In [1]:
import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, viz_tools
import gsw
import matplotlib.gridspec as gridspec
import matplotlib as mpl
import matplotlib.dates as mdates
import cmocean as cmo
import scipy.interpolate as sinterp
import pickle
import cmocean
import json
import f90nml
from collections import OrderedDict
fs=16
mpl.rc('xtick', labelsize=fs)
mpl.rc('ytick', labelsize=fs)
mpl.rc('legend', fontsize=fs)
mpl.rc('axes', titlesize=fs)
mpl.rc('axes', labelsize=fs)
mpl.rc('figure', titlesize=fs)
mpl.rc('font', size=fs)
mpl.rc('text', usetex=True)
mpl.rc('text.latex', preamble = r'''
\usepackage{txfonts}
\usepackage{lmodern}
''')
mpl.rc('font', family='sans-serif', weight='normal', style='normal')
import warnings
warnings.filterwarnings('ignore')
from IPython.display import Markdown, display
%matplotlib inline
In [2]:
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')
Out[2]:
In [3]:
PATH= '/results2/SalishSea/nowcast-green.201905/'
year=2007
In [4]:
# Parameters
year = 2016
In [5]:
display(Markdown('''# Year: '''+ str(year)))
Yearly model-data comparisons of nutrients, chlorophyll, temperature and salinity between 201905 runs and DFO observations¶
Define date range and load observations¶
In [6]:
start_date = dt.datetime(year,1,1)
end_date = dt.datetime(year,12,31)
flen=1
namfmt='nowcast'
filemap={'nitrate':'ptrc_T','silicon':'ptrc_T','ammonium':'ptrc_T','diatoms':'ptrc_T',
'ciliates':'ptrc_T','flagellates':'ptrc_T','vosaline':'grid_T','votemper':'grid_T'}
fdict={'ptrc_T':1,'grid_T':1}
df1=et.loadDFO(datelims=(start_date,end_date))
print(len(df1),'data points')
df1[['Year','Month','Day','Lat','Lon','Pressure','Depth','N','Si','Chlorophyll_Extracted',
'ConsT','AbsSal']].head()
Out[6]:
In [7]:
data=et.matchData(df1,filemap,fdict,start_date,end_date,'nowcast',PATH,1,quiet=True);
In [8]:
# density calculations:
data['rho']=gsw.rho(data['AbsSal'],data['ConsT'],data['Pressure'])
data['mod_rho']=gsw.rho(data['mod_vosaline'],data['mod_votemper'],
gsw.p_from_z(-1*data['Z'],data['Lat']))
In [9]:
# load chl to N ratio from namelist
nml=f90nml.read(os.path.join(PATH,'01jan'+str(year)[-2:],'namelist_smelt_cfg'))
mod_chl_N=nml['nampisopt']['zzn2chl']
print('Parameter values from 01jan'+str(year)[-2:]+' namelist_smelt_cfg:')
print(' Chl:N = ',mod_chl_N)
print(' zz_bfsi = ',nml['nampisrem']['zz_bfsi'])
print(' zz_remin_d_bsi = ',nml['nampisrem']['zz_remin_d_bsi'])
print(' zz_w_sink_d_bsi = ',nml['nampissink']['zz_w_sink_d_bsi'])
print(' zz_alpha_b_si = ',nml['nampissink']['zz_alpha_b_si'])
print(' zz_alpha_b_d = ',nml['nampissink']['zz_alpha_b_d'])
In [10]:
# chlorophyll calculations
data['l10_obsChl']=np.log10(data['Chlorophyll_Extracted']+0.01)
data['l10_modChl']=np.log10(mod_chl_N*(data['mod_diatoms']+data['mod_ciliates']+data['mod_flagellates'])+0.01)
data['mod_Chl']=mod_chl_N*(data['mod_diatoms']+data['mod_ciliates']+data['mod_flagellates'])
data['Chl']=data['Chlorophyll_Extracted']
In [11]:
# prep and load dictionary to save stats in
if os.path.isfile('vET-HC1905-DFO-NutChlPhys-stats.json'):
