zlev_pom_oper.shape[0])
POM_grid_exp = xr.open_dataset(pom_grid_exp, decode_times=False)
lon_pom_exp = np.asarray(POM_grid_exp['east_e'][:])
lat_pom_exp = np.asarray(POM_grid_exp['north_e'][:])
zlev_pom_exp = np.asarray(POM_grid_exp['zz'][:])
hpom_exp = np.asarray(POM_grid_exp['h'][:])
zmatrix = np.dot(hpom_exp.reshape(-1, 1), zlev_pom_exp.reshape(1, -1))
zmatrix_pom_exp = zmatrix.reshape(hpom_exp.shape[0], hpom_exp.shape[1],
zlev_pom_exp.shape[0])
#%% Reading HYCOM grid
# Reading lat and lon
lines_grid = [line.rstrip() for line in open(hycom_grid_exp + '.b')]
lon_hycom = np.array(readgrids(hycom_grid_exp, 'plon:', [0]))
lat_hycom = np.array(readgrids(hycom_grid_exp, 'plat:', [0]))
# Extracting the longitudinal and latitudinal size array
idm = int([line.split() for line in lines_grid
if 'longitudinal' in line][0][0])
jdm = int([line.split() for line in lines_grid if 'latitudinal' in line][0][0])
afiles = sorted(glob.glob(os.path.join(folder_hycom_exp,
prefix_hycom + '*.a')))
# Reading depths
lines = [line.rstrip() for line in open(afiles[0][:-2] + '.b')]
z = []
for line in lines[6:]:
if line.split()[2] == 'temp':
#%% GOFS 3.1
t = datetime.strptime(date_enterGoM, '%Y/%m/%d/%H/%M')
#t = datetime.strptime(date_midGoM,'%Y/%m/%d/%H/%M')
#t = datetime.strptime(date_landfallGoM,'%Y/%m/%d/%H/%M')
oktime31 = np.where(t31 == t)[0][0]
time31 = t31[oktime31]
# loading surface temperature and salinity
temp31 = GOFS31_ts['water_temp'][oktime31, :, oklat31, oklon31]
#%% Reading HMON-HYCOM ab files
# Reading lat and lon
lines_grid = [line.rstrip() for line in open(Dir_HMON_HYCOM + gridfile + '.b')]
hlon = np.array(readgrids(Dir_HMON_HYCOM + gridfile, 'plon:', [0]))
hlat = np.array(readgrids(Dir_HMON_HYCOM + gridfile, 'plat:', [0]))
# Extracting the longitudinal and latitudinal size array
idm = int([line.split() for line in lines_grid
if 'longitudinal' in line][0][0])
jdm = int([line.split() for line in lines_grid if 'latitudinal' in line][0][0])
afiles = sorted(glob.glob(os.path.join(Dir_HMON_HYCOM, prefix_ab + '*.a')))
# Reading depths
lines = [line.rstrip() for line in open(afiles[0][:-2] + '.b')]
z = []
for line in lines[6:]:
if line.split()[2] == var_name:
#print(line.split()[1])
def HMON_HYCOM_fields(cycle, storm_id, lon_forec_track, lat_forec_track,
lon_lim, lat_lim, temp_lim, salt_lim, temp200_lim,
salt200_lim, tempb_lim, tempt_lim, folder_fig):
#%%
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import os
import os.path
import glob
import cmocean
from matplotlib.dates import date2num, num2date
import xarray as xr
import sys
sys.path.append('/home/Maria.Aristizabal/NCEP_scripts/')
from utils4HYCOM import readgrids
