def check_final_grid (grid_path):
grid = Grid(grid_path)
problem = False
# Check there are no isolated bottom cells
# Find points which are open, their neighbour below is closed (i.e. they're at the seafloor), and their horizontal neighoburs are all closed
hfac = grid.hfac
num_valid_neighbours = neighbours(hfac, missing_val=0)[-1]
valid_below = neighbours_z(hfac, missing_val=0)[3]
num_isolated = np.count_nonzero((hfac!=0)*(valid_below==0)*(num_valid_neighbours==0))
if num_isolated > 0:
problem = True
print 'Problem!! There are ' + str(num_isolated) + ' locations with isolated bottom cells.'
# Check that every water column has at least 2 open cells (partial cells count)
open_cells = np.ceil(grid.hfac)
num_pinched = np.count_nonzero(np.sum(open_cells, axis=0)==1)
if num_pinched > 0:
problem = True
print 'Problem!! There are ' + str(num_pinched) + ' locations with only one open cell in the water column.'
# Check that neighbouring ocean cells have at least 2 open faces between
open_cells_w, open_cells_e, open_cells_s, open_cells_n = neighbours(open_cells)[:4]
open_cells_neighbours = [open_cells_w, open_cells_e, open_cells_s, open_cells_n]
loc_strings = ['western', 'eastern', 'southern', 'northern']
for i in range(len(loc_strings)):
problem = check_one_direction(open_cells, open_cells_neighbours[i], loc_strings[i], problem)
if problem:
print 'Something went wrong with the filling or digging. Are you sure that your values of hFacMin and hFacMinDr are correct? Are you working with a version of MITgcm that calculates Ro_sfc and R_low differently?'
else:
print 'Everything looks good!'
def calc_load_anomaly (grid, out_file, option='constant', ini_temp_file=None, ini_salt_file=None, ini_temp=None, ini_salt=None, constant_t=-1.9, constant_s=34.4, eosType='MDJWF', rhoConst=1035, tAlpha=None, sBeta=None, Tref=None, Sref=None, hfac=None, prec=64, check_grid=True):
errorTol = 1e-13 # convergence criteria
# Build the grid if needed
if check_grid:
grid = choose_grid(grid, None)
# Decide which hfac to use
if hfac is None:
hfac = grid.hfac
# Set temperature and salinity
if ini_temp is not None and ini_salt is not None:
# Deep copy of the arrays
temp = np.copy(ini_temp)
salt = np.copy(ini_salt)
elif ini_temp_file is not None and ini_salt_file is not None:
# Read from file
temp = read_binary(ini_temp_file, [grid.nx, grid.ny, grid.nz], 'xyz', prec=prec)
salt = read_binary(ini_salt_file, [grid.nx, grid.ny, grid.nz], 'xyz', prec=prec)
else:
print 'Error (calc_load_anomaly): Must either specify ini_temp and ini_salt OR ini_temp_file and ini_salt_file'
sys.exit()
# Fill in the ice shelves
# The bathymetry will get filled too, but that doesn't matter because pressure is integrated from the top down
closed = hfac==0
if option == 'constant':
# Fill with constant values
temp[closed] = constant_t
salt[closed] = constant_s
elif option == 'nearest':
# Select the layer immediately below the ice shelves and tile to make it 3D
temp_top = xy_to_xyz(select_top(np.ma.masked_where(closed, temp), return_masked=False), grid)
salt_top = xy_to_xyz(select_top(np.ma.masked_where(closed, salt), return_masked=False), grid)
# Fill the mask with these values
temp[closed] = temp_top[closed]
salt[closed] = salt_top[closed]
elif option == 'precomputed':
for data in [temp, salt]:
# Make sure there are no missing values
if (data[~closed]==0).any():
print 'Error (calc_load_anomaly): you selected the precomputed option, but there are appear to be missing values in the land mask.'
sys.exit()
# Make sure it's not a masked array as this will break the rms
if isinstance(data, np.ma.MaskedArray):
# Fill the mask with zeros
data[data.mask] = 0
data = data.data
else:
print 'Error (calc_load_anomaly): invalid option ' + option
sys.exit()
# Get vertical integrands considering z at both centres and edges of layers
dz_merged = np.zeros(2*grid.nz)
dz_merged[::2] = abs(grid.z - grid.z_edges[:-1]) # dz of top half of each cell
dz_merged[1::2] = abs(grid.z_edges[1:] - grid.z) # dz of bottom half of each cell
# Tile to make 3D
z = z_to_xyz(grid.z, grid)
dz_merged = z_to_xyz(dz_merged, grid)
# Initial guess for pressure (dbar) at centres of cells
press = abs(z)*gravity*rhoConst*1e-4
# Iteratively calculate pressure load anomaly until it converges
press_old = np.zeros(press.shape) # Dummy initial value for pressure from last iteration
rms_error = 0
while True:
rms_old = rms_error
rms_error = rms(press, press_old)
print 'RMS error = ' + str(rms_error)
if rms_error < errorTol or np.abs(rms_error-rms_old) < 0.1*errorTol:
print 'Converged'
break
# Save old pressure
press_old = np.copy(press)
# Calculate density anomaly at centres of cells
drho_c = density(eosType, salt, temp, press, rhoConst=rhoConst, Tref=Tref, Sref=Sref, tAlpha=tAlpha, sBeta=sBeta) - rhoConst
# Use this for both centres and edges of cells
drho = np.zeros(dz_merged.shape)
drho[::2,...] = drho_c
drho[1::2,...] = drho_c
# Integrate pressure load anomaly (Pa)
pload_full = np.cumsum(drho*gravity*dz_merged, axis=0)
# Update estimate of pressure
press = (abs(z)*gravity*rhoConst + pload_full[1::2,...])*1e-4
# Extract pload at each level edge (don't care about centres anymore)
pload_edges = pload_full[::2,...]
# Now find pload at the ice shelf base
# For each xy point, calculate three variables:
# (1) pload at the base of the last fully dry ice shelf cell
# (2) pload at the base of the cell beneath that
# (3) hFacC for that cell
# To calculate (1) we have to shift pload_3d_edges upward by 1 cell
pload_edges_above = neighbours_z(pload_edges)[0]
pload_above = select_top(np.ma.masked_where(closed, pload_edges_above), return_masked=False)
pload_below = select_top(np.ma.masked_where(closed, pload_edges), return_masked=False)
hfac_below = select_top(np.ma.masked_where(closed, hfac), return_masked=False)
# Now we can interpolate to the ice base
pload = pload_above + (1-hfac_below)*(pload_below - pload_above)
# Write to file
write_binary(pload, out_file, prec=prec)