The temperature in the bottom of the Salish Sea for nowcast-green is much too cold. Can this impact the density and in turn the deep water renewal?
In [1]:
import matplotlib.pyplot as plt
import numpy as np
import netCDF4 as nc
import datetime
from dateutil import tz
import os
import pandas as pd
from salishsea_tools import (
geo_tools,
places,
psu_tools,
teos_tools,
data_tools,
tidetools,
)
from nowcast import analyze
from nowcast.figures import shared
%matplotlib inline
In [2]:
grid_B=nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_downonegrid.nc')
mesh_mask = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/mesh_mask_downbyone.nc')
In [3]:
t_o=datetime.datetime(2016,2,7); t_f = datetime.datetime(2016,7,6)
fnames = analyze.get_filenames(t_o, t_f, '1d', 'grid_T', '/results/SalishSea/nowcast-green/')
In [4]:
def get_onc_TS_time_series(station, t_o, t_f):
"""Grab the ONC temperature and salinity time series for a station between dates
t_o and t_f. Return results as separate temperature and salinty data frames."""
numdays = (t_f-t_o).days
dates = [t_o + datetime.timedelta(days=num)
for num in range(0, numdays+1)]
sal_pd = pd.DataFrame({'time':[],
'data': []})
temp_pd = pd.DataFrame({'time': [],
'data': []})
station_code = places.PLACES[station]['ONC stationCode']
for date in dates:
onc_data = data_tools.get_onc_data(
'scalardata', 'getByStation', os.environ['ONC_USER_TOKEN'],
station=station_code,
deviceCategory='CTD', sensors='salinity,temperature',
dateFrom=data_tools.onc_datetime(date, 'utc'))
try:
ctd_data=data_tools.onc_json_to_dataset(onc_data)
#quality control
qc_sal = np.array(ctd_data.salinity.qaqcFlag)
qc_temp = np.array(ctd_data.temperature.qaqcFlag)
#average
sal_pd = sal_pd.append({'time': ctd_data.salinity.sampleTime.values[0],
'data': ctd_data.salinity.values[qc_sal==1].mean()},
ignore_index=True)
temp_pd = temp_pd.append({'time': ctd_data.temperature.sampleTime.values[0],
'data': ctd_data.temperature.values[qc_temp==1].mean()},
ignore_index=True)
except TypeError:
print('No data for {} at {}'.format(date, station))
return sal_pd, temp_pd
In [5]:
def get_model_time_series(station, fnames, grid_B, mesh_mask, nemo_36=True):
"""Retrieve the density, salinity and temperature time series at a station.
Time series is created from files listed in fnames"""
if nemo_36:
depth_var='gdept_0'
depth_var_w = 'gdepw_0'
else:
depth_var='gdept'
depth_var_w = 'gdepw'
#station info
lon = places.PLACES[station]['lon lat'][0]
lat = places.PLACES[station]['lon lat'][1]
depth = places.PLACES[station]['depth']
# model corresponding locations and variables
bathy, X, Y = tidetools.get_bathy_data(grid_B)
j, i = geo_tools.find_closest_model_point(lon,lat,X,Y, land_mask=bathy.mask)
model_depths = mesh_mask.variables[depth_var][0,:,j,i]
tmask = mesh_mask.variables['tmask'][0,:,j,i]
wdeps = mesh_mask.variables[depth_var_w][0,:,j,i]
sal, time = analyze.combine_files(fnames,'vosaline','None',j,i)
temp, time = analyze.combine_files(fnames,'votemper','None',j,i)
# interpolate:
sal_interp=np.array(
[shared.interpolate_tracer_to_depths(
sal[i,:],model_depths,depth,tmask,wdeps)
for i in range(sal.shape[0])])
temp_interp=np.array(
[shared.interpolate_tracer_to_depths(
temp[i,:],model_depths,depth,tmask,wdeps)
for i in range(temp.shape[0])])
# convert to psu for using density function
if nemo_36:
sal_interp = teos_tools.teos_psu(sal_interp)
density = psu_tools.calculate_density(temp_interp, sal_interp)
return density, sal_interp, temp_interp, time
In [6]:
rhos = {}
times = {}
sals={}
temps={}
stations = ['Central node', 'East node', 'Delta BBL node', 'Delta DDL node']
for station in stations:
print(station)
rhos[station],sals[station], temps[station], times[station] = get_model_time_series(station, fnames,
grid_B, mesh_mask)
Central node East node Delta BBL node Delta DDL node
In [7]:
fig, axs = plt.subplots(3,1,figsize=(10,10), sharex=True)