#from utils4HYCOM import readdepth, readVar
from utils4HYCOM2 import readBinz
# Increase fontsize of labels globally
plt.rc('xtick', labelsize=14)
plt.rc('ytick', labelsize=14)
plt.rc('legend', fontsize=14)
#%% Bathymetry file
bath_file = '/scratch2/NOS/nosofs/Maria.Aristizabal/bathymetry_files/GEBCO_2014_2D_-100.0_0.0_-10.0_70.0.nc'
#%% Reading bathymetry data
ncbath = xr.open_dataset(bath_file)
bath_lat = ncbath.variables['lat'][:]
bath_lon = ncbath.variables['lon'][:]
bath_elev = ncbath.variables['elevation'][:]
oklatbath = np.logical_and(bath_lat >= lat_lim[0], bath_lat <= lat_lim[-1])
oklonbath = np.logical_and(bath_lon >= lon_lim[0], bath_lon <= lon_lim[-1])
bath_latsub = bath_lat[oklatbath]
bath_lonsub = bath_lon[oklonbath]
bath_elevs = bath_elev[oklatbath, :]
bath_elevsub = bath_elevs[:, oklonbath]
#%% folder and file names
ti = datetime.today() - timedelta(1)
folder_hmon_hycom = '/scratch2/NOS/nosofs/Maria.Aristizabal/HMON_HYCOM_' + storm_id + '_' + str(
ti.year) + '/' + 'HMON_HYCOM_' + storm_id + '_' + cycle + '/'
#%% Reading RTOFS grid
grid_file = sorted(
glob.glob(os.path.join(folder_hmon_hycom, '*regional.grid.*')))[0][:-2]
#%% Reading RTOFS grid
print('Retrieving coordinates from RTOFS')
# Reading lat and lon
#lines_grid = [line.rstrip() for line in open(grid_file+'.b')]
lon_hycom = np.array(readgrids(grid_file, 'plon:', [0]))
lat_hycom = np.array(readgrids(grid_file, 'plat:', [0]))
#depth_HMON_HYCOM = np.asarray(readdepth(HMON_HYCOM_depth,'depth'))
# Reading depths
afiles = sorted(
glob.glob(os.path.join(folder_hmon_hycom, '*hat10_3z' + '*.a')))
lines = [line.rstrip() for line in open(afiles[0][:-2] + '.b')]
z = []
for line in lines[6:]:
if line.split()[2] == 'temp':
#print(line.split()[1])
z.append(float(line.split()[1]))
depth_HYCOM = np.asarray(z)
#%%
time_HYCOM = []
for x, file in enumerate(afiles):
print(x)
#lines=[line.rstrip() for line in open(file[:-2]+'.b')]
#Reading time stamp
year = int(file.split('/')[-1].split('.')[1][0:4])
month = int(file.split('/')[-1].split('.')[1][4:6])
day = int(file.split('/')[-1].split('.')[1][6:8])
hour = int(file.split('/')[-1].split('.')[1][8:10])
dt = int(file.split('/')[-1].split('.')[-2][1:])
timestamp_HYCOM = date2num(datetime(year, month, day, hour)) + dt / 24
time_HYCOM.append(num2date(timestamp_HYCOM))
# Reading 3D variable from binary file
oktime = 0 # first file
temp_HMON_HYCOM = readBinz(afiles[oktime][:-2], '3z', 'temp')
salt_HMON_HYCOM = readBinz(afiles[oktime][:-2], '3z', 'salinity')
uvel_HMON_HYCOM = readBinz(afiles[oktime][:-2], '3z', 'u-veloc.')
vvel_HMON_HYCOM = readBinz(afiles[oktime][:-2], '3z', 'v-veloc.')
