def common_grid(roms_file, cice_file, out_file):
# Resolution of common grid (degrees, same for lat and lon)
res = 0.25
# Northern boundary to interpolate to
nbdry = -50
# Radius of the Earth in metres
r = 6.371e6
# Degrees to radians conversion factor
deg2rad = pi / 180.0
N = 31
print 'Calculating grids'
# Make the latitude and longitude arrays for the common grid
lon_common = arange(-180, 180 + res, res)
lat_common = arange(-90, nbdry + res, res)
# Get a 2D version of each to calculate dx and dy in metres
lon_2d, lat_2d = meshgrid(lon_common, lat_common)
# dx = r*cos(lat)*dlon where lat and dlon (i.e. res) are in radians
dx = r * cos(lat_2d * deg2rad) * res * deg2rad
# dy = r*dlat where dlat (i.e. res) is in radians
# This is constant so reshape to an array of the right dimensions
dy = zeros(shape(dx)) + r * res * deg2rad
# Read the ROMS grid
id = Dataset(roms_file, 'r')
# We only need lat and lon on the rho grid
lon_rho = id.variables['lon_rho'][:, :]
lat_rho = id.variables['lat_rho'][:, :]
# Get shape of u and v grids
u_shape = id.variables['lon_u'].shape
v_shape = id.variables['lon_v'].shape
# Read land mask
mask_roms = id.variables['mask_rho'][:, :]
# Mask out ice shelves too
zice = id.variables['zice'][:, :]
mask_roms[zice != 0] = 0.0
# Read angle (for rotation of vector components)
angle_roms = id.variables['angle'][:, :]
# Get time as an array of Date objects
time_id = id.variables['ocean_time']
time = num2date(time_id[:],
units=time_id.units,
calendar=time_id.calendar.lower())
id.close()
# Read the CICE grid
id = Dataset(cice_file, 'r')
# We only need lat and lon on the velocity grid
lon_cice = id.variables['ULON'][:, :]
lat_cice = id.variables['ULAT'][:, :]
# Read angle (for rotation of vector components)
angle_cice = id.variables['ANGLE'][:, :]
id.close()
# Make sure longitude is between -180 and 180
index = lon_rho > 180
lon_rho[index] = lon_rho[index] - 360
index = lon_cice > 180
lon_cice[index] = lon_cice[index] - 360
print 'Counting months'
# Assume we start at the beginning of a year
# Figure out how many complete years have happened since then
num_full_years = time[-1].year - time[0].year
if time[-1].month == 12 and time[-1].day in range(29, 31 + 1):
# We happen to end at the very end of a year
num_full_years += 1
else:
# Count the complete months that have happened this year
num_extra_months = time[-1].month - 1
# Don't bother with the hassle of considering cases where we end at
# the very end of a month. Just ignore the month.
num_months = 12 * num_full_years + num_extra_months
print 'Interpolating land mask to new grid'
mask_common = interp_roms2common(lon_common, lat_common, lon_rho, lat_rho,
mask_roms)
mask_common[isnan(mask_common)] = 0
# Cut it off at 1
mask_common[mask_common < 0.5] = 0
mask_common[mask_common >= 0.5] = 1
# print 'Setting up ' + out_file
# id = Dataset(out_file, 'w')
# id.createDimension('longitude', size(lon_common))
# id.createDimension('latitude', size(lat_common))
# id.createDimension('time', None)
# id.createVariable('longitude', 'f8', ('longitude'))
# id.variables['longitude'].units = 'degrees'
# id.variables['longitude'][:] = lon_common
# id.createVariable('latitude', 'f8', ('latitude'))
# id.variables['latitude'].units = 'degrees'
# id.variables['latitude'][:] = lat_common
# id.createVariable('time', 'f8', ('time'))
# id.variables['time'].units = 'months'
# id.createVariable('mask', 'f8', ('latitude', 'longitude'))
# id.variables['mask'].units = '1'
# id.variables['mask'][:,:] = mask_common
# id.createVariable('sst', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['sst'].long_name = 'sea surface temperature'
# id.variables['sst'].units = 'C'
# id.createVariable('sss', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['sss'].long_name = 'sea surface salinity'
# id.variables['sss'].units = 'psu'
# id.createVariable('shflux', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['shflux'].long_name = 'surface heat flux into ocean'
# id.variables['shflux'].units = 'W/m^2'
# id.createVariable('ssflux', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['ssflux'].long_name = 'surface virtual salinity flux into ocean'
# id.variables['ssflux'].units = 'psu m/s'
# id.createVariable('aice', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['aice'].long_name = 'sea ice concentration'
# id.variables['aice'].units = '1'
# id.createVariable('hice', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['hice'].long_name = 'sea ice thickness'
# id.variables['hice'].units = 'm'
# id.createVariable('uocn', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['uocn'].long_name = 'ocean surface velocity eastward'
# id.variables['uocn'].units = 'm/s'
# id.createVariable('vocn', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['vocn'].long_name = 'ocean surface velocity northward'
# id.variables['vocn'].units = 'm/s'
# id.createVariable('uice', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['uice'].long_name = 'sea ice velocity eastward'
# id.variables['uice'].units = 'm/s'
# id.createVariable('vice', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['vice'].long_name = 'sea ice velocity northward'
# id.variables['vice'].units = 'm/s'
# id.createVariable('sustr', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['sustr'].long_name = 'zonal surface stress'
# id.variables['sustr'].units = 'N/m^2'
# id.createVariable('svstr', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['svstr'].long_name = 'meridional surface stress'
# id.variables['svstr'].units = 'N/m^2'
# id.createVariable('curl_str', 'f8', ('time', 'latitude', 'longitude'))
# id.variables['curl_str'].long_name = 'curl of surface stress'
# id.variables['curl_str'].units = 'N/m^3'
# id.close()
# Loop over months
for month in range(18, num_months):
print 'Processing month ' + str(month + 1) + ' of ' + str(num_months)
id = Dataset(out_file, 'a')
# Write time value for this month
id.variables['time'][month] = month + 1
print '...sea surface temperature'
# Get monthly average of 3D variable
temp_roms = monthly_avg_roms(
roms_file,
'temp',
[N, size(lon_rho, 0), size(lon_rho, 1)],
month % 12,
instance=month / 12 + 1)
# Select surface layer
sst_roms = temp_roms[-1, :, :]
# Interpolate to common grid
sst_common = interp_roms2common(lon_common, lat_common, lon_rho,
lat_rho, sst_roms)
# Apply land mask
sst = ma.masked_where(mask_common == 0, sst_common)
# Write to file
id.variables['sst'][month, :, :] = sst
print '...sea surface salinity'
# Get monthly average of 3D variable
salt_roms = monthly_avg_roms(
roms_file,
'salt',
[N, size(lon_rho, 0), size(lon_rho, 1)],
month % 12,
instance=month / 12 + 1)
# Select surface layer
sss_roms = salt_roms[-1, :, :]
# Interpolate to common grid
sss_common = interp_roms2common(lon_common, lat_common, lon_rho,
lat_rho, sss_roms)
# Apply land mask
sss = ma.masked_where(mask_common == 0, sss_common)
# Write to file
id.variables['sss'][month, :, :] = sss
print '...surface heat flux'
# Get monthly average
shflux_roms = monthly_avg_roms(roms_file,
'shflux',
shape(lon_rho),
month % 12,
instance=month / 12 + 1)
# Interpolate to common grid
shflux_common = interp_roms2common(lon_common, lat_common, lon_rho,
lat_rho, shflux_roms)
# Apply land mask
shflux = ma.masked_where(mask_common == 0, shflux_common)
# Write to file
id.variables['shflux'][month, :, :] = shflux
print '...surface salt flux'
# Get monthly average
ssflux_roms = monthly_avg_roms(roms_file,
'ssflux',
shape(lon_rho),
month % 12,
instance=month / 12 + 1)
# Interpolate to common grid
ssflux_common = interp_roms2common(lon_common, lat_common, lon_rho,
lat_rho, ssflux_roms)
# Apply land mask
ssflux = ma.masked_where(mask_common == 0, ssflux_common)
# Write to file
id.variables['ssflux'][month, :, :] = ssflux
print '...sea ice concentration'
# Get monthly average (use CICE file)
aice_cice = monthly_avg_cice(cice_file,
'aice',
shape(lon_cice),
month % 12,
instance=month / 12 + 1)
# Interpolate to common grid (note CICE grid not ROMS)
aice_common = interp_roms2common(lon_common, lat_common, lon_cice,
lat_cice, aice_cice)
# Apply land mask
aice = ma.masked_where(mask_common == 0, aice_common)
# Write to file
id.variables['aice'][month, :, :] = aice
print '...sea ice thickness'
# Get monthly average (use CICE file)
hice_cice = monthly_avg_cice(cice_file,
'hi',
shape(lon_cice),
month % 12,
instance=month / 12 + 1)
# Interpolate to common grid (note CICE grid not ROMS)
hice_common = interp_roms2common(lon_common, lat_common, lon_cice,
lat_cice, hice_cice)
# Apply land mask
hice = ma.masked_where(mask_common == 0, hice_common)
# Write to file
id.variables['hice'][month, :, :] = hice
print '...surface ocean velocity vector'
# Surface ocean velocity
# Get monthly averages of both 3D vector components
uocn_3d_tmp = monthly_avg_roms(roms_file,
'u', [N, u_shape[0], u_shape[1]],
month % 12,
instance=month / 12 + 1)
vocn_3d_tmp = monthly_avg_roms(roms_file,
'v', [N, v_shape[0], v_shape[1]],
month % 12,
instance=month / 12 + 1)
# Select surface layer
uocn_tmp = uocn_3d_tmp[-1, :, :]
vocn_tmp = vocn_3d_tmp[-1, :, :]
# Rotate to lon-lat space (note they are on the rho grid now)
uocn_roms, vocn_roms = rotate_vector_roms(uocn_tmp, vocn_tmp,
angle_roms)
# Interpolate to common grid
uocn_common = interp_roms2common(lon_common, lat_common, lon_rho,
lat_rho, uocn_roms)
vocn_common = interp_roms2common(lon_common, lat_common, lon_rho,
lat_rho, vocn_roms)
# Apply land mask
uocn = ma.masked_where(mask_common == 0, uocn_common)
vocn = ma.masked_where(mask_common == 0, vocn_common)
# Write to file
id.variables['uocn'][month, :, :] = uocn
id.variables['vocn'][month, :, :] = vocn
print '...sea ice velocity vector'
# Sea ice velocity (CICE variable not ROMS)
# Get monthly averages of both vector components
uice_tmp = monthly_avg_cice(cice_file,
'uvel',
shape(lon_cice),
month % 12,
instance=month / 12 + 1)
vice_tmp = monthly_avg_cice(cice_file,
'vvel',
shape(lon_cice),
month % 12,
instance=month / 12 + 1)
# Rotate to lon-lat space
uice_cice, vice_cice = rotate_vector_cice(uice_tmp, vice_tmp,
angle_cice)
# Interpolate to common grid (note CICE grid not ROMS)
uice_common = interp_roms2common(lon_common, lat_common, lon_cice,
lat_cice, uice_cice)
vice_common = interp_roms2common(lon_common, lat_common, lon_cice,
lat_cice, vice_cice)
# Apply land mask
uice = ma.masked_where(mask_common == 0, uice_common)
vice = ma.masked_where(mask_common == 0, vice_common)
# Write to file
id.variables['uice'][month, :, :] = uice
id.variables['vice'][month, :, :] = vice
print '...surface stress vector'
# Surface stresses
# Get monthly averages of both vector components
sustr_tmp = monthly_avg_roms(roms_file,
'sustr',
u_shape,
month % 12,
instance=month / 12 + 1)
svstr_tmp = monthly_avg_roms(roms_file,
'svstr',
v_shape,
month % 12,
instance=month / 12 + 1)
# Rotate to lon-lat space (note they are on the rho grid now)
sustr_roms, svstr_roms = rotate_vector_roms(sustr_tmp, svstr_tmp,
angle_roms)
# Interpolate to common grid
sustr_common = interp_roms2common(lon_common, lat_common, lon_rho,
lat_rho, sustr_roms)
svstr_common = interp_roms2common(lon_common, lat_common, lon_rho,
lat_rho, svstr_roms)
# Apply land mask
sustr = ma.masked_where(mask_common == 0, sustr_common)
svstr = ma.masked_where(mask_common == 0, svstr_common)
# Write to file
id.variables['sustr'][month, :, :] = sustr
id.variables['svstr'][month, :, :] = svstr
print '...curl of surface stress vector'
# Curl of surface stress = d/dx (svstr) - d/dy (sustr)
# First calculate the two derivatives
dsvstr_dx = ma.empty(shape(svstr_common))
# Forward difference approximation
dsvstr_dx[:, :-1] = (svstr_common[:, 1:] -
svstr_common[:, :-1]) / dx[:, :-1]
# Backward difference for the last row
dsvstr_dx[:,
-1] = (svstr_common[:, -1] - svstr_common[:, -2]) / dx[:, -1]
dsustr_dy = ma.empty(shape(sustr_common))
dsustr_dy[:-1, :] = (sustr_common[1:, :] -
sustr_common[:-1, :]) / dy[:-1, :]
dsustr_dy[-1, :] = (sustr_common[-1, :] -
sustr_common[-2, :]) / dy[-1, :]
curl_str = dsvstr_dx - dsustr_dy
curl_str = ma.masked_where(mask_common == 0, curl_str)
# Write to file
id.variables['curl_str'][month, :, :] = curl_str
id.close()
print 'Finished'
def mip_cavity_fields(var_name, roms_grid, roms_file, fesom_mesh_path,
fesom_file):
# Name of each ice shelf
shelf_names = [
'Larsen D Ice Shelf', 'Larsen C Ice Shelf',
'Wilkins & George VI & Stange Ice Shelves', 'Ronne-Filchner Ice Shelf',
'Abbot Ice Shelf', 'Pine Island Glacier Ice Shelf',
'Thwaites Ice Shelf', 'Dotson Ice Shelf', 'Getz Ice Shelf',
'Nickerson Ice Shelf', 'Sulzberger Ice Shelf', 'Mertz Ice Shelf',
'Totten & Moscow University Ice Shelves', 'Shackleton Ice Shelf',
'West Ice Shelf', 'Amery Ice Shelf', 'Prince Harald Ice Shelf',
'Baudouin & Borchgrevink Ice Shelves', 'Lazarev Ice Shelf',
'Nivl Ice Shelf', 'Fimbul & Jelbart & Ekstrom Ice Shelves',
'Brunt & Riiser-Larsen Ice Shelves', 'Ross Ice Shelf'
]
# Beginnings of filenames for figures
fig_heads = [
'larsen_d', 'larsen_c', 'wilkins_georgevi_stange', 'ronne_filchner',
'abbot', 'pig', 'thwaites', 'dotson', 'getz', 'nickerson',
'sulzberger', 'mertz', 'totten_moscowuni', 'shackleton', 'west',
'amery', 'prince_harald', 'baudouin_borchgrevink', 'lazarev', 'nivl',
'fimbul_jelbart_ekstrom', 'brunt_riiser_larsen', 'ross'
]
# Limits on longitude and latitude for each ice shelf
# Note Ross crosses 180W=180E
lon_min = [
-62.67, -65.5, -79.17, -85, -104.17, -102.5, -108.33, -114.5, -135.67,
-149.17, -155, 144, 115, 94.17, 80.83, 65, 33.83, 19, 12.9, 9.33,
-10.05, -28.33, 158.33
]
lon_max = [
-59.33, -60, -66.67, -28.33, -88.83, -99.17, -103.33, -111.5, -114.33,
-140, -145, 146.62, 123.33, 102.5, 89.17, 75, 37.67, 33.33, 16.17,
12.88, 7.6, -10.33, -146.67
]
lat_min = [
-73.03, -69.35, -74.17, -83.5, -73.28, -75.5, -75.5, -75.33, -74.9,
-76.42, -78, -67.83, -67.17, -66.67, -67.83, -73.67, -69.83, -71.67,
-70.5, -70.75, -71.83, -76.33, -85
]
lat_max = [
-69.37, -66.13, -69.5, -74.67, -71.67, -74.17, -74.67, -73.67, -73,
-75.17, -76.41, -66.67, -66.5, -64.83, -66.17, -68.33, -68.67, -68.33,
-69.33, -69.83, -69.33, -71.5, -77
]
num_shelves = len(shelf_names)
# Constants
sec_per_year = 365 * 24 * 3600
deg2rad = pi / 180.0
# Parameters for missing circle in ROMS grid
lon_c = 50
lat_c = -83
radius = 10.1
nbdry = -63 + 90
# ROMS vertical grid parameters
theta_s = 7.0
theta_b = 2.0
hc = 250
N = 31
# Number of bins in each direction for vector overlay
num_bins = 50
print 'Reading ROMS fields'
if var_name == 'draft':
id = Dataset(roms_grid, 'r')
else:
id = Dataset(roms_file, 'r')
roms_lon = id.variables['lon_rho'][:, :]
roms_lat = id.variables['lat_rho'][:, :]
roms_mask = id.variables['mask_rho'][:, :]
roms_zice = id.variables['zice'][:, :]
if var_name == 'draft':
# Switch signs
roms_data = -1 * id.variables['zice'][:, :]
elif var_name == 'melt':
# Convert from m/s to m/y
roms_data = id.variables['m'][0, :, :] * sec_per_year
elif var_name == 'temp':
# Bottom layer
roms_data = id.variables['temp'][0, 0, :, :]
elif var_name == 'salt':
# Bottom layer
roms_data = id.variables['salt'][0, 0, :, :]
elif var_name in ['vsfc', 'vavg']:
# Get angle from the grid file
id2 = Dataset(roms_grid, 'r')
angle = id2.variables['angle'][:, :]
id2.close()
if var_name == 'vsfc':
# Read surface u and v
u_tmp = id.variables['u'][0, -1, :, :]
v_tmp = id.variables['v'][0, -1, :, :]
# Interpolate to rho grid and unrotate
u_rho, v_rho = rotate_vector_roms(u_tmp, v_tmp, angle)
elif var_name == 'vavg':
# Read full 3D u and v
u_3d_tmp = id.variables['u'][0, :, :, :]
v_3d_tmp = id.variables['v'][0, :, :, :]
# Read bathymetry from grid file
id2 = Dataset(roms_grid, 'r')
roms_h = id2.variables['h'][:, :]
id2.close()
# Get integrands on 3D grid; we only care about dz
dx, dy, dz, z = cartesian_grid_3d(roms_lon, roms_lat, roms_h,
roms_zice, theta_s, theta_b, hc,
N)
# Unrotate each vertical level
u_3d = ma.empty(shape(dz))
v_3d = ma.empty(shape(dz))
for k in range(N):
u_k, v_k = rotate_vector_roms(u_3d_tmp[k, :, :],
v_3d_tmp[k, :, :], angle)
u_3d[k, :, :] = u_k
v_3d[k, :, :] = v_k
# Vertically average u and v
u_rho = sum(u_3d * dz, axis=0) / sum(dz, axis=0)
v_rho = sum(v_3d * dz, axis=0) / sum(dz, axis=0)
# Get speed
roms_data = sqrt(u_rho**2 + v_rho**2)
id.close()
# Get land/zice mask
open_ocn = copy(roms_mask)
open_ocn[roms_zice != 0] = 0
land_zice = ma.masked_where(open_ocn == 1, open_ocn)
# Mask the open ocean and land out of the data field
roms_data = ma.masked_where(roms_zice == 0, roms_data)
# Convert grid to spherical coordinates
roms_x = -(roms_lat + 90) * cos(roms_lon * deg2rad + pi / 2)
roms_y = (roms_lat + 90) * sin(roms_lon * deg2rad + pi / 2)
# Find centre in spherical coordinates
x_c = -(lat_c + 90) * cos(lon_c * deg2rad + pi / 2)
y_c = (lat_c + 90) * sin(lon_c * deg2rad + pi / 2)
# Build a regular x-y grid and select the missing circle
x_reg_roms, y_reg_roms = meshgrid(linspace(-nbdry, nbdry, num=1000),
linspace(-nbdry, nbdry, num=1000))
land_circle = zeros(shape(x_reg_roms))
land_circle = ma.masked_where(
sqrt((x_reg_roms - x_c)**2 + (y_reg_roms - y_c)**2) > radius,
land_circle)
print 'Reading FESOM fields'
# Mask open ocean
elements, mask_patches = make_patches(fesom_mesh_path,
circumpolar=True,
mask_cavities=True)
# Unmask ice shelves
patches = iceshelf_mask(elements)
if var_name == 'draft':
# Nothing more to read
pass
else:
id = Dataset(fesom_file, 'r')
if var_name == 'melt':
# Convert from m/s to m/y
node_data = id.variables['wnet'][0, :] * sec_per_year
elif var_name == 'temp':
# Read full 3D field for now
node_data = id.variables['temp'][0, :]
elif var_name == 'salt':
# Read full 3D field for now
node_data = id.variables['salt'][0, :]
elif var_name in ['vsfc', 'vavg']:
