Plot Arctic Ocean Map

This (notebook) demonstrates creating an animation of hourly tidal elevations for the Arctic Ocean

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

  • io.GOT.py: extract tidal harmonic constants from GOT 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 tidal maps.

Load modules

import os
import pyproj
import datetime
import numpy as np
import matplotlib
matplotlib.rcParams['axes.linewidth'] = 2.0
matplotlib.rcParams["animation.html"] = "jshtml"
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import cartopy.crs as ccrs
from IPython.display import HTML

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

# autoreload
%load_ext autoreload
%autoreload 2

Set parameters for program

  • Model directory

  • Tide model

  • Date to run

# 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 = 'TPXO8-atlas'
TMDwidgets.VBox([
    TMDwidgets.directory,
    TMDwidgets.model,
    TMDwidgets.compress,
    TMDwidgets.datepick
])

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 tides

# create an image around the Arctic Ocean
# use NSIDC Polar Stereographic definitions
# https://nsidc.org/data/polar-stereo/ps_grids.html
xlimits = [-3850000,3750000]
ylimits = [-5350000,5850000]
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(3413)
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

# convert from calendar date to days relative to Jan 1, 1992 (48622 MJD)
YMD = TMDwidgets.datepick.value
ts = timescale.time.Timescale().from_calendar(YMD.year, YMD.month,
    YMD.day, hour=np.arange(24))

# 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)
    DELTAT = np.zeros_like(ts.tide)
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)
    DELTAT = np.zeros_like(ts.tide)
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)
    # delta time (TT - UT1)
    DELTAT = ts.tt_ut1
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
    # delta time (TT - UT1)
    DELTAT = ts.tt_ut1

# calculate complex phase in radians for Euler's
cph = -1j*ph*np.pi/180.0
# calculate constituent oscillation
hc = amp*np.exp(cph)

# allocate for tide map calculated every hour
tide_cm = np.ma.zeros((ny,nx,24))
for hour in range(24):
    # predict tidal elevations at time and infer minor corrections
    TIDE = pyTMD.predict.map(ts.tide[hour], hc, c, deltat=DELTAT[hour],
        corrections=model.corrections)
    MINOR = pyTMD.predict.infer_minor(ts.tide[hour], hc, c,
        deltat=DELTAT[hour], corrections=model.corrections)
    # add major and minor components and reform grid
    # convert from meters to centimeters
    tide_cm[:,:,hour] = 100.0*np.reshape((TIDE+MINOR),(ny,nx))

Create animation of hourly tidal oscillation

# output Arctic Ocean Tide Animation
projection = ccrs.Stereographic(central_longitude=-45.0,
    central_latitude=+90.0,true_scale_latitude=+70.0)
fig, ax = plt.subplots(num=1, figsize=(8,9),
    subplot_kw=dict(projection=projection))
# plot tide height
vmin,vmax = (np.min(tide_cm), np.max(tide_cm))
extent = (xlimits[0],xlimits[1],ylimits[0],ylimits[1])
im = ax.imshow(np.zeros((ny,nx)), interpolation='nearest',
    vmin=vmin, vmax=vmax, transform=projection,
    extent=extent, origin='upper', animated=True)
# add 50m resolution cartopy coastlines
ax.coastlines('50m')

# 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='both',
    extendfrac=0.0375, shrink=0.90, aspect=25.5, drawedges=False)
# rasterized colorbar to remove lines
cbar.solids.set_rasterized(True)
# Add label to the colorbar
cbar.ax.set_ylabel(f'{model.name} Tide Height', 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=21,
    labelsize=13, direction='in')
# add title (date and time)
ttl = ax.set_title(None, fontsize=13)
# 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=0.95)

# animate each map
def animate_maps(hour):
    # set map data
    im.set_data(tide_cm[:,:,hour])
    # set title
    args = (YMD.year,YMD.month,YMD.day,hour)
    ttl.set_text('{0:4d}-{1:02d}-{2:02d}T{3:02d}:00:00'.format(*args))

# set animation
anim = animation.FuncAnimation(fig, animate_maps, frames=24)
%matplotlib inline
HTML(anim.to_jshtml())