#%%
oklon_HYCOM = np.where(
np.logical_and(lon_hycom[0, :] >= lon_lim[0] + 360,
lon_hycom[0, :] <= lon_lim[1] + 360))[0]
oklat_HYCOM = np.where(
np.logical_and(lat_hycom[:, 0] >= lat_lim[0],
lat_hycom[:, 0] <= lat_lim[1]))[0]
lon_HYCOM = lon_hycom[0, oklon_HYCOM] - 360
lat_HYCOM = lat_hycom[oklat_HYCOM, 0]
sst_HYCOM = temp_HMON_HYCOM[oklat_HYCOM, :, 0][:, oklon_HYCOM]
sss_HYCOM = salt_HMON_HYCOM[oklat_HYCOM, :, 0][:, oklon_HYCOM]
su_HYCOM = uvel_HMON_HYCOM[oklat_HYCOM, :, 0][:, oklon_HYCOM]
sv_HYCOM = vvel_HMON_HYCOM[oklat_HYCOM, :, 0][:, oklon_HYCOM]
#okdepth200 = np.where(depth_HYCOM >= 200)[0][0]
#temp200_HYCOM = temp_HMON_HYCOM[oklat_HYCOM,:,okdepth200][:,oklon_HYCOM]
#salt200_HYCOM = salt_HMON_HYCOM[oklat_HYCOM,:,okdepth200][:,oklon_HYCOM]
#%% SST
kw = dict(levels=np.arange(temp_lim[0], temp_lim[1], 0.5))
plt.figure()
plt.contour(bath_lonsub, bath_latsub, bath_elevsub, levels=[0], colors='k')
plt.contourf(bath_lonsub,
bath_latsub,
bath_elevsub,
levels=[0, 10000],
colors='papayawhip',
alpha=0.5)
plt.contourf(lon_HYCOM,
lat_HYCOM,
sst_HYCOM,
cmap=cmocean.cm.thermal,
**kw)
plt.plot(lon_forec_track, lat_forec_track, '.-', color='grey')
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=14)
cbar.ax.set_ylabel('($^\circ$C)', fontsize=14, labelpad=15)
plt.axis('scaled')
plt.xlim(lon_lim[0], lon_lim[1])
plt.ylim(lat_lim[0], lat_lim[1])
plt.title('HMON-HYCOM SST ' + 'Storm ' + storm_id + ' Cycle ' + cycle +
'\n on ' + str(time_HYCOM[oktime])[0:13],
fontsize=16)
file_name = folder_fig + 'HMON_HYCOM_SST_' + cycle
plt.savefig(file_name, bbox_inches='tight', pad_inches=0.1)
#%% SSS
kw = dict(levels=np.arange(salt_lim[0], salt_lim[1], 0.5))
plt.figure()
plt.contour(bath_lonsub, bath_latsub, bath_elevsub, levels=[0], colors='k')
plt.contourf(bath_lonsub,
bath_latsub,
bath_elevsub,
levels=[0, 10000],
colors='papayawhip',
alpha=0.5)
plt.contourf(lon_HYCOM, lat_HYCOM, sss_HYCOM, cmap=cmocean.cm.haline, **kw)
plt.plot(lon_forec_track, lat_forec_track, '.-', color='grey')
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=14)
plt.axis('scaled')
plt.xlim(lon_lim[0], lon_lim[1])
plt.ylim(lat_lim[0], lat_lim[1])
plt.title('HMON-HYCOM SSS ' + 'Storm ' + storm_id + ' Cycle ' + cycle +
'\n on ' + str(time_HYCOM[oktime])[0:13],
fontsize=16)
file_name = folder_fig + 'HMON_HYCOM_SSS_' + cycle
plt.savefig(file_name, bbox_inches='tight', pad_inches=0.1)
#%% SST and velocity vectors
kw = dict(levels=np.arange(temp_lim[0], temp_lim[1], 0.5))
plt.figure()
plt.contour(bath_lonsub, bath_latsub, bath_elevsub, levels=[0], colors='k')
plt.contourf(bath_lonsub,
bath_latsub,
bath_elevsub,
levels=[0, 10000],
colors='papayawhip',
alpha=0.5)
plt.contourf(lon_HYCOM,
lat_HYCOM,
sst_HYCOM,
cmap=cmocean.cm.thermal,
**kw)
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=14)
cbar.ax.set_ylabel('($^\circ$C)', fontsize=14, labelpad=15)
plt.axis('scaled')
plt.xlim(lon_lim[0], lon_lim[1])
