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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
'''
Model validation, for Supplimentary Figure Fig. S5
SSS comparison between SCARIBOS and MultiObs for 2022
Download before running the script: MultiObs dataset
'''

#%% Import libraries
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
import cmocean as cmo
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'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)

copernicus_file_pattern = 'croco/ANALYSIS/VALIDATION/data/SSS_2022/*.nc'
ds_coper = xr.open_mfdataset(copernicus_file_pattern, combine='by_coords')

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.sos.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 MultiObs

lon_regg = sss_all_months.longitude.values  # 1D array
lat_regg = sss_all_months.latitude.values  # 1D array
lon_regg2d, lat_regg2d = np.meshgrid(lon_regg, lat_regg)  # 2D grid

# Flatten target grid for interpolation
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, "MultiObs", fontsize=11, ha='left', va='bottom')

        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:
        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/SSS_validation_2022_regridded.png', dpi=300, bbox_inches='tight')