Comparisons of all-season thickness data with BGEP ULS sea ice draft estimates

Contents

Comparisons of all-season thickness data with BGEP ULS sea ice draft estimates#

Summary: In this notebook, we produce comparisons of all-season monthly gridded ICESat-2 and CryoSat-2 sea ice thickness data with draft measurements obtained from Upward Looking Sonar moorings deployed in the Beaufort Sea.

Version history: Version 1 (05/01/2025)

Import notebook dependencies#

import xarray as xr 
import pandas as pd
import numpy as np
import itertools
import pyproj 
from netCDF4 import Dataset
import scipy.interpolate 
from utils.read_data_utils import read_book_data, read_IS2SITMOGR4 # Helper function for reading the data from the bucket
from utils.plotting_utils import compute_gridcell_winter_means, interactiveArcticMaps, interactive_winter_mean_maps, interactive_winter_comparison_lineplot # Plotting
from extra_funcs import apply_interpolation_time
from scipy import stats
import datetime
# Plotting dependencies
import cartopy.crs as ccrs
from textwrap import wrap
import hvplot.pandas
import holoviews as hv
import matplotlib.pyplot as plt
from matplotlib.axes import Axes
from cartopy.mpl.geoaxes import GeoAxes
GeoAxes._pcolormesh_patched = Axes.pcolormesh # Helps avoid some weird issues with the polar projection 
%config InlineBackend.figure_format = 'retina'
import matplotlib as mpl

mpl.rcParams['figure.dpi'] = 00 # Sets figure size in the notebook

# Remove warnings to improve display
import warnings 
warnings.filterwarnings('ignore') 

# Set some plotting parameters
mpl.rcParams.update({
    "text.usetex": False,  # Use LaTeX for rendering
    "font.family": "sans-serif",
    "lines.linewidth": 1.,
    "font.size": 8,
    #"lines.alpha": 0.8,
    "axes.labelsize": 8,
    "xtick.labelsize": 8,
    "ytick.labelsize": 8,
    "legend.fontsize": 8
})
mpl.rcParams['font.sans-serif'] = ['Arial']
mpl.rcParams['figure.dpi'] = 300 

# DO ONE RUN APR 2021 WITH SM AS THIS IS WHEN IT IS AVAILABLE TO
start_date = "Nov 2018"
end_date = "July 2021"
options = {
    'IS-2/SM-LG': {'thickness': 'ice_thickness_sm_int', 'snow_depth': 'snow_depth_sm_int'},
    'CS-2/SM-LG': {'thickness': 'ice_thickness_cs2_ubris', 'snow_depth': 'snow_depth_sm_int'}
}

# Load the all-season wrangled dataset
IS2_CS2_allseason = xr.open_dataset('./data/book_data_allseason.nc')
print("Successfully loaded all-season wrangled dataset")

Successfully loaded all-season wrangled dataset

# Get some map proj info needed for later functions
out_proj = 'EPSG:3411'
mapProj = pyproj.Proj("+init=" + out_proj)

xIS2 = IS2_CS2_allseason.x.values
yIS2 = IS2_CS2_allseason.y.values
xptsIS2, yptsIS2 = np.meshgrid(xIS2, yIS2)

out_lons = IS2_CS2_allseason.longitude.values
out_lats = IS2_CS2_allseason.latitude.values

int_str = '_int'

# Estimate ice draft from the ICESat-2 data for more direct comparison with ULS draft measurements
# NB: Another option (not taken) is to simply multiply ULS drafts by 1.1 to convert to thickness (AWI do this)
# Include extra var = '_int' to use interpolated values, not recommended for validation purposes. 
# Define the variables for each option

IS2_CS2_allseason[f'ice_draft_IS-2/SM-LG'] = (IS2_CS2_allseason['ice_thickness_sm'+int_str] - IS2_CS2_allseason['freeboard'+int_str] + IS2_CS2_allseason['snow_depth_sm'+int_str])

IS2_CS2_allseason[f'ice_draft_CS-2/SM-LG'] = ((IS2_CS2_allseason['ice_thickness_cs2_ubris']*IS2_CS2_allseason['cs2_sea_ice_density_UBRIS']) + (IS2_CS2_allseason['snow_depth_sm'+int_str] + IS2_CS2_allseason['snow_density_sm'+int_str]))/1024.

# Initialize map projection and project data to it

IS2_date_range = pd.date_range(start=start_date, end=end_date, freq='MS')+ pd.Timedelta(days=14)# MS indicates a time frequency of start of the month
#IS2_date_range = IS2_date_range[((IS2_date_range.month <5) | (IS2_date_range.month > 8))]
IS2_date_range_strs=[str(date.year)+'-%02d'%(date.month) for date in IS2_date_range]
IS2_date_range_strs

['2018-11',
 '2018-12',
 '2019-01',
 '2019-02',
 '2019-03',
 '2019-04',
 '2019-05',
 '2019-06',
 '2019-07',
 '2019-08',
 '2019-09',
 '2019-10',
 '2019-11',
 '2019-12',
 '2020-01',
 '2020-02',
 '2020-03',
 '2020-04',
 '2020-05',
 '2020-06',
 '2020-07',
 '2020-08',
 '2020-09',
 '2020-10',
 '2020-11',
 '2020-12',
 '2021-01',
 '2021-02',
 '2021-03',
 '2021-04',
 '2021-05',
 '2021-06',
 '2021-07']

# This is the value in meters of the aggregation length-scale for the IS-2/CS-2 data. 
# A compromise between the 50km used in the original BGEP analysis and the 150km used in the Landy2022 analysis.
comp_res=100000

# Wrange ULS data
# Could really convert this to another data wrangling notebook and store the derived output as netcdfs

dataPathULS='./data/'

def get_ULS_dates(uls_mean_monthly_draft, uls_dates, date):
    #print(date)
    try:
        return uls_mean_monthly_draft[uls_dates==date].values[0]
    except:
        return np.nan

def get_uls_year(letter, year):
    if letter=='a':
        print('Mooring A (75.0 N, 150 W)')
        uls_x, uls_y = mapProj(-150., 75.)
    if letter=='b':
        print('Mooring B (78.4 N, 150.0 W)')
        uls_x, uls_y = mapProj(-150., 78.4)
    if letter=='d':
        print('Mooring D (74.0 N, 140.0 W)')
        uls_x, uls_y = mapProj(-140., 74.)
 
    uls = pd.read_csv(dataPathULS+'uls'+year+letter+'_draft.dat', sep='\s+',names = ['date', 'time', 'draft'], header=2)
    print(uls.head(10))
    utc_datetime_uls = pd.to_datetime(uls['date'], format='%Y%m%d')
    uls_mean_daily_draft = uls['draft'].groupby([utc_datetime_uls.dt.date]).mean() 

    uls_mean_monthly_draft = uls['draft'].groupby([utc_datetime_uls.dt.to_period('m')]).mean() 
    #print(uls_mean_monthly_draft)
    
    return uls_mean_daily_draft, uls_mean_monthly_draft, uls_x, uls_y

uls_mean_daily_draft_a_18, uls_mean_monthly_draft_a_18, uls_x_a, uls_y_a = get_uls_year('a', '18')
uls_mean_daily_draft_b_18, uls_mean_monthly_draft_b_18, uls_x_b, uls_y_b = get_uls_year('b', '18')
uls_mean_daily_draft_d_18, uls_mean_monthly_draft_d_18, uls_x_d, uls_y_d = get_uls_year('d', '18')

