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/abrupt-4xCO2/r1i1p1f1/day/tas/gr2/v20180701/tas_day_GFDL-CM4_abrupt-4xCO2_r1i1p1f1_gr2_00010101-00201231.nc"

grid_ds = xcdat.open_dataset(grid_urlpath)

output_grid = grid_ds.regridder.grid

Other related APIs available for creating grids: xcdat.create_grid() and xcdat.create_uniform_grid()

[4]:

output_grid = xcdat.create_gaussian_grid(32)

fig, axes = plt.subplots(ncols=2, figsize=(16, 4))

ds.regridder.grid.plot.scatter(x="lon", y="lat", s=4, ax=axes[0])
axes[0].set_title("Input Grid")

output_grid.plot.scatter(x="lon", y="lat", s=4, ax=axes[1])
axes[1].set_title("Output Grid")

plt.tight_layout()

../_images/examples_regridding-horizontal_7_0.png

3. Regrid the data#

Here we will regrid the input data to the ouptut grid using the xESMF tool and the bilinear method.

[5]:

output = ds.regridder.horizontal("tas", output_grid, tool="xesmf", method="bilinear")

fig, axes = plt.subplots(ncols=2, figsize=(16, 4))

ds.tas.isel(time=0).plot(ax=axes[0], vmin=-40, vmax=40, extend="both", cmap="RdBu_r")
axes[0].set_title("Input data")

output.tas.isel(time=0).plot(
    ax=axes[1], vmin=-40, vmax=40, extend="both", cmap="RdBu_r"
)
axes[1].set_title("Output data")

plt.tight_layout()

../_images/examples_regridding-horizontal_9_0.png

4. Regridding algorithms#

In this example, we will compare the different regridding methods supported by xESMF.

You can find a more in depth comparison on xESMF’s documentation.

Methods:

bilinear
conservative
nearest_s2d
nearest_d2s
patch

[6]:

methods = ["bilinear", "conservative", "nearest_s2d", "nearest_d2s", "patch"]

fig, axes = plt.subplots(3, 2, figsize=(16, 12))

axes = axes.flatten()

for i, method in enumerate(methods):
    output = ds.regridder.horizontal("tas", output_grid, tool="xesmf", method=method)

    output.tas.isel(time=0).plot(
        ax=axes[i], vmin=-40, vmax=40, extend="both", cmap="RdBu_r"
    )

    axes[i].set_title(method)

axes[-1].set_visible(False)

plt.tight_layout()

../_images/examples_regridding-horizontal_11_0.png

5. Masking#

xESMF supports masking by simply adding a data variable with the id mask.

See xESMF documentation for additonal details.

[7]:

ds["mask"] = xr.where(ds.tas.isel(time=0) < -10, 1, 0)

masked_output = ds.regridder.horizontal(
    "tas", output_grid, tool="xesmf", method="bilinear"
)

fig, axes = plt.subplots(ncols=2, figsize=(18, 4))

ds["mask"].plot(ax=axes[0], cmap="binary_r")
axes[0].set_title("Mask")

masked_output.tas.isel(time=0).plot(
    ax=axes[1], vmin=-40, vmax=40, extend="both", cmap="RdBu_r"
)
axes[1].set_title("Masked output")

plt.tight_layout()

../_images/examples_regridding-horizontal_13_0.png

6. Regridding using `regrid2`#

Regrid2 is a conservative regridder for rectilinear (lat/lon) grids originally from the cdutil package from CDAT.

This regridder assumes constant latitude lines when generating weights.

[8]:

output = ds.regridder.horizontal("tas", output_grid, tool="regrid2")

fig, axes = plt.subplots(ncols=2, figsize=(16, 4))

ds.tas.isel(time=0).plot(ax=axes[0], vmin=-40, vmax=40, extend="both", cmap="RdBu_r")

output.tas.isel(time=0).plot(
    ax=axes[1], vmin=-40, vmax=40, extend="both", cmap="RdBu_r"
)

/Users/zhang40/mambaforge/envs/xcdat_notebook/lib/python3.12/site-packages/xcdat/regridder/regrid2.py:195: RuntimeWarning: invalid value encountered in divide
  np.divide(

[8]:

<matplotlib.collections.QuadMesh at 0x198015a00>

../_images/examples_regridding-horizontal_15_2.png

Horizontal Regridding

Contents

Horizontal Regridding#

Other Resources#

Overview#

Notebook Kernel Setup#

1. Open the Dataset#

2. Create the output grid#

3. Regrid the data#

4. Regridding algorithms#

5. Masking#

6. Regridding using `regrid2`#

Horizontal Regridding

Contents

Horizontal Regridding#

Other Resources#

Overview#

Notebook Kernel Setup#

1. Open the Dataset#

2. Create the output grid#

3. Regrid the data#

4. Regridding algorithms#

5. Masking#

6. Regridding using regrid2#

6. Regridding using `regrid2`#