SCARIBOS_ConnectivityCuracao/SCARIBOS_model/SCARIBOS_analysis/3_validation_GLORYS_SST.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
137
138
'''
Model validation, for Supplimentary Figure Fig. S4
SST comparison between SCARIBOS and GLORYS for 2022
Download before running the script: GLORYS 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}_sst.nc') for month in months], dim='time')
ds_scaribos_sss = ds_scaribos_sss.where(ds_scaribos_sss.temp != 0)

# Load all the monthly-mean NetCDF files
globcur_file_pattern = 'croco/ANALYSIS/VALIDATION/data/GLORYS_MONTHLY/*.nc'
ds_coper = xr.open_mfdataset(globcur_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.thetao.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 SST of SCARIBOS

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.temp.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}))

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 = 23, 31
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.thermal, 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.thermal, 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')
cbar.set_label("Sea surface temperature [°C]", fontsize=11)
cbar.ax.tick_params(labelsize=11)

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