Mooring A (75.0 N, 150 W)

       date    time  draft
20180925  234211    0.0
20180925  234212    0.0
20180925  234213    0.0
20180925  234214    0.0
20180925  234215    0.0
20180925  234216    0.0
20180925  234217    0.0
20180925  234218    0.0
20180925  234219    0.0
20180925  234220    0.0

Mooring B (78.4 N, 150.0 W)

       date   time  draft
20180923  20901    0.0
20180923  20902    0.0
20180923  20903    0.0
20180923  20904    0.0
20180923  20905    0.0
20180923  20906    0.0
20180923  20907    0.0
20180923  20908    0.0
20180923  20909    0.0
20180923  20910    0.0

Mooring D (74.0 N, 140.0 W)

       date   time  draft
20180915  10011    0.0
20180915  10012    0.0
20180915  10013    0.0
20180915  10014    0.0
20180915  10015    0.0
20180915  10016    0.0
20180915  10017    0.0
20180915  10018    0.0
20180915  10019    0.0
20180915  10020    0.0

uls_mean_daily_draft_a_21, uls_mean_monthly_draft_a_21, _, _ = get_uls_year('a', '21')
uls_mean_daily_draft_b_21, uls_mean_monthly_draft_b_21, _, _ = get_uls_year('b', '21')
uls_mean_daily_draft_d_21, uls_mean_monthly_draft_d_21, _, _ = get_uls_year('d', '21')   

Mooring A (75.0 N, 150 W)

       date    time  draft
20210828  235411   2.11
20210828  235412   2.04
20210828  235413   1.96
20210828  235414   1.89
20210828  235415   1.82
20210828  235416   1.75
20210828  235417   1.68
20210828  235418   1.61
20210828  235419   1.54
20210828  235420   1.48

Mooring B (78.4 N, 150.0 W)

       date    time  draft
20210831  222825    0.0
20210831  222826    0.0
20210831  222827    0.0
20210831  222828    0.0
20210831  222829    0.0
20210831  222830    0.0
20210831  222831    0.0
20210831  222832    0.0
20210831  222833    0.0
20210831  222834    0.0

Mooring D (74.0 N, 140.0 W)

       date  time  draft
20210909  4745    0.0
20210909  4746    0.0
20210909  4747    0.0
20210909  4748    0.0
20210909  4749    0.0
20210909  4750    0.0
20210909  4751    0.0
20210909  4752    0.0
20210909  4753    0.0
20210909  4754    0.0

# Combine data for all years
uls_mean_daily_draft_a = pd.concat([uls_mean_daily_draft_a_18, uls_mean_daily_draft_a_21])
uls_mean_daily_draft_b = pd.concat([uls_mean_daily_draft_b_18, uls_mean_daily_draft_b_21])
uls_mean_daily_draft_d = pd.concat([uls_mean_daily_draft_d_18, uls_mean_daily_draft_d_21])

# Combine data for all years
uls_mean_monthly_draft_a = pd.concat([uls_mean_monthly_draft_a_18, uls_mean_monthly_draft_a_21])
uls_mean_monthly_draft_b = pd.concat([uls_mean_monthly_draft_b_18, uls_mean_monthly_draft_b_21])
uls_mean_monthly_draft_d = pd.concat([uls_mean_monthly_draft_d_18, uls_mean_monthly_draft_d_21])

end_date = "2021-08-01"
uls_mean_daily_draft_a = uls_mean_daily_draft_a[uls_mean_daily_draft_a.index < np.datetime64(end_date)].dropna()
uls_mean_daily_draft_b = uls_mean_daily_draft_b[uls_mean_daily_draft_b.index < np.datetime64(end_date)].dropna()
uls_mean_daily_draft_d = uls_mean_daily_draft_d[uls_mean_daily_draft_d.index < np.datetime64(end_date)].dropna()

uls_mean_monthly_draft_a = uls_mean_monthly_draft_a[uls_mean_monthly_draft_a.index < end_date].dropna()
uls_mean_monthly_draft_b = uls_mean_monthly_draft_b[uls_mean_monthly_draft_b.index < end_date].dropna()
uls_mean_monthly_draft_d = uls_mean_monthly_draft_d[uls_mean_monthly_draft_d.index < end_date].dropna()

start_date = "2018-11-01"
uls_mean_daily_draft_a = uls_mean_daily_draft_a[uls_mean_daily_draft_a.index >= np.datetime64(start_date)].dropna()
uls_mean_daily_draft_b = uls_mean_daily_draft_b[uls_mean_daily_draft_b.index >= np.datetime64(start_date)].dropna()
uls_mean_daily_draft_d = uls_mean_daily_draft_d[uls_mean_daily_draft_d.index >= np.datetime64(start_date)].dropna()

uls_mean_monthly_draft_a = uls_mean_monthly_draft_a[uls_mean_monthly_draft_a.index >= start_date].dropna()
uls_mean_monthly_draft_b = uls_mean_monthly_draft_b[uls_mean_monthly_draft_b.index >= start_date].dropna()
uls_mean_monthly_draft_d = uls_mean_monthly_draft_d[uls_mean_monthly_draft_d.index >= start_date].dropna()

def grid_IS2_nearby(date, option, uls_x, uls_y, res=50000):
    #print(date)
    IS2 = IS2_CS2_allseason['ice_draft_'+option].sel(time=date)
    #xptsIS2g, yptsIS2g = mapProj(IS2.longitude.values, IS2.latitude.values)
    
    dist = np.sqrt((xptsIS2 - uls_x)**2 + (yptsIS2 - uls_y)**2)
    
    IS2_uls = IS2.where(dist<res).mean(skipna=True).values
    
    #print('Number of valid IS-2 grid cells in month '+str(date)[0:7]+':', np.count_nonzero(~np.isnan(IS2.where(dist<res))))
    
    #Another option I first explored, coarsen the data then do nearest neighbor...provided similar results but above is more flexible.
    #if coarse_res>1:
    #Coarsen array by coarse_res in x/y directions (note that each grid-cell represents 25 km so 4 = 100 km)
    #    IS2 = IS2.coarsen(x=res, y=res, boundary='pad').mean()
    #IS2_uls = scipy.interpolate.griddata((xptsIS2.flatten(), yptsIS2.flatten()), IS2.values.flatten(), (uls_x, uls_y), method = 'nearest')
    print(date, option, IS2_uls)
    return IS2_uls


# Compute monthly ULS values for each option
monthly_IS2_at_ULS_a_options = {}
monthly_IS2_at_ULS_b_options = {}
monthly_IS2_at_ULS_d_options = {}
monthly_IS2_at_ULS_all_options = {}

for option in options.keys():
    monthly_IS2_at_ULS_a_options[option] = [
        grid_IS2_nearby(date, option, uls_x_a, uls_y_a, res=comp_res) for date in IS2_date_range
    ]
    monthly_IS2_at_ULS_b_options[option] = [
        grid_IS2_nearby(date, option,uls_x_b, uls_y_b, res=comp_res) for date in IS2_date_range
    ]
    monthly_IS2_at_ULS_d_options[option] = [
        grid_IS2_nearby(date, option,uls_x_d, uls_y_d, res=comp_res) for date in IS2_date_range
    ]
    # Combine all ULS values for the current option
    monthly_IS2_at_ULS_all_options[option] = (
        monthly_IS2_at_ULS_a_options[option] +
        monthly_IS2_at_ULS_b_options[option] +
        monthly_IS2_at_ULS_d_options[option]
    )