# The overlaid vectors are based on nodes not elements, so many
# of the fesom_grid data structures fail to apply and we need to
# read some of the FESOM grid files again.
# Read the cavity flag for each 2D surface node
fesom_cavity = []
f = open(fesom_mesh_path + 'cavity_flag_nod2d.out', 'r')
for line in f:
tmp = int(line)
if tmp == 1:
fesom_cavity.append(True)
elif tmp == 0:
fesom_cavity.append(False)
else:
print 'Problem'
return
f.close()
# Save the number of 2D nodes
fesom_n2d = len(fesom_cavity)
# Read rotated lat and lon for each node; also read depth which is
# needed for vertically averaged velocity
f = open(fesom_mesh_path + 'nod3d.out', 'r')
f.readline()
rlon = []
rlat = []
node_depth = []
for line in f:
tmp = line.split()
lon_tmp = float(tmp[1])
lat_tmp = float(tmp[2])
node_depth_tmp = -1 * float(tmp[3])
if lon_tmp < -180:
lon_tmp += 360
elif lon_tmp > 180:
lon_tmp -= 360
rlon.append(lon_tmp)
rlat.append(lat_tmp)
node_depth.append(node_depth_tmp)
f.close()
# For lat and lon, only care about the 2D nodes (the first
# fesom_n2d indices)
rlon = array(rlon[0:fesom_n2d])
rlat = array(rlat[0:fesom_n2d])
node_depth = array(node_depth)
# Unrotate longitude
fesom_lon, fesom_lat = unrotate_grid(rlon, rlat)
# Calculate polar coordinates of each node
fesom_x = -(fesom_lat + 90) * cos(fesom_lon * deg2rad + pi / 2)
fesom_y = (fesom_lat + 90) * sin(fesom_lon * deg2rad + pi / 2)
if var_name == 'vavg':
# Read lists of which nodes are directly below which
f = open(fesom_mesh_path + 'aux3d.out', 'r')
max_num_layers = int(f.readline())
node_columns = zeros([fesom_n2d, max_num_layers])
for n in range(fesom_n2d):
for k in range(max_num_layers):
node_columns[n, k] = int(f.readline())
node_columns = node_columns.astype(int)
f.close()
# Now we can actually read the data
# Read full 3D field for both u and v
node_ur_3d = id.variables['u'][0, :]
node_vr_3d = id.variables['v'][0, :]
if var_name == 'vsfc':
# Only care about the first fesom_n2d nodes (surface)
node_ur = node_ur_3d[0:fesom_n2d]
node_vr = node_vr_3d[0:fesom_n2d]
elif var_name == 'vavg':
# Vertically average
node_ur = zeros(fesom_n2d)
node_vr = zeros(fesom_n2d)
for n in range(fesom_n2d):
# Integrate udz, vdz, and dz over this water column
udz_col = 0
vdz_col = 0
dz_col = 0
for k in range(max_num_layers - 1):
if node_columns[n, k + 1] == -999:
# Reached the bottom
break
# Trapezoidal rule
top_id = node_columns[n, k]
bot_id = node_columns[n, k + 1]
dz_tmp = node_depth[bot_id - 1] - node_depth[top_id -
1]
udz_col += 0.5 * (node_ur_3d[top_id - 1] +
node_ur_3d[bot_id - 1]) * dz_tmp
vdz_col += 0.5 * (node_vr_3d[top_id - 1] +
node_vr_3d[bot_id - 1]) * dz_tmp
dz_col += dz_tmp
# Convert from integrals to averages
node_ur[n] = udz_col / dz_col
node_vr[n] = vdz_col / dz_col
# Unrotate
node_u, node_v = unrotate_vector(rlon, rlat, node_ur, node_vr)
# Calculate speed
node_data = sqrt(node_u**2 + node_v**2)
id.close()
# Calculate given field at each element
fesom_data = []
for elm in elements:
# For each element in an ice shelf cavity, append the mean value
# for the 3 component Nodes
if elm.cavity:
if var_name == 'draft':
# Ice shelf draft is depth of surface layer
fesom_data.append(
mean([
elm.nodes[0].depth, elm.nodes[1].depth,
elm.nodes[2].depth
]))
elif var_name in ['melt', 'vsfc', 'vavg']:
# Surface nodes (or 2D in the case of vavg)
fesom_data.append(
mean([
node_data[elm.nodes[0].id], node_data[elm.nodes[1].id],
node_data[elm.nodes[2].id]
]))
elif var_name in ['temp', 'salt']:
# Bottom nodes
fesom_data.append(
mean([
node_data[elm.nodes[0].find_bottom().id],
node_data[elm.nodes[1].find_bottom().id],
node_data[elm.nodes[2].find_bottom().id]
]))
# Loop over ice shelves
for index in range(num_shelves):
print 'Processing ' + shelf_names[index]
# Convert lat/lon bounds to polar coordinates for plotting
x1 = -(lat_min[index] + 90) * cos(lon_min[index] * deg2rad + pi / 2)
y1 = (lat_min[index] + 90) * sin(lon_min[index] * deg2rad + pi / 2)
x2 = -(lat_min[index] + 90) * cos(lon_max[index] * deg2rad + pi / 2)
y2 = (lat_min[index] + 90) * sin(lon_max[index] * deg2rad + pi / 2)
x3 = -(lat_max[index] + 90) * cos(lon_min[index] * deg2rad + pi / 2)
y3 = (lat_max[index] + 90) * sin(lon_min[index] * deg2rad + pi / 2)
x4 = -(lat_max[index] + 90) * cos(lon_max[index] * deg2rad + pi / 2)
y4 = (lat_max[index] + 90) * sin(lon_max[index] * deg2rad + pi / 2)
# Find the new bounds on x and y
x_min = amin(array([x1, x2, x3, x4]))
x_max = amax(array([x1, x2, x3, x4]))
y_min = amin(array([y1, y2, y3, y4]))
y_max = amax(array([y1, y2, y3, y4]))
# Now make the plot square: enlarge the smaller of delta_x and delta_y
# so they are equal
delta_x = x_max - x_min
delta_y = y_max - y_min
if delta_x > delta_y:
diff = 0.5 * (delta_x - delta_y)
y_min -= diff
y_max += diff
elif delta_y > delta_x:
diff = 0.5 * (delta_y - delta_x)
x_min -= diff
x_max += diff
# Set up a grey square for FESOM to fill the background with land
x_reg_fesom, y_reg_fesom = meshgrid(linspace(x_min, x_max, num=100),
linspace(y_min, y_max, num=100))
land_square = zeros(shape(x_reg_fesom))
# Find bounds on variable in this region, for both ROMS and FESOM
# Start with ROMS
loc = (roms_x >= x_min) * (roms_x <= x_max) * (roms_y >= y_min) * (
roms_y <= y_max)
var_min = amin(roms_data[loc])
var_max = amax(roms_data[loc])
# Modify with FESOM
i = 0
for elm in elements:
if elm.cavity:
if any(elm.x >= x_min) and any(elm.x <= x_max) and any(
elm.y >= y_min) and any(elm.y <= y_max):
if fesom_data[i] < var_min:
var_min = fesom_data[i]
if fesom_data[i] > var_max:
var_max = fesom_data[i]
i += 1
if var_name == 'melt':
# Special colour map
if var_min < 0:
# There is refreezing here; include blue for elements below 0
cmap_vals = array([
var_min, 0, 0.25 * var_max, 0.5 * var_max, 0.75 * var_max,
var_max
])
cmap_colors = [(0.26, 0.45, 0.86), (1, 1, 1), (1, 0.9, 0.4),
(0.99, 0.59, 0.18), (0.5, 0.0, 0.08),
(0.96, 0.17, 0.89)]
cmap_vals_norm = (cmap_vals - var_min) / (var_max - var_min)
cmap_list = []
for i in range(size(cmap_vals)):
cmap_list.append((cmap_vals_norm[i], cmap_colors[i]))
mf_cmap = LinearSegmentedColormap.from_list(
'melt_freeze', cmap_list)
else:
# No refreezing
cmap_vals = array([
0, 0.25 * var_max, 0.5 * var_max, 0.75 * var_max, var_max
])
cmap_colors = [(1, 1, 1), (1, 0.9, 0.4), (0.99, 0.59, 0.18),
(0.5, 0.0, 0.08), (0.96, 0.17, 0.89)]
cmap_vals_norm = cmap_vals / var_max
cmap_list = []
for i in range(size(cmap_vals)):
cmap_list.append((cmap_vals_norm[i], cmap_colors[i]))
mf_cmap = LinearSegmentedColormap.from_list(
'melt_freeze', cmap_list)
colour_map = mf_cmap
else:
colour_map = 'jet'
if var_name in ['vsfc', 'vavg']:
# Make vectors for overlay
# Set up bins (edges)
x_bins = linspace(x_min, x_max, num=num_bins + 1)
y_bins = linspace(y_min, y_max, num=num_bins + 1)
# Calculate centres of bins (for plotting)
x_centres = 0.5 * (x_bins[:-1] + x_bins[1:])
y_centres = 0.5 * (y_bins[:-1] + y_bins[1:])
# ROMS
# First set up arrays to integrate velocity in each bin
# Simple averaging of all the points inside each bin
roms_u = zeros([size(y_centres), size(x_centres)])
roms_v = zeros([size(y_centres), size(x_centres)])
roms_num_pts = zeros([size(y_centres), size(x_centres)])
# First convert to polar coordinates, rotate to account for
# longitude in circumpolar projection, and convert back to vector
# components
theta_roms = arctan2(v_rho, u_rho)
theta_circ_roms = theta_roms - roms_lon * deg2rad
u_circ_roms = roms_data * cos(
theta_circ_roms) # roms_data is speed
v_circ_roms = roms_data * sin(theta_circ_roms)
# Loop over all points (can't find a better way to do this)
for j in range(size(roms_data, 0)):
for i in range(size(roms_data, 1)):
# Make sure data isn't masked (i.e. land or open ocean)
if u_circ_roms[j, i] is not ma.masked:
# Check if we're in the region of interest
if roms_x[j, i] > x_min and roms_x[
j, i] < x_max and roms_y[
j, i] > y_min and roms_y[j, i] < y_max:
# Figure out which bins this falls into
x_index = nonzero(x_bins > roms_x[j, i])[0][0] - 1
y_index = nonzero(y_bins > roms_y[j, i])[0][0] - 1
# Integrate
roms_u[y_index, x_index] += u_circ_roms[j, i]
roms_v[y_index, x_index] += v_circ_roms[j, i]
roms_num_pts[y_index, x_index] += 1
# Convert from sums to averages
# First mask out points with no data
roms_u = ma.masked_where(roms_num_pts == 0, roms_u)
roms_v = ma.masked_where(roms_num_pts == 0, roms_v)
# Divide everything else by the number of points
flag = roms_num_pts > 0
roms_u[flag] = roms_u[flag] / roms_num_pts[flag]
roms_v[flag] = roms_v[flag] / roms_num_pts[flag]
# FESOM
fesom_u = zeros([size(y_centres), size(x_centres)])
fesom_v = zeros([size(y_centres), size(x_centres)])
fesom_num_pts = zeros([size(y_centres), size(x_centres)])
theta_fesom = arctan2(node_v, node_u)
theta_circ_fesom = theta_fesom - fesom_lon * deg2rad
u_circ_fesom = node_data * cos(
theta_circ_fesom) # node_data is speed
v_circ_fesom = node_data * sin(theta_circ_fesom)
# Loop over 2D nodes to fill in the velocity bins as before
for n in range(fesom_n2d):
if fesom_cavity[n]:
if fesom_x[n] > x_min and fesom_x[n] < x_max and fesom_y[
n] > y_min and fesom_y[n] < y_max:
x_index = nonzero(x_bins > fesom_x[n])[0][0] - 1
y_index = nonzero(y_bins > fesom_y[n])[0][0] - 1
fesom_u[y_index, x_index] += u_circ_fesom[n]
fesom_v[y_index, x_index] += v_circ_fesom[n]
fesom_num_pts[y_index, x_index] += 1
fesom_u = ma.masked_where(fesom_num_pts == 0, fesom_u)
fesom_v = ma.masked_where(fesom_num_pts == 0, fesom_v)
flag = fesom_num_pts > 0
fesom_u[flag] = fesom_u[flag] / fesom_num_pts[flag]
fesom_v[flag] = fesom_v[flag] / fesom_num_pts[flag]
# Plot
fig = figure(figsize=(30, 12))
fig.patch.set_facecolor('white')
# ROMS
ax1 = fig.add_subplot(1, 2, 1, aspect='equal')
# First shade land and zice in grey
contourf(roms_x, roms_y, land_zice, 1, colors=(('0.6', '0.6', '0.6')))
# Fill in the missing circle
contourf(x_reg_roms,
y_reg_roms,
land_circle,
1,
colors=(('0.6', '0.6', '0.6')))
# Now shade the data
pcolor(roms_x,
roms_y,
roms_data,
vmin=var_min,
vmax=var_max,
cmap=colour_map)
if var_name in ['vsfc', 'vavg']:
# Overlay vectors
quiver(x_centres,
y_centres,
roms_u,
roms_v,
scale=1.5,
color='black')
xlim([x_min, x_max])
ylim([y_min, y_max])
axis('off')
title('MetROMS', fontsize=24)
# FESOM
ax2 = fig.add_subplot(1, 2, 2, aspect='equal')
# Start with land background
contourf(x_reg_fesom,
y_reg_fesom,
land_square,
1,
colors=(('0.6', '0.6', '0.6')))
# Add ice shelf elements
img = PatchCollection(patches, cmap=colour_map)
img.set_array(array(fesom_data))
img.set_edgecolor('face')
img.set_clim(vmin=var_min, vmax=var_max)
ax2.add_collection(img)
# Mask out the open ocean in white
overlay = PatchCollection(mask_patches, facecolor=(1, 1, 1))
overlay.set_edgecolor('face')
ax2.add_collection(overlay)
if var_name in ['vsfc', 'vavg']:
quiver(x_centres,
y_centres,
fesom_u,
fesom_v,
scale=1.5,
color='black')
xlim([x_min, x_max])
ylim([y_min, y_max])
axis('off')
title('FESOM', fontsize=24)
# Colourbar on the right
cbaxes = fig.add_axes([0.92, 0.2, 0.01, 0.6])
cbar = colorbar(img, cax=cbaxes)
cbar.ax.tick_params(labelsize=20)
# Main title
if var_name == 'draft':
title_string = ' draft (m)'
elif var_name == 'melt':
title_string = ' melt rate (m/y)'
elif var_name == 'temp':
title_string = r' bottom water temperature ($^{\circ}$C)'
elif var_name == 'salt':
title_string = ' bottom water salinity (psu)'
elif var_name == 'vsfc':
title_string = ' surface velocity (m/s)'
elif var_name == 'vavg':
title_string = ' vertically averaged velocity (m/s)'
suptitle(shelf_names[index] + title_string, fontsize=30)
subplots_adjust(wspace=0.05)
#fig.show()
fig.savefig(fig_heads[index] + '_' + var_name + '.png')
def mip_sfc_stress():
# File paths
roms_grid = '/short/m68/kaa561/metroms_iceshelf/apps/common/grid/circ30S_quarterdegree.nc'
roms_file = '/short/m68/kaa561/metroms_iceshelf/tmproms/run/intercomparison/stress_firstyear.nc' # Already averaged over first year
fesom_mesh_path_lr = '/short/y99/kaa561/FESOM/mesh/meshA/'
fesom_mesh_path_hr = '/short/y99/kaa561/FESOM/mesh/meshB/'
fesom_file_lr = '/short/y99/kaa561/FESOM/intercomparison_lowres/output/MK44005.1992.forcing.diag.nc'
fesom_file_hr = '/short/y99/kaa561/FESOM/intercomparison_highres/output/MK44005.1992.forcing.diag.nc'
# Degrees to radians conversion factor
deg2rad = pi / 180.0
# Northern boundaries for plots
nbdry_acc = -30 + 90
nbdry_shelf = -64 + 90
# Bounds for colour scale
colour_bound_acc = 0.25
colour_bound_shelf = 0.25
print 'Processing ROMS'
# Read grid
id = Dataset(roms_grid, 'r')
roms_lat = id.variables['lat_rho'][:, :]
roms_lon = id.variables['lon_rho'][:, :]
angle = id.variables['angle'][:, :]
zice = id.variables['zice'][:, :]
id.close()
# Read surface stress
id = Dataset(roms_file, 'r')
sustr_tmp = id.variables['sustr'][0, :, :]
svstr_tmp = id.variables['svstr'][0, :, :]
id.close()
# Unrotate
sustr, svstr = rotate_vector_roms(sustr_tmp, svstr_tmp, angle)
# Get magnitude
roms_stress = sqrt(sustr**2 + svstr**2)
# Mask cavities
roms_stress = ma.masked_where(zice < 0, roms_stress)
# Calculate polar projection
roms_x = -(roms_lat + 90) * cos(roms_lon * deg2rad + pi / 2)
roms_y = (roms_lat + 90) * sin(roms_lon * deg2rad + pi / 2)
print 'Processing low-res FESOM'
# Build mesh and patches
elements_lr, patches_lr = make_patches(fesom_mesh_path_lr,
circumpolar=True,
mask_cavities=True)
# Read rotated and and lon
f = open(fesom_mesh_path_lr + 'nod2d.out', 'r')
f.readline()
rlon_lr = []
rlat_lr = []
for line in f:
tmp = line.split()
lon_tmp = float(tmp[1])
if lon_tmp < -180:
lon_tmp += 360
elif lon_tmp > 180:
lon_tmp -= 360
rlon_lr.append(lon_tmp)
rlat_lr.append(float(tmp[2]))
f.close()
rlon_lr = array(rlon_lr)
rlat_lr = array(rlat_lr)
# Read surface stress
id = Dataset(fesom_file_lr, 'r')
stress_x_tmp = mean(id.variables['stress_x'][:, :], axis=0)
stress_y_tmp = mean(id.variables['stress_y'][:, :], axis=0)
id.close()
# Unrotate
stress_x_lr, stress_y_lr = unrotate_vector(rlon_lr, rlat_lr, stress_x_tmp,
stress_y_tmp)
# Get magnitude
fesom_stress_lr_nodes = sqrt(stress_x_lr**2 + stress_y_lr**2)
# Average over elements
fesom_stress_lr = []
for elm in elements_lr:
if not elm.cavity:
fesom_stress_lr.append(
mean([
fesom_stress_lr_nodes[elm.nodes[0].id],
fesom_stress_lr_nodes[elm.nodes[1].id],
fesom_stress_lr_nodes[elm.nodes[2].id]
]))
print 'Processing high-res FESOM'
elements_hr, patches_hr = make_patches(fesom_mesh_path_hr,
circumpolar=True,
mask_cavities=True)
f = open(fesom_mesh_path_hr + 'nod2d.out', 'r')
f.readline()
rlon_hr = []
rlat_hr = []
for line in f:
tmp = line.split()
lon_tmp = float(tmp[1])
if lon_tmp < -180:
lon_tmp += 360
elif lon_tmp > 180:
lon_tmp -= 360
rlon_hr.append(lon_tmp)
rlat_hr.append(float(tmp[2]))
f.close()
rlon_hr = array(rlon_hr)
rlat_hr = array(rlat_hr)
id = Dataset(fesom_file_hr, 'r')
stress_x_tmp = mean(id.variables['stress_x'][:, :], axis=0)
stress_y_tmp = mean(id.variables['stress_y'][:, :], axis=0)
id.close()
stress_x_hr, stress_y_hr = unrotate_vector(rlon_hr, rlat_hr, stress_x_tmp,
stress_y_tmp)
fesom_stress_hr_nodes = sqrt(stress_x_hr**2 + stress_y_hr**2)
fesom_stress_hr = []
for elm in elements_hr:
if not elm.cavity:
fesom_stress_hr.append(
mean([
fesom_stress_hr_nodes[elm.nodes[0].id],
fesom_stress_hr_nodes[elm.nodes[1].id],
fesom_stress_hr_nodes[elm.nodes[2].id]
]))
print 'Plotting'
# ACC
fig = figure(figsize=(19, 8))
fig.patch.set_facecolor('white')
gs = GridSpec(1, 3)
gs.update(left=0.05, right=0.95, bottom=0.1, top=0.85, wspace=0.05)
# ROMS
ax = subplot(gs[0, 0], aspect='equal')
ax.pcolor(roms_x,
roms_y,
roms_stress,
vmin=0,
vmax=colour_bound_acc,
cmap='jet')
xlim([-nbdry_acc, nbdry_acc])
ylim([-nbdry_acc, nbdry_acc])
title('a) MetROMS', fontsize=28)
ax.set_xticks([])
ax.set_yticks([])
# FESOM (low-res)
ax = subplot(gs[0, 1], aspect='equal')
img = PatchCollection(patches_lr, cmap='jet')
img.set_array(array(fesom_stress_lr))
img.set_clim(vmin=0, vmax=colour_bound_acc)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([-nbdry_acc, nbdry_acc])
ylim([-nbdry_acc, nbdry_acc])
title('b) FESOM (low-res)', fontsize=28)
ax.set_xticks([])
ax.set_yticks([])
# FESOM (high-res)
ax = subplot(gs[0, 2], aspect='equal')
img = PatchCollection(patches_hr, cmap='jet')
img.set_array(array(fesom_stress_hr))
img.set_clim(vmin=0, vmax=colour_bound_acc)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([-nbdry_acc, nbdry_acc])
ylim([-nbdry_acc, nbdry_acc])
title('c) FESOM (high-res)', fontsize=28)
ax.set_xticks([])
ax.set_yticks([])
# Add a horizontal colourbar on the bottom
cbaxes = fig.add_axes([0.3, 0.05, 0.4, 0.04])
cbar = colorbar(img,
orientation='horizontal',
cax=cbaxes,
extend='max',
ticks=arange(0, colour_bound_acc + 0.05, 0.05))
cbar.ax.tick_params(labelsize=20)
# Main title
suptitle(r'Ocean surface stress (N/m$^2$), 1992 mean', fontsize=34)
fig.show()
fig.savefig('sfc_stress_acc.png')
# Continental shelf
fig = figure(figsize=(19, 8))
fig.patch.set_facecolor('white')
gs = GridSpec(1, 3)
