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SMOS_ESSD_Ant.ipynb
View file @ eb3ea04c
%% Cell type:markdown id:32432818 tags:
 
# SMOS-derived Antarctic thin sea-ice thickness from 2010 to 2020: data description and validation in the Weddell Sea
 
This is the main notebook that generates figures and statistics used in the ESSD paper with the same title.
 
 
## SMOS data
 
Tian-Kunze, Xiangshan; Kaleschke, Lars (2021): SMOS-derived sea ice thickness in the Antarctic from 2010 to 2020. PANGAEA, https://doi.org/10.1594/PANGAEA.934732
 
 
As a first step the notebook entitled **SMOS sea ice thickness monthly mean and climatology** has to be used to generate monthly means and the climatology from the daily product.
 
 
## Validation data
 
Three main datasets are used
 
* Helicopter-based EM Bird (HEM) data¶
* Surface and Under-Ice Trawl (SUIT)
* Upward-Looking Sonars (ULS)
 
%% Cell type:code id:718fd223 tags:
 
``` python
# Directory for daily product (data source)
smos_datadir ='/isibhv/projects/siral/product/smos/SMOS_v620_L3C_Sea_v3/south/'
# Directory for program output
smos_monthly ='repo_data/SMOS_monthly/'
```
 
%% Cell type:markdown id:7102d92a tags:
 
## Helicopter-based EM Bird (HEM) data
 
HEM data have been submitted to Pangaea but the reference is not yet available. Therefore, the HEM data are included in this repository.
 
%% Cell type:code id:5a5c52c4 tags:
 
``` python
# This directory contains the HEM data, profiles and gridded.
grdvaldir='repo_data/HEM'
valdir='repo_data/HEM'
#!ls $grdvaldir
!ls $valdir
```
 
%% Output
 
Ant_HEM_2013-06-19_1343_sit.nc HEM_Ant29_20130619T134315_20130619T134258.nc
Ant_HEM_2013-06-20_1405_sit.nc HEM_Ant29_20130620T140530_20130620T132327.nc
Ant_HEM_2013-06-21_1145_sit.nc HEM_Ant29_20130621T114536_20130621T114519.nc
Ant_HEM_2013-07-07_1221_sit.nc HEM_Ant29_20130707T122107_20130707T122051.nc
 
%% Cell type:code id:c27b6af0 tags:
 
``` python
#!gzip repo_data/HEM/*.nc
!gunzip repo_data/HEM/*.gz
```
 
%% Cell type:markdown id:50a679c0 tags:
 
## SMOS grid definitions
 
%% Cell type:code id:712e8b75 tags:
 
``` python
%matplotlib notebook
from pylab import *
from mpl_toolkits.basemap import Basemap
from matplotlib.colors import LinearSegmentedColormap,ListedColormap,Normalize
import cmocean
import pandas as pd
import xarray as xr
import glob
import os.path
import pyproj
 
pol=pyproj.Proj("+init=EPSG:3412") # NSIDC Polar Stereographic South
 
def mapxy(x,y):
lon,lat=pol(x*1000,y*1000, inverse=True)
return lat,lon
 
def mapll(lat,lon):
x,y=pol(lon,lat, inverse=False)
return x,y
 
def def_grids():
grids={}
grids.update({'Arc': [1,3750.0,5850.0,-3850.0,-5350.0]})
grids.update({'Ant': [-1,3950.0,4350.0,-3950.0,-3950.0]})
return grids
 
 
hemis='Ant'
grids=def_grids()
cs=12.5
sgn,cds= grids[hemis][0],grids[hemis][1:5]
X0,Y0,X2,Y2=cds[0],cds[1],cds[2],cds[3]
x0,y0,x2,y2=cds[0],cds[1],cds[2],cds[3]
 
```
 
%% Output
 
/isibhv/projects/SMOS_ESL/isibhv_miniconda3/envs/jupyterlab/lib/python3.9/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
in_crs_string = _prepare_from_proj_string(in_crs_string)
 
%% Cell type:markdown id:3f57b058 tags:
 
### SMOS climatological monthly mean and interannual standard deviation
 
%% Cell type:code id:acca6c4f tags:
 
``` python
smos_months=['04','05','06','07','08','09','10']
sy,sx=(664, 632)
SIT=zeros((sy,sx,7))
 
fdir='/isibhv/projects/siral/product/smos/SMOS_v620_L3C_Sea_v3/south/monthly/'
#fdir='/isibhv/projects/siral/product/smos/SMOS_v724_L3C_Sea_v3.3/south/monthly/'
 
for i,month in enumerate(smos_months):
smos_fn=fdir+'climatology_SMOS_Icethickness_v3.2_south_'+month+'.nc'
#smos_fn=fdir+'monthly_SMOS_Icethickness_v3.2_south_2010'+month+'.nc'
print(smos_fn)
sit=xr.open_dataarray(smos_fn)
SIT[:,:,i]=sit
SIT=ma.masked_invalid(SIT)
sitm=SIT.mean(axis=2)
 
# Interannual variability (std)
smos_months=['04','05','06','07','08','09','10']
sy,sx=(664, 632)
SITs=zeros((sy,sx,7))
 
fdir='/isibhv/projects/siral/product/smos/SMOS_v620_L3C_Sea_v3/south/monthly/'
#fdir='/isibhv/projects/siral/product/smos/SMOS_v724_L3C_Sea_v3.3/south/monthly/'
 
for i,month in enumerate(smos_months):
smos_fn=fdir+'climatology_SMOS_Icethickness_v3.2_south_'+month+'.nc'
#smos_fn=fdir+'monthly_SMOS_Icethickness_v3.2_south_2010'+month+'.nc'
flistpat=fdir+'monthly_SMOS_Icethickness_v3.2_south_20??'+month+'.nc'
flist=!ls $flistpat
 
nry=len(flist) # number years
SITsy=zeros((sy,sx,nry))
for j,smos_fn in enumerate(flist):
#print(j,smos_fn)
sit=xr.open_dataarray(smos_fn)
SITsy[:,:,j]=sit
SITsy=ma.masked_invalid(SITsy)
sits=SITsy.std(axis=2)
SITs[:,:,i]=sits
 