2018-11-15 00:00:00 IS-2/SM-LG 0.5943448
2018-12-15 00:00:00 IS-2/SM-LG 0.947996
2019-01-15 00:00:00 IS-2/SM-LG 1.123304
2019-02-15 00:00:00 IS-2/SM-LG 1.3832679
2019-03-15 00:00:00 IS-2/SM-LG 1.9343529
2019-04-15 00:00:00 IS-2/SM-LG 2.0559678
2019-05-15 00:00:00 IS-2/SM-LG 2.1249423
2019-06-15 00:00:00 IS-2/SM-LG 1.8776155
2019-07-15 00:00:00 IS-2/SM-LG nan
2019-08-15 00:00:00 IS-2/SM-LG nan
2019-09-15 00:00:00 IS-2/SM-LG nan
2019-10-15 00:00:00 IS-2/SM-LG nan
2019-11-15 00:00:00 IS-2/SM-LG 0.6100675
2019-12-15 00:00:00 IS-2/SM-LG 0.9386612
2020-01-15 00:00:00 IS-2/SM-LG 1.1106002
2020-02-15 00:00:00 IS-2/SM-LG 1.1858178
2020-03-15 00:00:00 IS-2/SM-LG 1.435097
2020-04-15 00:00:00 IS-2/SM-LG 1.5970328
2020-05-15 00:00:00 IS-2/SM-LG 1.8750578
2020-06-15 00:00:00 IS-2/SM-LG 1.8289809
2020-07-15 00:00:00 IS-2/SM-LG 0.70578855
2020-08-15 00:00:00 IS-2/SM-LG 0.55099994
2020-09-15 00:00:00 IS-2/SM-LG 0.6826465
2020-10-15 00:00:00 IS-2/SM-LG 0.76492584
2020-11-15 00:00:00 IS-2/SM-LG 0.8903983
2020-12-15 00:00:00 IS-2/SM-LG 0.8992391
2021-01-15 00:00:00 IS-2/SM-LG 0.99166137
2021-02-15 00:00:00 IS-2/SM-LG 1.1913469
2021-03-15 00:00:00 IS-2/SM-LG 1.5791435
2021-04-15 00:00:00 IS-2/SM-LG 1.8244846
2021-05-15 00:00:00 IS-2/SM-LG 2.0777307
2021-06-15 00:00:00 IS-2/SM-LG 1.71525
2021-07-15 00:00:00 IS-2/SM-LG 0.7386345
2018-11-15 00:00:00 IS-2/SM-LG 0.47448257
2018-12-15 00:00:00 IS-2/SM-LG 0.6522888
2019-01-15 00:00:00 IS-2/SM-LG 0.8879977
2019-02-15 00:00:00 IS-2/SM-LG 0.97024953
2019-03-15 00:00:00 IS-2/SM-LG 1.4155942
2019-04-15 00:00:00 IS-2/SM-LG 1.5527574
2019-05-15 00:00:00 IS-2/SM-LG 2.16676
2019-06-15 00:00:00 IS-2/SM-LG 2.27472
2019-07-15 00:00:00 IS-2/SM-LG nan
2019-08-15 00:00:00 IS-2/SM-LG 0.81884444
2019-09-15 00:00:00 IS-2/SM-LG 0.81331205
2019-10-15 00:00:00 IS-2/SM-LG 0.86675507
2019-11-15 00:00:00 IS-2/SM-LG 0.9571779
2019-12-15 00:00:00 IS-2/SM-LG 1.099113
2020-01-15 00:00:00 IS-2/SM-LG 1.2618959
2020-02-15 00:00:00 IS-2/SM-LG 1.5363593
2020-03-15 00:00:00 IS-2/SM-LG 1.540907
2020-04-15 00:00:00 IS-2/SM-LG 1.5984646
2020-05-15 00:00:00 IS-2/SM-LG 1.6374398
2020-06-15 00:00:00 IS-2/SM-LG 1.8070999
2020-07-15 00:00:00 IS-2/SM-LG 0.62468004
2020-08-15 00:00:00 IS-2/SM-LG 0.651
2020-09-15 00:00:00 IS-2/SM-LG 0.6152792
2020-10-15 00:00:00 IS-2/SM-LG 0.5213816
2020-11-15 00:00:00 IS-2/SM-LG 0.58381575
2020-12-15 00:00:00 IS-2/SM-LG 0.94671357
2021-01-15 00:00:00 IS-2/SM-LG 1.2533611
2021-02-15 00:00:00 IS-2/SM-LG 1.4482422
2021-03-15 00:00:00 IS-2/SM-LG 1.6480451
2021-04-15 00:00:00 IS-2/SM-LG 1.8098042
2021-05-15 00:00:00 IS-2/SM-LG 1.9285
2021-06-15 00:00:00 IS-2/SM-LG 1.64134
2021-07-15 00:00:00 IS-2/SM-LG 1.0927
2018-11-15 00:00:00 IS-2/SM-LG 0.7251236
2018-12-15 00:00:00 IS-2/SM-LG 1.0473033
2019-01-15 00:00:00 IS-2/SM-LG 1.4275311
2019-02-15 00:00:00 IS-2/SM-LG 1.7813711
2019-03-15 00:00:00 IS-2/SM-LG 2.0924714
2019-04-15 00:00:00 IS-2/SM-LG 2.0695682
2019-05-15 00:00:00 IS-2/SM-LG 1.99896
2019-06-15 00:00:00 IS-2/SM-LG 1.5218371
2019-07-15 00:00:00 IS-2/SM-LG nan
2019-08-15 00:00:00 IS-2/SM-LG nan
2019-09-15 00:00:00 IS-2/SM-LG nan
2019-10-15 00:00:00 IS-2/SM-LG 0.6043561
2019-11-15 00:00:00 IS-2/SM-LG 0.64539367
2019-12-15 00:00:00 IS-2/SM-LG 1.0899856
2020-01-15 00:00:00 IS-2/SM-LG 1.3011304
2020-02-15 00:00:00 IS-2/SM-LG 1.3999809
2020-03-15 00:00:00 IS-2/SM-LG 1.6867998
2020-04-15 00:00:00 IS-2/SM-LG 1.9885639
2020-05-15 00:00:00 IS-2/SM-LG 2.07406
2020-06-15 00:00:00 IS-2/SM-LG 1.76022
2020-07-15 00:00:00 IS-2/SM-LG 0.73486006
2020-08-15 00:00:00 IS-2/SM-LG 0.50064105
2020-09-15 00:00:00 IS-2/SM-LG nan
2020-10-15 00:00:00 IS-2/SM-LG 0.5091671
2020-11-15 00:00:00 IS-2/SM-LG 0.6787752
2020-12-15 00:00:00 IS-2/SM-LG 1.0654914
2021-01-15 00:00:00 IS-2/SM-LG 1.7295828
2021-02-15 00:00:00 IS-2/SM-LG 1.7133557
2021-03-15 00:00:00 IS-2/SM-LG 1.9272254
2021-04-15 00:00:00 IS-2/SM-LG 2.0230212
2021-05-15 00:00:00 IS-2/SM-LG 2.32064
2021-06-15 00:00:00 IS-2/SM-LG 1.7958002
2021-07-15 00:00:00 IS-2/SM-LG 0.82938004
2018-11-15 00:00:00 CS-2/SM-LG 0.6197224239619122
2018-12-15 00:00:00 CS-2/SM-LG 0.8183370701639411
2019-01-15 00:00:00 CS-2/SM-LG 0.8202187042801291
2019-02-15 00:00:00 CS-2/SM-LG 1.3767945674402606
2019-03-15 00:00:00 CS-2/SM-LG 1.5657275956859935
2019-04-15 00:00:00 CS-2/SM-LG 1.6086189625387741
2019-05-15 00:00:00 CS-2/SM-LG 1.327550512771373
2019-06-15 00:00:00 CS-2/SM-LG 1.598343082378904
2019-07-15 00:00:00 CS-2/SM-LG nan
2019-08-15 00:00:00 CS-2/SM-LG nan
2019-09-15 00:00:00 CS-2/SM-LG nan
2019-10-15 00:00:00 CS-2/SM-LG nan
2019-11-15 00:00:00 CS-2/SM-LG 0.4587474681488548
2019-12-15 00:00:00 CS-2/SM-LG 1.0275744967971536
2020-01-15 00:00:00 CS-2/SM-LG 1.056501641531382
2020-02-15 00:00:00 CS-2/SM-LG 1.1563959724953017
2020-03-15 00:00:00 CS-2/SM-LG 1.8724642986790283
2020-04-15 00:00:00 CS-2/SM-LG 2.129049423538323
2020-05-15 00:00:00 CS-2/SM-LG 2.0244427194398855
2020-06-15 00:00:00 CS-2/SM-LG 1.2937080339604892
2020-07-15 00:00:00 CS-2/SM-LG 0.5506746897488428
2020-08-15 00:00:00 CS-2/SM-LG 0.43253594256991484
2020-09-15 00:00:00 CS-2/SM-LG 0.33215796113007856
2020-10-15 00:00:00 CS-2/SM-LG 0.44811226629554707
2020-11-15 00:00:00 CS-2/SM-LG 0.7101341943868487
2020-12-15 00:00:00 CS-2/SM-LG 1.1303755169301701
2021-01-15 00:00:00 CS-2/SM-LG 1.2356852875475712
2021-02-15 00:00:00 CS-2/SM-LG 1.274310038487792
2021-03-15 00:00:00 CS-2/SM-LG 1.314928065145518
2021-04-15 00:00:00 CS-2/SM-LG 1.3398297581684473
2021-05-15 00:00:00 CS-2/SM-LG 1.46657827999922
2021-06-15 00:00:00 CS-2/SM-LG 1.0721816433032336
2021-07-15 00:00:00 CS-2/SM-LG 0.47533325648293123
2018-11-15 00:00:00 CS-2/SM-LG 0.8967005003656743
2018-12-15 00:00:00 CS-2/SM-LG 1.0118412412978637
2019-01-15 00:00:00 CS-2/SM-LG 1.0567229374230374
2019-02-15 00:00:00 CS-2/SM-LG 1.5320334052841644
2019-03-15 00:00:00 CS-2/SM-LG 2.264547641181853
2019-04-15 00:00:00 CS-2/SM-LG 2.6066323886066676
2019-05-15 00:00:00 CS-2/SM-LG 2.319994140315006
2019-06-15 00:00:00 CS-2/SM-LG 1.908852149348844
2019-07-15 00:00:00 CS-2/SM-LG nan
2019-08-15 00:00:00 CS-2/SM-LG 0.6280665893332168
2019-09-15 00:00:00 CS-2/SM-LG 0.7638313953087584
2019-10-15 00:00:00 CS-2/SM-LG 0.7396426005569511
2019-11-15 00:00:00 CS-2/SM-LG 1.139180883827949
2019-12-15 00:00:00 CS-2/SM-LG 1.2112879074085747
2020-01-15 00:00:00 CS-2/SM-LG 1.2650114944624538
2020-02-15 00:00:00 CS-2/SM-LG 1.387350722212974
2020-03-15 00:00:00 CS-2/SM-LG 1.929808448742545
2020-04-15 00:00:00 CS-2/SM-LG 2.626905442462057
2020-05-15 00:00:00 CS-2/SM-LG 2.7080473835427994
2020-06-15 00:00:00 CS-2/SM-LG 1.9263898358378833
2020-07-15 00:00:00 CS-2/SM-LG 0.7288139555772672
2020-08-15 00:00:00 CS-2/SM-LG 0.6928146691988778
2020-09-15 00:00:00 CS-2/SM-LG 0.6859333251102164
2020-10-15 00:00:00 CS-2/SM-LG 0.48472436367173044
2020-11-15 00:00:00 CS-2/SM-LG 1.0379473593219881
2020-12-15 00:00:00 CS-2/SM-LG 1.3097076061346697