with open('vET-HC1905-DFO-NutChlPhys-stats.json', 'r') as fstat:
statsDict = json.load(fstat);
statsDict[year]=dict();
else:
statsDict={year:dict()};
In [12]:
cm1=cmocean.cm.thermal
theta=-30
lon0=-123.9
lat0=49.3
with nc.Dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/bathymetry_201702.nc') as bathy:
bathylon=np.copy(bathy.variables['nav_lon'][:,:])
bathylat=np.copy(bathy.variables['nav_lat'][:,:])
bathyZ=np.copy(bathy.variables['Bathymetry'][:,:])
In [13]:
def byDepth(ax,obsvar,modvar,lims):
ps=et.varvarPlot(ax,data,obsvar,modvar,'Z',(15,22),'z','m',('mediumseagreen','darkturquoise','navy'))
l=ax.legend(handles=ps)
ax.set_xlabel('Obs')
ax.set_ylabel('Model')
ax.plot(lims,lims,'k-',alpha=.5)
ax.set_xlim(lims)
ax.set_ylim(lims)
ax.set_aspect(1)
return ps,l
def byRegion(ax,obsvar,modvar,lims):
ps1=et.varvarPlot(ax,dJDF,obsvar,modvar,cols=('b',),lname='SJDF')
ps2=et.varvarPlot(ax,dSJGI,obsvar,modvar,cols=('c',),lname='SJGI')
ps3=et.varvarPlot(ax,dSOG,obsvar,modvar,cols=('y',),lname='SOG')
ps4=et.varvarPlot(ax,dNSOG,obsvar,modvar,cols=('m',),lname='NSOG')
l=ax.legend(handles=[ps1[0][0],ps2[0][0],ps3[0][0],ps4[0][0]])
ax.set_xlabel('Obs')
ax.set_ylabel('Model')
ax.plot(lims,lims,'k-',alpha=.5)
ax.set_xlim(lims)
ax.set_ylim(lims)
ax.set_aspect(1)
return (ps1,ps2,ps3,ps4),l
def bySeason(ax,obsvar,modvar,lims):
for axi in ax:
axi.plot(lims,lims,'k-')
axi.set_xlim(lims)
axi.set_ylim(lims)
axi.set_aspect(1)
axi.set_xlabel('Obs')
axi.set_ylabel('Model')
ps=et.varvarPlot(ax[0],JFM,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[0].set_title('Jan-Mar')
ps=et.varvarPlot(ax[1],Apr,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[1].set_title('Apr')
ps=et.varvarPlot(ax[2],MJJA,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[2].set_title('May-Aug')
ps=et.varvarPlot(ax[3],SOND,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[3].set_title('Sep-Dec')
return
def ErrErr(fig,ax,obsvar1,modvar1,obsvar2,modvar2,lims1,lims2):
m=ax.scatter(data[modvar1]-data[obsvar1],data[modvar2]-data[obsvar2],c=data['Z'],s=1,cmap='gnuplot')
cb=fig.colorbar(m,ax=ax,label='Depth (m)')
ax.set_xlim(lims1)
ax.set_ylim(lims2)
ax.set_aspect((lims1[1]-lims1[0])/(lims2[1]-lims2[0]))
return m,cb
In [14]:
fig, ax = plt.subplots(1,2,figsize = (13,6))
viz_tools.set_aspect(ax[0], coords = 'map')
ax[0].plot(data['Lon'], data['Lat'], 'ro',label='data')
ax[0].plot(data.loc[data.Si>75,['Lon']],data.loc[data.Si>75,['Lat']],'*',color='y',label='high Si')
grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')
viz_tools.plot_coastline(ax[0], grid, coords = 'map',isobath=.1)
ax[0].set_ylim(48, 50.5)
ax[0].legend()
ax[0].set_xlim(-125.7, -122.5);
ax[0].set_title('Observation Locations');
viz_tools.set_aspect(ax[1], coords = 'map')
#ax[1].plot(data['Lon'], data['Lat'], 'ro',label='data')
dJDF=data.loc[(data.Lon<-123.6)&(data.Lat<48.6)]
ax[1].plot(dJDF['Lon'],dJDF['Lat'],'b.',label='JDF')
dSJGI=data.loc[(data.Lon>=-123.6)&(data.Lat<48.9)]
ax[1].plot(dSJGI['Lon'],dSJGI['Lat'],'c.',label='SJGI')
dSOG=data.loc[(data.Lat>=48.9)&(data.Lon>-124.0)]
ax[1].plot(dSOG['Lon'],dSOG['Lat'],'y.',label='SOG')
