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Commit 6cf0fc2e authored by thopri's avatar thopri
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updated FES scripts

parent 14ffd7d3
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Showing with 93 additions and 55 deletions
inputs/namelist_remote.bdy
View file @ 6cf0fc2e
......@@ -67,8 +67,8 @@
!------------------------------------------------------------------------------
! unstructured open boundaries tidal parameters
!------------------------------------------------------------------------------
ln_tide = .false. ! =T : produce bdy tidal conditions
sn_tide_model = 'FES' ! Name of tidal model (FES|TPXO)
ln_tide = .true. ! =T : produce bdy tidal conditions
sn_tide_model = 'fes' ! Name of tidal model (fes|tpxo)
clname(1) = 'M2' ! constituent name
clname(2) = 'S2'
clname(3) = 'K2'
......@@ -86,7 +86,7 @@
! TPXO file locations
sn_tide_grid = '/Users/thopri/Projects/PyNEMO/DATA/TPXO/grid_tpxo7.2.nc'
sn_tide_h = '/Users/thopri/Projects/PyNEMO/DATA/TPXO/h_tpxo7.2.nc'
sn_tide_u = './Users/thopri/Projects/PyNEMO/DATA/TPXO/u_tpxo7.2.nc'
sn_tide_u = '/Users/thopri/Projects/PyNEMO/DATA/TPXO/u_tpxo7.2.nc'
! location of FES data
sn_tide_fes = '/Users/thopri/Projects/PyNEMO/DATA/FES/'
......
pynemo/tide/fes_extract_HC.py
View file @ 6cf0fc2e
......@@ -19,70 +19,96 @@ class FesExtract(object):
Tidal files"""
# Set tide model
tide_model = 'fes'
if tide_model == 'fes': # Define stuff to generalise Tide model
hRe_name = 'hRe'
hIm_name = 'hIm'
lon_z_name = 'lon'
lat_z_name = 'lat'
lon_z_name = 'lon_z'
lat_z_name = 'lat_z'
URe_name = 'URe'
UIm_name = 'UIm'
lon_u_name = 'lon'
lat_u_name = 'lat'
lon_u_name = 'lon_u'
lat_u_name = 'lat_u'
VRe_name = 'VRe'
VIm_name = 'VIm'
lon_v_name = 'lon'
lat_v_name = 'lat'
mz_name = 'mz'
mu_name = 'mu'
mv_name = 'mv'
lon_v_name = 'lon_v'
lat_v_name = 'lat_v'
mz_name = 'mask_z'
mu_name = 'mask_u'
mv_name = 'mask_v'
#constituents = ['2N2', 'EPS2', 'J1', 'K1', 'K2', 'L2', 'LA2', 'M2', 'M3', 'M4', 'M6', 'M8', 'MF', 'MKS2',
#'MM', 'MN4', 'MS4', 'MSF', 'MSQM', 'MTM', 'MU2', 'N2', 'N4', 'NU2', 'O1', 'P1', 'Q1', 'R2',
#'S1', 'S2', 'S4', 'SA', 'SSA', 'T2']
constituents = ['M2','S2']
self.constituents = ['M2','S2']
# extract lon and lat z data
lon_z = Dataset[settings['tide_fes']+constituents[0]+'_Z.nc'].variables['lon']
lat_z = Dataset[settings['tide_fes']+constituents[0]+'_Z.nc'].variabels['lat']
lon_z = np.array(Dataset(settings['tide_fes']+self.constituents[0]+'_Z.nc').variables['lon'])
lat_z = np.array(Dataset(settings['tide_fes']+self.constituents[0]+'_Z.nc').variables['lat'])
lon_resolution = lon_z[1] - lon_z[0]
data_in_km = 0 # added to maintain the reference to matlab tmd code
self.cons = constituents
#read and convert the height_dataset file to complex
for ncon in constituents:
amp = Dataset(settings['tide_fes']+constituents[ncon]+'_Z.nc').variables['amplitude'][:]
phase = Dataset(settings['tide_fes']+constituents[ncon]+'_Z.nc').variables['phase'][:]