2021-01-15 00:00:00 CS-2/SM-LG 1.3513224211589752
2021-02-15 00:00:00 CS-2/SM-LG 1.3723881423941293
2021-03-15 00:00:00 CS-2/SM-LG 1.7320068559140407
2021-04-15 00:00:00 CS-2/SM-LG 1.947810176785558
2021-05-15 00:00:00 CS-2/SM-LG 1.957827067655267
2021-06-15 00:00:00 CS-2/SM-LG 1.4537153246116017
2021-07-15 00:00:00 CS-2/SM-LG 1.4101024227262426
2018-11-15 00:00:00 CS-2/SM-LG 0.7125446257149433
2018-12-15 00:00:00 CS-2/SM-LG 1.2747119181361521
2019-01-15 00:00:00 CS-2/SM-LG 1.2151625065962437
2019-02-15 00:00:00 CS-2/SM-LG 1.1433311840051774
2019-03-15 00:00:00 CS-2/SM-LG 1.242675186458999
2019-04-15 00:00:00 CS-2/SM-LG 1.433435808751596
2019-05-15 00:00:00 CS-2/SM-LG 1.3571687958459215
2019-06-15 00:00:00 CS-2/SM-LG 1.3812019906745028
2019-07-15 00:00:00 CS-2/SM-LG nan
2019-08-15 00:00:00 CS-2/SM-LG nan
2019-09-15 00:00:00 CS-2/SM-LG nan
2019-10-15 00:00:00 CS-2/SM-LG 0.29484394371111583
2019-11-15 00:00:00 CS-2/SM-LG 0.7809757556389103
2019-12-15 00:00:00 CS-2/SM-LG 1.0387914950362773
2020-01-15 00:00:00 CS-2/SM-LG 1.1059496046783206
2020-02-15 00:00:00 CS-2/SM-LG 1.1274789403414043
2020-03-15 00:00:00 CS-2/SM-LG 1.5037857274450461
2020-04-15 00:00:00 CS-2/SM-LG 1.684323961770233
2020-05-15 00:00:00 CS-2/SM-LG 1.5964569551456307
2020-06-15 00:00:00 CS-2/SM-LG 1.1641355360312244
2020-07-15 00:00:00 CS-2/SM-LG 0.356019329989811
2020-08-15 00:00:00 CS-2/SM-LG 0.40841616401340675
2020-09-15 00:00:00 CS-2/SM-LG nan
2020-10-15 00:00:00 CS-2/SM-LG 0.3694209133898091
2020-11-15 00:00:00 CS-2/SM-LG 0.8415768671640279
2020-12-15 00:00:00 CS-2/SM-LG 1.0447649324365154
2021-01-15 00:00:00 CS-2/SM-LG 1.2101189564359844
2021-02-15 00:00:00 CS-2/SM-LG 1.2633204330951793
2021-03-15 00:00:00 CS-2/SM-LG 1.3210533205725499
2021-04-15 00:00:00 CS-2/SM-LG 1.4012743351163999
2021-05-15 00:00:00 CS-2/SM-LG 1.3600454839920186
2021-06-15 00:00:00 CS-2/SM-LG 1.1294450466200452
2021-07-15 00:00:00 CS-2/SM-LG 0.441223336777232

uls_dates=uls_mean_monthly_draft_a.index.astype(str)
uls_mean_monthly_draft_a_IS2_period  = [get_ULS_dates(uls_mean_monthly_draft_a, uls_dates, date) for date in IS2_date_range_strs]

uls_dates=uls_mean_monthly_draft_b.index.astype(str)
uls_mean_monthly_draft_b_IS2_period  = [get_ULS_dates(uls_mean_monthly_draft_b, uls_dates, date) for date in IS2_date_range_strs]

uls_dates=uls_mean_monthly_draft_d.index.astype(str)
uls_mean_monthly_draft_d_IS2_period  = [get_ULS_dates(uls_mean_monthly_draft_d, uls_dates, date) for date in IS2_date_range_strs]
                                        
uls_mean_monthly_draft_IS2_period = uls_mean_monthly_draft_a_IS2_period+uls_mean_monthly_draft_b_IS2_period+uls_mean_monthly_draft_d_IS2_period