dNSOG=data.loc[(data.Lat>=48.9)&(data.Lon<=-124.0)]
ax[1].plot(dNSOG['Lon'],dNSOG['Lat'],'m.',label='NSOG')
grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')
viz_tools.plot_coastline(ax[1], grid, coords = 'map')
ax[1].set_ylim(48, 50.5)
ax[1].legend()
ax[1].set_xlim(-125.7, -122.5);
# Also set up seasonal groupings:
iz=(data.Z<15)
JFM=data.loc[iz&(data.dtUTC<=dt.datetime(year,4,1)),:]
Apr=data.loc[iz&(data.dtUTC<=dt.datetime(year,5,1))&(data.dtUTC>dt.datetime(year,4,1)),:]
MJJA=data.loc[iz&(data.dtUTC<=dt.datetime(year,9,1))&(data.dtUTC>dt.datetime(year,5,1)),:]
SOND=data.loc[iz&(data.dtUTC>dt.datetime(year,9,1)),:]
In [15]:
statsubs=OrderedDict({'z < 15 m':data.loc[data.Z<15],
'15 m < z < 22 m':data.loc[(data.Z>=15)&(data.Z<22)],
'z >= 22 m':data.loc[data.Z>=22],
'z > 50 m':data.loc[data.Z>50],
'all':data,
'z < 15 m, JFM':JFM,
'z < 15 m, Apr':Apr,
'z < 15 m, MJJA':MJJA,
'z < 15 m, SOND': SOND})
Nitrate¶
In [16]:
obsvar='N'
modvar='mod_nitrate'
statsDict[year]['NO3']=OrderedDict()
for isub in statsubs:
statsDict[year]['NO3'][isub]=dict()
var=statsDict[year]['NO3'][isub]
var['N'],mmean,omean,var['Bias'],var['RMSE'],var['WSS']=et.stats(statsubs[isub].loc[:,[obsvar]],
statsubs[isub].loc[:,[modvar]])
tbl,tdf=et.displayStats(statsDict[year]['NO3'],level='Subset',suborder=list(statsubs.keys()))
tbl
Out[16]:
In [17]:
fig, ax = plt.subplots(1,2,figsize = (16,7))
ps,l=byDepth(ax[0],obsvar,modvar,(0,40))
ax[0].set_title('NO$_3$ ($\mu$M) By Depth')
ps,l=byRegion(ax[1],obsvar,modvar,(0,40))
ax[1].set_title('NO$_3$ ($\mu$M) By Region');
In [18]:
fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,obsvar,modvar,(0,30))
fig,ax=plt.subplots(1,1,figsize=(20,.3))
ax.plot(data.dtUTC,np.ones(np.shape(data.dtUTC)),'k.')
ax.set_xlim((dt.datetime(year,1,1),dt.datetime(year,12,31)))
ax.set_title('Data Timing')
ax.yaxis.set_visible(False)
In [19]:
fig,ax=plt.subplots(1,2,figsize=(12,4))
ax[0].set_xlabel('Density Error (kg m$^{-3}$)')
ax[0].set_ylabel('NO$_3$ ($\mu$M) Error')
m,cb=ErrErr(fig,ax[0],'rho','mod_rho',obsvar,modvar,(-3,3),(-15,15))
ax[1].set_xlabel('Salinity Error (g kg$^{-1}$)')
ax[1].set_ylabel('NO$_3$ ($\mu$M) Error')
m,cb=ErrErr(fig,ax[1],'AbsSal','mod_vosaline',obsvar,modvar,(-2.5,2.5),(-15,15))
Dissolved Silica¶
In [20]:
obsvar='Si'
modvar='mod_silicon'
statsDict[year]['dSi']=OrderedDict()
for isub in statsubs:
statsDict[year]['dSi'][isub]=dict()
var=statsDict[year]['dSi'][isub]
var['N'],mmean,omean,var['Bias'],var['RMSE'],var['WSS']=et.stats(statsubs[isub].loc[:,[obsvar]],
statsubs[isub].loc[:,[modvar]])
tbl,tdf=et.displayStats(statsDict[year]['dSi'],level='Subset',suborder=list(statsubs.keys()))
tbl
Out[20]:
In [21]:
mv=(0,80)
fig, ax = plt.subplots(1,2,figsize = (16,7))
ps,l=byDepth(ax[0],obsvar,modvar,mv)
ax[0].set_title('Dissolved Silica ($\mu$M) By Depth')
ps,l=byRegion(ax[1],obsvar,modvar,mv)
ax[1].set_title('Dissolved Silica ($\mu$M) By Region');
In [22]:
fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,obsvar,modvar,mv)
fig,ax=plt.subplots(1,1,figsize=(20,.3))
ax.plot(data.dtUTC,np.ones(np.shape(data.dtUTC)),'k.')