gs.update(left=0.05, right=0.95, bottom=0.1, top=0.85, wspace=0.05)
# ROMS
ax = subplot(gs[0, 0], aspect='equal')
ax.pcolor(roms_x,
roms_y,
roms_stress,
vmin=0,
vmax=colour_bound_shelf,
cmap='jet')
xlim([-nbdry_shelf, nbdry_shelf])
ylim([-nbdry_shelf, nbdry_shelf])
title('a) MetROMS', fontsize=28)
ax.set_xticks([])
ax.set_yticks([])
# FESOM (low-res)
ax = subplot(gs[0, 1], aspect='equal')
img = PatchCollection(patches_lr, cmap='jet')
img.set_array(array(fesom_stress_lr))
img.set_clim(vmin=0, vmax=colour_bound_shelf)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([-nbdry_shelf, nbdry_shelf])
ylim([-nbdry_shelf, nbdry_shelf])
title('b) FESOM (low-res)', fontsize=28)
ax.set_xticks([])
ax.set_yticks([])
# FESOM (high-res)
ax = subplot(gs[0, 2], aspect='equal')
img = PatchCollection(patches_hr, cmap='jet')
img.set_array(array(fesom_stress_hr))
img.set_clim(vmin=0, vmax=colour_bound_shelf)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([-nbdry_shelf, nbdry_shelf])
ylim([-nbdry_shelf, nbdry_shelf])
title('c) FESOM (high-res)', fontsize=28)
ax.set_xticks([])
ax.set_yticks([])
# Add a horizontal colourbar on the bottom
cbaxes = fig.add_axes([0.3, 0.05, 0.4, 0.04])
cbar = colorbar(img,
orientation='horizontal',
cax=cbaxes,
extend='max',
ticks=arange(0, colour_bound_shelf + 0.05, 0.05))
cbar.ax.tick_params(labelsize=20)
# Main title
suptitle(r'Ocean surface stress (N/m$^2$), 1992 mean', fontsize=34)
fig.show()
fig.savefig('sfc_stress_shelf.png')
def holland_fig1(grid_path, file_path):
deg2rad = pi / 180.0
# Read grid
id = Dataset(grid_path, 'r')
lon = id.variables['lon_rho'][:-15, 1:]
lat = id.variables['lat_rho'][:-15, 1:]
h = id.variables['h'][:-15, 1:]
zice = id.variables['zice'][:-15, 1:]
angle = id.variables['angle'][:-15, :]
id.close()
# Set up figure
x = -(lat + 90) * cos(lon * deg2rad + pi / 2)
y = (lat + 90) * sin(lon * deg2rad + pi / 2)
fig = figure(figsize=(16, 12))
# Barotropic streamfunction
# First read bartropic velocity vector
id = Dataset(file_path, 'r')
ubar_xy = mean(id.variables['ubar'][:, :-15, :], axis=0)
vbar_xy = mean(id.variables['vbar'][:, :-15, :], axis=0)
id.close()
# Rotate to lon-lat space
ubar, vbar = rotate_vector_roms(ubar_xy, vbar_xy, angle)
# Throw away the overlapping periodic boundary
ubar = ubar[:, 1:]
# Mask ice shelves
ubar = ma.masked_where(zice != 0, ubar)
# Water column thickness
wct = h + zice
# Horizontal differentials
dx, dy = cartesian_grid_2d(lon, lat)
# Indefinite integral from south to north of u*dz*dy, convert to Sv
baro_strf = cumsum(ubar * wct * dy, axis=0) * 1e-6
# Colour levels
lev1 = arange(-50, 150 + 10, 10)
# Plot
ax1 = fig.add_subplot(2, 2, 1, aspect='equal')
img = contourf(x, y, baro_strf, lev1, extend='both')
# Contour 0 Sv in black
contour(x, y, baro_strf, levels=[0], colors=('black'))
title('Barotropic streamfunction (Sv)', fontsize=24)
xlim([-35, 39])
ylim([-35, 39])
axis('off')
cbaxes1 = fig.add_axes([0.07, 0.6, 0.02, 0.3])
cbar1 = colorbar(img, ticks=arange(-50, 150 + 50, 50), cax=cbaxes1)
cbar1.ax.tick_params(labelsize=16)
# JJA mixed layer depth
start_month = 6 # Start in June
end_month = 8 # End in August
start_day = 1 # First day in June
next_startday = 1 # First day in September
end_day = 31 # Last day in August
prev_endday = 31 # Last day in May
ndays_season = 92 # Number of days in June+July+August
id = Dataset(file_path, 'r')
# Read time axis and get dates
time_id = id.variables['ocean_time']
time = num2date(time_id[:],
units=time_id.units,
calendar=time_id.calendar.lower())
# Find the last timestep we care about
end_t = -1 # Missing value flag
for t in range(size(time) - 1, -1, -1):
if time[t].month == end_month and time[t].day in range(
end_day - 2, end_day + 1):
end_t = t
break
if time[t].month == end_month + 1 and time[t].day in range(
next_startday, next_startday + 2):
end_t = t
break
# Make sure we actually found it
if end_t == -1:
print 'Error: ' + file_path + ' does not contain a complete JJA'
return
# Find the first timestep we care about
start_t = -1 # Missing value flag
for t in range(end_t, -1, -1):
if time[t].month == start_month - 1 and time[t].day in range(
prev_endday - 1, prev_endday + 1):
start_t = t
break
if time[t].month == start_month and time[t].day in range(
start_day, start_day + 3):
start_t = t
break
# Make sure we found it
if start_t == -1:
print 'Error: ' + file_path + ' does not contain a complete JJA'
return
# Initialise time-averaged KPP boundary layer depth
hsbl = ma.empty(shape(lon))
hsbl[:, :] = 0.0
ndays = 0
# Figure out how many of the 5 days represented in start_t we care about
if time[start_t].month == start_month and time[
start_t].day == start_day + 2:
start_days = 5
elif time[start_t].month == start_month and time[
start_t].day == start_day + 1:
start_days = 4
elif time[start_t].month == start_month and time[start_t].day == start_day:
start_days = 3
elif time[start_t].month == start_month - 1 and time[
start_t].day == prev_endday:
start_days = 2
elif time[start_t].month == start_month - 1 and time[
start_t].day == prev_endday - 1:
start_days = 1
else:
print 'Error: starting index is month ' + str(
time[start_t].month) + ', day ' + str(time[start_t].day)
return
# Integrate Hsbl weighted by start_days
hsbl += id.variables['Hsbl'][start_t, :-15, 1:] * start_days
ndays += start_days
# Between start_t and end_t, we care about all the days
for t in range(start_t + 1, end_t):
hsbl += id.variables['Hsbl'][t, :-15, 1:] * 5
ndays += 5
# Figure out how many of the 5 days represented in end_t we care about
if time[end_t].month == end_month + 1 and time[
end_t].day == next_startday + 1:
end_days = 1
elif time[end_t].month == end_month + 1 and time[
end_t].day == next_startday:
end_days = 2
elif time[end_t].month == end_month and time[end_t].day == end_day:
end_days = 3
elif time[end_t].month == end_month and time[end_t].day == end_day - 1:
end_days = 4
elif time[end_t].month == end_month and time[end_t].day == end_day - 2:
end_days = 5
else:
print 'Error: ending index is month ' + str(
time[end_t].month) + ', day ' + str(time[end_t].day)
return
# Integrate weighted by end_days
hsbl += id.variables['Hsbl'][end_t, :-15, 1:] * end_days
ndays += end_days
if ndays != ndays_season:
print 'Error: found ' + str(ndays) + ' days instead of ' + str(
ndays_season)
return
id.close()
# Convert from integral to average
hsbl[:, :] = hsbl[:, :] / ndays
# Mask out ice shelves, change sign, and call it mixed layer depth
mld = ma.masked_where(zice != 0, -hsbl)
# Colour levels
lev2 = arange(0, 300 + 25, 25)
# Plot
ax2 = fig.add_subplot(2, 2, 2, aspect='equal')
img = contourf(x, y, mld, lev2, extend='both')
# Contour 100 m in black
contour(x, y, mld, levels=[100], colors=('black'))
title('Winter mixed layer depth (m)', fontsize=24)
xlim([-35, 39])
ylim([-35, 39])
axis('off')
cbaxes2 = fig.add_axes([0.9, 0.6, 0.02, 0.3])
cbar2 = colorbar(img, ticks=arange(0, 300 + 100, 100), cax=cbaxes2)
cbar2.ax.tick_params(labelsize=16)
# Bottom water temperature
id = Dataset(file_path, 'r')
bwtemp = mean(id.variables['temp'][:, 0, :-15, 1:], axis=0)
id.close()
# Mask ice shelves
bwtemp = ma.masked_where(zice != 0, bwtemp)
# Colour levels
lev3 = arange(-2, 2 + 0.2, 0.2)
# Plot
ax3 = fig.add_subplot(2, 2, 3, aspect='equal')
img = contourf(x, y, bwtemp, lev3, extend='both')
# Contour 0C in black
contour(x, y, bwtemp, levels=[0], colors=('black'))
title(r'Bottom temperature ($^{\circ}$C', fontsize=24)
xlim([-35, 39])
ylim([-35, 39])
axis('off')
cbaxes3 = fig.add_axes([0.07, 0.1, 0.02, 0.3])
cbar3 = colorbar(img, ticks=arange(-2, 2 + 1, 1), cax=cbaxes3)
cbar3.ax.tick_params(labelsize=16)
# Bottom water salinity
id = Dataset(file_path, 'r')
bwsalt = mean(id.variables['salt'][:, 0, :-15, 1:], axis=0)
bwsalt = ma.masked_where(zice != 0, bwsalt)
id.close()
lev4 = arange(34.5, 34.8 + 0.025, 0.025)
ax4 = fig.add_subplot(2, 2, 4, aspect='equal')
img = contourf(x, y, bwsalt, lev4, extend='both')
# Contour 34.65 psu in black
contour(x, y, bwsalt, levels=[34.65], colors=('black'))
title('Bottom salinity (psu)', fontsize=24)
xlim([-35, 39])
ylim([-35, 39])
axis('off')
cbaxes4 = fig.add_axes([0.9, 0.1, 0.02, 0.3])
cbar4 = colorbar(img, ticks=arange(34.5, 34.8 + 0.1, 0.1), cax=cbaxes4)
cbar4.ax.tick_params(labelsize=16)
fig.show()
def zonal_plot(file_path,
var_name,
tstep,
lon_key,
lon0,
lon_bounds,
depth_min,
colour_bounds=None,
save=False,
fig_name=None,
grid_path=None):
# Grid parameters
theta_s = 7.0
theta_b = 2.0
hc = 250
N = 31
if var_name in ['w', 'AKv', 'AKt', 'AKs']:
N = 32
# Read the variable
id = Dataset(file_path, 'r')
data_3d = id.variables[var_name][tstep - 1, :, :-15, :]
# Also read sea surface height
zeta = id.variables['zeta'][tstep - 1, :-15, :]
if var_name == 'salt':
units = 'psu'
else:
units = id.variables[var_name].units
long_name = id.variables[var_name].long_name
# Rotate velocity if necessary
if var_name in ['u', 'v']:
grid_id = Dataset(grid_path, 'r')
angle = grid_id.variables['angle'][:-15, :]
grid_id.close()
if var_name == 'u':
data_3d_ugrid = data_3d[:, :, :]
data_3d = ma.empty([
data_3d_ugrid.shape[0], data_3d_ugrid.shape[1],
data_3d_ugrid.shape[2] + 1
])
for k in range(N):
u_data = data_3d_ugrid[k, :, :]
v_data = id.variables['v'][tstep - 1, k, :-15, :]
u_data_lonlat, v_data_lonlat = rotate_vector_roms(
u_data, v_data, angle)
data_3d[k, :, :] = u_data_lonlat
elif var_name == 'v':
data_3d_vgrid = data_3d[:, :, :]
data_3d = ma.empty([
data_3d_vgrid.shape[0], data_3d_vgrid.shape[1] + 1,
data_3d_vgrid.shape[2]
])
for k in range(N):
v_data = data_3d_vgrid[k, :, :]
u_data = id.variables['u'][tstep - 1, k, :-15, :]
u_data_lonlat, v_data_lonlat = rotate_vector_roms(
u_data, v_data, angle)
data_3d[k, :, :] = v_data_lonlat
# Read grid variables
h = id.variables['h'][:-15, :]
zice = id.variables['zice'][:-15, :]
lon_2d = id.variables['lon_rho'][:-15, :]
lat_2d = id.variables['lat_rho'][:-15, :]
id.close()
# Get a 3D array of z-coordinates; sc_r and Cs_r are unused in this script
z_3d, sc_r, Cs_r = calc_z(h, zice, theta_s, theta_b, hc, N, zeta)
# Warning message for zonal averages
if lon_key != 0:
print 'WARNING: this script assumes regular i-indices for zonal averages. In heavily rotated regions eg inner Weddell Sea it may not be appropriate.'
# Choose what to write on the title about longitude
if lon_key == 0:
if lon0 < 0:
lon_string = 'at ' + str(int(round(-lon0))) + r'$^{\circ}$W'
else:
lon_string = 'at ' + str(int(round(lon0))) + r'$^{\circ}$E'
elif lon_key == 1:
lon_string = 'zonally averaged'
elif lon_key == 2:
lon_string = 'zonally averaged between '
if lon_bounds[0] < 0:
lon_string += str(int(round(-lon_bounds[0]))) + r'$^{\circ}$W and '
else:
lon_string += str(int(round(lon_bounds[0]))) + r'$^{\circ}$E and '
if lon_bounds[1] < 0:
lon_string += str(int(round(-lon_bounds[1]))) + r'$^{\circ}$W'
else:
lon_string += str(int(round(lon_bounds[1]))) + r'$^{\circ}$E'
# Edit longitude bounds to be from 0 to 360, to fit with ROMS convention
if lon_key == 0:
if lon0 < 0:
lon0 += 360
elif lon_key == 2:
if lon_bounds[0] < 0:
lon_bounds[0] += 360
if lon_bounds[1] < 0:
lon_bounds[1] += 360
# Interpolate or average data
if lon_key == 0:
# Interpolate to lon0
data, z, lat = interp_lon_roms(data_3d, z_3d, lat_2d, lon_2d, lon0)
elif lon_key == 1:
# Zonally average over all longitudes
# dlon is constant on this grid (0.25 degrees) so this is easy
data = mean(data_3d, axis=2)
z = mean(z_3d, axis=2)
# Zonally average latitude, and copy into N depth levels
lat = tile(mean(lat_2d, axis=1), (N, 1))
elif lon_key == 2:
# Zonally average between lon_bounds
data, z, lat = average_btw_lons(data_3d, z_3d, lat_2d, lon_2d,
lon_bounds)
if colour_bounds is not None:
# User has set bounds on colour scale
lev = linspace(colour_bounds[0], colour_bounds[1], num=40)
if colour_bounds[0] == -colour_bounds[1]:
# Bounds are centered on zero, so choose a blue-to-red colourmap
# centered on yellow
colour_map = 'RdYlBu_r'
else:
colour_map = 'jet'
else:
# Determine bounds automatically
if var_name in ['u', 'v']:
# Center levels on 0 for certain variables, with a blue-to-red
# colourmap
max_val = amax(abs(data))
lev = linspace(-max_val, max_val, num=40)
colour_map = 'RdYlBu_r'
else:
lev = linspace(amin(data), amax(data), num=40)
colour_map = 'jet'
# Plot
fig = figure(figsize=(18, 6))
contourf(lat, z, data, lev, cmap=colour_map, extend='both')
colorbar()
title(long_name + ' (' + units + ')\n' + lon_string)
xlabel('Latitude')
ylabel('Depth (m)')
# Choose latitude bounds based on land mask
data_sum = sum(data, axis=0)
# Find southernmost and northernmost unmasked j-indices
edges = ma.flatnotmasked_edges(data_sum)
j_min = edges[0]
j_max = edges[1]
if j_min == 0:
# There are ocean points right to the southern boundary
# Don't do anything special
lat_min = min(lat[:, j_min])
else:
# There is land everywhere at the southern boundary
# Show the last 2 degrees of this land mask
lat_min = min(lat[:, j_min]) - 2
if j_max == size(data_sum) - 1:
# There are ocean points right to the northern boundary
# Don't do anything special
lat_max = max(lat[:, j_max])
else:
# There is land everywhere at the northern boundary
# Show the first 2 degrees of this land mask
lat_max = max(lat[:, j_max]) + 2
# lat_max = -50
xlim([lat_min, lat_max])
ylim([depth_min, 0])
if save:
fig.savefig(fig_name)
else:
fig.show()
# Reset lon0 or lon_bounds to (-180, 180) range in case we
# use them again for the next plot
if lon_key == 0:
if lon0 > 180:
lon0 -= 360
elif lon_key == 2:
if lon_bounds[0] > 180:
lon_bounds[0] -= 360
if lon_bounds[1] > 180:
lon_bounds[1] -= 360
def mip_regions_1var ():
# Path to ROMS grid file
roms_grid = '/short/m68/kaa561/metroms_iceshelf/apps/common/grid/circ30S_quarterdegree.nc'
# Path to ROMS time-averaged file
roms_file = '/short/m68/kaa561/metroms_iceshelf/tmproms/run/intercomparison/2002_2016_avg.nc'
# Path to FESOM mesh directories
fesom_mesh_path_lr = '/short/y99/kaa561/FESOM/mesh/meshA/'
fesom_mesh_path_hr = '/short/y99/kaa561/FESOM/mesh/meshB/'
# Path to FESOM time-averaged ocean files (temp, salt, u, v)
fesom_file_lr_o = '/short/y99/kaa561/FESOM/intercomparison_lowres/output/oce_2002_2016_avg.nc'
fesom_file_hr_o = '/short/y99/kaa561/FESOM/intercomparison_highres/output/oce_2002_2016_avg.nc'
# Path to FESOM time-averaged ice shelf files (wnet)
fesom_file_lr_i = '/short/y99/kaa561/FESOM/intercomparison_lowres/output/wnet_2002_2016_avg.nc'
fesom_file_hr_i = '/short/y99/kaa561/FESOM/intercomparison_highres/output/wnet_2002_2016_avg.nc'
# Name of each region
region_names = ['Filchner-Ronne Ice Shelf', 'Eastern Weddell Region', 'Amery Ice Shelf', 'Australian Sector', 'Ross Sea', 'Amundsen Sea', 'Bellingshausen Sea', 'Larsen Ice Shelves']
num_regions = len(region_names)
# Beginning of filenames for figures
fig_heads = ['filchner_ronne', 'eweddell', 'amery', 'australian', 'ross', 'amundsen', 'bellingshausen', 'larsen']
# Bounds for each region (using polar coordinate transformation as below)
x_min = [-14, -8, 15.25, 12, -9.5, -15.5, -20.25, -22.5]
x_max = [-4.5, 13, 20.5, 25.5, 4, -10.5, -15.5, -14.5]
y_min = [1, 12, 4.75, -20, -13, -11.25, -4.5, 8.3]
y_max = [10, 21, 8, 4, -4.75, -2.25, 7.6, 13]
# Size of each plot in the y direction
ysize = [8, 6, 7, 9, 7, 9, 10, 7]
# Variables to process
var_names = ['vel'] #['bathy', 'draft', 'wct', 'melt', 'temp', 'salt', 'vel']
# Constants
sec_per_year = 365*24*3600
deg2rad = pi/180.0
# Parameters for missing circle in ROMS grid
lon_c = 50
lat_c = -83
radius = 10.1
nbdry = -63+90
# ROMS vertical grid parameters
theta_s = 7.0
theta_b = 2.0
hc = 250
N = 31
# Number of bins in each direction for vector overlay
num_bins = 30
print 'Reading ROMS grid'
# Read the fields we need
id = Dataset(roms_grid, 'r')
roms_lon = id.variables['lon_rho'][:,:]
roms_lat = id.variables['lat_rho'][:,:]
roms_h = id.variables['h'][:,:]
roms_mask = id.variables['mask_rho'][:,:]
roms_zice = id.variables['zice'][:,:]
roms_angle = id.variables['angle'][:,:]
id.close()
# Get land/zice mask
open_ocn = copy(roms_mask)
open_ocn[roms_zice!=0] = 0
land_zice = ma.masked_where(open_ocn==1, open_ocn)
# Convert grid to spherical coordinates
roms_x = -(roms_lat+90)*cos(roms_lon*deg2rad+pi/2)
roms_y = (roms_lat+90)*sin(roms_lon*deg2rad+pi/2)
# Find centre in spherical coordinates
x_c = -(lat_c+90)*cos(lon_c*deg2rad+pi/2)
y_c = (lat_c+90)*sin(lon_c*deg2rad+pi/2)
# Build a regular x-y grid and select the missing circle
x_reg_roms, y_reg_roms = meshgrid(linspace(-nbdry, nbdry, num=1000), linspace(-nbdry, nbdry, num=1000))
land_circle = zeros(shape(x_reg_roms))
land_circle = ma.masked_where(sqrt((x_reg_roms-x_c)**2 + (y_reg_roms-y_c)**2) > radius, land_circle)
print 'Building FESOM low-res mesh'
elements_lr = fesom_grid(fesom_mesh_path_lr, circumpolar=True)
# Make patches for all elements, ice shelf elements, and open ocean elements
patches_lr = []
patches_shelf_lr = []
patches_ocn_lr = []
for elm in elements_lr:
coord = transpose(vstack((elm.x, elm.y)))
patches_lr.append(Polygon(coord, True, linewidth=0.))