# Validation analysis for each option
validation_results = {}

for option in options.keys():
    print(option)
    # ULS A
    mask_a = ~np.isnan(monthly_IS2_at_ULS_a_options[option])& ~np.isnan(uls_mean_monthly_draft_a_IS2_period)
    res_a = stats.linregress(
        np.array(monthly_IS2_at_ULS_a_options[option])[mask_a],
        np.array(uls_mean_monthly_draft_a_IS2_period)[mask_a]
    )
    r_str_a = '%.02f' % (res_a[2]**2)
    mb_str_a = '%.02f' % (np.nanmean(np.array(monthly_IS2_at_ULS_a_options[option]) - np.array(uls_mean_monthly_draft_a_IS2_period)))
    sd_str_a = '%.02f' % (np.nanstd(np.array(monthly_IS2_at_ULS_a_options[option]) - np.array(uls_mean_monthly_draft_a_IS2_period)))

    # ULS B
    mask_b = ~np.isnan(monthly_IS2_at_ULS_b_options[option])& ~np.isnan(uls_mean_monthly_draft_b_IS2_period)
    res_b = stats.linregress(
        np.array(monthly_IS2_at_ULS_b_options[option])[mask_b],
        np.array(uls_mean_monthly_draft_b_IS2_period)[mask_b]
    )
    r_str_b = '%.02f' % (res_b[2]**2)
    mb_str_b = '%.02f' % (np.nanmean(np.array(monthly_IS2_at_ULS_b_options[option]) - np.array(uls_mean_monthly_draft_b_IS2_period)))
    sd_str_b = '%.02f' % (np.nanstd(np.array(monthly_IS2_at_ULS_b_options[option]) - np.array(uls_mean_monthly_draft_b_IS2_period)))

    # ULS D
    mask_d = ~np.isnan(monthly_IS2_at_ULS_d_options[option])& ~np.isnan(uls_mean_monthly_draft_b_IS2_period)
    res_d = stats.linregress(
        np.array(monthly_IS2_at_ULS_d_options[option])[mask_d],
        np.array(uls_mean_monthly_draft_d_IS2_period)[mask_d]
    )
    r_str_d = '%.02f' % (res_d[2]**2)
    mb_str_d = '%.02f' % (np.nanmean(np.array(monthly_IS2_at_ULS_d_options[option]) - np.array(uls_mean_monthly_draft_d_IS2_period)))
    sd_str_d = '%.02f' % (np.nanstd(np.array(monthly_IS2_at_ULS_d_options[option]) - np.array(uls_mean_monthly_draft_d_IS2_period)))

    # ULS ALL
    mask_all = ~np.isnan(monthly_IS2_at_ULS_all_options[option])& ~np.isnan(uls_mean_monthly_draft_IS2_period)
    res_all = stats.linregress(
        np.array(monthly_IS2_at_ULS_all_options[option])[mask_all],
        np.array(uls_mean_monthly_draft_IS2_period)[mask_all]
    )
    r_str_all = '%.02f' % (res_all[2]**2)
    mb_str_all = '%.02f' % (np.nanmean(np.array(monthly_IS2_at_ULS_all_options[option]) - np.array(uls_mean_monthly_draft_IS2_period)))
    sd_str_all = '%.02f' % (np.nanstd(np.array(monthly_IS2_at_ULS_all_options[option]) - np.array(uls_mean_monthly_draft_IS2_period)))

    # Store results
    validation_results[option] = {
        'r_str_a': r_str_a, 'mb_str_a': mb_str_a, 'sd_str_a': sd_str_a,
        'r_str_b': r_str_b, 'mb_str_b': mb_str_b, 'sd_str_b': sd_str_b,
        'r_str_d': r_str_d, 'mb_str_d': mb_str_d, 'sd_str_d': sd_str_d,
        'r_str_all': r_str_all, 'mb_str_all': mb_str_all, 'sd_str_all': sd_str_all
    }

validation_results

IS-2/SM-LG
CS-2/SM-LG

{'IS-2/SM-LG': {'r_str_a': '0.78',
  'mb_str_a': '0.13',
  'sd_str_a': '0.24',
  'r_str_b': '0.72',
  'mb_str_b': '0.15',
  'sd_str_b': '0.32',
  'r_str_d': '0.84',
  'mb_str_d': '0.21',
  'sd_str_d': '0.26',
  'r_str_all': '0.76',
  'mb_str_all': '0.16',
  'sd_str_all': '0.28'},
 'CS-2/SM-LG': {'r_str_a': '0.67',
  'mb_str_a': '-0.03',
  'sd_str_a': '0.30',
  'r_str_b': '0.80',
  'mb_str_b': '0.38',
  'sd_str_b': '0.29',
  'r_str_d': '0.67',
  'mb_str_d': '-0.13',
  'sd_str_d': '0.36',
  'r_str_all': '0.56',
  'mb_str_all': '0.07',
  'sd_str_all': '0.39'}}

uls_mean_monthly_draft_a.index.to_timestamp()+ pd.Timedelta(days=14)

DatetimeIndex(['2018-11-15', '2018-12-15', '2019-01-15', '2019-02-15',
               '2019-03-15', '2019-04-15', '2019-05-15', '2019-06-15',
               '2019-07-15', '2019-08-15', '2019-09-15', '2019-10-15',
               '2019-11-15', '2019-12-15', '2020-01-15', '2020-02-15',
               '2020-03-15', '2020-04-15', '2020-05-15', '2020-06-15',
               '2020-07-15', '2020-08-15', '2020-09-15', '2020-10-15',
               '2020-11-15', '2020-12-15', '2021-01-15', '2021-02-15',
               '2021-03-15', '2021-04-15', '2021-05-15', '2021-06-15',
               '2021-07-15'],
              dtype='datetime64[ns]', name='date', freq=None)

# Define a function to create plots for a given option
option_labels = ['IS-2/SM-LG', 'CS-2/SM-LG']
fig, axes = plt.subplots(3, 1, figsize=(6.8, 6.5), sharex=True)

# Plot for ULS A
axes[0].plot(uls_mean_daily_draft_a.index, uls_mean_daily_draft_a, label="Daily ULS", color='gray', linewidth=0.6, alpha=0.8)
axes[0].scatter(uls_mean_monthly_draft_a.index.to_timestamp() + pd.Timedelta(days=14), uls_mean_monthly_draft_a, label="Monthly ULS", marker='o', alpha=0.6, color='k')
axes[0].scatter(IS2_date_range, monthly_IS2_at_ULS_a_options[option_labels[0]], label=option_labels[0], color='m', marker='x')
axes[0].scatter(IS2_date_range, monthly_IS2_at_ULS_a_options[option_labels[1]], label=option_labels[1], color='b', marker='x')
#axes[0].scatter(IS2_date_range, uls_mean_monthly_draft_a_IS2_period, label=option, color='m', marker='x')

axes[0].annotate('(a) ULS A', xy=(0.02, 0.97), xycoords='axes fraction', verticalalignment='top')
axes[0].legend(loc='upper right', frameon=False, ncols=4)

