def cellWidthVsLatLon():
"""
Create cell width array for this mesh on a regular latitude-longitude grid.
Returns
-------
cellWidth : ndarray
m x n array, entries are desired cell width in km
lat : ndarray
latitude, vector of length m, with entries between -90 and 90,
degrees
lon : ndarray
longitude, vector of length n, with entries between -180 and 180,
degrees
"""
dlon = 0.1
dlat = dlon
nlon = int(360./dlon) + 1
nlat = int(180./dlat) + 1
lon = np.linspace(-180., 180., nlon)
lat = np.linspace(-90., 90., nlat)
cellWidthVsLat = mdt.EC_CellWidthVsLat(lat)
_, cellWidth = np.meshgrid(lon, cellWidthVsLat)
fc = read_feature_collection('north_mid_res_region.geojson')
earth_radius = constants['SHR_CONST_REARTH']
mr_signed_distance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
fc = read_feature_collection('arctic_high_res_region.geojson')
hr_signed_distance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
frac = (-mr_signed_distance / (-mr_signed_distance + hr_signed_distance))
frac = np.maximum(0., np.minimum(1., frac))
dx_min = 15.
dx_max = 30.
arctic_widths = dx_max + (dx_min - dx_max) * frac
trans_width = 1000e3
trans_start = 0.
weights = 0.5 * (1 + np.tanh((mr_signed_distance - trans_start) /
trans_width))
cellWidth = arctic_widths * (1 - weights) + cellWidth * weights
return cellWidth, lon, lat
def cellWidthVsLatLon():
"""
Create cell width array for this mesh on a regular latitude-longitude grid.
Returns
-------
cellWidth : ndarray
m x n array, entries are desired cell width in km
lat : ndarray
latitude, vector of length m, with entries between -90 and 90,
degrees
lon : ndarray
longitude, vector of length n, with entries between -180 and 180,
degrees
"""
dlon = 0.1
dlat = dlon
earth_radius = constants['SHR_CONST_REARTH']
nlon = int(360./dlon) + 1
nlat = int(180./dlat) + 1
lon = np.linspace(-180., 180., nlon)
lat = np.linspace(-90., 90., nlat)
cellWidthSouth = mdt.EC_CellWidthVsLat(lat, cellWidthEq=30.,
cellWidthMidLat=45.,
cellWidthPole=45.,
latPosEq=7.5, latWidthEq=3.0)
# Transition at Equator
cellWidthNorth = mdt.EC_CellWidthVsLat(lat, cellWidthEq=30.,
cellWidthMidLat=60.,
cellWidthPole=60.,
latPosEq=7.5, latWidthEq=3.0)
latTransition = 0.0
latWidthTransition = 2.5
cellWidthVsLat = mdt.mergeCellWidthVsLat(
lat,
cellWidthSouth,
cellWidthNorth,
latTransition,
latWidthTransition)
_, cellWidth = np.meshgrid(lon, cellWidthVsLat)
# now, add the high-res region
fc = read_feature_collection('high_res_region.geojson')
so_signed_distance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
# Equivalent to 20 degrees latitude
trans_width = 1600e3
trans_start = -500e3
dx_min = 12.
weights = 0.5 * (1 + np.tanh((so_signed_distance - trans_start) /
trans_width))
cellWidth = dx_min * (1 - weights) + cellWidth * weights
fc = read_feature_collection('north_mid_res_region.geojson')
ar_signed_distance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
fc = read_feature_collection('greenland.geojson')
gr_signed_distance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
frac = (-ar_signed_distance/(-ar_signed_distance + gr_signed_distance))
frac = np.maximum(0., np.minimum(1., frac))
dx_min = 15.
dx_max = 35.
arctic_widths = dx_max + (dx_min - dx_max)*frac
trans_width = 1000e3
trans_start = 0.
weights = 0.5 * (1 + np.tanh((ar_signed_distance - trans_start) /
trans_width))
cellWidth = arctic_widths * (1 - weights) + cellWidth * weights
return cellWidth, lon, lat
def cellWidthVsLatLon():
"""
Create cell width array for this mesh on a regular latitude-longitude grid.
