SCARIBOS_ConnectivityCuracao/SCARIBOS_model/SCARIBOS_analysis/3_validation_GLORYS_SSS.py at main · Parcels-code/SCARIBOS_ConnectivityCuracao · GitHub

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'''
Model validation, for Supplimentary Figure Fig. S6
SSS comparison between SCARIBOS and GLORYS for 2022
Download before running the script: GLORYS dataset
'''


#%% Import libraries
import numpy as np
import cmocean as cmo
import xarray as xr
import os
import pandas as pd
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
import matplotlib.gridspec as gridspec

dir   = 'croco/CONFIG/SCARIBOS_V8/CROCO_FILES/surface_currents/'
months = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
ds_scaribos_sss = xr.concat([xr.open_dataset(f'/nethome/berto006/croco/CONFIG/SCARIBOS_V8/CROCO_FILES/surface_currents/Y2022M{month}_sss.nc') for month in months], dim='time')
ds_scaribos_sss = ds_scaribos_sss.where(ds_scaribos_sss.salt != 0)

# Load all the monthly-mean NetCDF files
globcur_file_pattern = 'data/GLORYS_MONTHLY/*.nc'
ds_coper = xr.open_mfdataset(globcur_file_pattern, combine='by_coords')

# Subset coordinates
lon_slice = slice(-70.5, -66)
lat_slice = slice(10, 13.5)

sss_all_months = []
for t in range(len(ds_coper.time-1)):
    sss = ds_coper.so.isel(time=t).sel(longitude=lon_slice, latitude=lat_slice)
    sss = sss.where(sss != 0)
    sss_all_months.append(sss)

sss_all_months = xr.concat(sss_all_months, dim='time')


# %% regrid SSS of SCARIBOS to the same grid as GLORYS

lon_regg = sss_all_months.longitude.values
lat_regg = sss_all_months.latitude.values
lon_regg2d, lat_regg2d = np.meshgrid(lon_regg, lat_regg)
points_target = np.column_stack((lon_regg2d.ravel(), lat_regg2d.ravel()))

sss_regg = []

for t in range(len(ds_scaribos_sss.time)):
    sss = ds_scaribos_sss.salt.isel(time=t)

    # Extract original 2D lon/lat grids
    lon_u_2d = ds_scaribos_sss.lon_rho.values
    lat_u_2d = ds_scaribos_sss.lat_rho.values

    # Flatten original grid and values
    points_source = np.column_stack((lon_u_2d.ravel(), lat_u_2d.ravel()))
    values = sss.values.ravel()

    # Perform interpolation
    sss_interp = griddata(points_source, values, points_target, method="linear")

    # Reshape back to 2D
    sss_interp_2d = sss_interp.reshape(lon_regg2d.shape)

    # Store as xarray DataArray
    sss_regg.append(xr.DataArray(sss_interp_2d, dims=("lat", "lon"), coords={"lat": lat_regg, "lon": lon_regg}))

# Convert list to xarray DataArray along time
sss_regg = xr.concat(sss_regg, dim="time")
sss_regg = sss_regg.assign_coords(time=ds_scaribos_sss.time)
sss_regg = sss_regg.where(sss_regg != 0)

# %% FIGURE

vmin, vmax = 32, 37
months = ["JAN", "FEB", "MAR", "APR", "MAY", "JUN", "JUL", "AUG", "SEP", "OCT", "NOV", "DEC"]

fig = plt.figure(figsize=(11.5, 14.4), constrained_layout=True)
gs = gridspec.GridSpec(6, 5, width_ratios=[1, 1, 1,1, 0.1], wspace=0.1)
axs = np.array([[fig.add_subplot(gs[row, col]) for col in range(4)] for row in range(6)])

for row in range(6):
    for col in [0, 1, 2, 3]:
        axs[row, col].set_aspect('equal')
        month_index = row * 2 + (col // 2)
        if month_index >= 12:
            continue

        if col % 2 == 0:  # SCARIBOS
            sss = sss_regg.isel(time=month_index)
            pcm = axs[row, col].pcolormesh(
                sss.lon, sss.lat, sss,
                cmap=cmo.cm.haline, vmin=vmin, vmax=vmax, rasterized=True
            )
            axs[row, col].text(-70.3, 10.1, "SCARIBOS", fontsize=11, ha='left', va='bottom')

            axs[row, col].set_xlim(-70.5, -66)
            axs[row, col].set_ylim(10, 13.5)

        else:  # GlobCurrent
            sss = sss_all_months.isel(time=month_index)
            pcm = axs[row, col].pcolormesh(
                sss.longitude, sss.latitude, sss[0,:,:],
                cmap=cmo.cm.haline, vmin=vmin, vmax=vmax, rasterized=True
            )
            axs[row, col].text(-70.3, 10.1, "GLORYS12V1", fontsize=11, ha='left', va='bottom')
            axs[row, col].set_xlim(-70.5, -66)
            axs[row, col].set_ylim(10, 13.5)

        axs[row, col].set_aspect('equal')

    # Add month and year annotations
    axs[row, 0].text(-65.85, 13.6, months[row * 2], fontsize=12, ha='right', va='bottom', fontweight='bold')
    axs[row, 1].text(-70.6, 13.6, "2022", fontsize=12, ha='left', va='bottom', fontweight='bold')
    axs[row, 2].text(-65.85, 13.6, months[row * 2 + 1], fontsize=12, ha='right', va='bottom', fontweight='bold')
    axs[row, 3].text(-70.6, 13.6, "2022", fontsize=12, ha='left', va='bottom', fontweight='bold')

# Formatting ticks and labels
for row in range(6):
    for col in range(4):
        axs[row, col].set_xticks([])
        axs[row, col].set_yticks([])

    axs[row, 0].set_yticks(np.arange(10, 14, 1))
    axs[row, 0].set_yticklabels([f"{tick:.0f}° N" for tick in np.arange(10, 14, 1)], fontsize=11)
    axs[row, 0].set_aspect('equal')

    if row == 5:  # Add x-ticks for the last row
        for col in range(4):
            axs[row, col].set_xticks(np.arange(-70, -65, 1))
            axs[row, col].set_xticklabels([f"{abs(tick):.0f}° W" for tick in np.arange(-70, -65, 1)], rotation=90, fontsize=11)
            axs[row, col].set_aspect('equal')

cbar_ax = fig.add_subplot(gs[:, 4])
cbar = fig.colorbar(pcm, cax=cbar_ax, orientation='vertical', extend = 'max', extendfrac = 0.03)
cbar.set_label("Sea surface salinity [PSU]", fontsize=11)
cbar.ax.tick_params(labelsize=11)

# save
plt.savefig('figures/SCARIBOS_VS_GLORYS_SSS_regridded.png', dpi=300, bbox_inches='tight')