# Plot for ULS B
axes[1].plot(uls_mean_daily_draft_b.index, uls_mean_daily_draft_b, label="Daily ULS", color='gray', linewidth=0.6, alpha=0.8)
axes[1].scatter(uls_mean_monthly_draft_b.index.to_timestamp() + pd.Timedelta(days=14), uls_mean_monthly_draft_b, label="Monthly ULS", marker='o', alpha=0.6, color='k')
axes[1].scatter(IS2_date_range, monthly_IS2_at_ULS_b_options[option_labels[0]], label=option_labels[0], color='m', marker='x')
axes[1].scatter(IS2_date_range, monthly_IS2_at_ULS_b_options[option_labels[1]], label=option_labels[1], color='b', marker='x')

#axes[1].scatter(uls_mean_monthly_draft_b.index.to_timestamp() + pd.Timedelta(days=14), uls_mean_monthly_draft_b, label=option, color='b', marker='x')
axes[1].annotate('(b) ULS B', xy=(0.02, 0.97), xycoords='axes fraction', verticalalignment='top')


# Plot for ULS D
axes[2].plot(uls_mean_daily_draft_d.index, uls_mean_daily_draft_d, label="Daily ULS", color='gray', linewidth=0.6, alpha=0.8)
axes[2].scatter(uls_mean_monthly_draft_d.index.to_timestamp() + pd.Timedelta(days=14), uls_mean_monthly_draft_d, label="Monthly ULS", marker='o', alpha=0.6, color='k')
#axes[2].scatter(IS2_date_range, uls_mean_monthly_draft_d_IS2_period , label=option, color='m', marker='x')
axes[2].scatter(IS2_date_range, monthly_IS2_at_ULS_d_options[option_labels[0]], label=option_labels[0], color='m', marker='x')
axes[2].scatter(IS2_date_range, monthly_IS2_at_ULS_d_options[option_labels[1]], label=option_labels[1], color='b', marker='x')

axes[2].annotate('(c) ULS D', xy=(0.02, 0.97), xycoords='axes fraction', verticalalignment='top')
axes[2].set_ylabel('Sea ice draft (m)')


for ax in axes:
    # Remove top and right spines
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    # Add horizontal gridlines
    ax.grid(axis='y', linestyle=':', alpha=0.5)
    ax.set_ylim([0, 3.5])
    ax.set_ylabel('Sea ice draft (m)')
    ax.set_xlim([pd.Timestamp('2018-10-15'), pd.Timestamp('2021-08-15')])


plt.subplots_adjust(left=0.065, right=0.97, top=0.98, bottom=0.04, hspace=0.11)  # Adjust these values to reduce whitespace
plt.savefig('./figs/BGEP_allseason_res'+str(comp_res)+'_nn_'+start_date+end_date+int_str+'_inccs2.pdf', dpi=300)
plt.show()

../_images/cef2a839ae51641084797a6a972283f3eee360b550f4667aa880eaaa03025bf3.png

# Create a single column with two rows for the two options
fig, axes = plt.subplots(2, 1, figsize=(2.8, 4.5), gridspec_kw={'hspace': 0.05})  # Adjust hspace for less whitespace
axes = axes.flatten()  # Flatten the 2D array of axes for easy iteration
panel_labels = ['(a)', '(b)']  # Panel labels

# Iterate over the first two options
for i, option in enumerate(list(options.keys())[:2]):
    ax = axes[i]
    ax.scatter(uls_mean_monthly_draft_a_IS2_period, monthly_IS2_at_ULS_a_options[option], color='b', alpha=0.8, label='A')
    ax.scatter(uls_mean_monthly_draft_b_IS2_period, monthly_IS2_at_ULS_b_options[option], color='tab:red', alpha=0.8, label='B')
    ax.scatter(uls_mean_monthly_draft_d_IS2_period, monthly_IS2_at_ULS_d_options[option], color='tab:orange', alpha=0.8, label='D')

    # Retrieve validation results for the current option
    r_str_all = validation_results[option]['r_str_all']
    mb_str_all = validation_results[option]['mb_str_all']
    sd_str_all = validation_results[option]['sd_str_all']
    rmse_all = np.sqrt(np.nanmean((np.array(monthly_IS2_at_ULS_all_options[option]) - np.array(uls_mean_monthly_draft_IS2_period))**2))
    n_str = str(len(monthly_IS2_at_ULS_all_options[option]))
    # Annotate the plot with RMSE, mean bias, and standard deviation
    ax.annotate(f"r$^2$: {r_str_all} \nMB: {mb_str_all} (m)\nSD: {sd_str_all} (m)\nRMSE: {rmse_all:.02f} (m)\nN: {n_str}", color='k', xy=(0.98, 0.02), xycoords='axes fraction', horizontalalignment='right', verticalalignment='bottom')

    # Combine panel label with option label
    ax.annotate(f"{panel_labels[i]} "+option, xy=(0.02, 0.98), xycoords='axes fraction', horizontalalignment='left', verticalalignment='top')

    # Set x and y labels only for the leftmost and bottom plots
    if i == 0:
        ax.legend(frameon=False, loc="upper right")
        ax.set_xticklabels('')
        ax.set_xlabel('')
    else:
        ax.set_xlabel('ULS ice draft (m)')
    ax.set_ylabel('IS-2/CS-2 derived ice draft (m)')
    

    ax.set_xlim([0, 3])
    ax.set_ylim([0, 3])
    lims = [
        np.min([ax.get_xlim(), ax.get_ylim()]),  # min of both axes
        np.max([ax.get_xlim(), ax.get_ylim()]),  # max of both axes
    ]
    ax.plot(lims, lims, 'k--', linewidth=0.5, alpha=0.5, zorder=0)
    ax.set_aspect('equal')
    ax.set_xlim(lims)
    ax.set_ylim(lims)

plt.subplots_adjust(left=0.065, right=0.99, top=0.98, bottom=0.09)  # Adjust these values to reduce whitespace

plt.savefig('./figs/BGEP_allseason_scatter_res'+str(comp_res)+'_nn_'+start_date+end_date+int_str+'_inccs2.pdf', dpi=300)
plt.show()

../_images/be5a5f17fb2e824cbb738b1eb39219768430b8ad1fb001cdab519cd6903d6021.png

OK now just do summer month (May to August) comparisons!#

# Filter for summer months (May=5 through August=8)
IS2_date_range = IS2_date_range[IS2_date_range.month.isin([5, 6, 7, 8])]

# Create date strings for filtered range
IS2_date_range_strs = [str(date.year)+'-%02d'%(date.month) for date in IS2_date_range]

print("Summer dates:", IS2_date_range_strs)

Summer dates: ['2019-05', '2019-06', '2019-07', '2019-08', '2020-05', '2020-06', '2020-07', '2020-08', '2021-05', '2021-06', '2021-07']