Returns
-------
cellWidth : ndarray
m x n array, entries are desired cell width in km
lat : ndarray
latitude, vector of length m, with entries between -90 and 90,
degrees
lon : ndarray
longitude, vector of length n, with entries between -180 and 180,
degrees
"""
# To speed up for testing, set following line to 1.0 degrees
dlon = 0.1
dlat = dlon
earth_radius = constants['SHR_CONST_REARTH']
print('\nCreating cellWidth on a lat-lon grid of: {0:.2f} x {0:.2f} '
'degrees'.format(dlon,dlat))
print('This can be set higher for faster test generation\n')
nlon = int(360. / dlon) + 1
nlat = int(180. / dlat) + 1
lon = np.linspace(-180., 180., nlon)
lat = np.linspace(-90., 90., nlat)
km = 1.0e3
print('plotting ...')
fig = plt.figure()
plt.clf()
fig.set_size_inches(10.0, 14.0)
register_sci_viz_colormaps()
# Create cell width vs latitude for Atlantic and Pacific basins
QU1 = np.ones(lat.size)
EC60to30 = mdt.EC_CellWidthVsLat(lat)
EC60to30Narrow = mdt.EC_CellWidthVsLat(lat, latPosEq = 8.0, latWidthEq = 3.0)
# Expand from 1D to 2D
_, cellWidth = np.meshgrid(lon, EC60to30Narrow)
plot_cartopy(2, 'narrow EC60to30', cellWidth, '3Wbgy5')
plotFrame = 3
# global settings for regionally refines mesh
highRes = 14.0 #[km]
fileName = 'region_Central_America'
transitionWidth = 800.0*km
transitionOffset = 0.0
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat, earth_radius,
max_length=0.25)
mask = 0.5 * (1 + np.tanh((transitionOffset-signedDistance) /
(transitionWidth/2.)))
cellWidth = 30.0 * mask + cellWidth * (1 - mask)
fileName = 'coastline_CUSP'
distanceToTransition = 600.0*km
# transitionWidth is distance from 0.07 to 0.03 of transition within tanh
transitionWidth = 600.0*km
transitionOffset = distanceToTransition + transitionWidth/2.0
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat, earth_radius,
max_length=0.25)
mask = 0.5 * (1 + np.tanh((transitionOffset-signedDistance) /
(transitionWidth/2.)))
cellWidth = highRes * mask + cellWidth * (1 - mask)
plot_cartopy(plotFrame, fileName + ' mask', mask, 'Blues')
plot_cartopy(plotFrame+1, 'cellWidth ', cellWidth, '3Wbgy5')
plotFrame += 2
fileName = 'region_Gulf_of_Mexico'
transitionOffset = 600.0*km
transitionWidth = 600.0*km
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat, earth_radius,
max_length=0.25)
maskSmooth = 0.5 * (1 + np.tanh((transitionOffset-signedDistance) /
(transitionWidth/2.)))
maskSharp = 0.5 * (1 + np.sign(-signedDistance))
fc = read_feature_collection('land_mask_Mexico.geojson')
signedDistance = signed_distance_from_geojson(fc, lon, lat, earth_radius,
max_length=0.25)
landMask = 0.5 * (1 + np.sign(-signedDistance))
mask = maskSharp * landMask + maskSmooth * (1-landMask)
cellWidth = highRes * mask + cellWidth * (1 - mask)
plot_cartopy(plotFrame, fileName + ' mask', mask, 'Blues')
plot_cartopy(plotFrame+1, 'cellWidth ', cellWidth, '3Wbgy5')
plotFrame += 2
fileName = 'region_Bering_Sea'
transitionOffset = 0.0*km
transitionWidth = 600.0*km
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat, earth_radius,
max_length=0.25)
maskSmooth = 0.5 * (1 + np.tanh((transitionOffset-signedDistance) /
(transitionWidth/2.)))
maskSharp = 0.5 * (1 + np.sign(-signedDistance))
fc = read_feature_collection('land_mask_Kamchatka.geojson')
signedDistance = signed_distance_from_geojson(fc, lon, lat, earth_radius,
max_length=0.25)
landMask = 0.5 * (1 + np.sign(-signedDistance))
mask = maskSharp * landMask + maskSmooth * (1-landMask)
cellWidth = highRes * mask + cellWidth * (1 - mask)
plot_cartopy(plotFrame, fileName + ' mask', mask, 'Blues')
plot_cartopy(plotFrame+1, 'cellWidth ', cellWidth, '3Wbgy5')
plotFrame += 2
fileName = 'region_Arctic_Ocean'
transitionOffset = 0.0*km
transitionWidth = 600.0*km
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat, earth_radius,
max_length=0.25)
mask = 0.5 * (1 + np.tanh((transitionOffset-signedDistance) /
(transitionWidth/2.)))
cellWidth = highRes * mask + cellWidth * (1 - mask)
plot_cartopy(plotFrame, fileName + ' mask', mask, 'Blues')
plot_cartopy(plotFrame+1, 'cellWidth ', cellWidth, '3Wbgy5')
plotFrame += 2
fileName = 'region_Gulf_Stream_extension'
transitionOffset = 0.0*km
transitionWidth = 600.0*km
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat, earth_radius,
max_length=0.25)
mask = 0.5 * (1 + np.tanh((transitionOffset-signedDistance) /
(transitionWidth/2.)))