if elm.cavity:
patches_shelf_lr.append(Polygon(coord, True, linewidth=0.))
else:
patches_ocn_lr.append(Polygon(coord, True, linewidth=0.))
print 'Building FESOM high-res mesh'
elements_hr = fesom_grid(fesom_mesh_path_hr, circumpolar=True)
patches_hr = []
patches_shelf_hr = []
patches_ocn_hr = []
for elm in elements_hr:
coord = transpose(vstack((elm.x, elm.y)))
patches_hr.append(Polygon(coord, True, linewidth=0.))
if elm.cavity:
patches_shelf_hr.append(Polygon(coord, True, linewidth=0.))
else:
patches_ocn_hr.append(Polygon(coord, True, linewidth=0.))
for var in var_names:
print 'Processing variable ' + var
print 'Reading ROMS fields'
if var == 'draft':
# Swap sign on existing zice field; nothing more to read
roms_data = -1*roms_zice
elif var == 'bathy':
# Point to h field and mask out land mask; nothing more to read
roms_data = ma.masked_where(roms_mask==0, roms_h)
elif var == 'wct':
# Add h (positive) and zice (negative); nothing more to read
roms_data = roms_h + roms_zice
else:
id = Dataset(roms_file, 'r')
if var == 'melt':
# Convert from m/s to m/y
roms_data = id.variables['m'][0,:,:]*sec_per_year
elif var in ['temp', 'salt']:
# Bottom layer
roms_data = id.variables[var][0,0,:,:]
elif var == 'vel':
# Read full 3D u and v
u_3d_tmp = id.variables['u'][0,:,:,:]
v_3d_tmp = id.variables['v'][0,:,:,:]
# Get integrands on 3D grid; we only care about dz
dx, dy, dz, z = cartesian_grid_3d(roms_lon, roms_lat, roms_h, roms_zice, theta_s, theta_b, hc, N)
# Unrotate each vertical level
u_3d = ma.empty(shape(dz))
v_3d = ma.empty(shape(dz))
num_lat_u = size(u_3d_tmp,1)
num_lon_u = size(u_3d_tmp,2)
num_lat_v = size(v_3d_tmp,1)
num_lon_v = size(v_3d_tmp,2)
for k in range(N):
# Extend into land mask before interpolation to rho-grid so
# the land mask doesn't change in the final plot
for j in range(1,num_lat_u-1):
for i in range(1,num_lon_u-1):
# Check for masked points
if u_3d_tmp[k,j,i] is ma.masked:
# Look at 4 neighbours
neighbours = ma.array([u_3d_tmp[k,j-1,i], u_3d_tmp[k,j,i-1], u_3d_tmp[k,j+1,i], u_3d_tmp[k,j,i+1]])
# Find how many of them are unmasked
num_unmasked = MaskedArray.count(neighbours)
if num_unmasked > 0:
# There is at least one unmasked neighbour;
# set u_3d_tmp to their average
u_3d_tmp[k,j,i] = sum(neighbours)/num_unmasked
# Repeat for v
for j in range(1,num_lat_v-1):
for i in range(1,num_lon_v-1):
if v_3d_tmp[k,j,i] is ma.masked:
neighbours = ma.array([v_3d_tmp[k,j-1,i], v_3d_tmp[k,j,i-1], v_3d_tmp[k,j+1,i], v_3d_tmp[k,j,i+1]])
num_unmasked = MaskedArray.count(neighbours)
if num_unmasked > 0:
v_3d_tmp[k,j,i] = sum(neighbours)/num_unmasked
# Interpolate to rho grid and rotate
u_k, v_k = rotate_vector_roms(u_3d_tmp[k,:,:], v_3d_tmp[k,:,:], roms_angle)
u_3d[k,:,:] = u_k
v_3d[k,:,:] = v_k
# Vertically average u and v
u_rho = sum(u_3d*dz, axis=0)/sum(dz, axis=0)
v_rho = sum(v_3d*dz, axis=0)/sum(dz, axis=0)
# Get speed
roms_data = sqrt(u_rho**2 + v_rho**2)
# Mask out land
u_rho = ma.masked_where(roms_mask==0, u_rho)
v_rho = ma.masked_where(roms_mask==0, v_rho)
roms_data = ma.masked_where(roms_mask==0, roms_data)
id.close()
if var in ['draft', 'melt', 'wct']:
# Mask out open ocean
roms_data = ma.masked_where(roms_zice==0, roms_data)
print 'Reading FESOM low-res fields'
if var not in ['draft', 'bathy', 'wct']:
if var == 'melt':
id = Dataset(fesom_file_lr_i, 'r')
# Convert from m/s to m/y
node_data_lr = id.variables['wnet'][0,:]*sec_per_year
elif var in ['temp', 'salt']:
id = Dataset(fesom_file_lr_o, 'r')
# Read full 3D field for now
node_data_lr = id.variables[var][0,:]
elif var == 'vel':
id = Dataset(fesom_file_lr_o, 'r')
# The overlaid vectors are based on nodes not elements, so many
# of the fesom_grid data structures fail to apply and we need to
# read some of the FESOM grid files again.
# Read the cavity flag for each 2D surface node
fesom_cavity_lr = []
f = open(fesom_mesh_path_lr + 'cavity_flag_nod2d.out', 'r')
for line in f:
tmp = int(line)
if tmp == 1:
fesom_cavity_lr.append(True)
elif tmp == 0:
fesom_cavity_lr.append(False)
else:
print 'Problem'
#return
f.close()
# Save the number of 2D nodes
fesom_n2d_lr = len(fesom_cavity_lr)
# Read rotated lat and lon for each node, also depth
f = open(fesom_mesh_path_lr + 'nod3d.out', 'r')
f.readline()
rlon_lr = []
rlat_lr = []
node_depth_lr = []
for line in f:
tmp = line.split()
lon_tmp = float(tmp[1])
lat_tmp = float(tmp[2])
node_depth_tmp = -1*float(tmp[3])
if lon_tmp < -180:
lon_tmp += 360
elif lon_tmp > 180:
lon_tmp -= 360
rlon_lr.append(lon_tmp)
rlat_lr.append(lat_tmp)
node_depth_lr.append(node_depth_tmp)
f.close()
# For lat and lon, only care about the 2D nodes (the first
# fesom_n2d indices)
rlon_lr = array(rlon_lr[0:fesom_n2d_lr])
rlat_lr = array(rlat_lr[0:fesom_n2d_lr])
node_depth_lr = array(node_depth_lr)
# Unrotate longitude
fesom_lon_lr, fesom_lat_lr = unrotate_grid(rlon_lr, rlat_lr)
# Calculate polar coordinates of each node
fesom_x_lr = -(fesom_lat_lr+90)*cos(fesom_lon_lr*deg2rad+pi/2)
fesom_y_lr = (fesom_lat_lr+90)*sin(fesom_lon_lr*deg2rad+pi/2)
# Read lists of which nodes are directly below which
f = open(fesom_mesh_path_lr + 'aux3d.out', 'r')
max_num_layers_lr = int(f.readline())
node_columns_lr = zeros([fesom_n2d_lr, max_num_layers_lr])
for n in range(fesom_n2d_lr):
for k in range(max_num_layers_lr):
node_columns_lr[n,k] = int(f.readline())
node_columns_lr = node_columns_lr.astype(int)
f.close()
# Now we can actually read the data
# Read full 3D field for both u and v
node_ur_3d_lr = id.variables['u'][0,:]
node_vr_3d_lr = id.variables['v'][0,:]
# Vertically average
node_ur_lr = zeros(fesom_n2d_lr)
node_vr_lr = zeros(fesom_n2d_lr)
for n in range(fesom_n2d_lr):
# Integrate udz, vdz, and dz over this water column
udz_col = 0
vdz_col = 0
dz_col = 0
for k in range(max_num_layers_lr-1):
if node_columns_lr[n,k+1] == -999:
# Reached the bottom
break
# Trapezoidal rule
top_id = node_columns_lr[n,k]
bot_id = node_columns_lr[n,k+1]
dz_tmp = node_depth_lr[bot_id-1] - node_depth_lr[top_id-1]
udz_col += 0.5*(node_ur_3d_lr[top_id-1]+node_ur_3d_lr[bot_id-1])*dz_tmp
vdz_col += 0.5*(node_vr_3d_lr[top_id-1]+node_vr_3d_lr[bot_id-1])*dz_tmp
dz_col += dz_tmp
# Convert from integrals to averages
node_ur_lr[n] = udz_col/dz_col
node_vr_lr[n] = vdz_col/dz_col
# Unrotate
node_u_lr, node_v_lr = unrotate_vector(rlon_lr, rlat_lr, node_ur_lr, node_vr_lr)
# Calculate speed
node_data_lr = sqrt(node_u_lr**2 + node_v_lr**2)
id.close()
# Calculate given field at each element
fesom_data_lr = []
for elm in elements_lr:
# For each element, append the mean value for the 3 component Nodes
# Restrict to ice shelf cavities for draft, melt, wct
if elm.cavity or var not in ['draft', 'melt', 'wct']:
if var == 'draft':
# Ice shelf draft is depth of surface layer
fesom_data_lr.append(mean([elm.nodes[0].depth, elm.nodes[1].depth, elm.nodes[2].depth]))
elif var == 'bathy':
# Bathymetry is depth of bottom layer
fesom_data_lr.append(mean([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth]))
elif var == 'wct':
# Water column thickness is depth of bottom layer minus
# depth of surface layer
fesom_data_lr.append(mean([elm.nodes[0].find_bottom().depth - elm.nodes[0].depth, elm.nodes[1].find_bottom().depth - elm.nodes[1].depth, elm.nodes[2].find_bottom().depth - elm.nodes[2].depth]))
elif var in ['melt', 'vel']:
# Surface nodes
fesom_data_lr.append(mean([node_data_lr[elm.nodes[0].id], node_data_lr[elm.nodes[1].id], node_data_lr[elm.nodes[2].id]]))
elif var in ['temp', 'salt']:
# Bottom nodes
fesom_data_lr.append(mean([node_data_lr[elm.nodes[0].find_bottom().id], node_data_lr[elm.nodes[1].find_bottom().id], node_data_lr[elm.nodes[2].find_bottom().id]]))
print 'Reading FESOM high-res fields'
# As before
if var not in ['draft', 'bathy', 'wct']:
if var == 'melt':
id = Dataset(fesom_file_hr_i, 'r')
node_data_hr = id.variables['wnet'][0,:]*sec_per_year
elif var in ['temp', 'salt']:
id = Dataset(fesom_file_hr_o, 'r')
node_data_hr = id.variables[var][0,:]
elif var == 'vel':
id = Dataset(fesom_file_hr_o, 'r')
fesom_cavity_hr = []
f = open(fesom_mesh_path_hr + 'cavity_flag_nod2d.out', 'r')
for line in f:
tmp = int(line)
if tmp == 1:
fesom_cavity_hr.append(True)
elif tmp == 0:
fesom_cavity_hr.append(False)
else:
print 'Problem'
#return
f.close()
fesom_n2d_hr = len(fesom_cavity_hr)
f = open(fesom_mesh_path_hr + 'nod3d.out', 'r')
f.readline()
rlon_hr = []
rlat_hr = []
node_depth_hr = []
for line in f:
tmp = line.split()
lon_tmp = float(tmp[1])
lat_tmp = float(tmp[2])
node_depth_tmp = -1*float(tmp[3])
if lon_tmp < -180:
lon_tmp += 360
elif lon_tmp > 180:
lon_tmp -= 360
rlon_hr.append(lon_tmp)
rlat_hr.append(lat_tmp)
node_depth_hr.append(node_depth_tmp)
f.close()
rlon_hr = array(rlon_hr[0:fesom_n2d_hr])
rlat_hr = array(rlat_hr[0:fesom_n2d_hr])
node_depth_hr = array(node_depth_hr)
fesom_lon_hr, fesom_lat_hr = unrotate_grid(rlon_hr, rlat_hr)
fesom_x_hr = -(fesom_lat_hr+90)*cos(fesom_lon_hr*deg2rad+pi/2)
fesom_y_hr = (fesom_lat_hr+90)*sin(fesom_lon_hr*deg2rad+pi/2)
f = open(fesom_mesh_path_hr + 'aux3d.out', 'r')
max_num_layers_hr = int(f.readline())
node_columns_hr = zeros([fesom_n2d_hr, max_num_layers_hr])
for n in range(fesom_n2d_hr):
for k in range(max_num_layers_hr):
node_columns_hr[n,k] = int(f.readline())
node_columns_hr = node_columns_hr.astype(int)
f.close()
node_ur_3d_hr = id.variables['u'][0,:]
node_vr_3d_hr = id.variables['v'][0,:]
node_ur_hr = zeros(fesom_n2d_hr)
node_vr_hr = zeros(fesom_n2d_hr)
for n in range(fesom_n2d_hr):
udz_col = 0
vdz_col = 0
dz_col = 0
for k in range(max_num_layers_hr-1):
if node_columns_hr[n,k+1] == -999:
break
top_id = node_columns_hr[n,k]
bot_id = node_columns_hr[n,k+1]
dz_tmp = node_depth_hr[bot_id-1] - node_depth_hr[top_id-1]
udz_col += 0.5*(node_ur_3d_hr[top_id-1]+node_ur_3d_hr[bot_id-1])*dz_tmp
vdz_col += 0.5*(node_vr_3d_hr[top_id-1]+node_vr_3d_hr[bot_id-1])*dz_tmp
dz_col += dz_tmp
node_ur_hr[n] = udz_col/dz_col
node_vr_hr[n] = vdz_col/dz_col
node_u_hr, node_v_hr = unrotate_vector(rlon_hr, rlat_hr, node_ur_hr, node_vr_hr)
node_data_hr = sqrt(node_u_hr**2 + node_v_hr**2)
id.close()
fesom_data_hr = []
for elm in elements_hr:
if elm.cavity or var not in ['draft', 'melt', 'wct']:
if var == 'draft':
fesom_data_hr.append(mean([elm.nodes[0].depth, elm.nodes[1].depth, elm.nodes[2].depth]))
elif var == 'bathy':
fesom_data_hr.append(mean([elm.nodes[0].find_bottom().depth, elm.nodes[1].find_bottom().depth, elm.nodes[2].find_bottom().depth]))
elif var == 'wct':
fesom_data_hr.append(mean([elm.nodes[0].find_bottom().depth - elm.nodes[0].depth, elm.nodes[1].find_bottom().depth - elm.nodes[1].depth, elm.nodes[2].find_bottom().depth - elm.nodes[2].depth]))
elif var in ['melt', 'vel']:
fesom_data_hr.append(mean([node_data_hr[elm.nodes[0].id], node_data_hr[elm.nodes[1].id], node_data_hr[elm.nodes[2].id]]))
elif var in ['temp', 'salt']:
fesom_data_hr.append(mean([node_data_hr[elm.nodes[0].find_bottom().id], node_data_hr[elm.nodes[1].find_bottom().id], node_data_hr[elm.nodes[2].find_bottom().id]]))
# Loop over regions
for index in range(num_regions):
print 'Processing ' + region_names[index]
# Set up a grey square for FESOM to fill the background with land
x_reg_fesom, y_reg_fesom = meshgrid(linspace(x_min[index], x_max[index], num=100), linspace(y_min[index], y_max[index], num=100))
land_square = zeros(shape(x_reg_fesom))
# Find bounds on variable in this region, for both ROMS and FESOM
# Start with ROMS
loc = (roms_x >= x_min[index])*(roms_x <= x_max[index])*(roms_y >= y_min[index])*(roms_y <= y_max[index])
var_min = amin(roms_data[loc])
var_max = amax(roms_data[loc])
# Modify with FESOM
# Low-res
i = 0
for elm in elements_lr:
if elm.cavity or var not in ['draft', 'melt', 'wct']:
if any(elm.x >= x_min[index]) and any(elm.x <= x_max[index]) and any(elm.y >= y_min[index]) and any(elm.y <= y_max[index]):
if fesom_data_lr[i] < var_min:
var_min = fesom_data_lr[i]
if fesom_data_lr[i] > var_max:
var_max = fesom_data_lr[i]
i += 1
# High-res
i = 0
for elm in elements_hr:
if elm.cavity or var not in ['draft', 'melt', 'wct']:
if any(elm.x >= x_min[index]) and any(elm.x <= x_max[index]) and any(elm.y >= y_min[index]) and any(elm.y <= y_max[index]):
if fesom_data_hr[i] < var_min:
var_min = fesom_data_hr[i]
if fesom_data_hr[i] > var_max:
var_max = fesom_data_hr[i]
i += 1
if var == 'melt':
# Special colour map
if var_min < 0:
# There is refreezing here; include blue for elements < 0
cmap_vals = array([var_min, 0, 0.25*var_max, 0.5*var_max, 0.75*var_max, var_max])
cmap_colors = [(0.26, 0.45, 0.86), (1, 1, 1), (1, 0.9, 0.4), (0.99, 0.59, 0.18), (0.5, 0.0, 0.08), (0.96, 0.17, 0.89)]
cmap_vals_norm = (cmap_vals - var_min)/(var_max - var_min)
cmap_list = []
for i in range(size(cmap_vals)):
cmap_list.append((cmap_vals_norm[i], cmap_colors[i]))
mf_cmap = LinearSegmentedColormap.from_list('melt_freeze', cmap_list)
else:
# No refreezing
cmap_vals = array([0, 0.25*var_max, 0.5*var_max, 0.75*var_max, var_max])
cmap_colors = [(1, 1, 1), (1, 0.9, 0.4), (0.99, 0.59, 0.18), (0.5, 0.0, 0.08), (0.96, 0.17, 0.89)]
cmap_vals_norm = cmap_vals/var_max
cmap_list = []
for i in range(size(cmap_vals)):
cmap_list.append((cmap_vals_norm[i], cmap_colors[i]))
mf_cmap = LinearSegmentedColormap.from_list('melt_freeze', cmap_list)
colour_map = mf_cmap
elif var == 'vel':
colour_map = 'cool'
else:
colour_map = 'jet'
if var == 'vel':
# Make vectors for overlay
# Set up bins (edges)
x_bins = linspace(x_min[index], x_max[index], num=num_bins+1)
y_bins = linspace(y_min[index], y_max[index], num=num_bins+1)
# Calculate centres of bins (for plotting)
x_centres = 0.5*(x_bins[:-1] + x_bins[1:])
y_centres = 0.5*(y_bins[:-1] + y_bins[1:])