# create empty dictionaries to store harmonics in.
self.height_dataset = {}
self.Uvelocity_dataset = {}
self.Vvelocity_dataset = {}
self.mask_dataset = {}
# extract example amplitude grid for Z, U and V and change NaNs to 0 (for land) and other values to 1 (for water)
mask_z = np.array(np.rot90(Dataset(settings['tide_fes']+self.constituents[0]+'_Z.nc').variables['amplitude'][:]))
where_are_NaNs = np.isnan(mask_z)
where_no_NaNs = np.invert(np.isnan(mask_z))
mask_z[where_no_NaNs] = 1
mask_z[where_are_NaNs] = 0
self.mask_dataset[mz_name] = mask_z
mask_u = np.array(np.rot90(Dataset(settings['tide_fes']+self.constituents[0]+'_U.nc').variables['Ua'][:]))
where_are_NaNs = np.isnan(mask_u)
where_no_NaNs = np.invert(np.isnan(mask_u))
mask_u[where_no_NaNs] = 1
mask_u[where_are_NaNs] = 0
self.mask_dataset[mu_name] = mask_u
mask_v = np.array(np.rot90(Dataset(settings['tide_fes']+self.constituents[0]+'_V.nc').variables['Va'][:]))
where_are_NaNs = np.isnan(mask_v)
where_no_NaNs = np.invert(np.isnan(mask_v))
mask_v[where_no_NaNs] = 1
mask_v[where_are_NaNs] = 0
self.mask_dataset[mv_name] = mask_v
#read and convert the height_dataset file to complex and store in dicts
for ncon in range(len(self.constituents)):
amp = np.array(np.rot90(Dataset(settings['tide_fes']+str(self.constituents[ncon])+'_Z.nc').variables['amplitude'][:]))
phase = np.array(np.rot90(Dataset(settings['tide_fes']+self.constituents[ncon]+'_Z.nc').variables['phase'][:]))
lat_z = np.array(Dataset(settings['tide_fes']+self.constituents[ncon]+'_Z.nc').variables['lat'][:])
lon_z = np.array(Dataset(settings['tide_fes']+self.constituents[ncon]+'_Z.nc').variables['lon'][:])
hRe = amp*np.sin(phase)
hIm = amp*np.cos(phase)
self.height_dataset = {'hRe':hRe,'hIm':hIm}
self.height_dataset[self.constituents[ncon]] = {'lat_z':lat_z,'lon_z':lon_z,'hRe':hRe,'hIm':hIm}
#read and convert the velocity_dataset files to complex
for ncon in constituents:
amp = Dataset(settings['tide_fes']+constituents[ncon]+'_U.nc').variables['Ua'][:]
phase = Dataset(settings['tide_fes']+constituents[ncon]+'_U.nc').variables['Ug'][:]
for ncon in range(len(self.constituents)):
amp = np.array(np.rot90(Dataset(settings['tide_fes']+self.constituents[ncon]+'_U.nc').variables['Ua'][:]))
phase = np.array(np.rot90(Dataset(settings['tide_fes']+self.constituents[ncon]+'_U.nc').variables['Ug'][:]))
lat_u = np.array(Dataset(settings['tide_fes']+self.constituents[ncon]+'_U.nc').variables['lat'][:])
lon_u = np.array(Dataset(settings['tide_fes']+self.constituents[ncon]+'_U.nc').variables['lon'][:])
URe = amp*np.sin(phase)
UIm = amp*np.cos(phase)
self.Uvelocity_dataset = {'URe':URe,'UIm':UIm}
self.Uvelocity_dataset[self.constituents[ncon]] = {'lat_u':lat_u,'lon_u':lon_u,'URe':URe,'UIm':UIm}
for ncon in constituents:
amp = Dataset(settings['tide_fes']+constituents[ncon]+'_V.nc').variables['Va'][:]
phase = Dataset(settings['tide_fes']+constituents[ncon]+'_V.nc').variables['Vg'][:]
for ncon in range(len(self.constituents)):
amp = np.array(np.rot90(Dataset(settings['tide_fes']+self.constituents[ncon]+'_V.nc').variables['Va'][:]))
phase = np.array(np.rot90(Dataset(settings['tide_fes']+self.constituents[ncon]+'_V.nc').variables['Vg'][:]))
lat_v = np.array(Dataset(settings['tide_fes']+self.constituents[ncon]+'_V.nc').variables['lat'][:])
lon_v = np.array(Dataset(settings['tide_fes']+self.constituents[ncon]+'_V.nc').variables['lon'][:])
VRe = amp*np.sin(phase)
VIm = amp*np.cos(phase)
self.Vvelocity_dataset = {'VRe':VRe,'VIm':VIm}
self.Vvelocity_dataset[self.constituents[ncon]] = {'lat_v':lat_v,'lon_v':lon_v,'VRe':VRe,'VIm':VIm}
# extract example amplitude grid and change NaNs to 0 (for land) and other values to 1 (for water)
mask_z = Dataset(settings['tide_fes']+constituents[ncon]+'_Z.nc').variables['amplitude'][:]
where_are_NaNs = np.isnan(mask_z)
where_no_NaNs = np.invert(np.isnan(mask_z))
mask_z[where_no_NaNs] = 1
mask_z[where_are_NaNs] = 0
# need to sort for FES
self.grid = Dataset(settings['tide_grid'])#../data/tide/grid_tpxo7.2.nc')
height_z = self.grid.variables['hz']