# Repeat functions from above (should really streamline this more)
# Compute monthly ULS values for each option
monthly_IS2_at_ULS_a_options = {}
monthly_IS2_at_ULS_b_options = {}
monthly_IS2_at_ULS_d_options = {}
monthly_IS2_at_ULS_all_options = {}

for option in options.keys():
    monthly_IS2_at_ULS_a_options[option] = [
        grid_IS2_nearby(date, option, uls_x_a, uls_y_a, res=comp_res) for date in IS2_date_range
    ]
    monthly_IS2_at_ULS_b_options[option] = [
        grid_IS2_nearby(date, option,uls_x_b, uls_y_b, res=comp_res) for date in IS2_date_range
    ]
    monthly_IS2_at_ULS_d_options[option] = [
        grid_IS2_nearby(date, option,uls_x_d, uls_y_d, res=comp_res) for date in IS2_date_range
    ]
    # Combine all ULS values for the current option
    monthly_IS2_at_ULS_all_options[option] = (
        monthly_IS2_at_ULS_a_options[option] +
        monthly_IS2_at_ULS_b_options[option] +
        monthly_IS2_at_ULS_d_options[option]
    )

2019-05-15 00:00:00 IS-2/SM-LG 2.1249423
2019-06-15 00:00:00 IS-2/SM-LG 1.8776155
2019-07-15 00:00:00 IS-2/SM-LG nan
2019-08-15 00:00:00 IS-2/SM-LG nan
2020-05-15 00:00:00 IS-2/SM-LG 1.8750578
2020-06-15 00:00:00 IS-2/SM-LG 1.8289809
2020-07-15 00:00:00 IS-2/SM-LG 0.70578855
2020-08-15 00:00:00 IS-2/SM-LG 0.55099994
2021-05-15 00:00:00 IS-2/SM-LG 2.0777307
2021-06-15 00:00:00 IS-2/SM-LG 1.71525
2021-07-15 00:00:00 IS-2/SM-LG 0.7386345
2019-05-15 00:00:00 IS-2/SM-LG 2.16676
2019-06-15 00:00:00 IS-2/SM-LG 2.27472
2019-07-15 00:00:00 IS-2/SM-LG nan
2019-08-15 00:00:00 IS-2/SM-LG 0.81884444
2020-05-15 00:00:00 IS-2/SM-LG 1.6374398
2020-06-15 00:00:00 IS-2/SM-LG 1.8070999
2020-07-15 00:00:00 IS-2/SM-LG 0.62468004
2020-08-15 00:00:00 IS-2/SM-LG 0.651
2021-05-15 00:00:00 IS-2/SM-LG 1.9285
2021-06-15 00:00:00 IS-2/SM-LG 1.64134
2021-07-15 00:00:00 IS-2/SM-LG 1.0927
2019-05-15 00:00:00 IS-2/SM-LG 1.99896
2019-06-15 00:00:00 IS-2/SM-LG 1.5218371
2019-07-15 00:00:00 IS-2/SM-LG nan
2019-08-15 00:00:00 IS-2/SM-LG nan
2020-05-15 00:00:00 IS-2/SM-LG 2.07406
2020-06-15 00:00:00 IS-2/SM-LG 1.76022
2020-07-15 00:00:00 IS-2/SM-LG 0.73486006
2020-08-15 00:00:00 IS-2/SM-LG 0.50064105
2021-05-15 00:00:00 IS-2/SM-LG 2.32064
2021-06-15 00:00:00 IS-2/SM-LG 1.7958002
2021-07-15 00:00:00 IS-2/SM-LG 0.82938004
2019-05-15 00:00:00 CS-2/SM-LG 1.327550512771373
2019-06-15 00:00:00 CS-2/SM-LG 1.598343082378904
2019-07-15 00:00:00 CS-2/SM-LG nan
2019-08-15 00:00:00 CS-2/SM-LG nan
2020-05-15 00:00:00 CS-2/SM-LG 2.0244427194398855
2020-06-15 00:00:00 CS-2/SM-LG 1.2937080339604892
2020-07-15 00:00:00 CS-2/SM-LG 0.5506746897488428
2020-08-15 00:00:00 CS-2/SM-LG 0.43253594256991484
2021-05-15 00:00:00 CS-2/SM-LG 1.46657827999922
2021-06-15 00:00:00 CS-2/SM-LG 1.0721816433032336
2021-07-15 00:00:00 CS-2/SM-LG 0.47533325648293123
2019-05-15 00:00:00 CS-2/SM-LG 2.319994140315006
2019-06-15 00:00:00 CS-2/SM-LG 1.908852149348844
2019-07-15 00:00:00 CS-2/SM-LG nan
2019-08-15 00:00:00 CS-2/SM-LG 0.6280665893332168
2020-05-15 00:00:00 CS-2/SM-LG 2.7080473835427994
2020-06-15 00:00:00 CS-2/SM-LG 1.9263898358378833
2020-07-15 00:00:00 CS-2/SM-LG 0.7288139555772672
2020-08-15 00:00:00 CS-2/SM-LG 0.6928146691988778
2021-05-15 00:00:00 CS-2/SM-LG 1.957827067655267
2021-06-15 00:00:00 CS-2/SM-LG 1.4537153246116017
2021-07-15 00:00:00 CS-2/SM-LG 1.4101024227262426
2019-05-15 00:00:00 CS-2/SM-LG 1.3571687958459215
2019-06-15 00:00:00 CS-2/SM-LG 1.3812019906745028
2019-07-15 00:00:00 CS-2/SM-LG nan
2019-08-15 00:00:00 CS-2/SM-LG nan
2020-05-15 00:00:00 CS-2/SM-LG 1.5964569551456307
2020-06-15 00:00:00 CS-2/SM-LG 1.1641355360312244
2020-07-15 00:00:00 CS-2/SM-LG 0.356019329989811
2020-08-15 00:00:00 CS-2/SM-LG 0.40841616401340675
2021-05-15 00:00:00 CS-2/SM-LG 1.3600454839920186
2021-06-15 00:00:00 CS-2/SM-LG 1.1294450466200452
2021-07-15 00:00:00 CS-2/SM-LG 0.441223336777232

uls_dates=uls_mean_monthly_draft_a.index.astype(str)
uls_mean_monthly_draft_a_IS2_period  = [get_ULS_dates(uls_mean_monthly_draft_a, uls_dates, date) for date in IS2_date_range_strs]

uls_dates=uls_mean_monthly_draft_b.index.astype(str)
uls_mean_monthly_draft_b_IS2_period  = [get_ULS_dates(uls_mean_monthly_draft_b, uls_dates, date) for date in IS2_date_range_strs]

uls_dates=uls_mean_monthly_draft_d.index.astype(str)
uls_mean_monthly_draft_d_IS2_period  = [get_ULS_dates(uls_mean_monthly_draft_d, uls_dates, date) for date in IS2_date_range_strs]
                                        
uls_mean_monthly_draft_IS2_period = uls_mean_monthly_draft_a_IS2_period+uls_mean_monthly_draft_b_IS2_period+uls_mean_monthly_draft_d_IS2_period

# Validation analysis for each option
validation_results = {}

for option in options.keys():
    print(option)
    # ULS A
    mask_a = ~np.isnan(monthly_IS2_at_ULS_a_options[option])& ~np.isnan(uls_mean_monthly_draft_a_IS2_period)
    res_a = stats.linregress(
        np.array(monthly_IS2_at_ULS_a_options[option])[mask_a],
        np.array(uls_mean_monthly_draft_a_IS2_period)[mask_a]
    )
    r_str_a = '%.02f' % (res_a[2]**2)
    mb_str_a = '%.02f' % (np.nanmean(np.array(monthly_IS2_at_ULS_a_options[option]) - np.array(uls_mean_monthly_draft_a_IS2_period)))
    sd_str_a = '%.02f' % (np.nanstd(np.array(monthly_IS2_at_ULS_a_options[option]) - np.array(uls_mean_monthly_draft_a_IS2_period)))