```
 
%% Output
 
/isibhv/projects/siral/product/smos/SMOS_v620_L3C_Sea_v3/south/monthly/climatology_SMOS_Icethickness_v3.2_south_04.nc
/isibhv/projects/siral/product/smos/SMOS_v620_L3C_Sea_v3/south/monthly/climatology_SMOS_Icethickness_v3.2_south_05.nc
/isibhv/projects/siral/product/smos/SMOS_v620_L3C_Sea_v3/south/monthly/climatology_SMOS_Icethickness_v3.2_south_06.nc
/isibhv/projects/siral/product/smos/SMOS_v620_L3C_Sea_v3/south/monthly/climatology_SMOS_Icethickness_v3.2_south_07.nc
/isibhv/projects/siral/product/smos/SMOS_v620_L3C_Sea_v3/south/monthly/climatology_SMOS_Icethickness_v3.2_south_08.nc
/isibhv/projects/siral/product/smos/SMOS_v620_L3C_Sea_v3/south/monthly/climatology_SMOS_Icethickness_v3.2_south_09.nc
/isibhv/projects/siral/product/smos/SMOS_v620_L3C_Sea_v3/south/monthly/climatology_SMOS_Icethickness_v3.2_south_10.nc
 
%% Cell type:markdown id:37887b6b tags:
 
## ULS Data
 
### References
 
Behrendt, A., Dierking, W., Fahrbach, E., and Witte, H.: Sea ice draft in the Weddell Sea, measured by upward looking sonars, Earth Syst. Sci. Data, 5, 209–226, https://doi.org/10.5194/essd-5-209-2013, 2013.
 
Behrendt, A., W. Dierking, and H. Witte(2015), Thermodynamic sea ice growthin the central Weddell Sea, observed inupward-looking sonar data,J. Geophys.Res. Oceans,120, 2270–2286,doi:10.1002/2014JC010408
 
The sea ice thickness in the Atlantic sector of the Southern Ocean, PhD thesis Axel Behrendt, 2013
http://wwwdb.dbod.de/login?url=https://d-nb.info/1072077566/34
 
Harms, S., Fahrbach, E., & Strass, V. H. (2001). Sea ice transports in the Weddell Sea. Journal of Geophysical Research: Oceans, 106(C5), 9057-9073.
 
Behrendt, Axel; Dierking, Wolfgang; Fahrbach, Eberhard; Witte, Hannelore (2013): Sea ice draft measured by upward looking sonars in the Weddell Sea (Antarctica). PANGAEA, https://doi.org/10.1594/PANGAEA.785565, Supplement to: Behrendt, A et al. (2013): Sea ice draft in the Weddell Sea, measured by upward looking sonars. Earth System Science Data, 5(1), 209-226, https://doi.org/10.5194/essd-5-209-2013
 
%% Cell type:code id:e6b5bd4c tags:
 
``` python
# Download data from Pangaea https://doi.org/10.5194/essd-5-209-2013
# and store in the directory ULS_dir
# Download ZIP file containing all datasets as tab-delimited text
ULS_dir="repo_data/ULS_Ant_Behrendt_2013/"
!ls $ULS_dir
```
 
%% Output
 
AWI206-4_sea-ice_draft.tab AWI229-6_sea-ice_draft.tab
AWI206-6_sea-ice_draft.tab AWI229-8_sea-ice_draft.tab
AWI207-2_sea-ice_draft.tab AWI230-2_sea-ice_draft.tab
AWI207-4_sea-ice_draft.tab AWI230-3_sea-ice_draft.tab
AWI207-6_sea-ice_draft.tab AWI231-1_sea-ice_draft.tab
AWI207-7_sea-ice_draft.tab AWI231-2_sea-ice_draft.tab
AWI208-3_sea-ice_draft.tab AWI231-3_sea-ice_draft.tab
AWI208-5_sea-ice_draft.tab AWI231-4_sea-ice_draft.tab
AWI209-3_sea-ice_draft.tab AWI231-6_sea-ice_draft.tab
AWI210-2_sea-ice_draft.tab AWI231-7_sea-ice_draft.tab
AWI212-2_sea-ice_draft.tab AWI232-1_sea-ice_draft.tab
AWI217_sea-ice_draft.tab AWI232-4_sea-ice_draft.tab
AWI227-3_sea-ice_draft.tab AWI232-5_sea-ice_draft.tab
AWI227-4_sea-ice_draft.tab AWI232-6_sea-ice_draft.tab
AWI227-6_sea-ice_draft.tab AWI232-8_sea-ice_draft.tab
AWI229-1_sea-ice_draft.tab AWI232-9_sea-ice_draft.tab
AWI229-2_sea-ice_draft.tab AWI233-2_sea-ice_draft.tab
AWI229-3_sea-ice_draft.tab AWI233-6_sea-ice_draft.tab
AWI229-4_sea-ice_draft.tab summary.txt
AWI229-5_sea-ice_draft.tab
 
%% Cell type:markdown id:2a8102d4 tags:
 
### Functions to read in ULS data
 
%% Cell type:code id:9334555e tags:
 
``` python
from pylab import *
import pandas as pd
import xarray as xr
import glob
import os.path
 
def ts_fillnan(ts):
dr=pd.date_range(ts.index[0],ts.index[-1])
ind=pd.Series(data=ones(len(dr)),index=dr)
ind[:]=nan
ind[ts.index]=ts[ts.index]
return ind
 
def headerlenght(fn):
fid=open(fn)
for l in range(40):
line=fid.readline()
if "*/" in line:
return(l+1)
 
def latlon_fromheader(fn):
fid=open(fn)
for l in range(40):
line=fid.readline()
if "Coverage:" in line:
lat,lon=line.replace('\n','').split('LATITUDE: ')[1].split('* LONGITUDE:')
lat,lon=float(lat),float(lon)
return(lat,lon)
 
fl=!ls $ULS_dir/*.tab
 
L=[]
for fn in fl:
awi_id=os.path.basename(fn)[0:6]
L.append(awi_id)
 
awi_ids=unique(array(L))
print('Unique ULS data positions', awi_ids)
```
 