# ROMS
# First set up arrays to integrate velocity in each bin
# Simple averaging of all the points inside each bin
roms_u = zeros([size(y_centres), size(x_centres)])
roms_v = zeros([size(y_centres), size(x_centres)])
roms_num_pts = zeros([size(y_centres), size(x_centres)])
# First convert to polar coordinates, rotate to account for
# longitude in circumpolar projection, and convert back to vector
# components
theta_roms = arctan2(v_rho, u_rho)
theta_circ_roms = theta_roms - roms_lon*deg2rad
u_circ_roms = roms_data*cos(theta_circ_roms) # roms_data is speed
v_circ_roms = roms_data*sin(theta_circ_roms)
# Loop over all points (can't find a better way to do this)
for j in range(size(roms_data,0)):
for i in range(size(roms_data,1)):
# Make sure data isn't masked (i.e. land)
if u_circ_roms[j,i] is not ma.masked:
# Check if we're in the region of interest
if roms_x[j,i] > x_min[index] and roms_x[j,i] < x_max[index] and roms_y[j,i] > y_min[index] and roms_y[j,i] < y_max[index]:
# Figure out which bins this falls into
x_index = nonzero(x_bins > roms_x[j,i])[0][0]-1
y_index = nonzero(y_bins > roms_y[j,i])[0][0]-1
# Integrate
roms_u[y_index, x_index] += u_circ_roms[j,i]
roms_v[y_index, x_index] += v_circ_roms[j,i]
roms_num_pts[y_index, x_index] += 1
# Convert from sums to averages
# First mask out points with no data
roms_u = ma.masked_where(roms_num_pts==0, roms_u)
roms_v = ma.masked_where(roms_num_pts==0, roms_v)
# Divide everything else by the number of points
flag = roms_num_pts > 0
roms_u[flag] = roms_u[flag]/roms_num_pts[flag]
roms_v[flag] = roms_v[flag]/roms_num_pts[flag]
# FESOM low-res
fesom_u_lr = zeros([size(y_centres), size(x_centres)])
fesom_v_lr = zeros([size(y_centres), size(x_centres)])
fesom_num_pts_lr = zeros([size(y_centres), size(x_centres)])
theta_fesom_lr = arctan2(node_v_lr, node_u_lr)
theta_circ_fesom_lr = theta_fesom_lr - fesom_lon_lr*deg2rad
u_circ_fesom_lr = node_data_lr*cos(theta_circ_fesom_lr) # node_data is speed
v_circ_fesom_lr = node_data_lr*sin(theta_circ_fesom_lr)
# Loop over 2D nodes to fill in the velocity bins as before
for n in range(fesom_n2d_lr):
if fesom_x_lr[n] > x_min[index] and fesom_x_lr[n] < x_max[index] and fesom_y_lr[n] > y_min[index] and fesom_y_lr[n] < y_max[index]:
x_index = nonzero(x_bins > fesom_x_lr[n])[0][0]-1
y_index = nonzero(y_bins > fesom_y_lr[n])[0][0]-1
fesom_u_lr[y_index, x_index] += u_circ_fesom_lr[n]
fesom_v_lr[y_index, x_index] += v_circ_fesom_lr[n]
fesom_num_pts_lr[y_index, x_index] += 1
fesom_u_lr = ma.masked_where(fesom_num_pts_lr==0, fesom_u_lr)
fesom_v_lr = ma.masked_where(fesom_num_pts_lr==0, fesom_v_lr)
flag = fesom_num_pts_lr > 0
fesom_u_lr[flag] = fesom_u_lr[flag]/fesom_num_pts_lr[flag]
fesom_v_lr[flag] = fesom_v_lr[flag]/fesom_num_pts_lr[flag]
# FESOM high-res
fesom_u_hr = zeros([size(y_centres), size(x_centres)])
fesom_v_hr = zeros([size(y_centres), size(x_centres)])
fesom_num_pts_hr = zeros([size(y_centres), size(x_centres)])
theta_fesom_hr = arctan2(node_v_hr, node_u_hr)
theta_circ_fesom_hr = theta_fesom_hr - fesom_lon_hr*deg2rad
u_circ_fesom_hr = node_data_hr*cos(theta_circ_fesom_hr) # node_data is speed
v_circ_fesom_hr = node_data_hr*sin(theta_circ_fesom_hr)
# Loop over 2D nodes to fill in the velocity bins as before
for n in range(fesom_n2d_hr):
if fesom_x_hr[n] > x_min[index] and fesom_x_hr[n] < x_max[index] and fesom_y_hr[n] > y_min[index] and fesom_y_hr[n] < y_max[index]:
x_index = nonzero(x_bins > fesom_x_hr[n])[0][0]-1
y_index = nonzero(y_bins > fesom_y_hr[n])[0][0]-1
fesom_u_hr[y_index, x_index] += u_circ_fesom_hr[n]
fesom_v_hr[y_index, x_index] += v_circ_fesom_hr[n]
fesom_num_pts_hr[y_index, x_index] += 1
fesom_u_hr = ma.masked_where(fesom_num_pts_hr==0, fesom_u_hr)
fesom_v_hr = ma.masked_where(fesom_num_pts_hr==0, fesom_v_hr)
flag = fesom_num_pts_hr > 0
fesom_u_hr[flag] = fesom_u_hr[flag]/fesom_num_pts_hr[flag]
fesom_v_hr[flag] = fesom_v_hr[flag]/fesom_num_pts_hr[flag]
# Plot
fig = figure(figsize=(20, ysize[index]))
fig.patch.set_facecolor('white')
# MetROMS
ax = fig.add_subplot(1,3,1, aspect='equal')
# First shade land and zice in grey
contourf(roms_x, roms_y, land_zice, 1, colors=(('0.6', '0.6', '0.6')))
# Fill in the missing circle
contourf(x_reg_roms, y_reg_roms, land_circle, 1, colors=(('0.6', '0.6', '0.6')))
# Now shade the data
pcolor(roms_x, roms_y, roms_data, vmin=var_min, vmax=var_max, cmap=colour_map)
if var == 'vel':
# Overlay vectors
quiver(x_centres, y_centres, roms_u, roms_v, scale=1.5, headwidth=6, headlength=7, color='black')
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
axis('off')
title('MetROMS', fontsize=24)
# FESOM low-res
ax = fig.add_subplot(1,3,2, aspect='equal')
# Start with land background
contourf(x_reg_fesom, y_reg_fesom, land_square, 1, colors=(('0.6', '0.6', '0.6')))
# Add elements
if var in ['draft', 'melt', 'wct']:
# Ice shelf elements only
img = PatchCollection(patches_shelf_lr, cmap=colour_map)
else:
img = PatchCollection(patches_lr, cmap=colour_map)
img.set_array(array(fesom_data_lr))
img.set_edgecolor('face')
img.set_clim(vmin=var_min, vmax=var_max)
ax.add_collection(img)
if var in ['draft', 'melt', 'wct']:
# Mask out the open ocean in white
overlay = PatchCollection(patches_ocn_lr, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
if var == 'vel':
# Overlay vectors
quiver(x_centres, y_centres, fesom_u_lr, fesom_v_lr, scale=1.5, headwidth=6, headlength=7, color='black')
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
axis('off')
title('FESOM (low-res)', fontsize=24)
# FESOM high-res
ax = fig.add_subplot(1,3,3, aspect='equal')
contourf(x_reg_fesom, y_reg_fesom, land_square, 1, colors=(('0.6', '0.6', '0.6')))
if var in ['draft', 'melt', 'wct']:
# Ice shelf elements only
img = PatchCollection(patches_shelf_hr, cmap=colour_map)
else:
img = PatchCollection(patches_hr, cmap=colour_map)
img.set_array(array(fesom_data_hr))
img.set_edgecolor('face')
img.set_clim(vmin=var_min, vmax=var_max)
ax.add_collection(img)
if var in ['draft', 'melt', 'wct']:
overlay = PatchCollection(patches_ocn_hr, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
if var == 'vel':
# Overlay vectors
quiver(x_centres, y_centres, fesom_u_hr, fesom_v_hr, scale=1.5, headwidth=6, headlength=7, color='black')
xlim([x_min[index], x_max[index]])
ylim([y_min[index], y_max[index]])
axis('off')
title('FESOM (high-res)', fontsize=24)
# Colourbar on the right
cbaxes = fig.add_axes([0.92, 0.2, 0.01, 0.6])
cbar = colorbar(img, cax=cbaxes)
cbar.ax.tick_params(labelsize=20)
# Main title
if var == 'draft':
title_string = ' draft (m)'
elif var == 'bathy':
title_string = ' bathymetry (m)'
elif var == 'wct':
title_string = ' water column thickness (m)'
elif var == 'melt':
title_string = ' melt rate (m/y)'
elif var == 'temp':
title_string = r' bottom water temperature ($^{\circ}$C)'
elif var == 'salt':
title_string = ' bottom water salinity (psu)'
elif var == 'vel':
title_string = ' vertically averaged ocean velocity (m/s)'
suptitle(region_names[index] + title_string, fontsize=30)
subplots_adjust(wspace=0.05)
#fig.show()
fig.savefig(fig_heads[index] + '_' + var + '.png')
def timeseries_dpt (grid_path, file_path, log_path):
# Radius of the Earth in metres
r = 6.371e6
# Degrees to radians conversion factor
deg2rad = pi/180.0
# Longitude of Drake Passage zonal slice (convert to ROMS bounds 0-360)
lon0 = -67 + 360
# Latitude bounds on Drake Passage zonal slice
lat_min = -68
lat_max = -54.5
time = []
dpt = []
# Check if the log file exists
if exists(log_path):
print 'Reading previously calculated values'
f = open(log_path, 'r')
# Skip first line (header for time array)
f.readline()
for line in f:
try:
time.append(float(line))
except(ValueError):
# Reached the header for the next variable
break
for line in f:
dpt.append(float(line))
f.close()
print 'Reading grid'
id = Dataset(grid_path, 'r')
h = id.variables['h'][:-15,1:-1]
zice = id.variables['zice'][:-15,1:-1]
lon = id.variables['lon_rho'][:-15,1:-1]
lat = id.variables['lat_rho'][:-15,1:-1]
# Keep the overlapping periodic boundary on "angle" for now
angle = id.variables['angle'][:-15,:]
id.close()
print 'Reading data'
id = Dataset(file_path, 'r')
# Read time values and convert from seconds to years
new_time = id.variables['ocean_time'][:]/(60*60*24*365.25)
num_time = size(new_time)
# Concatenate with time values from log file
for t in range(num_time):
time.append(new_time[t])
# Calculate time-dependent water column thickness: h + zice + zeta
zeta = id.variables['zeta'][:,:-15,1:-1]
wct = tile(h, (num_time,1,1)) + tile(zice, (num_time,1,1)) + zeta
# Read barotropic velocities in x-y space
ubar_xy = id.variables['ubar'][:,:-15,:]
vbar_xy = id.variables['vbar'][:,:-15,:]
id.close()
print 'Rotating velocity vector'
ubar = ma.empty([num_time, size(lon,0), size(lon,1)])
# Rotate one time index at a time
for t in range(num_time):
ubar_tmp, vbar_tmp = rotate_vector_roms(ubar_xy[t,:,:], vbar_xy[t,:,:], angle)
# Throw away the overlapping periodic boundary before saving
ubar[t,:,:] = ubar_tmp[:,1:-1]
print 'Extracting zonal slice through Drake Passage' #
num_lat = size(lat,0)
# Set up arrays for zonal slices of ubar, water column thickness, latitude
ubar_DP = ma.empty([num_time, num_lat])
wct_DP = ma.empty([num_time, num_lat])
lat_DP = empty([num_lat])
# Loop over longitudes
for j in range(num_lat):
lon_tmp = lon[j,:]
# Find indices and coefficients to interpolate to lon0
ie, iw, coeffe, coeffw = interp_lon_helper(lon_tmp, lon0)
# Use these to interpolate all 3 variables we care about
ubar_DP[:,j] = coeffe*ubar[:,j,ie] + coeffw*ubar[:,j,iw]
wct_DP[:,j] = coeffe*wct[:,j,ie] + coeffw*wct[:,j,iw]
lat_DP[j] = coeffe*lat[j,ie] + coeffw*lat[j,iw]
# Find indices for latitude bounds
jS = nonzero(lat_DP > lat_min)[0][0]
jN = nonzero(lat_DP > lat_max)[0][0]
# Trim everything to these bounds
ubar_DP = ubar_DP[:,jS:jN]
wct_DP = wct_DP[:,jS:jN]
lat_DP = lat_DP[jS:jN]
# Calculate dy
# First calculate latitude on edges of each cell
middle_lat = 0.5*(lat_DP[:-1] + lat_DP[1:])
s_bdry = 2*lat_DP[0] - middle_lat[0]
n_bdry = 2*lat_DP[-1] - middle_lat[-1]
lat_edges = zeros(size(lat_DP)+1)
lat_edges[0] = s_bdry
lat_edges[1:-1] = middle_lat
lat_edges[-1] = n_bdry
# Now calculate difference in latitude across each cell
dlat_DP = lat_edges[1:] - lat_edges[:-1]
# Convert to Cartesian space for dy in metres
dy_DP = r*dlat_DP*deg2rad
for t in range(num_time):
# Integrate ubar*wct*dy and convert to Sv
dpt.append(sum(ubar_DP[t,:]*wct_DP[t,:]*dy_DP)*1e-6)
print 'Plotting'
clf()
plot(time, dpt)
xlabel('Years')
ylabel('Drake Passage Transport (Sv)')
grid(True)
savefig('drakepsgtrans.png')
print 'Saving results to log file'
f = open(log_path, 'w')
f.write('Time (years):\n')
for elm in time:
f.write(str(elm) + '\n')
f.write('Drake Passage Transport (Sv):\n')
for elm in dpt:
f.write(str(elm) + '\n')
f.close()
def slope_current ():
# File paths
roms_grid = '/short/m68/kaa561/metroms_iceshelf/apps/common/grid/circ30S_quarterdegree.nc'
roms_file = '/short/m68/kaa561/metroms_iceshelf/tmproms/run/intercomparison/2002_2016_avg.nc'
fesom_mesh_path_lr = '/short/y99/kaa561/FESOM/mesh/meshA/'
fesom_mesh_path_hr = '/short/y99/kaa561/FESOM/mesh/meshB/'
fesom_file_lr = '/short/y99/kaa561/FESOM/intercomparison_lowres/output/oce_2002_2016_avg.nc'
fesom_file_hr = '/short/y99/kaa561/FESOM/intercomparison_highres/output/oce_2002_2016_avg.nc'
# ROMS vertical grid parameters
theta_s = 7.0
theta_b = 2.0
hc = 250
N = 31
# FESOM mesh parameters
circumpolar = False
cross_180 = False
# Spacing of longitude bins
dlon = 1
# Parameters for continental shelf selection
lat0 = -64 # Maximum latitude to consider
h0 = 2500 # Deepest depth to consider
# Set up longitude bins
# Start with edges
lon_bins = arange(-180, 180+dlon, dlon)
# Centres for plotting
lon_centres = 0.5*(lon_bins[:-1] + lon_bins[1:])
num_bins = size(lon_centres)
# Set up arrays to store maximum barotropic speed in each bin
current_roms = zeros(num_bins)
current_fesom_lr = zeros(num_bins)
current_fesom_hr = zeros(num_bins)
print 'Processing MetROMS'
print 'Reading grid'
id = Dataset(roms_grid, 'r')
roms_lon = id.variables['lon_rho'][:,:]
roms_lat = id.variables['lat_rho'][:,:]
roms_h = id.variables['h'][:,:]
roms_zice = id.variables['zice'][:,:]
roms_angle = id.variables['angle'][:,:]
id.close()
print 'Reading data'
# Read full 3D u and v
id = Dataset(roms_file, 'r')
u_3d_tmp = id.variables['u'][0,:,:,:]
v_3d_tmp = id.variables['v'][0,:,:,:]
id.close()
print 'Vertically averaging velocity'
# Get integrands on 3D grid; we only care about dz
dx, dy, dz, z = cartesian_grid_3d(roms_lon, roms_lat, roms_h, roms_zice, theta_s, theta_b, hc, N)
# Unrotate each vertical level
u_3d = ma.empty(shape(dz))
v_3d = ma.empty(shape(dz))
num_lat_u = size(u_3d_tmp,1)
num_lon_u = size(u_3d_tmp,2)
num_lat_v = size(v_3d_tmp,1)
num_lon_v = size(v_3d_tmp,2)
for k in range(N):
u_k, v_k = rotate_vector_roms(u_3d_tmp[k,:,:], v_3d_tmp[k,:,:], roms_angle)
u_3d[k,:,:] = u_k
v_3d[k,:,:] = v_k
# Vertically average u and v
roms_u = sum(u_3d*dz, axis=0)/sum(dz, axis=0)
roms_v = sum(v_3d*dz, axis=0)/sum(dz, axis=0)
# Calculate speed
roms_speed = sqrt(roms_u**2 + roms_v**2)
print 'Selecting slope current'
# First make sure longitude is between -180 and 180
index = roms_lon > 180
roms_lon[index] = roms_lon[index] - 360
for j in range(size(roms_speed,0)):
for i in range(size(roms_speed,1)):
# Check if we care about this point
if roms_lat[j,i] <= lat0 and roms_h[j,i] <= h0 and roms_zice[j,i] == 0:
# Find longitude bin
lon_index = nonzero(lon_bins > roms_lon[j,i])[0][0] - 1
# Update slope current speed in this bin if needed
if roms_speed[j,i] > current_roms[lon_index]:
current_roms[lon_index] = roms_speed[j,i]
print 'Processing low-res FESOM'
print 'Building mesh'