cellWidth = highRes * mask + cellWidth * (1 - mask)
plot_cartopy(plotFrame, fileName + ' mask', mask, 'Blues')
plot_cartopy(plotFrame+1, 'cellWidth ', cellWidth, '3Wbgy5')
plotFrame += 2
# save signed distance to a file
# da = xarray.DataArray(signedDistance,
# dims=['y', 'x'],
# coords={'y': lat, 'x': lon},
# name='signedDistance')
#cw_filename = 'signedDistance.nc'
# da.to_netcdf(cw_filename)
ax = plt.subplot(6, 2, 1)
ax.plot(lat, EC60to30, label='original EC60to30')
ax.plot(lat, EC60to30Narrow, label='narrow EC60to30')
ax.grid(True)
plt.title('Grid cell size [km] versus latitude')
plt.legend(loc="upper left")
plt.savefig('mesh_construction.png')
return cellWidth, lon, lat
def build_cell_width_lat_lon(self):
"""
Create cell width array for this mesh on a regular latitude-longitude
grid
Returns
-------
cellWidth : numpy.array
m x n array of cell width in km
lon : numpy.array
longitude in degrees (length n and between -180 and 180)
lat : numpy.array
longitude in degrees (length m and between -90 and 90)
"""
dlon = 0.1
dlat = dlon
earth_radius = constants['SHR_CONST_REARTH']
nlon = int(360./dlon) + 1
nlat = int(180./dlat) + 1
lon = np.linspace(-180., 180., nlon)
lat = np.linspace(-90., 90., nlat)
cellWidthSouth = mdt.EC_CellWidthVsLat(lat, cellWidthEq=30.,
cellWidthMidLat=45.,
cellWidthPole=45.,
latPosEq=7.5, latWidthEq=3.0)
cellWidthNorth = mdt.EC_CellWidthVsLat(lat, cellWidthEq=30.,
cellWidthMidLat=60.,
cellWidthPole=35.,
latPosEq=7.5, latWidthEq=3.0)
# Transition at Equator
latTransition = 0.0
latWidthTransition = 2.5
cellWidthVsLat = mdt.mergeCellWidthVsLat(
lat,
cellWidthSouth,
cellWidthNorth,
latTransition,
latWidthTransition)
_, cellWidth = np.meshgrid(lon, cellWidthVsLat)
cellWidthAtlantic = mdt.EC_CellWidthVsLat(lat, cellWidthEq=30.,
cellWidthMidLat=30.,
cellWidthPole=35.,
latPosEq=7.5, latWidthEq=3.0)
cellWidthAtlantic = mdt.mergeCellWidthVsLat(
lat,
cellWidthSouth,
cellWidthAtlantic,
latTransition,
latWidthTransition)
_, cellWidthAtlantic = np.meshgrid(lon, cellWidthAtlantic)
fc = read_feature_collection('atlantic.geojson')
atlantic_signed_distance = signed_distance_from_geojson(
fc, lon, lat, earth_radius, max_length=0.25)
trans_width = 400e3
trans_start = 0.
weights = 0.5 * (1 + np.tanh((atlantic_signed_distance - trans_start) /
trans_width))
cellWidth = cellWidthAtlantic * (1 - weights) + cellWidth * weights
fc = read_feature_collection('high_res_region.geojson')
so_signed_distance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
# Equivalent to 20 degrees latitude
trans_width = 1600e3
trans_start = 500e3
dx_min = 12.
weights = 0.5 * (1 + np.tanh((so_signed_distance - trans_start) /
trans_width))
cellWidth = dx_min * (1 - weights) + cellWidth * weights
return cellWidth, lon, lat
def cellWidthVsLatLon():
"""
Create cell width array for this mesh on a regular latitude-longitude grid.