colours = ['b', 'g', 'r', 'c']
variables = [rhos, sals, temps]
names = ['Density [kg/m^3]', 'Salinty [psu]', 'Temperature [C]']
titles = ['density', 'salinity', 'temperature']
for station, col in zip(stations, colours):
obs_sal, obs_temp = get_onc_TS_time_series(station, t_o, t_f)
obs_rho = pd.DataFrame({'time':[], 'data':[]})
obs_rho.data = psu_tools.calculate_density(obs_temp['data'].values, obs_sal['data'].values )
obs_rho.time = obs_temp.time
obs = [obs_rho, obs_sal,obs_temp]
for var, name, title, ax, ob in zip(variables, names, titles, axs, obs):
ax.plot(ob.time, ob.data, '--',c=col, label='obs',lw=2)
ax.plot(times[station], var[station], c=col, label=station,lw=2)
ax.set_ylabel(name)
for ax, title in zip(axs,titles):
ax.grid()
ax.legend(loc=(1,0.25))
ax.get_yaxis().get_major_formatter().set_useOffset(False)
ax.set_title('Daily averaged {} field'.format(title))
No data for 2016-05-02 00:00:00 at Central node No data for 2016-04-07 00:00:00 at East node No data for 2016-04-08 00:00:00 at East node No data for 2016-04-09 00:00:00 at East node No data for 2016-04-10 00:00:00 at East node No data for 2016-04-11 00:00:00 at East node No data for 2016-04-12 00:00:00 at East node No data for 2016-04-13 00:00:00 at East node No data for 2016-04-14 00:00:00 at East node No data for 2016-04-15 00:00:00 at East node No data for 2016-04-16 00:00:00 at East node No data for 2016-04-17 00:00:00 at East node No data for 2016-04-18 00:00:00 at East node No data for 2016-04-19 00:00:00 at East node No data for 2016-04-20 00:00:00 at East node No data for 2016-04-21 00:00:00 at East node No data for 2016-04-22 00:00:00 at East node No data for 2016-04-23 00:00:00 at East node No data for 2016-04-24 00:00:00 at East node No data for 2016-04-25 00:00:00 at East node No data for 2016-04-26 00:00:00 at East node No data for 2016-04-27 00:00:00 at East node No data for 2016-04-28 00:00:00 at East node No data for 2016-04-29 00:00:00 at East node No data for 2016-04-30 00:00:00 at East node No data for 2016-04-30 00:00:00 at Delta BBL node No data for 2016-05-01 00:00:00 at Delta BBL node No data for 2016-02-07 00:00:00 at Delta DDL node No data for 2016-02-08 00:00:00 at Delta DDL node No data for 2016-02-09 00:00:00 at Delta DDL node No data for 2016-02-10 00:00:00 at Delta DDL node No data for 2016-02-11 00:00:00 at Delta DDL node No data for 2016-02-12 00:00:00 at Delta DDL node No data for 2016-02-13 00:00:00 at Delta DDL node No data for 2016-02-14 00:00:00 at Delta DDL node No data for 2016-02-15 00:00:00 at Delta DDL node No data for 2016-02-16 00:00:00 at Delta DDL node No data for 2016-02-17 00:00:00 at Delta DDL node No data for 2016-02-18 00:00:00 at Delta DDL node No data for 2016-02-19 00:00:00 at Delta DDL node No data for 2016-02-20 00:00:00 at Delta DDL node No data for 2016-02-21 00:00:00 at Delta DDL node No data for 2016-02-22 00:00:00 at Delta DDL node No data for 2016-02-23 00:00:00 at Delta DDL node No data for 2016-02-24 00:00:00 at Delta DDL node No data for 2016-02-25 00:00:00 at Delta DDL node No data for 2016-02-26 00:00:00 at Delta DDL node No data for 2016-02-27 00:00:00 at Delta DDL node No data for 2016-02-28 00:00:00 at Delta DDL node No data for 2016-02-29 00:00:00 at Delta DDL node No data for 2016-03-01 00:00:00 at Delta DDL node No data for 2016-03-02 00:00:00 at Delta DDL node No data for 2016-03-03 00:00:00 at Delta DDL node No data for 2016-03-04 00:00:00 at Delta DDL node No data for 2016-03-05 00:00:00 at Delta DDL node No data for 2016-03-06 00:00:00 at Delta DDL node No data for 2016-03-07 00:00:00 at Delta DDL node No data for 2016-03-08 00:00:00 at Delta DDL node No data for 2016-03-09 00:00:00 at Delta DDL node No data for 2016-03-10 00:00:00 at Delta DDL node No data for 2016-03-11 00:00:00 at Delta DDL node No data for 2016-03-12 00:00:00 at Delta