Regular lat-lon grid — plotting methods¶
The regular lat-lon grid (regular_ll) is the most familiar grid type in meteorology: points are evenly spaced in both latitude and longitude, forming a uniform rectangular mesh. ERA5 reanalysis data is distributed on a 0.25° × 0.25° regular lat-lon grid.
Because the grid is regular, all three plotting methods work natively without any interpolation step:
Method |
How it works |
Best for |
|---|---|---|
|
Draws each grid point as a rectangular cell |
Seeing exact cell geometry |
|
Plots a coloured marker at each grid point |
Quick inspection |
|
Draws filled contours directly on the grid |
Smooth, publication-quality maps |
This notebook uses an ERA5 0.25° 2-metre temperature field.
[1]:
import earthkit.data as ekd
import earthkit.plots as ekp
data = ekd.from_source("sample", "era5-2t-msl-1985122512.grib").to_fieldlist()
temperature = data[0]
All methods side-by-side¶
We zoom to Europe where the 0.25° cell size is large enough to be visible in grid_cells and point_cloud.
[3]:
figure = ekp.Figure(rows=2, columns=2, domain=["France", "Spain"])
for method in ["point_cloud", "grid_cells", "contourf", "grid_points"]:
subplot = figure.add_map()
getattr(subplot, method)(data, style=style)
subplot.title(method)
figure.coastlines()
figure.gridlines()
figure.title("Plotting regular lat-lon grid data with various methods")
figure.legend(location="right")
figure.show()
What to notice¶
``grid_cells`` shows perfectly uniform rectangular cells — the hallmark of the regular lat-lon grid. Unlike HEALPix or reduced Gaussian grids, every cell has the same angular width, though their physical area shrinks towards the poles.
``point_cloud`` at 0.25° resolution is dense enough that the dots form a nearly continuous field. The uniform spacing is clearly visible compared to the irregular spacing of the reduced Gaussian grid.
``contourf`` is typically the best choice for regular lat-lon data in final figures: it requires no interpolation and produces clean, smooth contours directly from the grid values.