%% Output
 
Unique ULS data positions ['AWI206' 'AWI207' 'AWI208' 'AWI209' 'AWI210' 'AWI212' 'AWI217' 'AWI227'
'AWI229' 'AWI230' 'AWI231' 'AWI232' 'AWI233']
 
%% Cell type:markdown id:05037629 tags:
 
### Read ULS data and calculate monthly means
 
%% Cell type:code id:58aef07b tags:
 
``` python
ULS_Dict_daily={}
ULS_Dict_monthly={}
ULS_latlon={}
 
for uls_id in awi_ids:
fl=glob.glob(ULS_dir+uls_id+'*.tab')
fl.sort()
ULS_dict={}
for fn in fl:
print(fn)
ULS_name=os.path.basename(fn)[0:8]
skiprows=headerlenght(fn)
ULS_latlon[ULS_name[0:6]]=latlon_fromheader(fn)
uls=pd.read_csv(fn,sep='\t',skiprows=skiprows)
#print(ULS_name,uls.keys())
for k in uls.keys():
if 'Draft' in k:
if 'model' in k:
draft_param=k
else:
if 'zero line' in k:
draft_param=k
print('--> Selected parameter:',draft_param)
draft=array(uls[draft_param].values).astype(float)
index=pd.to_datetime(uls['Date/Time'])
index.name='Date'
ULS_dict[ULS_name]=pd.Series(data=draft,index=index)
ULS_1d={}
for ULS_name in ULS_dict:
print('Calculating daily averages', ULS_name)
ULS_1d[ULS_name]=ULS_dict[ULS_name].resample('1d').mean()
L=[]
for uls in ULS_1d:
L.append(ULS_1d[uls])
ts=pd.concat(L)
ts1m=ts.resample('m').mean()
ULS_Dict_monthly[uls_id]=ts1m
print('Finished ',uls_id)
 
xa1=xr.Dataset(ULS_Dict_monthly)# Monthly mean
```
 
%% Output
 
repo_data/ULS_Ant_Behrendt_2013/AWI206-4_sea-ice_draft.tab
--> Selected parameter: Draft [m] (zero line correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI206-6_sea-ice_draft.tab
--> Selected parameter: Draft [m] (zero line correction, Calculated)
Calculating daily averages AWI206-4
Calculating daily averages AWI206-6
Finished AWI206
repo_data/ULS_Ant_Behrendt_2013/AWI207-2_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI207-4_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI207-6_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI207-7_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
Calculating daily averages AWI207-2
Calculating daily averages AWI207-4
Calculating daily averages AWI207-6
Calculating daily averages AWI207-7
Finished AWI207
repo_data/ULS_Ant_Behrendt_2013/AWI208-3_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI208-5_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
Calculating daily averages AWI208-3
Calculating daily averages AWI208-5
Finished AWI208
repo_data/ULS_Ant_Behrendt_2013/AWI209-3_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
Calculating daily averages AWI209-3
Finished AWI209
repo_data/ULS_Ant_Behrendt_2013/AWI210-2_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
Calculating daily averages AWI210-2
Finished AWI210
repo_data/ULS_Ant_Behrendt_2013/AWI212-2_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
Calculating daily averages AWI212-2
Finished AWI212
repo_data/ULS_Ant_Behrendt_2013/AWI217_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
Calculating daily averages AWI217_s
Finished AWI217
repo_data/ULS_Ant_Behrendt_2013/AWI227-3_sea-ice_draft.tab
--> Selected parameter: Draft [m] (zero line correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI227-4_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI227-6_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
Calculating daily averages AWI227-3
Calculating daily averages AWI227-4
Calculating daily averages AWI227-6
Finished AWI227
repo_data/ULS_Ant_Behrendt_2013/AWI229-1_sea-ice_draft.tab
--> Selected parameter: Draft [m] (zero line correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI229-2_sea-ice_draft.tab
--> Selected parameter: Draft [m] (zero line correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI229-3_sea-ice_draft.tab
--> Selected parameter: Draft [m] (zero line correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI229-4_sea-ice_draft.tab
--> Selected parameter: Draft [m] (zero line correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI229-5_sea-ice_draft.tab
--> Selected parameter: Draft [m] (zero line correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI229-6_sea-ice_draft.tab
--> Selected parameter: Draft [m] (zero line correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI229-8_sea-ice_draft.tab
--> Selected parameter: Draft [m] (zero line correction, Calculated)
Calculating daily averages AWI229-1
Calculating daily averages AWI229-2
Calculating daily averages AWI229-3
Calculating daily averages AWI229-4
Calculating daily averages AWI229-5
Calculating daily averages AWI229-6
Calculating daily averages AWI229-8
Finished AWI229
repo_data/ULS_Ant_Behrendt_2013/AWI230-2_sea-ice_draft.tab
--> Selected parameter: Draft [m] (zero line correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI230-3_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
Calculating daily averages AWI230-2
Calculating daily averages AWI230-3
Finished AWI230
repo_data/ULS_Ant_Behrendt_2013/AWI231-1_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI231-2_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI231-3_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI231-4_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI231-6_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI231-7_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
Calculating daily averages AWI231-1
Calculating daily averages AWI231-2
Calculating daily averages AWI231-3
Calculating daily averages AWI231-4
Calculating daily averages AWI231-6
Calculating daily averages AWI231-7
Finished AWI231
repo_data/ULS_Ant_Behrendt_2013/AWI232-1_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI232-4_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI232-5_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI232-6_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI232-8_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI232-9_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
Calculating daily averages AWI232-1
Calculating daily averages AWI232-4
Calculating daily averages AWI232-5
Calculating daily averages AWI232-6
Calculating daily averages AWI232-8
Calculating daily averages AWI232-9
Finished AWI232
repo_data/ULS_Ant_Behrendt_2013/AWI233-2_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
repo_data/ULS_Ant_Behrendt_2013/AWI233-6_sea-ice_draft.tab
--> Selected parameter: Draft [m] (model correction, Calculated)
Calculating daily averages AWI233-2
Calculating daily averages AWI233-6
Finished AWI233
 