plt.ylim(lat_lim[0], lat_lim[1])
plt.title('HMON-HYCOM SST ' + 'Storm ' + storm_id + ' Cycle ' + cycle +
'\n on ' + str(time_HYCOM[oktime])[0:13],
fontsize=16)
q = plt.quiver(lon_HYCOM[::10], lat_HYCOM[::10], su_HYCOM[::10, ::10],
sv_HYCOM[::10, ::10])
#plt.quiverkey(q,np.min(lon_HYCOM)-5,np.max(lat_HYCOM)-5,1,"1 m/s",coordinates='data',color='k',fontproperties={'size': 14})
file_name = folder_fig + 'HMON_HYCOM_SST_UV_' + cycle
plt.savefig(file_name, bbox_inches='tight', pad_inches=0.1)
#%% Temp 200 meters
'''
kw = dict(levels = np.linspace(10,25,31))
plt.figure()
plt.contour(bath_lonsub,bath_latsub,bath_elevsub,levels=[0],colors='k')
plt.contourf(bath_lonsub,bath_latsub,bath_elevsub,levels=[0,10000],colors='papayawhip',alpha=0.5)
plt.contourf(lon_HYCOM,lat_HYCOM,temp200_HYCOM,cmap=cmocean.cm.thermal) #,**kw)
cbar = plt.colorbar()
cbar.ax.set_ylabel('($^\circ$C)',fontsize=14,labelpad=15)
cbar.ax.tick_params(labelsize=14)
plt.title('HMON-HYCOM Temperatute at 200 m \n on '+str(time_HYCOM[oktime])[0:13],fontsize=16)
file_name = folder_fig + 'HWRF_POM_temp200_' + str(time_HYCOM[0])[0:10]
plt.savefig(file_name,bbox_inches = 'tight',pad_inches = 0.1)
'''
#%% Salt 200 meters
'''
plt.figure()
plt.contour(bath_lonsub,bath_latsub,bath_elevsub,levels=[0],colors='k')
plt.contourf(bath_lonsub,bath_latsub,bath_elevsub,levels=[0,10000],colors='papayawhip',alpha=0.5)
plt.contourf(lon_HYCOM,lat_HYCOM,salt200_HYCOM,cmap=cmocean.cm.haline) #,**kw)
cbar = plt.colorbar()
cbar.ax.set_ylabel('($^\circ$C)',fontsize=14,labelpad=15)
cbar.ax.tick_params(labelsize=14)
plt.title('HMON-HYCOM Salinity at 200 m \n on '+str(time_HYCOM[oktime])[0:13],fontsize=16)
file_name = folder_fig + 'HWRF_POM_salt200_' + str(time_HYCOM[0])[0:10]
plt.savefig(file_name,bbox_inches = 'tight',pad_inches = 0.1)
'''
#%% Figure temp transect along storm path
lat_forec_tracku, ind = np.unique(lat_forec_track, return_index=True)
lon_forec_tracku = lon_forec_track[ind]
lon_forec_track_interp = np.interp(lat_hycom[:, 0],
lat_forec_tracku,
lon_forec_tracku,
left=np.nan,
right=np.nan)
lat_forec_track_interp = np.copy(lat_hycom[:, 0])
lat_forec_track_interp[np.isnan(lon_forec_track_interp)] = np.nan
lon_forec_track_int = lon_forec_track_interp[np.isfinite(
lon_forec_track_interp)]
lat_forec_track_int = lat_forec_track_interp[np.isfinite(
lat_forec_track_interp)]
oklon = np.round(
np.interp(lon_forec_track_int, lon_hycom[0, :] - 360,
np.arange(len(lon_hycom[0, :])))).astype(int)
oklat = np.round(
np.interp(lat_forec_track_int, lat_hycom[:, 0],
np.arange(len(lat_hycom[:, 0])))).astype(int)
trans_temp_HYCOM = temp_HMON_HYCOM[oklat, oklon, :]
kw = dict(levels=np.arange(tempt_lim[0], tempt_lim[1], 1))
plt.figure()
plt.contourf(lat_hycom[oklat, 0],
-depth_HYCOM,
trans_temp_HYCOM.T,
cmap=cmocean.cm.thermal,
**kw)
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=16)
plt.contour(lat_hycom[oklat, 0],
-depth_HYCOM,