# We only care about nodes, not elements, so don't need to use the
# fesom_grid function.
# Read cavity flag for each 2D surface node
fesom_cavity_lr = []
f = open(fesom_mesh_path_lr + 'cavity_flag_nod2d.out', 'r')
for line in f:
tmp = int(line)
if tmp == 1:
fesom_cavity_lr.append(True)
elif tmp == 0:
fesom_cavity_lr.append(False)
else:
print 'Problem'
f.close()
# Save the number of 2D nodes
fesom_n2d_lr = len(fesom_cavity_lr)
# Read rotated lat and lon for each node, also depth
f = open(fesom_mesh_path_lr + 'nod3d.out', 'r')
f.readline()
rlon_lr = []
rlat_lr = []
node_depth_lr = []
for line in f:
tmp = line.split()
lon_tmp = float(tmp[1])
lat_tmp = float(tmp[2])
node_depth_tmp = -1*float(tmp[3])
if lon_tmp < -180:
lon_tmp += 360
elif lon_tmp > 180:
lon_tmp -= 360
rlon_lr.append(lon_tmp)
rlat_lr.append(lat_tmp)
node_depth_lr.append(node_depth_tmp)
f.close()
# For lat and lon, only care about the 2D nodes (the first
# fesom_n2d indices)
rlon_lr = array(rlon_lr[0:fesom_n2d_lr])
rlat_lr = array(rlat_lr[0:fesom_n2d_lr])
node_depth_lr = array(node_depth_lr)
# Unrotate longitude
fesom_lon_lr, fesom_lat_lr = unrotate_grid(rlon_lr, rlat_lr)
# Read lists of which nodes are directly below which
f = open(fesom_mesh_path_lr + 'aux3d.out', 'r')
max_num_layers_lr = int(f.readline())
node_columns_lr = zeros([fesom_n2d_lr, max_num_layers_lr])
for n in range(fesom_n2d_lr):
for k in range(max_num_layers_lr):
node_columns_lr[n,k] = int(f.readline())
node_columns_lr = node_columns_lr.astype(int)
f.close()
# Now figure out the bottom depth of each 2D node
bottom_depth_lr = zeros(fesom_n2d_lr)
for n in range(fesom_n2d_lr):
node_id = node_columns_lr[n,0] - 1
for k in range(1, max_num_layers_lr):
if node_columns_lr[n,k] == -999:
# Reached the bottom
break
node_id = node_columns_lr[n,k] - 1
# Save the last valid depth
bottom_depth_lr[n] = node_depth_lr[n]
print 'Reading data'
# Read full 3D field for both u and v
id = Dataset(fesom_file_lr, 'r')
node_ur_3d_lr = id.variables['u'][0,:]
node_vr_3d_lr = id.variables['v'][0,:]
id.close()
print 'Vertically averaging velocity'
# Vertically average
node_ur_lr = zeros(fesom_n2d_lr)
node_vr_lr = zeros(fesom_n2d_lr)
for n in range(fesom_n2d_lr):
# Integrate udz, vdz, and dz over this water column
udz_col = 0
vdz_col = 0
dz_col = 0
for k in range(max_num_layers_lr-1):
if node_columns_lr[n,k+1] == -999:
# Reached the bottom
break
# Trapezoidal rule
top_id = node_columns_lr[n,k]
bot_id = node_columns_lr[n,k+1]
dz_tmp = node_depth_lr[bot_id-1] - node_depth_lr[top_id-1]
udz_col += 0.5*(node_ur_3d_lr[top_id-1]+node_ur_3d_lr[bot_id-1])*dz_tmp
vdz_col += 0.5*(node_vr_3d_lr[top_id-1]+node_vr_3d_lr[bot_id-1])*dz_tmp
dz_col += dz_tmp
# Convert from integrals to averages
node_ur_lr[n] = udz_col/dz_col
node_vr_lr[n] = vdz_col/dz_col
# Unrotate
node_u_lr, node_v_lr = unrotate_vector(rlon_lr, rlat_lr, node_ur_lr, node_vr_lr)
# Calculate speed
node_speed_lr = sqrt(node_u_lr**2 + node_v_lr**2)
print 'Selecting slope current'
for n in range(fesom_n2d_lr):
# Check if we care about this node
if fesom_lat_lr[n] <= lat0 and bottom_depth_lr[n] <= h0 and not fesom_cavity_lr[n]:
# Find longitude bin
lon_index = nonzero(lon_bins > fesom_lon_lr[n])[0][0] - 1
# Update slope current speed in this bin if needed
if node_speed_lr[n] > current_fesom_lr[lon_index]:
current_fesom_lr[lon_index] = node_speed_lr[n]
print 'Processing high-res FESOM'
print 'Building mesh'
fesom_cavity_hr = []
f = open(fesom_mesh_path_hr + 'cavity_flag_nod2d.out', 'r')
for line in f:
tmp = int(line)
if tmp == 1:
fesom_cavity_hr.append(True)
elif tmp == 0:
fesom_cavity_hr.append(False)
else:
print 'Problem'
f.close()
fesom_n2d_hr = len(fesom_cavity_hr)
f = open(fesom_mesh_path_hr + 'nod3d.out', 'r')
f.readline()
rlon_hr = []
rlat_hr = []
node_depth_hr = []
for line in f:
tmp = line.split()
lon_tmp = float(tmp[1])
lat_tmp = float(tmp[2])
node_depth_tmp = -1*float(tmp[3])
if lon_tmp < -180:
lon_tmp += 360
elif lon_tmp > 180:
lon_tmp -= 360
rlon_hr.append(lon_tmp)
rlat_hr.append(lat_tmp)
node_depth_hr.append(node_depth_tmp)
f.close()
rlon_hr = array(rlon_hr[0:fesom_n2d_hr])
rlat_hr = array(rlat_hr[0:fesom_n2d_hr])
node_depth_hr = array(node_depth_hr)
fesom_lon_hr, fesom_lat_hr = unrotate_grid(rlon_hr, rlat_hr)
f = open(fesom_mesh_path_hr + 'aux3d.out', 'r')
max_num_layers_hr = int(f.readline())
node_columns_hr = zeros([fesom_n2d_hr, max_num_layers_hr])
for n in range(fesom_n2d_hr):
for k in range(max_num_layers_hr):
node_columns_hr[n,k] = int(f.readline())
node_columns_hr = node_columns_hr.astype(int)
f.close()
bottom_depth_hr = zeros(fesom_n2d_hr)
for n in range(fesom_n2d_hr):
node_id = node_columns_hr[n,0] - 1
for k in range(1, max_num_layers_hr):
if node_columns_hr[n,k] == -999:
break
node_id = node_columns_hr[n,k] - 1
bottom_depth_hr[n] = node_depth_hr[n]
print 'Reading data'
id = Dataset(fesom_file_hr, 'r')
node_ur_3d_hr = id.variables['u'][0,:]
node_vr_3d_hr = id.variables['v'][0,:]
id.close()
print 'Vertically averaging velocity'
node_ur_hr = zeros(fesom_n2d_hr)
node_vr_hr = zeros(fesom_n2d_hr)
for n in range(fesom_n2d_hr):
udz_col = 0
vdz_col = 0
dz_col = 0
for k in range(max_num_layers_hr-1):
if node_columns_hr[n,k+1] == -999:
break
top_id = node_columns_hr[n,k]
bot_id = node_columns_hr[n,k+1]
dz_tmp = node_depth_hr[bot_id-1] - node_depth_hr[top_id-1]
udz_col += 0.5*(node_ur_3d_hr[top_id-1]+node_ur_3d_hr[bot_id-1])*dz_tmp
vdz_col += 0.5*(node_vr_3d_hr[top_id-1]+node_vr_3d_hr[bot_id-1])*dz_tmp
dz_col += dz_tmp
node_ur_hr[n] = udz_col/dz_col
node_vr_hr[n] = vdz_col/dz_col
node_u_hr, node_v_hr = unrotate_vector(rlon_hr, rlat_hr, node_ur_hr, node_vr_hr)
node_speed_hr = sqrt(node_u_hr**2 + node_v_hr**2)
print 'Selecting slope current'
for n in range(fesom_n2d_hr):
if fesom_lat_hr[n] <= lat0 and bottom_depth_hr[n] <= h0 and not fesom_cavity_hr[n]:
lon_index = nonzero(lon_bins > fesom_lon_hr[n])[0][0] - 1
if node_speed_hr[n] > current_fesom_hr[lon_index]:
current_fesom_hr[lon_index] = node_speed_hr[n]
print 'Plotting'
fig = figure(figsize=(12,8))
plot(lon_centres, current_roms, color='blue', label='MetROMS')
plot(lon_centres, current_fesom_lr, color='green', label='FESOM low-res')
plot(lon_centres, current_fesom_hr, color='magenta', label='FESOM high-res')
grid(True)
title('Slope current speed', fontsize=20)
xlabel('Longitude', fontsize=14)
ylabel('m/s', fontsize=14)
xlim([-180, 180])
legend()
fig.savefig('slope_current.png')
print 'Mean slope current in MetROMS: ' + str(mean(current_roms)) + ' m/s'
print 'Mean slope current in low-res FESOM: ' + str(mean(current_fesom_lr)) + ' m/s'
print 'Mean slope current in high-res FESOM: ' + str(mean(current_fesom_hr)) + ' m/s'
# Command-line interface
if __name__ == "__main__":
coastal_current()
def uv_vectorplot (grid_path, file_path, tstep, depth_key, save=False, fig_name=None):
# Radius of the Earth in metres
r = 6.371e6
# Degrees to radians conversion factor
deg2rad = pi/180
# Side length of blocks to average vectors over (can't plot vector at every
# single point or the plot will be way too crowded)
block = 15
# Read angle from grid file
grid_id = Dataset(grid_path, 'r')
angle = grid_id.variables['angle'][:-15,:]
grid_id.close()
# Read grid and velocity data
id = Dataset(file_path, 'r')
lon = id.variables['lon_rho'][:-15,:-1]
lat = id.variables['lat_rho'][:-15,:-1]
if depth_key == 1:
# Surface u and v
u = id.variables['u'][tstep-1,-1,:-15,:]
v = id.variables['v'][tstep-1,-1,:-15,:]
elif depth_key == 2:
# Bottom u and v
u = id.variables['u'][tstep-1,0,:-15,:]
v = id.variables['v'][tstep-1,0,:-15,:]
elif depth_key == 3:
# Vertically averaged u and v
u = id.variables['ubar'][tstep-1,:-15,:]
v = id.variables['vbar'][tstep-1,:-15,:]
id.close()
# Rotate velocities to lat-lon space
u_lonlat, v_lonlat = rotate_vector_roms(u, v, angle)
# Throw away the overlapping periodic boundary
u_rho = u_lonlat[:,:-1]
v_rho = v_lonlat[:,:-1]
# Calculate speed for the background filled contour plot
speed = sqrt(u_rho**2 + v_rho**2)
# Calculate X and Y coordinates for plotting circumpolar projection
X = -(lat+90)*cos(lon*deg2rad+pi/2)
Y = (lat+90)*sin(lon*deg2rad+pi/2)
# Calculate velocity components in spherical coordinate space
# (just differentiate and rearrange spherical coordinate transformations)
dlon_dt = u_rho/(r*cos(lat*deg2rad)*deg2rad)
dlat_dt = v_rho/(r*deg2rad)
# Calculate velocity components in X-Y space (just differentiate and
# rearrange equations for X and Y above)
dX_dt = -dlat_dt*cos(lon*deg2rad+pi/2) + (lat+90)*sin(lon*deg2rad+pi/2)*dlon_dt*deg2rad
dY_dt = dlat_dt*sin(lon*deg2rad+pi/2) + (lat+90)*cos(lon*deg2rad+pi/2)*dlon_dt*deg2rad
# Average X, Y, dX_dt, and dY_dt over block x block intervals
# Calculate number of blocks
size0 = int(ceil(size(X,0)/float(block)))
size1 = int(ceil((size(X,1)-1)/float(block)))
# Set up arrays for averaged fields
X_block = ma.empty([size0, size1])
Y_block = ma.empty([size0, size1])
dX_dt_block = ma.empty([size0, size1])
dY_dt_block = ma.empty([size0, size1])
# Set up arrays containing boundary indices
posn0 = range(0, size(X,0), block)
posn0.append(size(X,0))
posn1 = range(0, size(X,1), block)
posn1.append(size(X,1))
# Double loop to average each block (can't find a more efficient way to do
# this)
for j in range(size0):
for i in range(size1):
start0 = posn0[j]
end0 = posn0[j+1]
start1 = posn1[i]
end1 = posn1[i+1]
X_block[j,i] = mean(X[start0:end0, start1:end1])
Y_block[j,i] = mean(Y[start0:end0, start1:end1])
dX_dt_block[j,i] = mean(dX_dt[start0:end0, start1:end1])
dY_dt_block[j,i] = mean(dY_dt[start0:end0, start1:end1])
# Make the plot
fig = figure(figsize=(16,12))
fig.add_subplot(1,1,1, aspect='equal')
# Contour speed values at every point
# Use pastel colour map so overlaid vectors will show up
contourf(X, Y, speed, 50, cmap='Paired')
cbar = colorbar()
cbar.ax.tick_params(labelsize=20)
# Add vectors for each block
quiver(X_block, Y_block, dX_dt_block, dY_dt_block, color='black')
if depth_key == 1:
title('Surface velocity (m/s)', fontsize=30)
elif depth_key == 2:
title('Bottom velocity (m/s)', fontsize=30)
elif depth_key == 3:
title('Vertically averaged velocity (m/s)', fontsize=30)
axis('off')
if save:
fig.savefig(fig_name)
else:
fig.show()
def circumpolar_plot(
file_path,
var_name,
tstep,
depth_key,
depth,
depth_bounds,
colour_bounds=None,
save=False,
fig_name=None,
grid_path=None,
):
# Grid parameters
theta_s = 4.0
theta_b = 0.9
hc = 40
N = 31
deg2rad = pi / 180
# Read the variable and figure out if 2D or 3D (not including time)
id = Dataset(file_path, "r")
if len(id.variables[var_name].shape) == 4:
# 3D variable; will have to choose depth later
data_full = id.variables[var_name][tstep - 1, :, :-15, :]
choose_depth = True
elif len(id.variables[var_name].shape) == 3:
# 2D variable
data = id.variables[var_name][tstep - 1, :-15, :]
choose_depth = False
if var_name == "salt":
units = "psu"
elif var_name == "m":
# Convert ice shelf melt rate from m/s to m/yr
units = "m/year"
data = data * 60.0 * 60.0 * 24.0 * 365.25
else:
units = id.variables[var_name].units
long_name = id.variables[var_name].long_name
# Check for vector variables that need to be rotated
if var_name in ["ubar", "vbar", "u", "v", "sustr", "svstr", "bustr", "bvstr"]:
grid_id = Dataset(grid_path, "r")
angle = grid_id.variables["angle"][:-15, :]
grid_id.close()
if var_name in ["ubar", "sustr", "bustr"]:
# 2D u-variable
u_data = data[:, :]
v_data = id.variables[var_name.replace("u", "v")][tstep - 1, :-15, :]
u_data_lonlat, v_data_lonlat = rotate_vector_roms(u_data, v_data, angle)
data = u_data_lonlat
elif var_name in ["vbar", "svstr", "bvstr"]:
# 2D v-variable
v_data = data[:, :]
u_data = id.variables[var_name.replace("v", "u")][tstep - 1, :-15, :]
u_data_lonlat, v_data_lonlat = rotate_vector_roms(u_data, v_data, angle)
data = v_data_lonlat
elif var_name in ["u"]:
# 3D u-variable
data_full_ugrid = data_full[:, :, :]
data_full = ma.empty([data_full_ugrid.shape[0], data_full_ugrid.shape[1], data_full_ugrid.shape[2] + 1])
for k in range(N):
u_data = data_full_ugrid[k, :, :]
v_data = id.variables[var_name.replace("u", "v")][tstep - 1, k, :-15, :]
u_data_lonlat, v_data_lonlat = rotate_vector_roms(u_data, v_data, angle)
data_full[k, :, :] = u_data_lonlat
elif var_name in ["v"]:
# 3D v-variable
data_full_vgrid = data_full[:, :, :]
data_full = ma.empty([data_full_vgrid.shape[0], data_full_vgrid.shape[1] + 1, data_full_vgrid.shape[2]])
for k in range(N):
v_data = data_full_vgrid[k, :, :]
u_data = id.variables[var_name.replace("v", "u")][tstep - 1, k, :-15, :]
u_data_lonlat, v_data_lonlat = rotate_vector_roms(u_data, v_data, angle)
data_full[k, :, :] = v_data_lonlat
# Read grid variables
h = id.variables["h"][:-15, :]
zice = id.variables["zice"][:-15, :]
lon = id.variables["lon_rho"][:-15, :]
lat = id.variables["lat_rho"][:-15, :]
id.close()
# Throw away the overlapping periodic boundary
if choose_depth:
data_full = data_full[:, :, :-1]
else:
data = data[:, :-1]
lon = lon[:, :-1]
lat = lat[:, :-1]
h = h[:, :-1]
zice = zice[:, :-1]
# Convert to spherical coordinates
x = -(lat + 90) * cos(lon * deg2rad + pi / 2)
y = (lat + 90) * sin(lon * deg2rad + pi / 2)
# Choose what to write on the title about depth
if choose_depth:
if depth_key == 0:
depth_string = "at surface"
elif depth_key == 1:
depth_string = "at bottom"
elif depth_key == 2:
depth_string = "at " + str(int(round(-depth))) + " m"
elif depth_key == 3:
depth_string = "vertically averaged"
elif depth_key == 4:
depth_string = (
"vertically averaged between "
+ str(int(round(-depth_bounds[0])))
+ " and "
+ str(int(round(-depth_bounds[1])))
+ " m"
)
else:
depth_string = ""
if choose_depth:
# For 3D variables, select data corresponding to depth choice
if depth_key == 0:
# Surface layer
data = data_full[-1, :, :]
elif depth_key == 1:
# Bottom layer
data = data_full[0, :, :]
else:
# We will need z-coordinates and possibly dz
dx, dy, dz, z = cartesian_grid_3d(lon, lat, h, zice, theta_s, theta_b, hc, N)
if depth_key == 2:
# Interpolate to given depth
data = interp_depth(data_full, z, depth)
elif depth_key == 3:
# Vertically average entire water column
data = sum(data_full * dz, axis=0) / sum(dz, axis=0)
elif depth_key == 4:
# Vertically average between given depths
data = average_btw_depths(data_full, z, dz, depth_bounds)
if colour_bounds is not None:
# User has set bounds on colour scale
lev = linspace(colour_bounds[0], colour_bounds[1], num=40)
if colour_bounds[0] == -colour_bounds[1]:
# Bounds are centered on zero, so choose a blue-to-red colourmap
# centered on yellow
colour_map = "RdYlBu_r"
else:
colour_map = "jet"
else:
# Determine bounds automatically
if var_name in ["u", "v", "ubar", "vbar", "m", "shflux", "ssflux", "sustr", "svstr", "bustr", "bvstr"]:
# Center levels on 0 for certain variables, with a blue-to-red
# colourmap
max_val = amax(abs(data))
lev = linspace(-max_val, max_val, num=40)
colour_map = "RdYlBu_r"
else:
lev = linspace(amin(data), amax(data), num=40)
colour_map = "jet"
# Plot
fig = figure(figsize=(16, 12))
fig.add_subplot(1, 1, 1, aspect="equal")
contourf(x, y, data, lev, cmap=colour_map, extend="both")
cbar = colorbar()
cbar.ax.tick_params(labelsize=20)
title(long_name + " (" + units + ")\n" + depth_string, fontsize=30)
axis("off")
if save:
fig.savefig(fig_name)
else:
fig.show()
def bugs_acc_fig(grid_path, laplacian_file, biharmonic_file, tstep):
# Month names for title
month_names = [
'January', 'February', 'March', 'April', 'May', 'June', 'July',
'August', 'September', 'October', 'November', 'December'
]
# Degrees to radians conversion factor
deg2rad = pi / 180.0
# Bounds on plot (in polar coordinate transformation)
x_min = -40
x_max = 0
y_min = 0
y_max = 40
# Minimum speed to plot (mask out values below)
threshold = 0.12
# Maximum speed to plot
bound = 1
# Read angle and grid
id = Dataset(grid_path, 'r')
angle = id.variables['angle'][:-15, :]
lon = id.variables['lon_rho'][:-15, :-1]
lat = id.variables['lat_rho'][:-15, :-1]
mask = id.variables['mask_rho'][:-15, :-1]
id.close()
# Set up grey shading of land
mask = ma.masked_where(mask == 1, mask)