%% Cell type:markdown id:95268a3d tags:
 
### Calculate seasonal cycle - climatological monthly means and interannual standard deviation
 
%% Cell type:code id:1572d837 tags:
 
``` python
ULS_seasonal={}
ULS_seasonal_std={}
 
for k in xa1:
ts=xa1[k]
dates=pd.to_datetime(ts.indexes['Date'])
month_index=dates.to_period(freq='M')
df=pd.DataFrame(ts)
df=pd.DataFrame(data=ts.data,index=dates,columns=['Draft'])
#df.index.name='Date'
df['year-month']=month_index
df['month']=month_index.month
seasonal=df.groupby('month').mean()
seasonal_std=df.groupby('month').std()
 
ULS_seasonal[k]=seasonal
ULS_seasonal_std[k]=seasonal_std
 
xas1=xr.Dataset(ULS_seasonal)
xass1=xr.Dataset(ULS_seasonal_std)
xas2=xr.Dataset(ULS_seasonal,attrs=ULS_latlon)
#xas2.to_netcdf('ULS_draft_monthly_seasonal.nc')
```
 
%% Cell type:markdown id:2cd221dc tags:
 
### Select suitable ULS data
 
The data have to fulfill following criteria:
 
* Range of SMOS sea ice thickness 0 - 1.5 m
* Long time series, at least 3 years of data
 
%% Cell type:code id:0e0dc14c tags:
 
``` python
for k in xa1:
if xa1[k].max()<1.5:
xa1[k].plot(label=k)
ylabel('Draft [m]')
legend(fontsize=8)
title('ULS < 1.5 m')
 
figure()
 
#Selected: AWI227, AWI229, AWI231
k='AWI227'
xa1[k].plot(label=k)
k='AWI229'
xa1[k].plot(label=k)
k='AWI231'
xa1[k].plot(label=k)
legend()
title('Selected ULS with 47+ months of data')
```
 
%% Output
 
 
 
 
 
Text(0.5, 1.0, 'Selected ULS with 47+ months of data')
 
%% Cell type:markdown id:98affb73 tags:
 
### Extract SMOS values at ULS positions
 
%% Cell type:code id:08558000 tags:
 
``` python
SMOS_dict={}
SMOS_dict_s={}
 
for k in xas2:
lat,lon=xas2.attrs[k]
ts1=xas2[k]
psxy=mapll(lat,lon)
posxy=(int((psxy[0]/1000-x2)/cs),int((psxy[1]/1000-y2)/cs))
xi,yi=posxy
#SIT_0[yi-1:yi+1,xi-1:xi+1]=-1
#print(k,lat,lon,xi,yi)
L=[nan]*3 # Month 1-3 not available
Ls=[nan]*3
for mi in range(7):
L.append(SIT[yi,xi,mi])
Ls.append(SITs[yi,xi,mi])
 
L.append(nan)
L.append(nan)
Ls.append(nan)
Ls.append(nan)
 
SMOS_dict[k]=array(L)
SMOS_dict_s[k]=array(Ls)
```
 
%% Cell type:code id:32971b21 tags:
 
``` python
selected=['AWI209','AWI227','AWI229','AWI231']
selected=['AWI227','AWI229','AWI231']
symdic={'AWI227':'o','AWI229':'v','AWI231':'^'}
coldic={'AWI227':'b','AWI229':'r','AWI231':'c'}
coldic={'AWI227':'k','AWI229':'k','AWI231':'k'}
 
XL=[] # List for mean
YL=[]
XLS=[] # Std
YLS=[]
Lab=[] # Labels
 
xx=arange(12)+1
ci=1
for k in selected:
subplot(3,1,ci)
lat,lon=xas2.attrs[k]
mu=float(xas2[k].mean())
mumax=float(xas2[k].max())
nrm=sum(isfinite(array(xa1[k])))
print(k,nrm)
ts1=xas2[k].values[:,0]
ts1=0.028+ts1*1.012
tss1=xass1[k].values[:,0]
fill_between(xx,ts1-tss1,ts1+tss1,alpha=0.3,color=coldic[k])
 
plot(xx,ts1,label='ULS '+k,linestyle=':',color=coldic[k])
ts2=SMOS_dict[k]
tss2=SMOS_dict_s[k]
errorbar(xx,ts2,yerr=tss2,color=coldic[k],label='SMOS @'+k)
ci+=1
xlim([3.9,10.5])
ylim([0,1.3])
 
legend(fontsize=8,loc=2)
xticks([])
ylabel('Thickness [m]')
# Store value pairs into list (mean, std)
ind=isfinite(ts2)
YL.extend(ts2[ind])
XL.extend(ts1[ind])
XLS.extend(tss1[ind])
YLS.extend(tss2[ind])
Lab.extend(array(((k+',')*12).split(',')[:-1])[ind])
 
xticks(ticks=[4,5,6,7,8,9,10],labels=['April','May','June','July','Aug.','Sept.','Oct.'])
tight_layout()
#savefig('Seasonal_thickness_ULS_SMOS.pdf')
x=array(XL) # ULS
y=array(YL) # SMOS
xstd=array(XLS)
ystd=array(YLS)
```
 
