def calculate(x, mass, mask):
output = iris.cube.CubeList()
for cube in x:
xcoord = grid.extract_dim_coord(cube, 'X')
ycoord = grid.extract_dim_coord(cube, 'Y')
zcoord = grid.extract_dim_coord(cube, 'Z')
cube.data = np.ma.masked_where(mask, cube.data)
output.append(
cube.collapsed([xcoord, ycoord, zcoord], MEAN, weights=mass.data))
output.append(cube.collapsed([xcoord, ycoord, zcoord], STD_DEV))
return output
def main(cubes):
"""Find all points within 1km of the tropopause that have a negative PV
anomaly
"""
# Find negative pv anomalies
diff = convert.calc('total_minus_advection_only_pv', cubes)
negative_pv = diff.data < -1
# Find the tropopause height
ztrop, fold_t, fold_b = tropopause.height(cubes)
# Find point below the tropopause
z = diff.coord('altitude').points
tropopause_relative = np.logical_and(z < ztrop, z > ztrop - 2000)
# Combine criteria
criteria = np.logical_and(negative_pv, tropopause_relative)
# Get indices where criterion is met
indices = np.where(criteria)
# Convert to lon, lat, z
longitude = diff.coord('grid_longitude').points
lons = np.array([longitude[idx] for idx in indices[2]])
latitude = diff.coord('grid_latitude').points
lats = np.array([latitude[idx] for idx in indices[1]])
height = grid.extract_dim_coord(diff, 'z').points
altitude = np.array([height[idx] for idx in indices[0]])
# Scatterplot the locations
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(lons, lats, altitude)
plt.show()
def make_plot(cubes, name, mass, mask, **kwargs):
pv = convert.calc(name, cubes)
pv.data = np.ma.masked_where(mask, pv.data)
z = grid.extract_dim_coord(pv, 'z')
pv_ave = pv.collapsed(z.name(), MEAN, weights=mass.data)
im = iplt.pcolormesh(pv_ave, **kwargs)
add_map()
return im
def redo_cubes(cubes, basis_cube, stash_maps=[], slices=None, time=None):
# Coordinates to copy to analyses
z = grid.extract_dim_coord(basis_cube, 'z')
# Define attributes of custom variables by stash mapping
for stash_map in stash_maps:
stash_map.remap_cubelist(cubes)
newcubelist = CubeList()
for cube in cubes:
print(cube)
newcube = cube.copy()
# Remove unneccessary time coordinates
for coord in ['forecast_period', 'forecast_reference_time']:
try:
newcube.remove_coord(coord)
except iris.exceptions.CoordinateNotFoundError:
pass
if newcube.ndim == 3:
# Use the hybrid height coordinate as the main vertical coordinate
try:
newcube.remove_coord('model_level_number')
except iris.exceptions.CoordinateNotFoundError:
pass
newcube.coord('level_height').rename(z.name())
iris.util.promote_aux_coord_to_dim_coord(newcube, z.name())
# Remap the cubes to theta points
newcube = interpolate.remap_3d(newcube, basis_cube, z.name())
else:
# Regrid in the horizontal
newcube = newcube.regrid(basis_cube, Linear())
# Convert the main time coordinate
if time is not None:
newcube.coord('time').convert_units(time)
newcubelist.append(newcube)
return newcubelist
"""
import datetime
import iris
from iris.cube import CubeList
from iris.util import squeeze
from irise import constants, grid
from myscripts import datadir
from myscripts import files
# Define which area of grid to subset
slices = slice(0, 50), slice(15, -15), slice(15, -15)
# Load basis cube
basis_cube = iris.load_cube(datadir + 'xjjhl/basis_cube.nc', 'air_temperature')
lat = grid.extract_dim_coord(basis_cube, 'y')
lon = grid.extract_dim_coord(basis_cube, 'x')
raw_cubes = iris.load(datadir + 'xjjhl/xjjhl.astart')
# Define how to rename variables
name_pairs = [('u', 'x_wind'), ('v', 'y_wind'),
('dz_dt', 'upward_air_velocity'),
('theta', 'air_potential_temperature'),
('unspecified', 'air_density'),
('field7', 'dimensionless_exner_function'),
('q', 'specific_humidity'),
('QCL', 'mass_fraction_of_cloud_liquid_water_in_air'),
('QCF', 'mass_fraction_of_cloud_ice_in_air')]
# Correct units for some cubes