trans_temp_HYCOM.T, [26],
color='k')
cbar.ax.set_ylabel('($^\circ$C)', fontsize=14)
cbar.ax.tick_params(labelsize=14)
plt.ylabel('Depth (m)', fontsize=14)
plt.xlabel('Latitude ($^o$)', fontsize=14)
plt.title('HMON-HYCOM Temperature ' + 'Storm ' + storm_id + ' Cycle ' +
cycle + '\n along Forecasted Storm Track',
fontsize=16)
plt.ylim([-300, 0])
file = folder_fig + 'HMON_HYCOM_temp_along_forecasted_track_' + cycle
plt.savefig(file, bbox_inches='tight', pad_inches=0.1)
okt = np.isfinite(tempg_gridded[t, :])
if sum(depthg_gridded[okt] > 100) > 10:
if tempg_gridded[t, 0] != tempg_gridded[t, 20]:
tempg_full.append(tempg_gridded[t, :])
timeg_full.append(tt)
tempg_full = np.asarray(tempg_full)
timeg_full = np.asarray(timeg_full)
#%% Reading RTOFS grid
# Reading lat and lon
lines_grid = [line.rstrip() for line in open(gridfile + '.b')]
#hlon = np.array(readgrids(gridfile,'relax'))
hlon = np.array(readgrids(gridfile, 'plon:', [0]))
hlat = np.array(readgrids(gridfile, 'plat:', [0]))
# Extracting the longitudinal and latitudinal size array
idm = int([line.split() for line in lines_grid
if 'longitudinal' in line][0][0])
jdm = int([line.split() for line in lines_grid if 'latitudinal' in line][0][0])
#%% Reading RTOFS ab files
nz = 41
layers = np.arange(0, nz)
target_temp_RTOFS = np.empty((
(tmax - tmin).days + 1,
nz,
))
lonRTOFS = np.asarray(ncRTOFS.Longitude[:])
depthRTOFS = np.asarray(ncRTOFS.Depth[:])
tRTOFS = []
for t in np.arange(len(nc_files_RTOFS)):
ncRTOFS = xr.open_dataset(folder_RTOFS + fol + '/' + nc_files_RTOFS[t])
tRTOFS.append(np.asarray(ncRTOFS.MT[:])[0])
tRTOFS = np.asarray([mdates.num2date(mdates.date2num(tRTOFS[t])) \
for t in np.arange(len(nc_files_RTOFS))])
#%% Reading RTOFS-DA grid
print('Retrieving coordinates from RTOFS')
# Reading lat and lon
lines_grid = [line.rstrip() for line in open(RTOFS_DA_grid + '.b')]
lon_RTOFS_DA = np.array(readgrids(RTOFS_DA_grid, 'plon:', [0]))
lat_RTOFS_DA = np.array(readgrids(RTOFS_DA_grid, 'plat:', [0]))
depth_RTOFS_DA = np.asarray(readdepth(RTOFS_DA_depth, 'depth'))
#%% Looping through all gliders found
for id in gliders[1:]:
#id=gliders[1]
print('Reading ' + id)
e.dataset_id = id
e.constraints = constraints
e.variables = variables
# Converting glider data to data frame
df = e.to_pandas(
index_col='time (UTC)',
#%% folder and file names
ti = datetime.today() - timedelta(1)
folder_hmon_hycom = '/scratch2/NOS/nosofs/Maria.Aristizabal/HMON_HYCOM_' + storm_id + '_' + str(
ti.year) + '/' + 'HMON_HYCOM_' + storm_id + '_' + cycle + '/'
#%% Reading RTOFS grid
grid_file = sorted(
glob.glob(os.path.join(folder_hmon_hycom, '*regional.grid.*')))[0][:-2]
#%% Reading RTOFS grid
print('Retrieving coordinates from RTOFS')
# Reading lat and lon
#lines_grid = [line.rstrip() for line in open(grid_file+'.b')]
lon_hycom = np.array(readgrids(grid_file, 'plon:', [0]))
lat_hycom = np.array(readgrids(grid_file, 'plat:', [0]))