    # ULS B
    mask_b = ~np.isnan(monthly_IS2_at_ULS_b_options[option])& ~np.isnan(uls_mean_monthly_draft_b_IS2_period)
    res_b = stats.linregress(
        np.array(monthly_IS2_at_ULS_b_options[option])[mask_b],
        np.array(uls_mean_monthly_draft_b_IS2_period)[mask_b]
    )
    r_str_b = '%.02f' % (res_b[2]**2)
    mb_str_b = '%.02f' % (np.nanmean(np.array(monthly_IS2_at_ULS_b_options[option]) - np.array(uls_mean_monthly_draft_b_IS2_period)))
    sd_str_b = '%.02f' % (np.nanstd(np.array(monthly_IS2_at_ULS_b_options[option]) - np.array(uls_mean_monthly_draft_b_IS2_period)))

    # ULS D
    mask_d = ~np.isnan(monthly_IS2_at_ULS_d_options[option])& ~np.isnan(uls_mean_monthly_draft_b_IS2_period)
    res_d = stats.linregress(
        np.array(monthly_IS2_at_ULS_d_options[option])[mask_d],
        np.array(uls_mean_monthly_draft_d_IS2_period)[mask_d]
    )
    r_str_d = '%.02f' % (res_d[2]**2)
    mb_str_d = '%.02f' % (np.nanmean(np.array(monthly_IS2_at_ULS_d_options[option]) - np.array(uls_mean_monthly_draft_d_IS2_period)))
    sd_str_d = '%.02f' % (np.nanstd(np.array(monthly_IS2_at_ULS_d_options[option]) - np.array(uls_mean_monthly_draft_d_IS2_period)))

    # ULS ALL
    mask_all = ~np.isnan(monthly_IS2_at_ULS_all_options[option])& ~np.isnan(uls_mean_monthly_draft_IS2_period)
    res_all = stats.linregress(
        np.array(monthly_IS2_at_ULS_all_options[option])[mask_all],
        np.array(uls_mean_monthly_draft_IS2_period)[mask_all]
    )
    r_str_all = '%.02f' % (res_all[2]**2)
    mb_str_all = '%.02f' % (np.nanmean(np.array(monthly_IS2_at_ULS_all_options[option]) - np.array(uls_mean_monthly_draft_IS2_period)))
    sd_str_all = '%.02f' % (np.nanstd(np.array(monthly_IS2_at_ULS_all_options[option]) - np.array(uls_mean_monthly_draft_IS2_period)))

    # Store results
    validation_results[option] = {
        'r_str_a': r_str_a, 'mb_str_a': mb_str_a, 'sd_str_a': sd_str_a,
        'r_str_b': r_str_b, 'mb_str_b': mb_str_b, 'sd_str_b': sd_str_b,
        'r_str_d': r_str_d, 'mb_str_d': mb_str_d, 'sd_str_d': sd_str_d,
        'r_str_all': r_str_all, 'mb_str_all': mb_str_all, 'sd_str_all': sd_str_all
    }

validation_results

IS-2/SM-LG
CS-2/SM-LG

{'IS-2/SM-LG': {'r_str_a': '0.71',
  'mb_str_a': '0.10',
  'sd_str_a': '0.33',
  'r_str_b': '0.70',
  'mb_str_b': '0.28',
  'sd_str_b': '0.36',
  'r_str_d': '0.73',
  'mb_str_d': '0.03',
  'sd_str_d': '0.33',
  'r_str_all': '0.67',
  'mb_str_all': '0.12',
  'sd_str_all': '0.36'},
 'CS-2/SM-LG': {'r_str_a': '0.59',
  'mb_str_a': '-0.26',
  'sd_str_a': '0.33',
  'r_str_b': '0.89',
  'mb_str_b': '0.41',
  'sd_str_b': '0.30',
  'r_str_d': '0.52',
  'mb_str_d': '-0.46',
  'sd_str_d': '0.38',
  'r_str_all': '0.42',
  'mb_str_all': '-0.14',
  'sd_str_all': '0.49'}}

# Create a single column with two rows for the two options
fig, axes = plt.subplots(2, 1, figsize=(2.8, 4.5), gridspec_kw={'hspace': 0.05})  # Adjust hspace for less whitespace
axes = axes.flatten()  # Flatten the 2D array of axes for easy iteration
panel_labels = ['(c)', '(d)']  # Panel labels

# Iterate over the first two options
for i, option in enumerate(list(options.keys())[:2]):
    ax = axes[i]
    ax.scatter(uls_mean_monthly_draft_a_IS2_period, monthly_IS2_at_ULS_a_options[option], color='b', alpha=0.8, label='A')
    ax.scatter(uls_mean_monthly_draft_b_IS2_period, monthly_IS2_at_ULS_b_options[option], color='tab:red', alpha=0.8, label='B')
    ax.scatter(uls_mean_monthly_draft_d_IS2_period, monthly_IS2_at_ULS_d_options[option], color='tab:orange', alpha=0.8, label='D')

    # Retrieve validation results for the current option
    r_str_all = validation_results[option]['r_str_all']
    mb_str_all = validation_results[option]['mb_str_all']
    sd_str_all = validation_results[option]['sd_str_all']
    rmse_all = np.sqrt(np.nanmean((np.array(monthly_IS2_at_ULS_all_options[option]) - np.array(uls_mean_monthly_draft_IS2_period))**2))
    n_str = str(len(monthly_IS2_at_ULS_all_options[option]))
    # Annotate the plot with RMSE, mean bias, and standard deviation
    ax.annotate(f"r$^2$: {r_str_all} \nMB: {mb_str_all} (m)\nSD: {sd_str_all} (m)\nRMSE: {rmse_all:.02f} (m)\nN: {n_str}", color='k', xy=(0.98, 0.02), xycoords='axes fraction', horizontalalignment='right', verticalalignment='bottom')

    # Combine panel label with option label
    ax.annotate(f"{panel_labels[i]} "+option+' summer', xy=(0.02, 0.98), xycoords='axes fraction', horizontalalignment='left', verticalalignment='top')

    # Set x and y labels only for the leftmost and bottom plots
    if i == 0:
        #ax.legend(frameon=False, loc="upper right")
        ax.set_xticklabels('')
        ax.set_xlabel('')
    else:
        ax.set_xlabel('ULS ice draft (m)')
    #ax.set_ylabel('IS-2/CS-2 derived ice draft (m)')
    

    ax.set_xlim([0, 3])
    ax.set_ylim([0, 3])
    lims = [
        np.min([ax.get_xlim(), ax.get_ylim()]),  # min of both axes
        np.max([ax.get_xlim(), ax.get_ylim()]),  # max of both axes
    ]
    ax.plot(lims, lims, 'k--', linewidth=0.5, alpha=0.5, zorder=0)
    ax.set_aspect('equal')
    ax.set_xlim(lims)
    ax.set_ylim(lims)

plt.subplots_adjust(left=0.065, right=0.99, top=0.98, bottom=0.09)
plt.savefig('./figs/BGEP_summer_scatter_res'+str(comp_res)+'_nn_'+start_date+end_date+int_str+'_inccs2.pdf', dpi=300)
plt.show()

../_images/d3f23697220533e1dbe98107b4e91136e2ede74ec7ff310ebe7476944655fa26.png