%% Output
 
 
 
AWI227 47
AWI229 129
AWI231 99
 
%% Cell type:code id:0041713f tags:
 
``` python
from sklearn.metrics import mean_squared_error
import scipy.stats as stats
 
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
 
print('Standard metric -----------')
print("Mean VAL, SMOS: %1.2f +- %1.2f , %1.2f +- %1.2f" % (mean(x),std(x),mean(y),std(y)))
print("N: ",len(x))
md=np.mean(x-y)
print("Mean difference VAL-SMOS: %1.2f" % md)
rmsd=mean_squared_error(x,y,squared=False)
print("RMSD VAL-SMOS: %1.2f" % rmsd)
print("R: %1.2f" % r_value)
 
plt.figure(figsize=(6,6))
xn=np.linspace(0,1.4)
plt.plot(xn,xn,'k:',label='Identity')
plt.plot(xn,xn*slope+intercept,'k-',label='Regression')
 
for i,lab in enumerate(Lab):
marker=symdic[lab]
if i/7==int(i/7):
label=lab
else:
label=''
plt.plot([x[i]],[y[i]],color='k',marker=marker,ms=10,alpha=1.0,label=label)
 
plt.errorbar(x,y,xerr=xstd,yerr=ystd,fmt='.',color='k')
 
 
grid()
xlabel('ULS sea ice thickness [m]')
ylabel('SMOS sea ice thickness [m]')
 
xlim([-0.1,1.3])
ylim([-0.1,1.3])
 
legend()
tight_layout()
#savefig('Scatter_SMOS_ULS_seasonal.pdf')
#savefig('Scatter_SMOS_ULS_seasonal.png')
 
```
 
%% Output
 
Standard metric -----------
Mean VAL, SMOS: 0.46 +- 0.32 , 0.49 +- 0.38
N: 21
Mean difference VAL-SMOS: -0.03
RMSD VAL-SMOS: 0.14
R: 0.94
 
 
 
%% Cell type:markdown id:68f8da1c tags:
 
### SUIT
 
Castellani, Giulia; Flores, Hauke; Lange, Benjamin Allen; Schaafsma, Fokje L; Ehrlich, Julia; David, Carmen L; van Franeker, Jan Andries; Meijboom, Andre; van Dorssen, Michiel; Vortkamp, Martina; Van de Putte, Anton P (2019): Sea ice draft, under-ice irradiance and radiance, and surface water temperature, salinity and chlorophyl a from Surface and Under Ice Trawl (SUIT) measurements in 2012-2017. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, https://doi.org/10.1594/PANGAEA.902056
 
%% Cell type:code id:9506be52 tags:
 
``` python
fn0='repo_data/PANGAEA_902260_PS81.tab'
dat=pd.read_csv(fn0,sep='\t',skiprows=35)
di=pd.to_datetime(dat['Date/Time'])+pd.to_timedelta(dat['Time [s]'].values,unit='s')
Lat=pd.Series(data=dat['Latitude'].values,index=di)
Lon=pd.Series(data=dat['Longitude'].values,index=di)
Thick=pd.Series(data=dat['Ice thick [m]'].values,index=di)
Distance=pd.Series(data=dat['Distance [m]'].values,index=di)
 
from numpy import unique, array
 
days=unique(array(dat['Date/Time']))
for day in days:
print(day)
 
dat.head()
```
 
%% Output
 
2013-08-29
2013-09-09
2013-09-10
2013-09-11
2013-09-12
2013-09-16
2013-09-28
2013-09-29
2013-09-30
2013-10-02
 
Event Date/Time Latitude Longitude Haul Station Time [s] \
0 PS81/549-1 2013-08-29 -61.251660 -42.066770 2 549 43027.002375
1 PS81/549-1 2013-08-29 -61.249863 -42.066779 2 549 43028.032945
2 PS81/549-1 2013-08-29 -61.252591 -42.066787 2 549 43029.054942
3 PS81/549-1 2013-08-29 -61.251435 -42.066796 2 549 43030.066205
4 PS81/549-1 2013-08-29 -61.251739 -42.066805 2 549 43031.082798
Distance [m] Sea ice draft [m] Ice thick [m] Chl a [mg/m**3] Temp [°C] \
0 0.0 -0.802515 0.875152 0.122137 -1.859637
1 0.5 -0.698829 0.762081 0.126799 -1.859591
2 1.0 -0.426149 0.464721 0.130338 -1.859484
3 1.5 -0.711586 0.775993 0.132577 -1.859596
4 2.0 -0.801220 0.873740 0.134420 -1.859658
Sal
0 34.241937
1 34.186280
2 34.247329
3 34.253710
4 34.194110
 
%% Cell type:code id:ea04ace6 tags:
 
``` python
# Create SUIT profile plots, not used in the paper
close('all')
figure(figsize=(9,18))
spn=1
thresh=0.03
 
L=[]
for day in days:
thick0=Thick.loc[day]
dist0=Distance.loc[day]
lat0=Lat.loc[day]
lon0=Lon.loc[day]
mlat=mean(lat0)
mlon=mean(lon0)
if len(thick0)>1000:
subplot(9,1,spn)
spn+=1
plot(dist0,thick0,'.',ms=1,alpha=0.5)
h_median=median(thick0)
h_mean=mean(thick0)
h0_str='$h_{median}$'+str('={:01.2f} m'.format(h_median))
h1_str='$h_{mean}$'+str('={:01.2f} m'.format(h_mean))
x0,x1=0,max(dist0)
plot([x0,x1],[h_median,h_median],'k:',label=h0_str)
plot([x0,x1],[h_mean,h_mean],'k.-',ms=0,lw=0.5,label=h1_str)
# Ice concentration / open water fraction
c0=thick0<=0.05
c1=thick0>0.05
ice_conc=100-sum(c0)/sum(c1)*100
c_str=str('$C$={:01.1f} %'.format(ice_conc))
plot(dist0[c0],thick0[c0],'r.',ms=1,alpha=0.5,label=c_str)
legend(loc=1,fontsize=8,title=day)
ylim([-0.1,5])
L.append([day,mlat,mlon,h_median,h_mean,ice_conc])
ylabel('Thickness [m]',fontsize=8)
xlabel('SUIT profile length [m]')
#savefig('figs/SUIT_profiles.pdf',dpi=200)
 
L=array(L)
D={'Date':L[:,0],'Latitude':L[:,1].astype(float),'Longitude':L[:,2].astype(float),
'H median':L[:,3].astype(float),'H mean':L[:,4].astype(float),'Ice concentration':L[:,5].astype(float)}
SUIT_xa=xr.Dataset(D)
#SUIT_xa.to_netcdf('data/SUIT_thickness_validation.nc')
#close('all')
```
 