#depth_HMON_HYCOM = np.asarray(readdepth(HMON_HYCOM_depth,'depth'))
# Reading depths
afiles = sorted(glob.glob(os.path.join(folder_hmon_hycom,
'*hat10_3z' + '*.a')))
lines = [line.rstrip() for line in open(afiles[0][:-2] + '.b')]
z = []
for line in lines[6:]:
if line.split()[2] == 'temp':
#print(line.split()[1])
z.append(float(line.split()[1]))
depth_HYCOM = np.asarray(z)
import sys
sys.path.append('/home/Maria.Aristizabal/NCEP_scripts/')
from utils4HYCOM import readgrids
#from utils4HYCOM import readdepth, readVar
from utils4HYCOM2 import readBinz
# Increase fontsize of labels globally
plt.rc('xtick', labelsize=14)
plt.rc('ytick', labelsize=14)
plt.rc('legend', fontsize=14)
#%% Reading RTOFS grid
print('Retrieving coordinates from RTOFS')
# Reading lat and lon
lines_grid = [line.rstrip() for line in open(HMON_HYCOM_grid + '.b')]
lon_HMON_HYCOM = np.array(readgrids(HMON_HYCOM_grid, 'plon:', [0]))
lat_HMON_HYCOM = np.array(readgrids(HMON_HYCOM_grid, 'plat:', [0]))
#depth_HMON_HYCOM = np.asarray(readdepth(HMON_HYCOM_depth,'depth'))
# Reading depths
afiles = sorted(
glob.glob(os.path.join(folder_HMON_HYCOM, '*' + prefix + '*.a')))
lines = [line.rstrip() for line in open(afiles[0][:-2] + '.b')]
z = []
for line in lines[6:]:
if line.split()[2] == 'temp':
#print(line.split()[1])
z.append(float(line.split()[1]))
z_HMON_HYCOM = np.asarray(z)
tt31 = np.asarray(tim31)
tti = datetime.strptime(date_ini, '%Y-%m-%dT%H:%M:%SZ')
tte = datetime.strptime(date_end, '%Y-%m-%dT%H:%M:%SZ')
oktime31 = np.logical_and(tt31 >= tti, tt31 <= tte)
time31 = tt31[oktime31]
timestamp31 = date2num(time31)
temp31 = tem31[:, oktime31]
#%% Reading HMON-HYCOM ab files
# Reading lat and lon
lines_grid = [line.rstrip() for line in open(Dir_HMON_HYCOM + gridfile + '.b')]
hlon = np.array(readgrids(Dir_HMON_HYCOM + gridfile, 'plon:', [0]))
hlat = np.array(readgrids(Dir_HMON_HYCOM + gridfile, 'plat:', [0]))
# Extracting the longitudinal and latitudinal size array
idm = int([line.split() for line in lines_grid
if 'longitudinal' in line][0][0])
jdm = int([line.split() for line in lines_grid if 'latitudinal' in line][0][0])
afiles = sorted(glob.glob(os.path.join(Dir_HMON_HYCOM, prefix_ab + '*.a')))
# Reading depths
lines = [line.rstrip() for line in open(afiles[0][:-2] + '.b')]
z = []
for line in lines[6:]:
if line.split()[2] == var_name:
#print(line.split()[1])
def HMON_HYCOM_cross_transect(storm_id, cycle, tempt_lim_MAB, tempt_lim_GoM):
#%%
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import os
import os.path
import glob
import cmocean
from matplotlib.dates import date2num, num2date
import xarray as xr
import sys
sys.path.append('/home/Maria.Aristizabal/NCEP_scripts/')
from utils4HYCOM import readgrids
#from utils4HYCOM import readdepth, readVar
from utils4HYCOM2 import readBinz
# Increase fontsize of labels globally
plt.rc('xtick', labelsize=14)
plt.rc('ytick', labelsize=14)