Returns
-------
cellWidth : ndarray
m x n array, entries are desired cell width in km
lat : ndarray
latitude, vector of length m, with entries between -90 and 90,
degrees
lon : ndarray
longitude, vector of length n, with entries between -180 and 180,
degrees
"""
# To speed up for testing, set following line to 1.0 degrees
dlon = 1.0
dlat = dlon
earth_radius = constants['SHR_CONST_REARTH']
print('\nCreating cellWidth on a lat-lon grid of: {0:.2f} x {0:.2f} '
'degrees'.format(dlon, dlat))
print('This can be set higher for faster test generation\n')
nlon = int(360. / dlon) + 1
nlat = int(180. / dlat) + 1
lon = np.linspace(-180., 180., nlon)
lat = np.linspace(-90., 90., nlat)
km = 1.0e3
print('plotting ...')
fig = plt.figure()
plt.clf()
fig.set_size_inches(10.0, 10.0)
register_sci_viz_colormaps()
# Create cell width vs latitude for Atlantic and Pacific basins
QU1 = np.ones(lat.size)
EC60to30 = mdt.EC_CellWidthVsLat(lat)
RRS30to10 = mdt.RRS_CellWidthVsLat(lat, 30, 10)
AtlNH = RRS30to10
AtlVsLat = mdt.mergeCellWidthVsLat(lat, EC60to30, AtlNH, 0, 6)
PacNH = mdt.mergeCellWidthVsLat(lat, 30 * QU1, RRS30to10, 50, 10)
PacVsLat = mdt.mergeCellWidthVsLat(lat, EC60to30, PacNH, 0, 6)
# Expand from 1D to 2D
_, AtlGrid = np.meshgrid(lon, AtlVsLat)
_, PacGrid = np.meshgrid(lon, PacVsLat)
# Signed distance of Atlantic region
fc = read_feature_collection('Atlantic_region.geojson')
signedDistance = signed_distance_from_geojson(fc,
lon,
lat,
earth_radius,
max_length=0.25)
# Merge Atlantic and Pacific distrubutions smoothly
transitionWidth = 500.0 * km
maskSmooth = 0.5 * (1 + np.tanh(signedDistance / transitionWidth))
cellWidthSmooth = PacGrid * maskSmooth + AtlGrid * (1 - maskSmooth)
# Merge Atlantic and Pacific distrubutions with step function
maskSharp = 0.5 * (1 + np.sign(signedDistance))
cellWidthSharp = PacGrid * maskSharp + AtlGrid * (1 - maskSharp)
# Create a land mask that is 1 over land
fc = read_feature_collection('Americas_land_mask.geojson')
Americas_land_mask = mask_from_geojson(fc, lon, lat)
fc = read_feature_collection('Europe_Africa_land_mask.geojson')
Europe_Africa_land_mask = mask_from_geojson(fc, lon, lat)
landMask = np.fmax(Americas_land_mask, Europe_Africa_land_mask)
# Merge: step transition over land, smooth transition over water
cellWidth = cellWidthSharp * landMask + cellWidthSmooth * (1 - landMask)
ax = plt.subplot(4, 2, 1)
ax.plot(lat, AtlVsLat, label='Atlantic')
ax.plot(lat, PacVsLat, label='Pacific')
ax.grid(True)
plt.title('Grid cell size [km] versus latitude')
plt.legend()
varNames = [
'signedDistance', 'maskSmooth', 'cellWidthSmooth', 'maskSharp',
'cellWidthSharp', 'landMask', 'cellWidth'
]
j = 2
for varName in varNames:
plot_cartopy(j, varName, vars()[varName], '3Wbgy5')
j += 1
fig.canvas.draw()
plt.tight_layout()
plt.savefig('mesh_construction.png')
return cellWidth, lon, lat
def build_cell_width_lat_lon(self):
"""
Create cell width array for this mesh on a regular latitude-longitude
grid
Returns
-------
cellWidth : numpy.array
m x n array of cell width in km
lon : numpy.array
longitude in degrees (length n and between -180 and 180)
lat : numpy.array
longitude in degrees (length m and between -90 and 90)
"""
dlon = 0.1
dlat = dlon
earth_radius = constants['SHR_CONST_REARTH']
print('\nCreating cellWidth on a lat-lon grid of: {0:.2f} x {0:.2f} '
'degrees'.format(dlon, dlat))
print('This can be set higher for faster test generation\n')
nlon = int(360. / dlon) + 1
nlat = int(180. / dlat) + 1
lon = np.linspace(-180., 180., nlon)
lat = np.linspace(-90., 90., nlat)
km = 1.0e3
print('plotting ...')
plt.switch_backend('Agg')
fig = plt.figure()
plt.clf()
fig.set_size_inches(10.0, 14.0)
register_sci_viz_colormaps()
# Create cell width vs latitude for Atlantic and Pacific basins
EC60to30 = mdt.EC_CellWidthVsLat(lat)
EC60to30Narrow = mdt.EC_CellWidthVsLat(lat, latPosEq=8.0,
latWidthEq=3.0)
# Expand from 1D to 2D
_, cellWidth = np.meshgrid(lon, EC60to30Narrow)
_plot_cartopy(2, 'narrow EC60to30', cellWidth, '3Wbgy5')
plotFrame = 3
# global settings for regionally refines mesh
highRes = 14.0 # [km]
fileName = 'region_Central_America'
transitionWidth = 800.0 * km
transitionOffset = 0.0
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
mask = 0.5 * (1 + np.tanh((transitionOffset - signedDistance) /
(transitionWidth / 2.)))