ax.set_xlim((dt.datetime(year,1,1),dt.datetime(year,12,31)))
ax.set_title('Data Timing')
ax.yaxis.set_visible(False)
In [23]:
fig,ax=plt.subplots(1,2,figsize=(12,4))
ax[0].set_xlabel('Density Error (kg m$^{-3}$)')
ax[0].set_ylabel('dSi Error ($\mu$M)')
m,cb=ErrErr(fig,ax[0],'rho','mod_rho',obsvar,modvar,(-3,3),(-25,25))
ax[1].set_xlabel('Salinity Error (g kg$^{-1}$)')
ax[1].set_ylabel('dSi Error ($\mu$M)')
m,cb=ErrErr(fig,ax[1],'AbsSal','mod_vosaline',obsvar,modvar,(-2.5,2.5),(-25,25))
Profiles of NO3 and Dissolved Silica¶
In [24]:
fig, ax = plt.subplots(1,2,figsize = (15,8))
cols=('crimson','red','orangered','darkorange','gold','chartreuse','green','lightseagreen','cyan',
'darkturquoise','royalblue','lightskyblue','blue','darkblue','mediumslateblue','blueviolet',
'darkmagenta','fuchsia','deeppink','pink')
ii0=start_date
for ii in range(0,int((end_date-start_date).days/30)):
iii=(data.dtUTC>=(start_date+dt.timedelta(days=ii*30)))&(data.dtUTC<(start_date+dt.timedelta(days=(ii+1)*30)))
ax[0].plot(data.loc[iii,['mod_nitrate']].values-data.loc[iii,['N']].values, data.loc[iii,['Z']].values,
'.', color = cols[ii],label=str(ii))
ax[1].plot(data.loc[iii,['mod_silicon']].values-data.loc[iii,['Si']].values, data.loc[iii,['Z']].values,
'.', color = cols[ii],label=str(ii))
for axi in (ax[0],ax[1]):
axi.legend(loc=4)
axi.set_ylim(400,0)
axi.set_ylabel('Depth (m)')
ax[0].set_xlabel('Model - Obs')
ax[1].set_xlabel('Model - Obs')
ax[0].set_xlim(-15,15)
ax[1].set_xlim(-40,20)
ax[0].set_title('NO3')
ax[1].set_title('dSi')
Out[24]:
dSi:NO3 Ratios¶
In [25]:
fig,ax=plt.subplots(1,2,figsize=(15,6))
p1=ax[0].plot(dJDF['N'],dJDF['Si'],'b.',label='SJDF')
p2=ax[0].plot(dSJGI['N'],dSJGI['Si'],'c.',label='SJGI')
p3=ax[0].plot(dSOG['N'],dSOG['Si'],'y.',label='SOG')
p4=ax[0].plot(dNSOG['N'],dNSOG['Si'],'m.',label='NSOG')
ax[0].plot(np.arange(0,41),1.35*np.arange(0,41)+6.46,'k-',label='OBC')
ax[0].set_title('Observed')
ax[0].set_xlabel('NO3')
ax[0].set_ylabel('dSi')
ax[0].set_xlim(0,40)
ax[0].set_ylim(0,85)
ax[0].legend()
p5=ax[1].plot(dJDF['mod_nitrate'],dJDF['mod_silicon'],'b.',label='SJDF')
p6=ax[1].plot(dSJGI['mod_nitrate'],dSJGI['mod_silicon'],'c.',label='SJGI')
p7=ax[1].plot(dSOG['mod_nitrate'],dSOG['mod_silicon'],'y.',label='SOG')
p8=ax[1].plot(dNSOG['mod_nitrate'],dNSOG['mod_silicon'],'m.',label='NSOG')
ax[1].plot(np.arange(0,41),1.35*np.arange(0,41)+6.46,'k-',label='OBC')
ax[1].set_title('Model')
ax[1].set_xlabel('NO3')
ax[1].set_ylabel('dSi')
ax[1].set_xlim(0,40)
ax[1].set_ylim(0,85)
ax[1].legend()
#ax[0].plot(np.arange(0,35),1.3*np.arange(0,35),'k-')
#ax[1].plot(np.arange(0,35),1.3*np.arange(0,35),'k-')
Out[25]:
In [26]:
fig,ax=plt.subplots(1,2,figsize=(15,6))
p1=ax[0].plot(dJDF['AbsSal'], dJDF['Si']-1.3*dJDF['N'],'b.',label='SJDF')
p2=ax[0].plot(dSJGI['AbsSal'],dSJGI['Si']-1.3*dSJGI['N'],'c.',label='SJGI')
p3=ax[0].plot(dSOG['AbsSal'],dSOG['Si']-1.3*dSOG['N'],'y.',label='SOG')
p4=ax[0].plot(dNSOG['AbsSal'],dNSOG['Si']-1.3*dNSOG['N'],'m.',label='NSOG')
ax[0].set_title('Observed')
ax[0].set_xlabel('S (g/kg)')
ax[0].set_ylabel('dSi-1.3NO3')
ax[0].set_xlim(10,35)
ax[0].set_ylim(0,45)
ax[0].legend()
p5=ax[1].plot(dJDF['mod_vosaline'],dJDF['mod_silicon']-1.3*dJDF['mod_nitrate'],'b.',label='SJDF')
p6=ax[1].plot(dSJGI['mod_vosaline'],dSJGI['mod_silicon']-1.3*dSJGI['mod_nitrate'],'c.',label='SJGI')
p7=ax[1].plot(dSOG['mod_vosaline'],dSOG['mod_silicon']-1.3*dSOG['mod_nitrate'],'y.',label='SOG')
p8=ax[1].plot(dNSOG['mod_vosaline'],dNSOG['mod_silicon']-1.3*dNSOG['mod_nitrate'],'m.',label='NSOG')
ax[1].set_title('Model')
ax[1].set_xlabel('S (g/kg)')
ax[1].set_ylabel('dSi-1.3NO3')
ax[1].set_xlim(10,35)
ax[1].set_ylim(0,45)
ax[1].legend()
Out[26]:
Chlorophyll¶
In [27]:
obsvar='l10_obsChl'
modvar='l10_modChl'
statsDict[year]['Chl log10']=OrderedDict()
for isub in statsubs:
statsDict[year]['Chl log10'][isub]=dict()
var=statsDict[year]['Chl log10'][isub]
var['N'],mmean,omean,var['Bias'],var['RMSE'],var['WSS']=et.stats(statsubs[isub].loc[:,[obsvar]],
statsubs[isub].loc[:,[modvar]])
obsvar='Chlorophyll_Extracted'
modvar='mod_Chl'
statsDict[year]['Chl']=OrderedDict()
for isub in statsubs:
statsDict[year]['Chl'][isub]=dict()
var=statsDict[year]['Chl'][isub]
var['N'],mmean,omean,var['Bias'],var['RMSE'],var['WSS']=et.stats(statsubs[isub].loc[:,[obsvar]],
statsubs[isub].loc[:,[modvar]])
tempD={'Chl log10':statsDict[year]['Chl log10'],'Chl':statsDict[year]['Chl']}
tbl,tdf=et.displayStatsFlex(tempD,('Variable','Subset','Metric',''),
['Order','Subset','Metric'],
['Variable','Metric'],
suborder=list(statsubs.keys()))
tbl
Out[27]:
In [28]:
fig, ax = plt.subplots(1,2,figsize = (14,6))
ax[0].plot(np.arange(-.6,1.6,.1),np.arange(-.6,1.6,.1),'k-')
ps=et.varvarPlot(ax[0],data,'l10_obsChl','l10_modChl','Z',(5,10,15,20,25),'z','m',('crimson','darkorange','lime','mediumseagreen','darkturquoise','navy'))
ax[0].legend(handles=ps)
ax[0].set_xlabel('Obs')
ax[0].set_ylabel('Model')
ax[0].set_title('log10[Chl ($\mu$g/L)+0.01] By Depth')
ax[1].plot(np.arange(0,35),np.arange(0,35),'k-')
ps=et.varvarPlot(ax[1],data,'Chlorophyll_Extracted','mod_Chl','Z',(5,10,15,20,25),'z','m',('crimson','darkorange','lime','mediumseagreen','darkturquoise','navy'))
ax[1].legend(handles=ps)
ax[1].set_xlabel('Obs')
ax[1].set_ylabel('Model')
ax[1].set_title('Chl ($\mu$g/L) By Depth');