plt.rc('legend', fontsize=14)
#%%
ti = datetime.today()
ffig = '/home/Maria.Aristizabal/Figures/' + str(
ti.year) + '/' + ti.strftime('%b-%d')
folder_fig = ffig + '/' + storm_id + '_' + cycle + '/'
os.system('mkdir ' + ffig)
os.system('mkdir ' + folder_fig)
#%% Bathymetry file
bath_file = '/scratch2/NOS/nosofs/Maria.Aristizabal/bathymetry_files/GEBCO_2014_2D_-100.0_0.0_-10.0_70.0.nc'
#%% Reading bathymetry data
ncbath = xr.open_dataset(bath_file)
bath_lat = ncbath.variables['lat'][:]
bath_lon = ncbath.variables['lon'][:]
bath_elev = ncbath.variables['elevation'][:]
#%% folder and file names
ti = datetime.today() - timedelta(1)
folder_hmon_hycom = '/scratch2/NOS/nosofs/Maria.Aristizabal/HMON_HYCOM_' + storm_id + '_' + str(
ti.year) + '/' + 'HMON_HYCOM_' + storm_id + '_' + cycle + '/'
#%% Reading RTOFS grid
grid_file = sorted(
glob.glob(os.path.join(folder_hmon_hycom, '*regional.grid.*')))[0][:-2]
#%% Reading RTOFS grid
print('Retrieving coordinates from RTOFS')
# Reading lat and lon
#lines_grid = [line.rstrip() for line in open(grid_file+'.b')]
lon_hycom = np.array(readgrids(grid_file, 'plon:', [0]))
lat_hycom = np.array(readgrids(grid_file, 'plat:', [0]))
#depth_HMON_HYCOM = np.asarray(readdepth(HMON_HYCOM_depth,'depth'))
# Reading depths
afiles = sorted(
glob.glob(os.path.join(folder_hmon_hycom, '*hat10_3z' + '*.a')))
lines = [line.rstrip() for line in open(afiles[0][:-2] + '.b')]
z = []
for line in lines[6:]:
if line.split()[2] == 'temp':
#print(line.split()[1])
z.append(float(line.split()[1]))
depth_HYCOM = np.asarray(z)
time_HYCOM = []
for x, file in enumerate(afiles):
print(x)
#lines=[line.rstrip() for line in open(file[:-2]+'.b')]
#Reading time stamp
year = int(file.split('/')[-1].split('.')[1][0:4])
month = int(file.split('/')[-1].split('.')[1][4:6])
day = int(file.split('/')[-1].split('.')[1][6:8])
hour = int(file.split('/')[-1].split('.')[1][8:10])
dt = int(file.split('/')[-1].split('.')[-2][1:])
timestamp_HYCOM = date2num(datetime(year, month, day, hour)) + dt / 24
time_HYCOM.append(num2date(timestamp_HYCOM))
# Reading 3D variable from binary file
oktime = 0 # first file
temp_HMON_HYCOM = readBinz(afiles[oktime][:-2], '3z', 'temp')
#salt_HMON_HYCOM = readBinz(afiles[oktime][:-2],'3z','salinity')
#%%
x1 = -74.1
y1 = 39.4
x2 = -73.0
y2 = 38.6
# Slope
m = (y1 - y2) / (x1 - x2)
# Intercept
b = y1 - m * x1
X = np.arange(x1, -72, 0.05)
Y = b + m * X
dist = np.sqrt((X - x1)**2 +
(Y - y1)**2) * 111 # approx along transect distance in km
oklon = np.round(
np.interp(X, lon_hycom[0, :] - 360,
np.arange(len(lon_hycom[0, :])))).astype(int)
oklat = np.round(
np.interp(Y, lat_hycom[:, 0],
np.arange(len(lat_hycom[:, 0])))).astype(int)
trans_temp_HYCOM = temp_HMON_HYCOM[oklat, oklon, :]
#min_valt = 4
#max_valt = 27
nlevelst = tempt_lim_MAB[1] - tempt_lim_MAB[0] + 1
kw = dict(levels=np.linspace(tempt_lim_MAB[0], tempt_lim_MAB[1], nlevelst))
fig, ax = plt.subplots(figsize=(9, 3))
