Comparisons between nowcast, spinup, and observed salinity in the Strait of Juan de Fuca

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')

Load observations¶

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=-124.6; lat_min=48;
lon_max=-123.6; lat_max=48.6;

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, 48.6)

In [7]:

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

Out[7]:

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8e6f02c110>]], 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)

Data model comparisons¶

Compare model salinity profiles with observed in this region. Apply to both spinup and nowcasts.
Focus on all depths

In [9]:

paths = {'nowcast': '/data/dlatorne/MEOPAR/SalishSea/nowcast/',
        'spinup': '/ocean/dlatorne/MEOPAR/SalishSea/results/spin-up/'}
zmax= 250

November

In [10]:

month=11
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2014,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)

December - no data

January - no data

February

In [11]:

month=2
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)

March

In [12]:

month=3
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)

April

In [13]:

month=4
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)

May - no data

June

In [14]:

month=6
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)

July

In [15]:

month=7
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)

Consider comparing locations individually.

2014¶

In [16]:

data_2014 = data_region[data_region['Year']==2014]
data_2014.hist('Month')

Out[16]:

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8e6af7ddd0>]], dtype=object)

Unfortunately, no overlapping data with nowcasts

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