%% Output
 
 
 
%% Cell type:markdown id:65151b57 tags:
 
### Generate overview map
 
%% Cell type:code id:61064cd1 tags:
 
``` python
import warnings
warnings.filterwarnings('ignore')
 
i=4
# 0 April 1 May 2 June 3 July 4 August 5 Septmber 6 October
#sit0=SIT[:,:,i]
sit0=sitm
#sit0=SIT[:,:] # September 2010
if True:
sit0=ma.masked_invalid(sit0)
 
#Extract subimage
x0,y0,x2,y2=1200,3700.0,-3000.0,700.0
 
sit_region=sit0[int((y2-Y2)/cs):int((y0-Y2)/cs),int((x2-X2)/cs):int((x0-X2)/cs)]
 
Cur=array(mapxy(x0,y0))
Cll=array(mapxy(x2,y2))
 
palette = cm.gist_earth#bone
palette = cm.viridis
palette = cm.plasma
 
palette.set_over("#ffffff")
palette.set_bad("#d0d0d0")
palette.set_under("#0000f0")
 
close()
fig=plt.figure()
 
m = Basemap(projection='stere',resolution='l',llcrnrlon=Cll[1],llcrnrlat=Cll[0],\
urcrnrlon=Cur[1],urcrnrlat=Cur[0],lat_ts=-70,lon_0=0,lat_0=-90,rsphere=(6378273.,6356889.))#,round=True)
 
m.imshow(sit_region,interpolation='nearest',cmap=palette,vmin=0,vmax=1.0)
m.drawmeridians(arange(0, 360, 30),labels=[0,0,1,0],linewidth=0.5)
m.drawparallels(arange(-90, 90, 5),labels=[0,0,0,1],linewidth=0.5)
 
plt.colorbar(shrink=0.5,extend='max',ticks=[0,0.5,1.0],pad=0.01,orientation='vertical').set_label(label='Sea-ice thickness [m]',size=10)#.set_label_position('left')
for k in ['AWI227', 'AWI229', 'AWI231']:
lat,lon=xas2.attrs[k]
 
#mu=float(xas2[k][i+4].mean())
mu=float(xas2[k].mean())
 
xsc,ysc=m(lon,lat)
ec='k'
mumax=float(xas2[k].max())
label=''
ec='w'
label='ULS '+k
msize=50
scatter(xsc,ysc,s=msize,color=palette(mu),vmin=0,vmax=1,edgecolors=ec,label=label)
va='bottom'
text(xsc,ysc,k[3:],va=va,fontsize=10)
# SUIT
for day in days:
thick0=mean(Thick.loc[day])
dist0=Distance.loc[day]
lat0=Lat.loc[day]
lon0=Lon.loc[day]
mlat=mean(lat0)
mlon=mean(lon0)
xsc,ysc=m(mlon,mlat)
ec='g'
label=''
scatter(xsc,ysc,s=30,marker='s',color=palette(thick0),vmin=0,vmax=1,edgecolors=ec,label=label)
label='SUIT'
scatter(xsc,ysc,s=30,marker='s',color=palette(thick0),vmin=0,vmax=1,edgecolors=ec,label=label)
 
# HEM
fl=!ls $valdir/HEM_Ant29*.nc
 
for fn in fl:
xhem=xr.open_mfdataset(fn)
thickness=xhem.THICKNESS.values
ind=isfinite(thickness)
thickness=thickness[ind]
xlat=xhem.LATITUDE.values[ind][::100]
xlon=xhem.LONGITUDE.values[ind][::100]
x,y=m(xlon,xlat)
#plot(x,y,'c.',ms=0.1)
mxlat=mean(xlat)
mxlon=mean(xlon)
mthickness=mean(thickness)
xsc,ysc=m(mxlon,mxlat)
ec='c'
label=''
scatter(xsc,ysc,marker='d',s=40,color=palette(mthickness),vmin=0,vmax=1,edgecolors=ec,label=label)
label='HEM'
scatter(xsc,ysc,marker='d',s=40,color=palette(mthickness),vmin=0,vmax=1,edgecolors=ec,label=label)
 
legend(loc='lower right',title='Validation sites',fontsize=6)
tight_layout()
#savefig('ULS_map_SMOS_mean.pdf')
print('finished')
```
 
%% Output
 
 
 
finished
 
%% Cell type:markdown id:33fa8270 tags:
 