DDL node No data for 2016-03-13 00:00:00 at Delta DDL node No data for 2016-03-14 00:00:00 at Delta DDL node No data for 2016-03-15 00:00:00 at Delta DDL node No data for 2016-03-16 00:00:00 at Delta DDL node No data for 2016-03-17 00:00:00 at Delta DDL node No data for 2016-03-18 00:00:00 at Delta DDL node No data for 2016-03-19 00:00:00 at Delta DDL node No data for 2016-03-20 00:00:00 at Delta DDL node No data for 2016-03-21 00:00:00 at Delta DDL node No data for 2016-03-22 00:00:00 at Delta DDL node No data for 2016-03-23 00:00:00 at Delta DDL node No data for 2016-03-24 00:00:00 at Delta DDL node No data for 2016-03-25 00:00:00 at Delta DDL node No data for 2016-03-26 00:00:00 at Delta DDL node No data for 2016-03-27 00:00:00 at Delta DDL node No data for 2016-03-28 00:00:00 at Delta DDL node No data for 2016-03-29 00:00:00 at Delta DDL node No data for 2016-03-30 00:00:00 at Delta DDL node No data for 2016-03-31 00:00:00 at Delta DDL node No data for 2016-04-01 00:00:00 at Delta DDL node No data for 2016-04-02 00:00:00 at Delta DDL node No data for 2016-04-03 00:00:00 at Delta DDL node No data for 2016-04-04 00:00:00 at Delta DDL node No data for 2016-04-05 00:00:00 at Delta DDL node No data for 2016-04-06 00:00:00 at Delta DDL node No data for 2016-04-07 00:00:00 at Delta DDL node No data for 2016-04-08 00:00:00 at Delta DDL node No data for 2016-04-09 00:00:00 at Delta DDL node No data for 2016-04-10 00:00:00 at Delta DDL node No data for 2016-04-11 00:00:00 at Delta DDL node No data for 2016-04-12 00:00:00 at Delta DDL node No data for 2016-04-13 00:00:00 at Delta DDL node No data for 2016-04-14 00:00:00 at Delta DDL node No data for 2016-04-15 00:00:00 at Delta DDL node No data for 2016-04-16 00:00:00 at Delta DDL node No data for 2016-04-17 00:00:00 at Delta DDL node No data for 2016-04-18 00:00:00 at Delta DDL node No data for 2016-04-19 00:00:00 at Delta DDL node No data for 2016-04-20 00:00:00 at Delta DDL node No data for 2016-04-21 00:00:00 at Delta DDL node No data for 2016-04-22 00:00:00 at Delta DDL node No data for 2016-04-23 00:00:00 at Delta DDL node No data for 2016-04-24 00:00:00 at Delta DDL node No data for 2016-04-25 00:00:00 at Delta DDL node No data for 2016-04-26 00:00:00 at Delta DDL node No data for 2016-04-27 00:00:00 at Delta DDL node No data for 2016-04-28 00:00:00 at Delta DDL node No data for 2016-04-29 00:00:00 at Delta DDL node No data for 2016-04-30 00:00:00 at Delta DDL node No data for 2016-05-01 00:00:00 at Delta DDL node No data for 2016-05-02 00:00:00 at Delta DDL node
** East renewal event in late June**
- Why did the temperature increase so much in model? Summer renewals typically bring in colder water. Could it be related to boundary conditions at JDF? I modified salinity but not temperature, although I believe there was a warm bias in June:
- Compared to observatiobns, we are cold at East and above so maybe the warm spike is not that bizarre...
- Even so, the cold temperature at Central is making the density so high that I doubt we will renew this summer. A quick look at ONC State of the ocean plots suggests that observed density spikes at East node are typically less than .5 kg/m^3 during renweal events. http://www.oceannetworks.ca/in%C2%ADstal%C2%ADla%C2%ADtions/observatories/salish-sea/eastern-strait-georgia#SOO-SG-East
** DDL and BBL**
- It looks like we might start off salty at the DDL and BBL depths (107m, 143m)
** Note on QA/QC**
- Only used ONC data with qcflag =1, meaning data passed all tests. http://www.oceannetworks.ca/data-tools/data-quality
- I wonder if there is a way to get their 'cleaned' data through the api. The data on their plotting utility looks very different from what we download.
** Other notes**
- I started the comparison in February because we switched to TEOS-10 then...
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