cellWidth = 30.0 * mask + cellWidth * (1 - mask)
fileName = 'coastline_CUSP'
distanceToTransition = 600.0 * km
# transitionWidth is distance from 0.07 to 0.03 of transition within
# tanh
transitionWidth = 600.0 * km
transitionOffset = distanceToTransition + transitionWidth / 2.0
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
mask = 0.5 * (1 + np.tanh((transitionOffset - signedDistance) /
(transitionWidth / 2.)))
cellWidth = highRes * mask + cellWidth * (1 - mask)
_plot_cartopy(plotFrame, fileName + ' mask', mask, 'Blues')
_plot_cartopy(plotFrame + 1, 'cellWidth ', cellWidth, '3Wbgy5')
plotFrame += 2
fileName = 'region_Gulf_of_Mexico'
transitionOffset = 600.0 * km
transitionWidth = 600.0 * km
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
maskSmooth = 0.5 * (1 + np.tanh((transitionOffset - signedDistance) /
(transitionWidth / 2.)))
maskSharp = 0.5 * (1 + np.sign(-signedDistance))
fc = read_feature_collection('land_mask_Mexico.geojson')
signedDistance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
landMask = 0.5 * (1 + np.sign(-signedDistance))
mask = maskSharp * landMask + maskSmooth * (1 - landMask)
cellWidth = highRes * mask + cellWidth * (1 - mask)
_plot_cartopy(plotFrame, fileName + ' mask', mask, 'Blues')
_plot_cartopy(plotFrame + 1, 'cellWidth ', cellWidth, '3Wbgy5')
plotFrame += 2
fileName = 'region_Bering_Sea'
transitionOffset = 0.0 * km
transitionWidth = 600.0 * km
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
maskSmoothEast = 0.5 * (
1 + np.tanh((transitionOffset - signedDistance) /
(transitionWidth / 2.)))
fc = read_feature_collection('region_Bering_Sea_reduced.geojson')
signedDistance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
maskSmoothWest = 0.5 * (
1 + np.tanh((transitionOffset - signedDistance) /
(transitionWidth / 2.)))
fc = read_feature_collection('land_mask_Kamchatka.geojson')
maskWest = mask_from_geojson(fc, lon, lat)
mask = maskSmoothWest * maskWest + maskSmoothEast * (1 - maskWest)
cellWidth = highRes * mask + cellWidth * (1 - mask)
_plot_cartopy(plotFrame, fileName + ' mask', mask, 'Blues')
_plot_cartopy(plotFrame + 1, 'cellWidth ', cellWidth, '3Wbgy5')
plotFrame += 2
fileName = 'region_Arctic_Ocean'
transitionOffset = 0.0 * km
transitionWidth = 600.0 * km
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
mask = 0.5 * (1 + np.tanh((transitionOffset - signedDistance) /
(transitionWidth / 2.)))
cellWidth = highRes * mask + cellWidth * (1 - mask)
_plot_cartopy(plotFrame, fileName + ' mask', mask, 'Blues')
_plot_cartopy(plotFrame + 1, 'cellWidth ', cellWidth, '3Wbgy5')
plotFrame += 2
fileName = 'region_Gulf_Stream_extension'
transitionOffset = 0.0 * km
transitionWidth = 600.0 * km
fc = read_feature_collection('{}.geojson'.format(fileName))
signedDistance = signed_distance_from_geojson(fc, lon, lat,
earth_radius,
max_length=0.25)
mask = 0.5 * (1 + np.tanh((transitionOffset - signedDistance) /
(transitionWidth / 2.)))
cellWidth = highRes * mask + cellWidth * (1 - mask)
_plot_cartopy(plotFrame, fileName + ' mask', mask, 'Blues')
_plot_cartopy(plotFrame + 1, 'cellWidth ', cellWidth, '3Wbgy5')
plotFrame += 2
ax = plt.subplot(6, 2, 1)
ax.plot(lat, EC60to30, label='original EC60to30')
ax.plot(lat, EC60to30Narrow, label='narrow EC60to30')
ax.grid(True)
plt.title('Grid cell size [km] versus latitude')
plt.legend(loc="upper left")
plt.savefig('mesh_construction.png', dpi=300)
return cellWidth, lon, lat
