Compare a single cast from IOS observations to model.

IOS observations span many years. So, compare observations with model on day of, 7 days before and 7 days after. This can account for spring/neap variability but not interannual variabilty in discharge, winds, etc

In [1]:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import ACTDR
import netCDF4 as nc
from salishsea_tools import viz_tools
import numpy as np
import datetime
from salishsea_tools import tidetools

import comparisons

%matplotlib inline

In [2]:

sns.set_style('darkgrid')

In [3]:

grid_B = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')
paths = {'nowcast': '/data/dlatorne/MEOPAR/SalishSea/nowcast/',
        'spinup': '/ocean/dlatorne/MEOPAR/SalishSea/results/spin-up/'}

In [4]:

ACTDR.load_dat('SOG_2000.dat')

> open  SOG_2000.dat
> load CTD_DAT
> load STANDARD_KEYS
> close  SOG_2000.dat
> complete

In [5]:

data = pd.DataFrame(ACTDR.CTD_DAT)

lon_min=-123.1; lat_min=48.5;
lon_max=-123; lat_max=48.8;

data_region = comparisons.isolate_region(data, lon_min, lon_max, lat_min, lat_max)

In [6]:

fig,axm = plt.subplots(1,figsize=(8,5))
data_region.plot(x='Longitude',y='Latitude',kind='scatter', marker='o',ax=axm)
viz_tools.plot_coastline(axm,grid_B,coords='map')
axm.set_xlim([lon_min,lon_max])
axm.set_ylim([lat_min, lat_max])

Out[6]:

(48.5, 48.8)

In [7]:

data_region.hist('Month',bins=np.arange(0.5,13.5))

Out[7]:

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fd94d71c150>]], dtype=object)

In [8]:

data_region.hist('Year', bins= np.arange(data_region['Year'].min()-0.5, data_region['Year'].max() +1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)

In [9]:

zmax= 200
grouped = data_region.groupby('Month')
for month in grouped.groups.keys():
    if month != 8 and month != 9 and month != 10: 
        if month ==11 or month ==12:
            model_year=2014
        else:
            model_year=2015
        fig = comparisons.compare_model_obs(month,model_year,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=28,
                                            vmax=34, xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)

These comparisons are still difficult. Let's separate each cast out. Focus on the eastern Juan de Fuca region.

Cast by cast comparison, organized by month¶

Question - What does the stratification in Boundary Pass look like?

Plot 7 days before and after cast yeardate to attempt to capture spring/neap variability.

Nowcasts¶

In [10]:

vmin=28
month = 11
fig=comparisons.compare_cast_model(month,2014,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=vmin,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)

What is wrong with our depths?

In [11]:

month = 12
fig=comparisons.compare_cast_model(month,2014,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=vmin,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)

In [12]:

month = 1
fig=comparisons.compare_cast_model(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=vmin,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)

In [13]:

month = 4
fig=comparisons.compare_cast_model(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=vmin,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)

In [14]:

month = 7
fig=comparisons.compare_cast_model(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=vmin,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)

Summary¶

Model is fresher in almost all comparisons at pretty much all depths. Does this mean that the water entering Haro Strait is too fresh?
Again, worried about this comparing mode/observations in different years when I know there is a lot of interannual variability in the surface salinity of SoG.
Notice how the model is more or less stratified depending on the tidal cycle.
There seems to be something strange about our model depths.

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