def plot_residual_forcing(ax, runs_list, t_orig):
""" Plots the observed water level residual at Neah Bay against
forced residuals from existing ssh*.txt files for Neah Bay.
Function may produce none, any, or all (nowcast, forecast, forecast 2)
forced residuals depending on availability for specified date (runs_list).
:arg ax: The axis where the residuals are plotted.
:type ax: axis object
:arg runs_list: Runs that are verified as complete.
:type runs_list: list
:arg t_orig: Date being considered.
:type t_orig: datetime object
"""
# truncation times
sdt = t_orig.replace(tzinfo=tz.tzutc())
edt = sdt + datetime.timedelta(days=1)
# retrieve observations, tides and residual
tides = figures.shared.get_tides('Neah Bay', path=paths['tides'])
res_obs, obs = obs_residual_ssh_NOAA('Neah Bay', tides, sdt, sdt)
# truncate and plot
res_obs_trun, time_trun = analyze.truncate_data(np.array(res_obs),
np.array(obs.time), sdt,
edt)
ax.plot(time_trun,
res_obs_trun,
colours['observed'],
label='observed',
lw=2.5)
# plot forcing for each simulation
for mode in runs_list:
filename_NB, run_date = analyze.create_path(mode, t_orig, 'ssh*.txt')
if filename_NB:
dates, surge, fflag = NeahBay_forcing_anom(filename_NB, run_date,
paths['tides'])
surge_t, dates_t = analyze.truncate_data(np.array(surge),
np.array(dates), sdt, edt)
ax.plot(dates_t, surge_t, label=mode, lw=2.5, color=colours[mode])
ax.set_title('Comparison of observed and forced sea surface'
' height residuals at Neah Bay:'
'{t_forcing:%d-%b-%Y}'.format(t_forcing=t_orig))
def get_error_forcing(runs_list, t_orig):
""" Sets up the calculation for the forcing residual error.
:arg runs_list: Runs that have been verified as complete.
:type runs_list: list
:arg t_orig: Date being considered.
:type t_orig: datetime object
:returns: error_frc_dict, t_frc_dict
"""
# truncation times
sdt = t_orig.replace(tzinfo=tz.tzutc())
edt = sdt + datetime.timedelta(days=1)
# retrieve observed residual
tides = figures.shared.get_tides('Neah Bay', path=paths['tides'])
res_obs, obs = obs_residual_ssh_NOAA('Neah Bay', tides, sdt, sdt)
res_obs_trun, time_trun = analyze.truncate_data(np.array(res_obs),
np.array(obs.time), sdt,
edt)
# calculate forcing error
error_frc_dict = {}
t_frc_dict = {}
for mode in runs_list:
filename_NB, run_date = analyze.create_path(mode, t_orig, 'ssh*.txt')
if filename_NB:
dates, surge, fflag = NeahBay_forcing_anom(filename_NB, run_date,
paths['tides'])
surge_t, dates_t = analyze.truncate_data(np.array(surge),
np.array(dates), sdt, edt)
error_frc = analyze.calculate_error(surge_t, dates_t, res_obs_trun,
obs.time)
error_frc_dict[mode] = error_frc
t_frc_dict[mode] = dates_t
return error_frc_dict, t_frc_dict
def get_error_model(names, runs_list, grid_B, t_orig):
""" Sets up the calculation for the model residual error.
:arg names: Names of station.
:type names: list of strings
:arg runs_list: Runs that have been verified as complete.
:type runs_list: list
:arg grid_B: Bathymetry dataset for the Salish Sea NEMO model.
:type grid_B: :class:`netCDF4.Dataset`
:arg t_orig: Date being considered.