grey_cmap = ListedColormap([(0.6, 0.6, 0.6)])
x_reg, y_reg = meshgrid(linspace(x_min, x_max, num=1000),
linspace(y_min, y_max, num=1000))
land_circle = zeros(shape(x_reg))
lon_c = 50
lat_c = -83
radius = 10.1
x_c = -(lat_c + 90) * cos(lon_c * deg2rad + pi / 2)
y_c = (lat_c + 90) * sin(lon_c * deg2rad + pi / 2)
land_circle = ma.masked_where(
sqrt((x_reg - x_c)**2 + (y_reg - y_c)**2) > radius, land_circle)
# Truncate colourmap
min_colour = threshold / bound
max_colour = 1
trunc_cmap = truncate_colormap(get_cmap('jet'), min_colour, max_colour)
# Read surface velocity
id = Dataset(laplacian_file, 'r')
ur_lap = id.variables['u'][tstep - 1, -1, :-15, :]
vr_lap = id.variables['v'][tstep - 1, -1, :-15, :]
# Rotate to lon-lat space
u_lap, v_lap = rotate_vector_roms(ur_lap, vr_lap, angle)
# Now that they're on the same grid, get the speed
speed_laplacian = sqrt(u_lap**2 + v_lap**2)
# Also read time and convert to Date object
time_id = id.variables['ocean_time']
time = num2date(time_id[tstep - 1],
units=time_id.units,
calendar=time_id.calendar.lower())
# Get the date for the title
date_string = str(
time.day) + ' ' + month_names[time.month - 1] + ' ' + str(time.year)
id.close()
# Repeat velocity for biharmonic simulation
id = Dataset(biharmonic_file, 'r')
ur_bih = id.variables['u'][tstep - 1, -1, :-15, :]
vr_bih = id.variables['v'][tstep - 1, -1, :-15, :]
u_bih, v_bih = rotate_vector_roms(ur_bih, vr_bih, angle)
speed_biharmonic = sqrt(u_bih**2 + v_bih**2)
id.close()
# Calculate x and y coordinates for plotting circumpolar projection
x = -(lat + 90) * cos(lon * deg2rad + pi / 2)
y = (lat + 90) * sin(lon * deg2rad + pi / 2)
speed_laplacian = ma.masked_where(speed_laplacian < threshold,
speed_laplacian)
speed_biharmonic = ma.masked_where(speed_biharmonic < threshold,
speed_biharmonic)
# Make the plot
fig = figure(figsize=(20, 10))
# Laplacian
ax = fig.add_subplot(1, 2, 1, aspect='equal')
# First shade land in grey
pcolor(x, y, mask, cmap=grey_cmap)
pcolor(x_reg, y_reg, land_circle, cmap=grey_cmap)
pcolor(x, y, speed_laplacian, vmin=threshold, vmax=bound, cmap=trunc_cmap)
title('Laplacian viscosity', fontsize=24)
xlim([x_min, x_max])
ylim([y_min, y_max])
ax.set_xticks([])
ax.set_yticks([])
# Biharmonic
ax = fig.add_subplot(1, 2, 2, aspect='equal')
pcolor(x, y, mask, cmap=grey_cmap)
pcolor(x_reg, y_reg, land_circle, cmap=grey_cmap)
img = pcolor(x,
y,
speed_biharmonic,
vmin=threshold,
vmax=bound,
cmap=trunc_cmap)
title('Biharmonic viscosity', fontsize=24)
xlim([-40, 0])
ylim([0, 40])
ax.set_xticks([])
ax.set_yticks([])
# Colourbar on the right
cbaxes = fig.add_axes([0.94, 0.3, 0.02, 0.4])
cbar = colorbar(img,
cax=cbaxes,
extend='max',
ticks=arange(0.2, 1 + 0.2, 0.2))
cbar.ax.tick_params(labelsize=16)
suptitle('Surface speed (m/s): snapshot on ' + date_string, fontsize=30)
subplots_adjust(wspace=0.05)
fig.show()
fig.savefig('bugs_acc.png')
def uv_vectorplot(grid_path,
file_path,
tstep,
depth_key,
save=False,
fig_name=None):
# Radius of the Earth in metres
r = 6.371e6
# Degrees to radians conversion factor
deg2rad = pi / 180
# Side length of blocks to average vectors over (can't plot vector at every
# single point or the plot will be way too crowded)
block = 15
# Read angle from grid file
grid_id = Dataset(grid_path, 'r')
angle = grid_id.variables['angle'][:-15, :]
grid_id.close()
# Read grid and velocity data
id = Dataset(file_path, 'r')
lon = id.variables['lon_rho'][:-15, :-1]
lat = id.variables['lat_rho'][:-15, :-1]
if depth_key == 1:
# Surface u and v
u = id.variables['u'][tstep - 1, -1, :-15, :]
v = id.variables['v'][tstep - 1, -1, :-15, :]
elif depth_key == 2:
# Bottom u and v
u = id.variables['u'][tstep - 1, 0, :-15, :]
v = id.variables['v'][tstep - 1, 0, :-15, :]
elif depth_key == 3:
# Vertically averaged u and v
u = id.variables['ubar'][tstep - 1, :-15, :]
v = id.variables['vbar'][tstep - 1, :-15, :]
id.close()
# Rotate velocities to lat-lon space
u_lonlat, v_lonlat = rotate_vector_roms(u, v, angle)
# Throw away the overlapping periodic boundary
u_rho = u_lonlat[:, :-1]
v_rho = v_lonlat[:, :-1]
# Calculate speed
speed = sqrt(u_rho**2 + v_rho**2)
# Convert velocity to polar coordinates, rotate to account for longitude in
# circumpolar projection, and convert back to vector components
theta = arctan2(v_rho, u_rho)
theta_circ = theta - lon * deg2rad
u_circ = speed * cos(theta_circ)
v_circ = speed * sin(theta_circ)
# Calculate x and y coordinates for plotting circumpolar projection
x = -(lat + 90) * cos(lon * deg2rad + pi / 2)
y = (lat + 90) * sin(lon * deg2rad + pi / 2)
# Average x, y, u_circ, and v_circ over block x block intervals
# Calculate number of blocks
size0 = int(ceil(size(x, 0) / float(block)))
size1 = int(ceil((size(x, 1) - 1) / float(block)))
# Set up arrays for averaged fields
x_block = ma.empty([size0, size1])
y_block = ma.empty([size0, size1])
u_circ_block = ma.empty([size0, size1])
v_circ_block = ma.empty([size0, size1])
# Set up arrays containing boundary indices
posn0 = range(0, size(x, 0), block)
posn0.append(size(x, 0))
posn1 = range(0, size(x, 1), block)
posn1.append(size(x, 1))
# Double loop to average each block (can't find a more efficient way to do
# this)
for j in range(size0):
for i in range(size1):
start0 = posn0[j]
end0 = posn0[j + 1]
start1 = posn1[i]
end1 = posn1[i + 1]
x_block[j, i] = mean(x[start0:end0, start1:end1])
y_block[j, i] = mean(y[start0:end0, start1:end1])
u_circ_block[j, i] = mean(u_circ[start0:end0, start1:end1])
v_circ_block[j, i] = mean(v_circ[start0:end0, start1:end1])
# Make the plot
fig = figure(figsize=(16, 12))
fig.add_subplot(1, 1, 1, aspect='equal')
# Contour speed values at every point
# Use pastel colour map so overlaid vectors will show up
contourf(x, y, speed, 50, cmap='Paired')
cbar = colorbar()
cbar.ax.tick_params(labelsize=20)
# Add vectors for each block
quiver(x_block, y_block, u_circ_block, v_circ_block, color='black')
if depth_key == 1:
title('Surface velocity (m/s)', fontsize=30)
elif depth_key == 2:
title('Bottom velocity (m/s)', fontsize=30)
elif depth_key == 3:
title('Vertically averaged velocity (m/s)', fontsize=30)
axis('off')
if save:
fig.savefig(fig_name)
else:
fig.show()
def overturning_plot (grid_path, file_path, fig_name):
# Grid parameters
theta_s = 4.0
theta_b = 0.9
hc = 40
N = 31
# Read angle from the grid file
grid_id = Dataset(grid_path, 'r')
angle = grid_id.variables['angle'][:-15,:]
grid_id.close()
# Read grid variables
id = Dataset(file_path, 'r')
h = id.variables['h'][:-15,1:-1]
zice = id.variables['zice'][:-15,1:-1]
zeta = id.variables['zeta'][-1,:-15,1:-1]
lon = id.variables['lon_rho'][:-15,1:-1]
lat = id.variables['lat_rho'][:-15,1:-1]
# Read both velocities in x-y space
u_xy = id.variables['u'][-1,:,:-15,:]
v_xy = id.variables['v'][-1,:,:-15,:]
id.close()
# Rotate velocities to lat-lon space
v = ma.empty([N,v_xy.shape[1]+1,v_xy.shape[2]])
for k in range(N):
u_lonlat, v_lonlat = rotate_vector_roms(u_xy[k,:,:], v_xy[k,:,:], angle)
v[k,:,:] = v_lonlat[:,:]
# Throw away the periodic boundary overlap
v = v[:,:,1:-1]
# Calculate Cartesian integrands and z-coordinates
dx, dy, dz, z = cartesian_grid_3d(lon, lat, h, zice, theta_s, theta_b, hc, N, zeta)
# Calculate transport in each cell
transport = v*dx*dz
# Definite integral over longitude
transport = sum(transport, axis=2)
# Indefinite integral over depth; flip before and after so the integral
# starts at the surface, not the bottom. Also convert to Sv.
transport = flipud(cumsum(flipud(transport), axis=0))*1e-6
# Calculate latitude and z coordinates, averaged over longitude,
# for plotting
avg_lat = mean(lat, axis=1)
avg_lat = tile(avg_lat, (N,1))
avg_z = mean(z, axis=2)
# Centre colour scale on 0
max_val = amax(abs(transport))
lev = linspace(-max_val, max_val, num=40)
# Make the plot
figure(figsize=(16,8))
contourf(avg_lat, avg_z, transport, lev, cmap='RdBu_r')
colorbar()
xlabel('Latitude')
ylabel('Depth (m)')
title('Meridional Overturning Streamfunction (Sv)')
#savefig(fig_name)
show()
def timeseries_3D (grid_path, file_path, log_path):
# Grid parameters
theta_s = 4.0
theta_b = 0.9
hc = 40
N = 31
rho0 = 1000.0 # Reference density (kg/m^3)
Cp = 3974 # Specific heat of polar seawater (J/K/kg)
C2K = 273.15 # Celsius to Kelvin conversion
time = []
ohc = []
avgsalt = []
tke = []
# Check if the log file exists
if exists(log_path):
print 'Reading previously calculated values'
f = open(log_path, 'r')
# Skip first line (header for time array)
f.readline()
for line in f:
try:
time.append(float(line))
except(ValueError):
# Reached the header for the next variable
break
for line in f:
try:
ohc.append(float(line))
except(ValueError):
break
for line in f:
try:
avgsalt.append(float(line))
except(ValueError):
break
for line in f:
tke.append(float(line))
f.close()
print 'Analysing grid'
id = Dataset(grid_path, 'r')
h = id.variables['h'][:-15,1:-1]
zice = id.variables['zice'][:-15,1:-1]
lon = id.variables['lon_rho'][:-15,1:-1]
lat = id.variables['lat_rho'][:-15,1:-1]
mask = id.variables['mask_rho'][:-15,1:-1]
# Keep the overlapping periodic boundary on "angle" for now
angle = id.variables['angle'][:-15,:]
id.close()
id = Dataset(file_path, 'r')
# Read time values and convert from seconds to years
new_time = id.variables['ocean_time'][:]/(60*60*24*365.25)
num_time = size(new_time)
# Concatenate with time values from log file
for t in range(num_time):
time.append(new_time[t])
# Process 10 time indices at a time so we don't use too much memory
start_t = 0
while True:
end_t = min(start_t+10, num_time)
print 'Processing time indices ' + str(start_t+1) + ' to ' + str(end_t)
num_time_curr = end_t-start_t
print 'Calculating time-dependent dV'
# Read time-dependent sea surface height
zeta = id.variables['zeta'][start_t:end_t,:-15,1:-1]
# Calculate time-dependent dz
dz = ma.empty([num_time_curr, N, size(lon,0), size(lon,1)])
for t in range(num_time_curr):
# dx and dy will be recomputed unnecessarily each timestep
# but that's ok
dx, dy, dz_tmp, z = cartesian_grid_3d(lon, lat, h, zice, theta_s, theta_b, hc, N, zeta[t,:,:])
dz[t,:,:,:] = dz_tmp
# Calculate time-dependent dV and mask with land mask
# Here mask, dx, dy are all copied into arrays of dimension
# time x depth x lat x lon
dV = ma.masked_where(tile(mask, (num_time_curr,N,1,1))==0, tile(dx, (num_time_curr,1,1,1))*tile(dy, (num_time_curr,1,1,1))*dz)
print 'Reading data'
temp = id.variables['temp'][start_t:end_t,:,:-15,1:-1]
salt = id.variables['salt'][start_t:end_t,:,:-15,1:-1]
rho = id.variables['rho'][start_t:end_t,:,:-15,1:-1] + rho0
# Keep overlapping periodic boundary for u and v
u_xy = id.variables['u'][start_t:end_t,:,:-15,:]
v_xy = id.variables['v'][start_t:end_t,:,:-15,:]
print 'Interpolating velocities onto rho-grid'
# We are actually rotating them at the same time as interpolating
# which is a bit of unnecessary work (sum of squares won't change with
# rotation) but not much extra work, and it's conveneint
u = ma.empty(shape(temp))
v = ma.empty(shape(temp))
for t in range(num_time_curr):
for k in range(N):
u_tmp, v_tmp = rotate_vector_roms(u_xy[t,k,:,:], v_xy[t,k,:,:], angle)
u[t,k,:,:] = u_tmp[:,1:-1]
v[t,k,:,:] = v_tmp[:,1:-1]
print 'Building timeseries'
for t in range(num_time_curr):
# Integrate temp*rho*Cp*dV to get OHC
ohc.append(sum((temp[t,:,:,:]+C2K)*rho[t,:,:,:]*Cp*dV[t,:,:,:]))
# Average salinity (weighted with rho*dV)
avgsalt.append(sum(salt[t,:,:,:]*rho[t,:,:,:]*dV[t,:,:,:])/sum(rho[t,:,:,:]*dV[t,:,:,:]))
# Integrate 0.5*rho*speed^2*dV to get TKE
tke.append(sum(0.5*rho[t,:,:,:]*(u[t,:,:,:]**2 + v[t,:,:,:]**2)*dV[t,:,:,:]))
# Get ready for next 10 time indices
if end_t == num_time:
break
start_t = end_t
id.close()
print 'Plotting ocean heat content'
clf()
plot(time, ohc)
xlabel('Years')
ylabel('Southern Ocean Heat Content (J)')
grid(True)
savefig('ohc.png')
print 'Plotting average salinity'
clf()
plot(time, avgsalt)
xlabel('Years')
ylabel('Southern Ocean Average Salinity (psu)')
grid(True)
savefig('avgsalt.png')
print 'Plotting total kinetic energy'
clf()
plot(time, tke)
xlabel('Years')
ylabel('Southern Ocean Total Kinetic Energy (J)')
grid(True)
savefig('tke.png')
print 'Saving results to log file'
f = open(log_path, 'w')
f.write('Time (years):\n')
for elm in time:
f.write(str(elm) + '\n')
f.write('Southern Ocean Heat Content (J):\n')
for elm in ohc:
f.write(str(elm) + '\n')
f.write('Southern Ocean Average Salinity (psu):\n')
for elm in avgsalt:
f.write(str(elm) + '\n')
f.write('Southern Ocean Total Kinetic Energy (J):\n')
for elm in tke:
f.write(str(elm) + '\n')
f.close()
def circumpolar_plot(file_path,
var_name,
depth_key,
depth,
depth_bounds,
colour_bounds=None,
save=False,
fig_name=None,
grid_path=None):
# Grid parameters
theta_s = 4.0
theta_b = 0.9
hc = 50
N = 31
deg2rad = pi / 180
# Read the variable and figure out if 2D or 3D (not including time)
id = Dataset(file_path, 'r')
if len(id.variables[var_name].shape) == 4:
# 3D variable; will have to choose depth later
data_full = mean(id.variables[var_name][:, :, :, :], axis=0)
choose_depth = True
elif len(id.variables[var_name].shape) == 3:
# 2D variable
data = mean(id.variables[var_name][:, :, :], axis=0)
choose_depth = False
if var_name == 'salt':
units = 'psu'
elif var_name == 'm':
# Convert ice shelf melt rate from m/s to m/yr
units = 'm/year'
data = data * 60. * 60. * 24. * 365.25
else:
units = id.variables[var_name].units
long_name = id.variables[var_name].long_name
# Check for vector variables that need to be rotated
if var_name in [
'ubar', 'vbar', 'u', 'v', 'sustr', 'svstr', 'bustr', 'bvstr'
]:
grid_id = Dataset(grid_path, 'r')
angle = grid_id.variables['angle'][:, :]
grid_id.close()
if var_name in ['ubar', 'sustr', 'bustr']:
# 2D u-variable
u_data = data[:, :]
v_data = mean(id.variables[var_name.replace('u', 'v')][:, :, :],
axis=0)
u_data_lonlat, v_data_lonlat = rotate_vector_roms(
u_data, v_data, angle)
data = u_data_lonlat
elif var_name in ['vbar', 'svstr', 'bvstr']:
# 2D v-variable
v_data = data[:, :]
u_data = mean(id.variables[var_name.replace('v', 'u')][:, :, :],
axis=0)
u_data_lonlat, v_data_lonlat = rotate_vector_roms(
u_data, v_data, angle)
data = v_data_lonlat
elif var_name in ['u']:
# 3D u-variable
data_full_ugrid = data_full[:, :, :]
data_full = ma.empty([
data_full_ugrid.shape[0], data_full_ugrid.shape[1],
data_full_ugrid.shape[2] + 1
])
for k in range(N):
u_data = data_full_ugrid[k, :, :]
v_data = mean(id.variables[var_name.replace('u',
'v')][:, k, :, :],
axis=0)
u_data_lonlat, v_data_lonlat = rotate_vector_roms(
u_data, v_data, angle)
data_full[k, :, :] = u_data_lonlat
elif var_name in ['v']:
# 3D v-variable
data_full_vgrid = data_full[:, :, :]
data_full = ma.empty([
data_full_vgrid.shape[0], data_full_vgrid.shape[1] + 1,
data_full_vgrid.shape[2]
])
for k in range(N):
v_data = data_full_vgrid[k, :, :]
u_data = mean(id.variables[var_name.replace('v',
'u')][:, k, :, :],
axis=0)
u_data_lonlat, v_data_lonlat = rotate_vector_roms(
u_data, v_data, angle)
data_full[k, :, :] = v_data_lonlat
# Read grid variables
h = id.variables['h'][:, :]
zice = id.variables['zice'][:, :]
lon = id.variables['lon_rho'][:, :]
lat = id.variables['lat_rho'][:, :]
mask = id.variables['mask_rho'][:, :]
id.close()
# Throw away the overlapping periodic boundary
#if choose_depth:
# data_full = data_full[:,:,:-1]
#else:
# data = data[:,:-1]
#lon = lon[:,:-1]
#lat = lat[:,:-1]
#h = h[:,:-1]
#zice = zice[:,:-1]
# Convert to spherical coordinates
#x = -(lat+90)*cos(lon*deg2rad+pi/2)
#y = (lat+90)*sin(lon*deg2rad+pi/2)
# Choose what to write on the title about depth
if choose_depth:
if depth_key == 0:
depth_string = 'at surface'
elif depth_key == 1:
depth_string = 'at bottom'
elif depth_key == 2:
depth_string = 'at ' + str(int(round(-depth))) + ' m'
elif depth_key == 3:
depth_string = 'vertically averaged'
elif depth_key == 4:
depth_string = 'vertically averaged between ' + str(
int(round(-depth_bounds[0]))) + ' and ' + str(
int(round(-depth_bounds[1]))) + ' m'
else:
depth_string = ''
if choose_depth:
