def _generate_mask(self):
"""
Generates a boolean mask of areas NOT to calculate orographic
enhancement. Criteria for calculating orographic enhancement are that
all of the following are true:
- 3x3 mean topography height >= threshold (20 m)
- Relative humidity (fraction) >= threshold (0.8)
- v dot grad z (wind x topography gradient) >= threshold (0.0005)
The mask is therefore "True" if any of these conditions are false.
Returns:
numpy.ndarray:
Boolean mask - where True, set orographic enhancement to a
default zero value
"""
# calculate mean 3x3 (square nbhood) orography heights
radius = number_of_grid_cells_to_distance(self.topography, 1)
topo_nbhood = NeighbourhoodProcessing(
'square', radius)(self.topography)
topo_nbhood.convert_units('m')
# create mask
mask = np.full(topo_nbhood.shape, False, dtype=bool)
mask = np.where(topo_nbhood.data < self.orog_thresh_m, True, mask)
mask = np.where(self.humidity.data < self.rh_thresh_ratio, True, mask)
mask = np.where(abs(self.vgradz) < self.vgradz_thresh_ms, True, mask)
return mask
def find_max_in_nbhood_orography(self, orography_cube):
"""
Find the maximum value of the orography in the neighbourhood around
each grid point. If self.grid_point_radius is zero, the orography is used
without neighbourhooding.
Args:
orography_cube (iris.cube.Cube):
The cube containing a single 2 dimensional array of orography
data
Returns:
iris.cube.Cube:
The cube containing the maximum in the grid_point_radius neighbourhood
of the orography data or the orography data itself if the radius is zero
"""
if self.grid_point_radius >= 1:
radius_in_metres = number_of_grid_cells_to_distance(
orography_cube, self.grid_point_radius
)
max_in_nbhood_orog = OccurrenceWithinVicinity(radius_in_metres)(
orography_cube
)
return max_in_nbhood_orog
else:
return orography_cube.copy()
def test_check_input_in_km(self):
"""
Test that the output is still in metres when the input coordinates
are in a different unit.
"""
result_radius = number_of_grid_cells_to_distance(self.cube, 2)
for coord in self.cube.coords():
coord.convert_units("km")
expected_result = 4000.0
self.assertAlmostEqual(result_radius, expected_result)
self.assertIs(type(expected_result), float)
def find_max_in_nbhood_orography(self, orography_cube):
"""
Find the maximum value of the orography in the region around each grid
point in your orography field by finding the maximum in a neighbourhood
around that point.
Args:
orography_cube (iris.cube.Cube):
The cube containing a single 2 dimensional array of orography
data
Returns:
iris.cube.Cube:
The cube containing the maximum in a neighbourhood of the
orography data.
"""
radius_in_metres = number_of_grid_cells_to_distance(
orography_cube, self.grid_point_radius)
max_in_nbhood_orog = OccurrenceWithinVicinity(radius_in_metres)(
orography_cube)
return max_in_nbhood_orog
def test_check_different_input_radius(self):
"""Check it works for different input values."""
result_radius = number_of_grid_cells_to_distance(self.cube, 5)
expected_result = 10000.0
self.assertAlmostEqual(result_radius, expected_result)
self.assertIs(type(expected_result), float)
def test_basic(self):
"""Test the function does what it's meant to in a simple case."""
result_radius = number_of_grid_cells_to_distance(self.cube, 2)
expected_result = 4000.0
self.assertAlmostEqual(result_radius, expected_result)
self.assertIs(type(expected_result), float)
