Meteorological, 2D{1,1,2,1,1} dataset¶
The following dataset is obtained from NOAA/NCEP Global Forecast System (GFS) Atmospheric Model and subsequently converted to the CSD model file-format. The dataset consists of two spatial dimensions describing the geographical coordinates of the earth surface and five dependent variables with 1) surface temperature, 2) air temperature at 2 m, 3) relative humidity, 4) air pressure at sea level as the four scalar quantity_type dependent variable, and 5) wind velocity as the two-component vector, quantity type dependent variable.
Let’s import the csdmpy module and load this dataset.
>>> import csdmpy as cp
>>> filename = 'Test Files/correlatedDataset/forecast/NCEI.csdfe'
>>> multi_dataset = cp.load(filename)
Let’s get the tuple of dimension and dependent variable objects from
multi_dataset
instance.
>>> x = multi_dataset.dimensions
>>> y = multi_dataset.dependent_variables
The dataset contains two dimension objects representing the longitude and latitude of the earth’s surface. The respective dimensions are labeled as
>>> x[0].label
'longitude'
>>> x[1].label
'latitude'
There are a total of five dependent variables stored in this dataset. The first dependent variable is the surface air temperature. The data structure of this dependent variable is
>>> print(y[0].data_structure)
{
"type": "internal",
"description": "The label 'tmpsfc' is the standard attribute name for 'surface air temperature'.",
"name": "Surface temperature",
"unit": "K",
"quantity_name": "temperature",
"numeric_type": "float64",
"quantity_type": "scalar",
"component_labels": [
"tmpsfc - surface air temperature"
],
"components": [
[
"292.8152160644531, 293.0152282714844, ..., 301.8152160644531, 303.8152160644531"
]
]
}
If you have followed all previous examples, the above data structure should be self-explanatory. The following snippet plots a dependent variable of scalar quantity_type.
Tip
Plotting a scalar intensity plot
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from mpl_toolkits.axes_grid1 import make_axes_locatable
>>> def plot_scalar(yx):
... fig, ax = plt.subplots(1,1, figsize=(6,3))
...
... # Set the extents of the image plot.
... extent = [x[0].coordinates[0].value, x[0].coordinates[-1].value,
... x[1].coordinates[0].value, x[1].coordinates[-1].value]
...
... # Add the image plot.
... im = ax.imshow(yx.components[0], origin='lower', extent=extent,
... cmap='coolwarm')
...
... # Add a colorbar.
... divider = make_axes_locatable(ax)
... cax = divider.append_axes("right", size="5%", pad=0.05)
... cbar = fig.colorbar(im, cax)
... cbar.ax.set_ylabel(yx.axis_label[0])
...
... # Set up the axes label and figure title.
... ax.set_xlabel(x[0].axis_label)
... ax.set_ylabel(x[1].axis_label)
... ax.set_title(yx.name)
...
... # Set up the grid lines.
... ax.grid(color='k', linestyle='--', linewidth=0.5)
...
... plt.tight_layout(pad=0, w_pad=0, h_pad=0)
... plt.show()
Now to plot the data from the dependent variable.
>>> plot_scalar(y[0])

Similarly, other dependent variables with their respective plots are
>>> y[1].name
'Air temperature at 2m'
>>> plot_scalar(y[1])

>>> y[3].name
'Relative humidity'
>>> plot_scalar(y[3])

>>> y[4].name
'Air pressure at sea level'
>>> plot_scalar(y[4])

Notice, we didn’t plot the dependent variable at index 2. This is because this particular dependent variable is a vector dataset representing the wind velocity.
>>> y[2].quantity_type
'vector_2'
>>> y[2].name
'Wind velocity'
To visualize the vector data, we use matplotlib streamline plot.
Tip
Plotting a vector quiver plot
>>> def plot_vector(yx):
... fig, ax = plt.subplots(1,1, figsize=(6,3))
... X, Y = np.meshgrid(x[0].coordinates, x[1].coordinates)
... magnitude = np.sqrt(yx.components[0]**2 + yx.components[1]**2)
...
... cf = ax.quiver(x[0].coordinates, x[1].coordinates,
... yx.components[0], yx.components[1],
... magnitude, pivot ='middle', cmap='inferno')
... divider = make_axes_locatable(ax)
... cax = divider.append_axes("right", size="5%", pad=0.05)
... cbar = fig.colorbar(cf, cax)
... cbar.ax.set_ylabel(yx.name+' / '+str(yx.unit))
...
... ax.set_xlim([x[0].coordinates[0].value, x[0].coordinates[-1].value])
... ax.set_ylim([x[1].coordinates[0].value, x[1].coordinates[-1].value])
...
... # Set axes labels and figure title.
... ax.set_xlabel(x[0].axis_label)
... ax.set_ylabel(x[1].axis_label)
... ax.set_title(yx.name)
...
... # Set grid lines.
... ax.grid(color='gray', linestyle='--', linewidth=0.5)
...
... plt.tight_layout(pad=0, w_pad=0, h_pad=0)
... plt.show()
>>> plot_vector(y[2])