# For 3D variables, select data corresponding to depth choice
if depth_key == 0:
# Surface layer
data = data_full[-1, :, :]
elif depth_key == 1:
# Bottom layer
data = data_full[0, :, :]
else:
# We will need z-coordinates and possibly dz
dx, dy, dz, z = cartesian_grid_3d(lon, lat, h, zice, theta_s,
theta_b, hc, N)
if depth_key == 2:
# Interpolate to given depth
data = interp_depth(data_full, z, depth)
elif depth_key == 3:
# Vertically average entire water column
data = sum(data_full * dz, axis=0) / sum(dz, axis=0)
elif depth_key == 4:
# Vertically average between given depths
data = average_btw_depths(data_full, z, dz, depth_bounds)
if colour_bounds is not None:
# User has set bounds on colour scale
lev = linspace(colour_bounds[0], colour_bounds[1], num=40)
if colour_bounds[0] == -colour_bounds[1]:
# Bounds are centered on zero, so choose a blue-to-red colourmap
# centered on yellow
colour_map = 'RdYlBu_r'
else:
colour_map = 'jet'
else:
# Determine bounds automatically
if var_name in [
'u', 'v', 'ubar', 'vbar', 'm', 'shflux', 'ssflux', 'sustr',
'svstr', 'bustr', 'bvstr', 'ssflux_restoring'
]:
# Center levels on 0 for certain variables, with a blue-to-red
# colourmap
max_val = amax(abs(data))
lev = linspace(-max_val, max_val, num=40)
colour_map = 'RdYlBu_r'
else:
lev = linspace(amin(data), amax(data), num=40)
colour_map = 'jet'
data = ma.masked_where(mask == 0, data)
# Plot
fig = figure(figsize=(16, 12))
fig.add_subplot(1, 1, 1, aspect='equal')
pcolormesh(mask, cmap='gray_r')
pcolormesh(data,
cmap=colour_map,
vmin=colour_bounds[0],
vmax=colour_bounds[1])
ylim(0, len(data[:, 0]))
xlim(0, len(data[0, :]))
#contourf(x, y, data, lev, cmap=colour_map, extend='both')
cbar = colorbar()
cbar.ax.tick_params(labelsize=20)
title('average ' + long_name + ' (' + units + ')\n' + depth_string,
fontsize=30)
axis('off')
if save:
fig.savefig(fig_name)
else:
fig.show()
def timeseries_3D(grid_path, file_path, log_path):
# Grid parameters
theta_s = 4.0
theta_b = 0.9
hc = 50
N = 31
rho0 = 1027.0 # Reference density (kg/m^3)
Cp = 3974 # Specific heat of polar seawater (J/K/kg)
C2K = 273.15 # Celsius to Kelvin conversion
time = []
ohc = []
ohc_deep = []
avgsalt = []
tke = []
# Check if the log file exists
if exists(log_path):
print('Reading previously calculated values')
f = open(log_path, 'r')
# Skip first line (header for time array)
f.readline()
for line in f:
try:
time.append(float(line))
except (ValueError):
# Reached the header for the next variable
break
for line in f:
try:
ohc.append(float(line))
except (ValueError):
break
for line in f:
try:
ohc_deep.append(float(line))
except (ValueError):
break
for line in f:
try:
avgsalt.append(float(line))
except (ValueError):
break
for line in f:
tke.append(float(line))
f.close()
print('Analysing grid')
id = Dataset(grid_path, 'r')
h = id.variables['h'][:, :]
zice = id.variables['zice'][:, :]
lon = id.variables['lon_rho'][:, :]
lat = id.variables['lat_rho'][:, :]
lon_u = id.variables['lon_u'][:, :]
lat_u = id.variables['lat_u'][:, :]
lon_v = id.variables['lon_v'][:, :]
lat_v = id.variables['lat_v'][:, :]
mask = id.variables['mask_rho'][:, :]
# Keep the overlapping periodic boundary on "angle" for now
angle = id.variables['angle'][:, :]
id.close()
id = Dataset(file_path, 'r')
# Read time values and convert from seconds to years
new_time = id.variables['ocean_time'][:] / (60 * 60 * 24 * 365.25)
num_time = size(new_time)
# Concatenate with time values from log file
for t in range(num_time):
time.append(new_time[t])
# Process 10 time indices at a time so we don't use too much memory
start_t = 0
while True:
end_t = min(start_t + 10, num_time)
print('Processing time indices ' + str(start_t + 1) + ' to ' +
str(end_t))
num_time_curr = end_t - start_t
print('Calculating time-dependent dV')
# Read time-dependent sea surface height
zeta = id.variables['zeta'][start_t:end_t, :, :]
# Calculate time-dependent dz
dz = ma.empty([num_time_curr, N, size(lon, 0), size(lon, 1)])
for t in range(num_time_curr):
# dx and dy will be recomputed unnecessarily each timestep
# but that's ok
dx, dy, dz_tmp, z = cartesian_grid_3d(lon, lat, lon_u, lat_u,
lon_v, lat_v, h, zice,
theta_s, theta_b, hc, N,
zeta[t, :, :])
dz[t, :, :, :] = dz_tmp
# Calculate time-dependent dV and mask with land mask
# Here mask, dx, dy are all copied into arrays of dimension
# time x depth x lat x lon
dV = ma.masked_where(
tile(mask, (num_time_curr, N, 1, 1)) == 0,
tile(dx, (num_time_curr, 1, 1, 1)) *
tile(dy, (num_time_curr, 1, 1, 1)) * dz)
print('Reading data')
temp = id.variables['temp'][start_t:end_t, :, :, :]
salt = id.variables['salt'][start_t:end_t, :, :, :]
rho = id.variables['rho'][start_t:end_t, :, :, :] + rho0
# Keep overlapping periodic boundary for u and v
u_xy = id.variables['u'][start_t:end_t, :, :, :]
v_xy = id.variables['v'][start_t:end_t, :, :, :]
print('Interpolating velocities onto rho-grid')
# We are actually rotating them at the same time as interpolating
# which is a bit of unnecessary work (sum of squares won't change with
# rotation) but not much extra work, and it's conveneint
u = ma.empty(shape(temp))
v = ma.empty(shape(temp))
for t in range(num_time_curr):
for k in range(N):
u_tmp, v_tmp = rotate_vector_roms(u_xy[t, k, :, :],
v_xy[t, k, :, :], angle)
u[t, k, :, :] = u_tmp[:, :]
v[t, k, :, :] = v_tmp[:, :]
print('Building timeseries')
index = z < -2000.0
for t in range(num_time_curr):
# Integrate temp*rho*Cp*dV to get OHC
ohc.append(
sum((temp[t, :, :, :] + C2K) * rho[t, :, :, :] * Cp *
dV[t, :, :, :]))
# Integrate temp*rho*Cp*dV, where deeper than 2000m to get OHC deep
ohc_deep.append(
sum((temp[t, index] + C2K) * rho[t, index] * Cp *
dV[t, index]))
# Average salinity (weighted with rho*dV)
avgsalt.append(
sum(salt[t, :, :, :] * rho[t, :, :, :] * dV[t, :, :, :]) /
sum(rho[t, :, :, :] * dV[t, :, :, :]))
# Integrate 0.5*rho*speed^2*dV to get TKE
tke.append(
sum(0.5 * rho[t, :, :, :] *
(u[t, :, :, :]**2 + v[t, :, :, :]**2) * dV[t, :, :, :]))
# Get ready for next 10 time indices
if end_t == num_time:
break
start_t = end_t
id.close()
print('Plotting ocean heat content')
clf()
plot(time, ohc)
title('Southern Ocean Heat Content')
xlabel('Years')
ylabel('Heat Content (J)')
grid(True)
savefig('ohc.png')
print('Plotting ocean heat content below 2000m')
clf()
plot(time, ohc_deep)
xlabel('Years')
ylabel('Southern Ocean Heat Content (J)')
grid(True)
savefig('ohc_deep.png')
print('Plotting average salinity')
clf()
plot(time, avgsalt)
xlabel('Years')
ylabel('Southern Ocean Average Salinity (psu)')
grid(True)
savefig('avgsalt.png')
print('Plotting total kinetic energy')
clf()
plot(time, tke)
xlabel('Years')
ylabel('Southern Ocean Total Kinetic Energy (J)')
grid(True)
savefig('tke.png')
print('Saving results to log file')
f = open(log_path, 'w')
f.write('Time (years):\n')
for elm in time:
f.write(str(elm) + '\n')
f.write('Southern Ocean Heat Content (J):\n')
for elm in ohc:
f.write(str(elm) + '\n')
f.write('Southern Ocean Heat Content below 2000 m(J):\n')
for elm in ohc_deep:
f.write(str(elm) + '\n')
f.write('Southern Ocean Average Salinity (psu):\n')
for elm in avgsalt:
f.write(str(elm) + '\n')
f.write('Southern Ocean Total Kinetic Energy (J):\n')
for elm in tke:
f.write(str(elm) + '\n')
f.close()
def timeseries_dpt(grid_path, file_path, log_path, add_years=0):
# Radius of the Earth in metres
r = 6.371e6
# Degrees to radians conversion factor
deg2rad = pi / 180.0
# Longitude of Drake Passage zonal slice (convert to ROMS bounds 0-360)
lon0 = -67 + 360
# Latitude bounds on Drake Passage zonal slice
lat_min = -68
lat_max = -54.5
time = []
dpt = []
# Check if the log file exists
if exists(log_path):
print 'Reading previously calculated values'
f = open(log_path, 'r')
# Skip first line (header for time array)
f.readline()
for line in f:
try:
time.append(float(line))
except (ValueError):
# Reached the header for the next variable
break
for line in f:
dpt.append(float(line))
f.close()
print 'Reading grid'
id = Dataset(grid_path, 'r')
h = id.variables['h'][:-15, 1:-1]
zice = id.variables['zice'][:-15, 1:-1]
lon = id.variables['lon_rho'][:-15, 1:-1]
lat = id.variables['lat_rho'][:-15, 1:-1]
# Keep the overlapping periodic boundary on "angle" for now
angle = id.variables['angle'][:-15, :]
id.close()
print 'Reading data'
id = Dataset(file_path, 'r')
# Read time values and convert from seconds to years
new_time = id.variables['ocean_time'][:] / (60 * 60 * 24 *
365.25) + add_years
num_time = size(new_time)
# Concatenate with time values from log file
for t in range(num_time):
time.append(new_time[t])
# Calculate time-dependent water column thickness: h + zice + zeta
zeta = id.variables['zeta'][:, :-15, 1:-1]
wct = tile(h, (num_time, 1, 1)) + tile(zice, (num_time, 1, 1)) + zeta
# Read barotropic velocities in x-y space
ubar_xy = id.variables['ubar'][:, :-15, :]
vbar_xy = id.variables['vbar'][:, :-15, :]
id.close()
print 'Rotating velocity vector'
ubar = ma.empty([num_time, size(lon, 0), size(lon, 1)])
# Rotate one time index at a time
for t in range(num_time):
ubar_tmp, vbar_tmp = rotate_vector_roms(ubar_xy[t, :, :],
vbar_xy[t, :, :], angle)
# Throw away the overlapping periodic boundary before saving
ubar[t, :, :] = ubar_tmp[:, 1:-1]
print 'Extracting zonal slice through Drake Passage' #
num_lat = size(lat, 0)
# Set up arrays for zonal slices of ubar, water column thickness, latitude
ubar_DP = ma.empty([num_time, num_lat])
wct_DP = ma.empty([num_time, num_lat])
lat_DP = empty([num_lat])
# Loop over longitudes
for j in range(num_lat):
lon_tmp = lon[j, :]
# Find indices and coefficients to interpolate to lon0
ie, iw, coeffe, coeffw = interp_lon_helper(lon_tmp, lon0)
# Use these to interpolate all 3 variables we care about
ubar_DP[:, j] = coeffe * ubar[:, j, ie] + coeffw * ubar[:, j, iw]
wct_DP[:, j] = coeffe * wct[:, j, ie] + coeffw * wct[:, j, iw]
lat_DP[j] = coeffe * lat[j, ie] + coeffw * lat[j, iw]
# Find indices for latitude bounds
jS = nonzero(lat_DP > lat_min)[0][0]
jN = nonzero(lat_DP > lat_max)[0][0]
# Trim everything to these bounds
ubar_DP = ubar_DP[:, jS:jN]
wct_DP = wct_DP[:, jS:jN]
lat_DP = lat_DP[jS:jN]
# Calculate dy
# First calculate latitude on edges of each cell
middle_lat = 0.5 * (lat_DP[:-1] + lat_DP[1:])
s_bdry = 2 * lat_DP[0] - middle_lat[0]
n_bdry = 2 * lat_DP[-1] - middle_lat[-1]
lat_edges = zeros(size(lat_DP) + 1)
lat_edges[0] = s_bdry
lat_edges[1:-1] = middle_lat
lat_edges[-1] = n_bdry
# Now calculate difference in latitude across each cell
dlat_DP = lat_edges[1:] - lat_edges[:-1]
# Convert to Cartesian space for dy in metres
dy_DP = r * dlat_DP * deg2rad
for t in range(num_time):
# Integrate ubar*wct*dy and convert to Sv
dpt.append(sum(ubar_DP[t, :] * wct_DP[t, :] * dy_DP) * 1e-6)
print 'Plotting'
clf()
plot(time, dpt)
xlabel('Years')
ylabel('Drake Passage Transport (Sv)')
grid(True)
savefig('drakepsgtrans.png')
print 'Saving results to log file'
f = open(log_path, 'w')
f.write('Time (years):\n')
for elm in time:
f.write(str(elm) + '\n')
f.write('Drake Passage Transport (Sv):\n')
for elm in dpt:
f.write(str(elm) + '\n')
f.close()
def zonal_plot (file_path, var_name, tstep, lon_key, lon0, lon_bounds, depth_min, colour_bounds=None, save=False, fig_name=None, grid_path=None):
# Grid parameters
theta_s = 4.0
theta_b = 0.9
hc = 40
N = 31
# Read the variable
id = Dataset(file_path, 'r')
data_3d = id.variables[var_name][tstep-1,:,:-15,:]
# Also read sea surface height
zeta = id.variables['zeta'][tstep-1,:-15,:]
if var_name == 'salt':
units = 'psu'
else:
units = id.variables[var_name].units
long_name = id.variables[var_name].long_name
# Rotate velocity if necessary
if var_name in ['u', 'v']:
grid_id = Dataset(grid_path, 'r')
angle = grid_id.variables['angle'][:-15,:]
grid_id.close()
if var_name == 'u':
data_3d_ugrid = data_3d[:,:,:]
data_3d = ma.empty([data_3d_ugrid.shape[0], data_3d_ugrid.shape[1], data_3d_ugrid.shape[2]+1])
for k in range(N):
u_data = data_3d_ugrid[k,:,:]
v_data = id.variables['v'][tstep-1,k,:-15,:]
u_data_lonlat, v_data_lonlat = rotate_vector_roms(u_data, v_data, angle)
data_3d[k,:,:] = u_data_lonlat
elif var_name == 'v':
data_3d_vgrid = data_3d[:,:,:]
data_3d = ma.empty([data_3d_vgrid.shape[0], data_3d_vgrid.shape[1]+1, data_3d_vgrid.shape[2]])
for k in range(N):
v_data = data_3d_vgrid[k,:,:]
u_data = id.variables['u'][tstep-1,k,:-15,:]
u_data_lonlat, v_data_lonlat = rotate_vector_roms(u_data, v_data, angle)
data_3d[k,:,:] = v_data_lonlat
# Read grid variables
h = id.variables['h'][:-15,:]
zice = id.variables['zice'][:-15,:]
lon_2d = id.variables['lon_rho'][:-15,:]
lat_2d = id.variables['lat_rho'][:-15,:]
id.close()
# Get a 3D array of z-coordinates; sc_r and Cs_r are unused in this script
z_3d, sc_r, Cs_r = calc_z(h, zice, theta_s, theta_b, hc, N, zeta)
# Choose what to write on the title about longitude
if lon_key == 0:
if lon0 < 0:
lon_string = 'at ' + str(int(round(-lon0))) + r'$^{\circ}$W'
else:
lon_string = 'at ' + str(int(round(lon0))) + r'$^{\circ}$E'
elif lon_key == 1:
lon_string = 'zonally averaged'
elif lon_key == 2:
lon_string = 'zonally averaged between '
if lon_bounds[0] < 0:
lon_string += str(int(round(-lon_bounds[0]))) + r'$^{\circ}$W and '
else:
lon_string += str(int(round(lon_bounds[0]))) + r'$^{\circ}$E and '
if lon_bounds[1] < 0:
lon_string += str(int(round(-lon_bounds[1]))) + r'$^{\circ}$W'
else:
lon_string += str(int(round(lon_bounds[1]))) + r'$^{\circ}$E'
# Edit longitude bounds to be from 0 to 360, to fit with ROMS convention
if lon_key == 0:
if lon0 < 0:
lon0 += 360
elif lon_key == 2:
if lon_bounds[0] < 0:
lon_bounds[0] += 360
if lon_bounds[1] < 0:
lon_bounds[1] += 360
# Interpolate or average data
if lon_key == 0:
# Interpolate to lon0
data, z, lat = interp_lon(data_3d, z_3d, lat_2d, lon_2d, lon0)
elif lon_key == 1:
# Zonally average over all longitudes
# dlon is constant on this grid (0.25 degrees) so this is easy
data = mean(data_3d, axis=2)
z = mean(z_3d, axis=2)
# Zonally average latitude, and copy into N depth levels
lat = tile(mean(lat_2d, axis=1), (N,1))
elif lon_key == 2:
# Zonally average between lon_bounds
data, z, lat = average_btw_lons(data_3d, z_3d, lat_2d, lon_2d, lon_bounds)
if colour_bounds is not None:
# User has set bounds on colour scale
lev = linspace(colour_bounds[0], colour_bounds[1], num=40)
if colour_bounds[0] == -colour_bounds[1]:
# Bounds are centered on zero, so choose a blue-to-red colourmap
# centered on yellow
colour_map = 'RdYlBu_r'
else:
colour_map = 'jet'
else:
# Determine bounds automatically
if var_name in ['u', 'v']:
# Center levels on 0 for certain variables, with a blue-to-red
# colourmap
max_val = amax(abs(data))
lev = linspace(-max_val, max_val, num=40)
colour_map = 'RdYlBu_r'
else:
lev = linspace(amin(data), amax(data), num=40)
colour_map = 'jet'
# Plot
fig = figure(figsize=(18,6))
contourf(lat, z, data, lev, cmap=colour_map, extend='both')
colorbar()
title(long_name + ' (' + units + ')\n' + lon_string)
xlabel('Latitude')
ylabel('Depth (m)')
# Choose latitude bounds based on land mask
data_sum = sum(data, axis=0)
# Find southernmost and northernmost unmasked j-indices
edges = ma.flatnotmasked_edges(data_sum)
j_min = edges[0]
j_max = edges[1]
if j_min == 0:
# There are ocean points right to the southern boundary
# Don't do anything special
lat_min = min(lat[:,j_min])
else:
# There is land everywhere at the southern boundary
# Show the last 2 degrees of this land mask
lat_min = min(lat[:,j_min]) - 2
if j_max == size(data_sum) - 1:
# There are ocean points right to the northern boundary
# Don't do anything special
lat_max = max(lat[:,j_max])
else:
# There is land everywhere at the northern boundary
# Show the first 2 degrees of this land mask
lat_max = max(lat[:,j_max]) + 2
# lat_max = -65
xlim([lat_min, lat_max])
ylim([depth_min, 0])
if save:
fig.savefig(fig_name)
else:
fig.show()
# Reset lon0 or lon_bounds to (-180, 180) range in case we
# use them again for the next plot
if lon_key == 0:
if lon0 > 180:
lon0 -= 360
elif lon_key == 2:
if lon_bounds[0] > 180:
lon_bounds[0] -= 360
if lon_bounds[1] > 180:
lon_bounds[1] -= 360