:type t_orig: datetime object
:returns: error_mod_dict, t_mod_dict, t_orig_dict
"""
bathy, X, Y = tidetools.get_bathy_data(grid_B)
t_orig_obs = t_orig + datetime.timedelta(days=-1)
t_final_obs = t_orig + datetime.timedelta(days=1)
# truncation times
sdt = t_orig.replace(tzinfo=tz.tzutc())
edt = sdt + datetime.timedelta(days=1)
error_mod_dict = {}
t_mod_dict = {}
for name in names:
error_mod_dict[name] = {}
t_mod_dict[name] = {}
# Look up model grid
lat = figures.SITES[name]['lat']
lon = figures.SITES[name]['lon']
j, i = geo_tools.find_closest_model_point(lon,
lat,
X,
Y,
land_mask=bathy.mask)
# Observed residuals and wlevs and tides
ttide = figures.shared.get_tides(name, path=paths['tides'])
res_obs, wlev_meas = obs_residual_ssh(name, ttide, t_orig_obs,
t_final_obs)
res_obs_trun, time_obs_trun = analyze.truncate_data(
np.array(res_obs), np.array(wlev_meas.time), sdt, edt)
for mode in runs_list:
filename, run_date = analyze.create_path(
mode, t_orig, 'SalishSea_1h_*_grid_T.nc')
grid_T = nc.Dataset(filename)
res_mod, t_model, ssh_corr, ssh_mod = model_residual_ssh(
grid_T, j, i, ttide)
# Truncate
res_mod_trun, t_mod_trun = analyze.truncate_data(
res_mod, t_model, sdt, edt)
# Error
error_mod = analyze.calculate_error(res_mod_trun, t_mod_trun,
res_obs_trun, time_obs_trun)
error_mod_dict[name][mode] = error_mod
t_mod_dict[name][mode] = t_mod_trun
return error_mod_dict, t_mod_dict
def plot_residual_model(axs, names, runs_list, grid_B, t_orig):
""" Plots the observed sea surface height residual against the
sea surface height model residual (calculate_residual) at
specified stations. Function may produce none, any, or all
(nowcast, forecast, forecast 2) model residuals depending on
availability for specified date (runs_list).
:arg ax: The axis where the residuals are plotted.
:type ax: list of axes
:arg names: Names of station.
:type names: list of names
:arg runs_list: Runs that have been verified as complete.
:type runs_list: list
:arg grid_B: Bathymetry dataset for the Salish Sea NEMO model.
:type grid_B: :class:`netCDF4.Dataset`
:arg t_orig: Date being considered.
:type t_orig: datetime object
"""
bathy, X, Y = tidetools.get_bathy_data(grid_B)
t_orig_obs = t_orig + datetime.timedelta(days=-1)
t_final_obs = t_orig + datetime.timedelta(days=1)
# truncation times
sdt = t_orig.replace(tzinfo=tz.tzutc())
edt = sdt + datetime.timedelta(days=1)
for ax, name in zip(axs, names):
# Identify model grid point
lat = figures.SITES[name]['lat']
lon = figures.SITES[name]['lon']
j, i = geo_tools.find_closest_model_point(lon,
lat,
X,
Y,
land_mask=bathy.mask)
# Observed residuals and wlevs and tides
ttide = figures.shared.get_tides(name, path=paths['tides'])
res_obs, wlev_meas = obs_residual_ssh(name, ttide, t_orig_obs,
t_final_obs)
# truncate and plot
res_obs_trun, time_obs_trun = analyze.truncate_data(
np.array(res_obs), np.array(wlev_meas.time), sdt, edt)
ax.plot(time_obs_trun,
res_obs_trun,
c=colours['observed'],
lw=2.5,
label='observed')
for mode in runs_list:
filename, run_date = analyze.create_path(
mode, t_orig, 'SalishSea_1h_*_grid_T.nc')
grid_T = nc.Dataset(filename)
res_mod, t_model, ssh_corr, ssh_mod = model_residual_ssh(
grid_T, j, i, ttide)
# truncate and plot
res_mod_trun, t_mod_trun = analyze.truncate_data(
res_mod, t_model, sdt, edt)
ax.plot(t_mod_trun,
res_mod_trun,
label=mode,
c=colours[mode],
lw=2.5)
ax.set_title('Comparison of modelled sea surface height residuals at'
' {station}: {t:%d-%b-%Y}'.format(station=name, t=t_orig))