In [1]:

from __future__ import division

import datetime
from glob import glob
import os

from IPython.core.display import HTML
import netCDF4 as nc

from salishsea_tools.nowcast import figures
from salishsea_tools.nowcast import residuals

import scipy.io as sio

%matplotlib inline

In [2]:

def results_dataset(period, grid, results_dir):
    """Return the results dataset for period (e.g. 1h or 1d)
    and grid (e.g. grid_T, grid_U) from results_dir.
    """
    filename_pattern = 'SalishSea_{period}_*_{grid}.nc'
    filepaths = glob(os.path.join(results_dir, filename_pattern.format(period=period, grid=grid)))
    return nc.Dataset(filepaths[0])

In [3]:

run_date = datetime.datetime(2014,12,20)

# Results dataset location
results_home = '/data/dlatorne/MEOPAR/SalishSea/nowcast/'
results_dir = os.path.join(results_home, run_date.strftime('%d%b%y').lower())
# model winds
model_path = '/ocean/sallen/allen/research/MEOPAR/Operational/'

In [4]:

grid_T_hr = results_dataset('1h', 'grid_T', results_dir)

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bathy = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')

In [6]:

PNW_coastline = sio.loadmat('/ocean/rich/more/mmapbase/bcgeo/PNW.mat')

Strait of Georgia Modelling, a Pictorial Memoir¶

In [7]:

fig = figures.plot_threshold_website(bathy, grid_T_hr, model_path, PNW_coastline)

The Salish Sea MEOPAR Project: biological-chemical-physical model
Analysis and visualization of model results
Since November, nowcast (noon) and 2 forecasts (1 - 1.5 days) at different times (couple hours after nowcast)
One of the motivations of model is to predict storm surge
Make a difference for coastal communities to know before hand
Website in effort to disseminate results (http://salishsea.eos.ubc.ca/storm-surge/index.html)

In [35]:

fig = figures.compare_tidalpredictions_maxSSH(grid_T_hr, bathy, model_path)

Sea surface height is influenced by tides, atmospheric conditions (winds), and rivers
The corrected model is represented by 8 constituents
The residual is the water levels without tides and it helps us understand how the model represents the water motion without tidal forcing

Questions:

how many constituents was non-corrected model
why was residual helpful

In [36]:

fig = figures.average_winds_at_station(grid_T_hr, bathy, model_path, coastline, 'all')

Website provides average and max winds
Modelled winds are from the High Resolution Deterministic Prediction System of Environment Canada

Add:

Example from paper where you see the importance of winds

In [37]:

fig = figures.PA_tidal_predictions(grid_T_hr)

The website displays a 4 week period centred around the simulation start date
Tidal predictions at point atkinson are calculated using the T_Tide Harmonic Analysis Toolbox on Rich's website
Allows us to see upcoming high/low tide
Like winds, gives us context for what we see in ssh

In [38]:

fig = figures.plot_thresholds_all(grid_T_hr, bathy, model_path, coastline)

In [42]:

t_orig=datetime.datetime(2015,1,1)
t_final = datetime.datetime(2015,1,31)

In [43]:

fig = residuals.compare_errors('Point Atkinson', 'nowcast', t_orig, t_final, bathy)

In [44]:

fig = residuals.compare_errors('Point Atkinson', 'forecast', t_orig, t_final, bathy)

forecast simulation for 2015-01-18 00:00:00 did not occur
forecast simulation for 2015-01-19 00:00:00 did not occur

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