Using grids

Grids are a core component of athmospheric modelling. They are used to discretize the domain of the problem and to store the values of the variables at the grid points.

In this notebook we will see how to create grids and use them in emiproc.

Regular grids

Most of the time, regular latitude longitude grids are used. They are usually define by the number of points in the latitude and longitude directions and the spacing between the points. When not modelling the whole globe, the longitude and latitude ranges are also needed.

from emiproc.grids import RegularGrid

grid = RegularGrid(xmin=110, xmax=155, ymin=-45, ymax=-10, nx=20, ny=16)
grid

RegularGrid(x(110,155)_y(-45,-10)_nx(20)_ny(16))

This created an emiproc object of the RegularGrid type.

We can visualize the grid using geopandas capabilities. Interanly the geometry is stored in the gdf attribute of the grid.

# Be careful, this can be slow for large grids
grid.gdf.explore()

Make this Notebook Trusted to load map: File -> Trust Notebook

There are other paramters you can use to define the grid, such as resolution.

resolution_basedgrid = RegularGrid(
    xmin=110, xmax=160, ymin=-45, ymax=-10, dx=5, dy=5)
# This creates a grid with 5x5 degree cells size
resolution_basedgrid

RegularGrid(x(110,160)_y(-45,-10)_nx(10)_ny(7))

Hexagonal grid

Similarly to regular grid, hexagonal grids or “honeycomb” grids can be used.

from emiproc.grids import HexGrid
grid = HexGrid(xmin=110, xmax=155, ymin=-45, ymax=-10,spacing=5)
grid.gdf.explore()

Make this Notebook Trusted to load map: File -> Trust Notebook

Geopandas grid

As emiproc is based on geopandas, you can also use any geometry that is a geopandas dataframe as a grid.

For example we could create a “country grid” where each country is a polygon belonging to the grid. We can use emiproc utilities to get the data about the countries and create the grid.

from emiproc.grids import GeoPandasGrid

# Utitlies of countries based on natural earth data
from emiproc.utilities import get_natural_earth

countries = get_natural_earth(
    resolution='10m',
    category='cultural',
    name='admin_0_countries',

)
# This data contains the geometry of the countries
countries.head()

	featurecla	scalerank	LABELRANK	SOVEREIGNT	SOV_A3	ADM0_DIF	LEVEL	TYPE	TLC	ADMIN	...	FCLASS_TR	FCLASS_ID	FCLASS_PL	FCLASS_GR	FCLASS_IT	FCLASS_NL	FCLASS_SE	FCLASS_BD	FCLASS_UA	geometry
0	Admin-0 country	0	2	Indonesia	IDN	0	2	Sovereign country	1	Indonesia	...	None	None	None	None	None	None	None	None	None	MULTIPOLYGON (((117.70361 4.16341, 117.70361 4...
1	Admin-0 country	0	3	Malaysia	MYS	0	2	Sovereign country	1	Malaysia	...	None	None	None	None	None	None	None	None	None	MULTIPOLYGON (((117.70361 4.16341, 117.69711 4...
2	Admin-0 country	0	2	Chile	CHL	0	2	Sovereign country	1	Chile	...	None	None	None	None	None	None	None	None	None	MULTIPOLYGON (((-69.51009 -17.50659, -69.50611...
3	Admin-0 country	0	3	Bolivia	BOL	0	2	Sovereign country	1	Bolivia	...	None	None	None	None	None	None	None	None	None	POLYGON ((-69.51009 -17.50659, -69.51009 -17.5...
4	Admin-0 country	0	2	Peru	PER	0	2	Sovereign country	1	Peru	...	None	None	None	None	None	None	None	None	None	MULTIPOLYGON (((-69.51009 -17.50659, -69.63832...

5 rows × 169 columns

# Now create the grid from the country shapes
grid = GeoPandasGrid(countries, name="country_grid")
grid.gdf.explore()

Make this Notebook Trusted to load map: File -> Trust Notebook

Note that this grid is note covering the oceans, so if you use it, you will have to take care of the ocean values in an other way.

Conclusion

After this tutorial you should have understood the concept of grids in emiproc. You should be able to create a grid and use it in your simulation.

Grids are very flexible and they can use for remapping very efficiently.