## SUIT single days
 
%% Cell type:code id:4972e4b9 tags:
 
``` python
Outdir0=smos_datadir
 
year2=2020 # This year
year1=2010 # Start of SMOS data
Ny=year2-year1
years=list(range(year1,year2+1))
dr=pd.date_range(str(year1)+'-01-01',str(year2)+'-12-31',freq='1d')
#dr=dr.to_period('D')
Nd=len(dr) # Total days in period
nans=zeros(Nd)
nans[:]=nan
ts2=pd.Series(data=nans,index=dr)
 
ts=pd.Series()
df=pd.DataFrame()
 
date_list=list(dr.astype(str))
 
fl=[]
L_date=[]
for datestr in date_list:
yyyy,mm,dd,hh=datestr[0:4],datestr[5:7],datestr[8:10],datestr[11:13]
Outdir=Outdir0+'/'+yyyy+'/'+mm+'/'
fn=Outdir+'SMOS_Icethickness_v3.2_south_'+yyyy+mm+dd+'.nc'
 
fl1=glob.glob(Outdir+'*'+yyyy+mm+dd+'*.nc')
if fl1==[]:
fn=''
else:
fn=fl1[0]
if os.path.exists(fn):
L_date.append(pd.to_datetime( yyyy+mm+dd))
fl.append(fn)
ind=array(L_date)
 
ind2=pd.Series(data=ones(len(ind)),index=ind)
#ind2=ind2.to_period('12h')
smos_index=ts2.add(ind2,fill_value=0)
file_ind=pd.Series(array(fl),index=ind)
 
figure(figsize=(15,5))
isfinite(smos_index).astype(float).plot.area(stacked=False)
#savefig('SMOS_L3_TB_NH_available.png',dpi=150)
#savefig('SMOS_L3_SH_available.png',dpi=150)
```
 
%% Output
 
 
 
<AxesSubplot:>
 
%% Cell type:code id:960ee8b3 tags:
 
``` python
file_ind=pd.Series(array(fl),index=ind)
fn0=file_ind[day]
xa=xr.open_mfdataset(fn0)
land=array(xa.land)[0,:,:]
sit=array(xa.sea_ice_thickness)[0,:,:]
sy,sx=sit.shape[0],sit.shape[1]
lat=array(xa.latitude)
lon=array(xa.longitude)
tc='k'
#sit=sit0
```
 
%% Cell type:code id:d8f63bf7 tags:
 
``` python
days=array(['2013-08-29', '2013-09-09', '2013-09-10', '2013-09-11',
'2013-09-12', '2013-09-16', '2013-09-28', '2013-09-29',
'2013-09-30', '2013-10-02'], dtype=object)
 
days2=array(['2013-08-29', '2013-09-09', '2013-09-10', '2013-09-11',
'2013-09-12', '2013-09-16', '2013-09-29',
'2013-09-30', ], dtype=object)
#days=days2
 
dayL=[]
fl0=[]
for day in days:
fn0=file_ind[day]
fl0.append(fn0)
 
 
xa=xr.open_mfdataset(fl0)
sit=xa.sea_ice_thickness.mean(dim='time')
situ=xa.ice_thickness_uncertainty.mean(dim='time')
satr=xa.saturation_ratio.mean(dim='time')
```
 
%% Cell type:code id:b05dfedd tags:
 
``` python
SITL=[]
 
vals=[]
#days_selected=days
days=array(['2013-08-29', '2013-09-09', '2013-09-10', '2013-09-11',
'2013-09-12', '2013-09-16', '2013-09-28', '2013-09-29',
'2013-09-30', '2013-10-02'], dtype=object)
 
days2=array(['2013-08-29', '2013-09-09', '2013-09-10', '2013-09-11',
'2013-09-12', '2013-09-16', '2013-09-29',
'2013-09-30', ], dtype=object)
#days=days2
 
dayL=[]
 
for day in days:
fn0=file_ind[day]
xa=xr.open_mfdataset(fn0)
sit=ma.masked_invalid((xa.sea_ice_thickness)[0,:,:])
situ=ma.masked_invalid((xa.ice_thickness_uncertainty)[0,:,:])
satr=ma.masked_invalid((xa.saturation_ratio)[0,:,:])
 
#SITL.append(sit)
lat0=Lat.loc[day].median()
lon0=Lon.loc[day].median()
thick0=Thick.loc[day]#.median()
mthick=thick0.median()
#mthick=thick0.mean()
 
sthick=thick0.std()
q25thick=quantile(thick0,0.25)
q75thick=quantile(thick0,0.75)
 
c0=thick0<=0.05
c1=thick0>0.05
ice_conc=100-sum(c0)/sum(c1)*100
#if day=='2013-10-02':
 
 
if len(Thick.loc[day])>1000:
iy,ix=unravel_index(argmin(abs(lat[:]-lat0)+abs(lon[:]-lon0)),lat.shape) # get closest index
vals.append([mthick,sthick,q25thick,q75thick,ice_conc,(sit[iy,ix]),situ[iy,ix],satr[iy,ix]])
dayL.append(day)
 
v=array(vals)
```
 
%% Cell type:code id:9a11a24b tags:
 
``` python
from sklearn.metrics import mean_squared_error
 
#x,y,xe,ye=v[:,0],v[:,3],v[:,1],v[:,4]
x,y,xe0,xe1,ye1=v[:,0],v[:,5],v[:,2],v[:,3],v[:,6]
dmax=v[:,5]/v[:,7]*100
 
 
ye=array([ye1,dmax-y])
 
xe=array([xe0,xe1])
 
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
print("slope: %f intercept: %f" % (slope, intercept))
print("R: %f" % r_value)
 
print("R-squared: %f" % r_value**2)
 
print("Std err: %f" % std_err)
md=np.mean(x-y)
print("Mean difference VAL-SMOS: %f" % md)
 
 
print('Standard metric -----------')
print("Mean VAL, SMOS: %1.2f +- %1.2f , %1.2f +- %1.2f" % (mean(x),std(x),mean(y),std(y)))
print("N: ",len(x))
md=np.mean(x-y)
print("Mean difference VAL-SMOS: %1.2f" % md)
rmsd=mean_squared_error(x,y,squared=False)
print("RMSD VAL-SMOS: %1.2f" % rmsd)
print("R: %1.2f" % r_value)
 
#plt.plot(x,y,'o')
plt.figure(figsize=(6,6))
xn=np.linspace(0,2)
plt.plot(xn,xn,'k:',label='Identity')
#plt.plot(xn,xn*slope+intercept,'k-',label='Ordinary regression')
plt.plot(x,y,'ko',ms=5,alpha=1.0,label='SMOS pixel based')
plt.errorbar(x,y,ms=5,xerr=xe,yerr=ye,fmt='ko',alpha=1.0)
 
#plt.errorbar(x,y,xerr=xe,yerr=ye,fmt='ko',alpha=0.2,label='SMOS pixel based')
plt.plot(x,dmax,'r^',ms=5,alpha=1.0,label='$d_{max}$')
#plt.plot(xn,xn*wslope+wintercept,'k:',label='Weighted regression')
 
#plt.plot(xn,xn,'k:')
#plt.grid()
plt.xlabel('Validation thickness [m]')
plt.ylabel('SMOS thickness [m]')
#plt.title('SMOS SIT version 3.2')
#plt.title('SMOS SIT version 2')
 
plt.legend(loc=2)
plt.xlim([-0.1,1.5])
plt.ylim([-0.1,1.5])
#plt.savefig('SMOS_v2_scatter_fits.png',dpi=200)
 
 
for i,day in enumerate(dayL):
text(x[i]-0.05,y[i]-0.05,day[5:],color='r',fontsize=8)
 
plt.tight_layout()
grid()
#plt.savefig('figs/SMOS_SUIT_scatter.pdf')
 