# open grid variables these are resampled TPXO parameters so may not work correctly.
self.grid = Dataset(settings['tide_fes']+'grid_fes.nc')
height_z = np.array(np.rot90(self.grid.variables['hz']))
else:
print('Don''t know that tide model')
......@@ -92,8 +118,7 @@ class FesExtract(object):
if lon_z[-1]-lon_z[0] == 360-lon_resolution:
glob = 1
if glob == 1:
lon_z = np.concatenate(([lon_z[0]-lon_resolution, ], lon_z,
[lon_z[-1]+lon_resolution, ]))
lon_z = np.concatenate(([lon_z[0]-lon_resolution, ], lon_z,[lon_z[-1]+lon_resolution, ]))
height_z = np.concatenate(([height_z[-1, :], ], height_z, [height_z[0, :],]), axis=0)
mask_z = np.concatenate(([mask_z[-1, :], ], mask_z, [mask_z[0, :], ]), axis=0)
......@@ -158,17 +183,18 @@ class FesExtract(object):
def interpolate_constituents(self, nc_dataset, real_var_name, img_var_name, lon_var_name,
lat_var_name, lon, lat, height_data=None, maskname=None):
""" Interpolates the tidal constituents along the given lat lon coordinates """
amp = np.zeros((nc_dataset.variables['con'].shape[0], lon.shape[0],))
gph = np.zeros((nc_dataset.variables['con'].shape[0], lon.shape[0],))
data = np.array(np.ravel(nc_dataset.variables[real_var_name]), dtype=complex)
data.imag = np.array(np.ravel(nc_dataset.variables[img_var_name]))
amp = np.zeros((len(nc_dataset), nc_dataset[list(nc_dataset)[0]]['lon_z'].shape[0]))
gph = np.zeros((len(nc_dataset), nc_dataset[list(nc_dataset)[0]]['lon_z'].shape[0]))
data = np.array(np.ravel(nc_dataset['M2'][real_var_name]), dtype=complex)
data.imag = np.array(np.ravel(nc_dataset['M2'][img_var_name]))
data = data.reshape(nc_dataset.variables[real_var_name].shape)
#data[data==0] = np.NaN
data = data.reshape(nc_dataset['M2'][real_var_name].shape)
# data[data==0] = np.NaN
#Lat Lon values
x_values = nc_dataset.variables[lon_var_name][:, 0]
y_values = nc_dataset.variables[lat_var_name][0, :]
# Lat Lon values
x_values = nc_dataset['M2'][lon_var_name]
y_values = nc_dataset['M2'][lat_var_name]
x_resolution = x_values[1] - x_values[0]
glob = 0
if x_values[-1]-x_values[0] == 360-x_resolution:
......@@ -188,12 +214,13 @@ class FesExtract(object):
lonlat = np.concatenate((lon, lat))
lonlat = np.reshape(lonlat, (lon.size, 2), order='F')
mask = self.grid.variables[maskname]
mask = self.mask_dataset[maskname]
mask = np.concatenate(([mask[-1, :], ], mask, [mask[0, :], ]), axis=0)
#interpolate the mask values
maskedpoints = interpolate.interpn((x_values, y_values), mask, lonlat)
data_temp = np.zeros((data.shape[0], lon.shape[0], 2, ))
#check at same point in TPXO extract HC script.
for cons_index in range(data.shape[0]):
#interpolate real values
data_temp[cons_index, :, 0] = interpolate_data(x_values, y_values,
......
pynemo/tide/nemo_bdy_tide3.py
View file @ 6cf0fc2e
......@@ -34,16 +34,27 @@ def nemo_bdy_tide_rot(setup, DstCoord, Grid_T, Grid_U, Grid_V, comp,tide_model):
#convert the dst_lon into TMD Conventions (0E/360E)
dst_lon[dst_lon < 0.0] = dst_lon[dst_lon < 0.0]+360.0
#extract the surface elevation at each z-point
tpxo_z = tpxo_extract_HC.TpxoExtract(setup.settings, dst_lat, dst_lon, g_type)
if tide_model == 'tpxo':
tpxo_z = tpxo_extract_HC.TpxoExtract(setup.settings, dst_lat, dst_lon, g_type)
if tide_model == 'fes':
fes_z = fes_extract_HC.FesExtract(setup.settings,dst_lat,dst_lon,g_type)
#convert back the z-longitudes into the usual conventions (-180E/+180E)
dst_lon[dst_lon > 180.0] = dst_lon[dst_lon > 180.0]-360.0
#check if elevation data are missing
ind = np.where((np.isnan(tpxo_z.amp)) | (np.isnan(tpxo_z.gph)))
if tide_model == 'tpxo':
ind = np.where((np.isnan(tpxo_z.amp)) | (np.isnan(tpxo_z.gph)))
if tide_model == 'fes':
ind = np.where((np.isnan(fes_z.amp)) | (np.isnan(fes_z.gph)))
if ind[0].size > 0:
logger.warning('Missing elveation along the open boundary')
if tide_model == 'tpxo':
ampz = tpxo_z.amp
phaz = tpxo_z.gph
if tide_model == 'fes':
ampz = fes_z.amp
phaz = fes_z.gph
ampz = tpxo_z.amp
phaz = tpxo_z.gph
ampz[ind] = 0.0
phaz[ind] = 0.0
......
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