plt.contourf(dist,
-depth_HYCOM,
trans_temp_HYCOM.T,
cmap=cmocean.cm.thermal,
**kw)
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=16)
plt.contour(dist, -depth_HYCOM, trans_temp_HYCOM.T, [26], color='k')
cbar.ax.set_ylabel('($^\circ$C)', fontsize=14)
cbar.ax.tick_params(labelsize=14)
plt.ylabel('Depth (m)', fontsize=14)
plt.xlabel('Along Transect Distance (km)', fontsize=14)
plt.title('HMON-HYCOM Endurance Line ' + 'Storm ' + storm_id + ' Cycle ' +
cycle + ' \n on ' + str(time_HYCOM[oktime])[0:13],
fontsize=16)
plt.ylim([-100, 0])
plt.xlim([0, 200])
file = folder_fig + 'HMON_HYCOM_temp_MAB_endurance_line_cycle_' + cycle
plt.savefig(file, bbox_inches='tight', pad_inches=0.1)
#%% Bathymetry GEBCO HYCOM domain
kw = dict(levels=np.arange(-5000, 1, 200))
plt.figure()
plt.contour(bath_lon, bath_lat, bath_elev, levels=[0], colors='k')
plt.contourf(bath_lon, bath_lat, bath_elev, cmap=cmocean.cm.topo, **kw)
plt.plot(X, Y, '-k')
plt.colorbar()
plt.axis('scaled')
plt.title('GEBCO Bathymetry')
plt.xlim(-76, -70)
plt.ylim(35, 42)
file = folder_fig + 'MAB_transect'
plt.savefig(file, bbox_inches='tight', pad_inches=0.1)
#%%
x1 = -90
y1 = 20 + 52 / 60
x2 = -90
y2 = 30
Y = np.arange(y1, y2, 0.05)
X = np.tile(x1, len(Y))
dist = np.sqrt((X - x1)**2 +
(Y - y1)**2) * 111 # approx along transect distance in km
oklon = np.round(
np.interp(X, lon_hycom[0, :] - 360,
np.arange(len(lon_hycom[0, :])))).astype(int)
oklat = np.round(
np.interp(Y, lat_hycom[:, 0],
np.arange(len(lat_hycom[:, 0])))).astype(int)
trans_temp_HYCOM = temp_HMON_HYCOM[oklat, oklon, :]
#min_valt = 12
#max_valt = 32
nlevelst = tempt_lim_GoM[1] - tempt_lim_GoM[0] + 1
kw = dict(levels=np.linspace(tempt_lim_GoM[0], tempt_lim_GoM[1], nlevelst))
fig, ax = plt.subplots(figsize=(9, 3))
plt.contourf(dist,
-depth_HYCOM,
trans_temp_HYCOM.T,
cmap=cmocean.cm.thermal,
**kw)
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=16)
plt.contour(dist, -depth_HYCOM, trans_temp_HYCOM.T, [26], color='k')
cbar.ax.set_ylabel('($^\circ$C)', fontsize=14)
cbar.ax.tick_params(labelsize=14)
plt.ylabel('Depth (m)', fontsize=14)
plt.xlabel('Along Transect Distance (km)', fontsize=14)
plt.title('HMON-HYCOM Across GoMex ' + 'Storm ' + storm_id + ' Cycle ' +
cycle + ' \n on ' + str(time_HYCOM[oktime])[0:13],
fontsize=16)
plt.ylim([-300, 0])
#plt.xlim([0,200])
file = folder_fig + 'HMON_HYCOM_temp_GoMex_across_cycle_' + cycle
plt.savefig(file, bbox_inches='tight', pad_inches=0.1)
#%% Bathymetry GEBCO HYCOM domain
kw = dict(levels=np.arange(-5000, 1, 200))
plt.figure()
plt.contour(bath_lon, bath_lat, bath_elev, levels=[0], colors='k')
plt.contourf(bath_lon, bath_lat, bath_elev, cmap=cmocean.cm.topo, **kw)
plt.plot(X, Y, '-k')
plt.colorbar()
plt.axis('scaled')
plt.title('GEBCO Bathymetry')
plt.xlim(-98, -80)
plt.ylim(18, 32)
file = folder_fig + 'GoMex_transect'
plt.savefig(file, bbox_inches='tight', pad_inches=0.1)