```
 
%% Output
 
slope: 0.486081 intercept: 0.055360
R: 0.714217
R-squared: 0.510105
Std err: 0.180045
Mean difference VAL-SMOS: 0.246493
Standard metric -----------
Mean VAL, SMOS: 0.59 +- 0.24 , 0.34 +- 0.16
N: 9
Mean difference VAL-SMOS: 0.25
RMSD VAL-SMOS: 0.30
R: 0.71
 
 
 
%% Cell type:markdown id:fcda86b3 tags:
 
## HEM single days
 
%% Cell type:code id:a4b3bcca tags:
 
``` python
XA=xr.open_mfdataset('repo_data/HEM/Ant_*_*_sit.nc')
XA
```
 
%% Output
 
<xarray.Dataset>
Dimensions: (x: 632, y: 664, time: 4)
Coordinates:
* x (x) float64 -3.95e+03 -3.937e+03 ... 3.937e+03 3.95e+03
* y (y) float64 -3.95e+03 -3.937e+03 ... 4.337e+03 4.35e+03
* time (time) datetime64[ns] 2013-06-19T13:43:00 ... 2013-07-...
Data variables:
sea_ice_thickness (time, y, x) float32 dask.array<chunksize=(1, 664, 632), meta=np.ndarray>
 
%% Cell type:code id:ba8503a1 tags:
 
``` python
 
zval=[]
zsmos=[]
zsmosu=[] # Uncertainty
zsmosr=[] # Saturation
 
zvalm=[] # Mean for days
zsmosm=[]
zvals=[] # std for days
zsmoss=[]
 
 
for ti in XA.time:
SITval=ma.masked_invalid(XA.sel(time=ti).to_array())[0,:,:]
datestr=str(np.array(ti))[0:10]
yyyy,mm,dd,hh=datestr[0:4],datestr[5:7],datestr[8:10],datestr[11:13]
Outdir=Outdir0+'/'+yyyy+'/'+mm+'/'
fn=Outdir+'SMOS_Icethickness_v3.2_south_'+yyyy+mm+dd+'.nc'
print(fn)
xa=xr.open_mfdataset(fn)
SIT0=np.array(xa.sea_ice_thickness)[0,:,:]
SITU=np.array(xa.ice_thickness_uncertainty)[0,:,:]
SATR=np.array(xa.saturation_ratio)[0,:,:]
 
ind=np.logical_not(SITval.mask)
zval.extend(SITval[ind].flatten().data)
zsmos.extend(SIT0[ind].flatten().data)
zsmosu.extend(SITU[ind].flatten().data)
zsmosr.extend(SATR[ind].flatten().data)
 
zvalm.append(mean(SITval[ind].flatten().data))
zsmosm.append(mean(SIT0[ind].flatten().data))
zvals.append(std(SITval[ind].flatten().data))
zsmoss.append(std(SIT0[ind].flatten().data))
print(len(zval))
 
x=np.array(zval)
y=np.array(zsmos)
ye=np.array(zsmosu)
dmax=y/np.array(zsmosr)*100
 
 
xm=np.array(zvalm)
ym=np.array(zsmosm)
 
xms=np.array(zvals)
yms=np.array(zsmoss)
 
```
 
%% Output
 
/isibhv/projects/siral/product/smos/SMOS_v620_L3C_Sea_v3/south//2013/06/SMOS_Icethickness_v3.2_south_20130619.nc
11
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%% Cell type:code id:c42f298b tags:
 
``` python
ye2=array([ye,dmax-y])
ye3=array([0,dmax-y])
plt.figure(figsize=(6,6))
xn=np.linspace(0,2)
plt.plot(xn,xn,'k:',label='Identity')
plt.plot(x,y,'ko',ms=3,alpha=0.5,label='SMOS pixel based')
plt.errorbar(xm,ym,xerr=xms,yerr=yms,ms=10,fmt='ko',label='Flight mean')
plt.plot(x,dmax,'r^',ms=5,alpha=1.0,label='$d_{max}$')
plt.errorbar(x,y,yerr=ye2,ms=3,fmt='ko',alpha=0.3)#,label='SMOS pixel based')
plt.xlabel('Validation thickness [m]')
plt.ylabel('SMOS thickness [m]')
plt.legend(loc=2)
plt.xlim([0.0,2.0])
plt.ylim([0.0,2.0])
plt.grid()
#savefig('Ant_Val_HEM_Suit_Scatter.png',dpi=300)
#savefig('figs/Ant_Val_HEM_Scatter.pdf')
```
 
%% Output
 
 
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