Plot Antarctic Cotidal Charts

This (notebook) demonstrates plotting cotidal charts for selected constituents around Antarctica

OTIS format tidal solutions provided by Oregon State University and ESR

Global Tide Model (GOT) solutions provided by Richard Ray at GSFC

Finite Element Solution (FES) provided by AVISO

Python Dependencies

Program Dependencies

  • arguments.py: load the nodal corrections for tidal constituents

  • astro.py: computes the basic astronomical mean longitudes

  • crs.py: Coordinate Reference System (CRS) routines

  • io.model.py: retrieves tide model parameters for named tide models

  • io.OTIS.py: extract tidal harmonic constants from OTIS tide models

  • io.ATLAS.py: extract tidal harmonic constants from ATLAS netcdf models

  • io.GOT.py: extract tidal harmonic constants from GOT tide models

  • io.FES.py: extract tidal harmonic constants from FES tide models

  • predict.py: predict tidal values using harmonic constants

  • time.py: utilities for calculating time operations

  • utilities.py: download and management utilities for files

This notebook uses Jupyter widgets to set parameters for calculating the cotidal maps.

Load modules

import pyproj
import numpy as np
import matplotlib
matplotlib.rcParams['axes.linewidth'] = 2.0
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

# import tide programs
import pyTMD.io
import pyTMD.predict
import pyTMD.tools
import pyTMD.utilities

# autoreload
%load_ext autoreload
%autoreload 2

Set parameters for program

  • Model directory

  • Tide model

# available model list
model_list = sorted(pyTMD.io.model.ocean_elevation())
# display widgets for setting directory and model
TMDwidgets = pyTMD.tools.widgets()
TMDwidgets.model.options = model_list
TMDwidgets.model.value = 'CATS2008'
TMDwidgets.VBox([
    TMDwidgets.directory,
    TMDwidgets.model,
    TMDwidgets.compress,
])

Setup tide model parameters

# get model parameters
model = pyTMD.io.model(TMDwidgets.directory.value,
    compressed=TMDwidgets.compress.value
   ).elevation(TMDwidgets.model.value)

Setup coordinates for calculating tidal amplitudes

# create an image around Antarctica
xlimits = [-560.*5e3,560.*5e3]
ylimits = [-560.*5e3,560.*5e3]
spacing = [20e3,-20e3]
# x and y coordinates
x = np.arange(xlimits[0],xlimits[1]+spacing[0],spacing[0])
y = np.arange(ylimits[1],ylimits[0]+spacing[1],spacing[1])
xgrid,ygrid = np.meshgrid(x,y)
# x and y dimensions
nx = int((xlimits[1]-xlimits[0])/spacing[0])+1
ny = int((ylimits[0]-ylimits[1])/spacing[1])+1
# convert image coordinates from polar stereographic to latitude/longitude
crs1 = pyproj.CRS.from_epsg(3031)
crs2 = pyproj.CRS.from_epsg(4326)
transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)
lon,lat = transformer.transform(xgrid.flatten(), ygrid.flatten())

Calculate tide map

# read tidal constants and interpolate to grid points
if model.format in ('OTIS','ATLAS-compact','TMD3'):
    amp,ph,D,c = pyTMD.io.OTIS.extract_constants(lon, lat, model.grid_file,
        model.model_file, model.projection, type=model.type, crop=True,
        method='spline', grid=model.file_format)
elif (model.format == 'ATLAS-netcdf'):
    amp,ph,D,c = pyTMD.io.ATLAS.extract_constants(lon, lat, model.grid_file,
        model.model_file, type=model.type, crop=True, method='spline',
        scale=model.scale, compressed=model.compressed)
elif model.format in ('GOT-ascii', 'GOT-netcdf'):
    amp,ph,c = pyTMD.io.GOT.extract_constants(lon, lat, model.model_file,
        grid=model.file_format, crop=True, method='spline', scale=model.scale,
        compressed=model.compressed)
elif (model.format == 'FES-netcdf'):
    amp,ph = pyTMD.io.FES.extract_constants(lon, lat, model.model_file,
        type=model.type, version=model.version, crop=True,
        method='spline', scale=model.scale, compressed=model.compressed)
    c = model.constituents

Select constituent for cotidal chart

TMDwidgets.constituents.options = c
TMDwidgets.constituents.value = c[0]
display(TMDwidgets.constituents)

Create cotidal chart

# extract amplitude and phase for constituent
i = TMDwidgets.constituents.index
cons = TMDwidgets.constituents.value
amplitude = amp[:,i].reshape(ny,nx)
phase = ph[:,i].reshape(ny,nx)

# plot Antarctic cotidal charts
projection = ccrs.Stereographic(central_longitude=0.0,
    central_latitude=-90,true_scale_latitude=-71.0)
fig, ax = plt.subplots(num=1, figsize=(9,8),
    subplot_kw=dict(projection=projection))
# plot tide amplitude
extent = (xlimits[0],xlimits[1],ylimits[0],ylimits[1])
im = ax.imshow(amplitude, interpolation='nearest',
    vmin=0, vmax=amplitude.max(), transform=projection,
    extent=extent, origin='upper')
# plot tide phase
contour_levels = np.arange(0, 360, 30)
contour = ax.contour(xgrid, ygrid, phase,
    contour_levels, colors='0.2', linestyles='solid',
    transform=projection)
# add high resolution cartopy coastlines
ax.coastlines('10m')

# Add colorbar and adjust size
# pad = distance from main plot axis
# extend = add extension triangles to upper and lower bounds
# options: neither, both, min, max
# shrink = percent size of colorbar
# aspect = lengthXwidth aspect of colorbar
cbar = plt.colorbar(im, ax=ax, pad=0.025, extend='max',
    extendfrac=0.0375, shrink=0.85, aspect=22.5, drawedges=False)
# rasterized colorbar to remove lines
cbar.solids.set_rasterized(True)
# Add label to the colorbar
cbar.ax.set_ylabel(f'{model.name} {cons} Tide Chart', labelpad=10, fontsize=13)
cbar.ax.set_title('cm', fontsize=13, va='bottom')
# ticks lines all the way across
cbar.ax.tick_params(which='both', width=1, length=20,
    labelsize=13, direction='in')

# set x and y limits
ax.set_xlim(xlimits)
ax.set_ylim(ylimits)

# stronger linewidth on frame
ax.spines['geo'].set_linewidth(2.0)
ax.spines['geo'].set_capstyle('projecting')
# adjust subplot within figure
fig.subplots_adjust(left=0.02,right=0.98,bottom=0.05,top=0.98)

# show the plot
plt.show()