Please note that this repository is used for development and review, so quality assessments should be considered work in progress until they are merged into the main branch
1.6.1. Satellite Sea Surface Temperature Consistency in Resolving Oceanic Features#
Production date: 22-07-2025
Produced by: CNR-ISMAR
🌍 Use case: Using Sea Surface Temperature to characterise oceanic features in key areas#
❓ Quality assessment question#
How well do ESA CCI L4 v2.1 and GHRSST Multi-Product Ensemble (GMPE) SSTs resolve ocean features?
Satellite L4 Sea Surface Temperature (SST) products are known to capture oceanographic meso- to sub-mesoscale features. Many studies (e.g., [1], [2], [3], [4], [5], [6]) have leveraged L4 SST imagery to analyse ocean dynamics at regional and global scales, largely due to the gap-free nature of these datasets. However, L4 SSTs are generated through a range of methods — such as optimal interpolation, deep learning (e.g., [7], [8], [9]), and multiscale wavelet analysis — which often introduce smoothing effects, leading to differences in the effective resolution of ocean features despite the identical nominal resolution. This can lead to inconsistent results when studying mesoscale features ([10], [11]).
To address this, feature resolution is here assessed using Fast Fourier Transform (FFT) analysis of the SST Climate Data Records (CDRs) available in the Climate Data Store (CDS).
📢 Quality assessment statement#
These are the key outcomes of this assessment
Both ESA CCI v2.1 and GMPE SST datasets capture mesoscale ocean features, but ESA CCI v2.1 consistently provides sharper frontal structures and higher spatial variance across multiple regions, particularly at scales between \(\sim 800\) and \(40 \, km\).
Power Spectral Density (PSD) analysis reveals that GMPE SSTs closely follow the expected \(k^{-11/3}\) spectral slope of geostrophic turbulence across the 0.3–2 \(deg^{-1}\) wavenumber range, while ESA CCI SSTs match this slope at larger scales but diverge at finer scales (above 2 \(deg^{-1}\)), indicating limitations in capturing small mesoscale dynamics.
At wavenumbers larger than 2 \(deg^{-1}\) (scales <40 \(km\)), ESA CCI SST exhibits a loss of spectral fidelity, with PSDs flattening and showing oscillatory behaviour - implying that feature resolution is compromised and signal may be dominated by noise.
Fig. 1.6.1.1 Snapshot of the Gulf Stream region on July 9th, 1992 showing SSTs from ESA CCI (top left) and GMPE (bottom left), alongside a graphical illustration of the area (right).#
📋 Methodology#
We evaluate here the consistency among SST CDRs available in the CDS in representing oceanic mesoscale features. To this end, two different SST CDRs have been intercompared over a common period (1982-2016). Respectively, they are:
Sea surface temperature daily gridded data from 1981 to 2016 derived from a multi-product satellite-based ensemble – from the Group for High Resolution Sea Surface Temperature (GHRSST) multi-product ensemble (GMPE) produced by the European Space Agency Climate Change Initiative Sea Surface Temperature (ESA CCI SST) ([12] GMPE SST in the following). The GMPE SST is obtained as the ensemble median of 16 global contributing Level-4 (L4) analyses (including the ESA CCI v2.0 and v1.1). This dataset is provided on a daily basis and on a regular 0.25° latitude-longitude grid;
Sea Surface Temperature (SST) daily data from 1981 to present derived from satellite observations, ESA CCI SST L4 dataset v2.1 ([13] ESA CCI SST in the following). This L4 dataset provides daily global SST fields from the merging of space-borne Infrared SSTs through a variational assimilation algorithm. This dataset is provided on a daily basis and on a regular 0.05° latitude-longitude grid.
We specifically looked into Power Spectral Density (PSD), which is computed using the scipy.signal package [14] for the above-mentioned CDRs, over the following Areas of Interest (AOIs), relevant for dynamical processes (e.g., air-sea interactions, vertical mixing, eddy shedding, turbulence, etc.): Gulf Stream, Kuroshio, Agulhas, Brazilian–Malvinas and Tropical–Pacific. For each AOI, PSDs are estimated longitudinally at each latitude, then averaged to obtain daily PSDs across the common coverage period. These daily spectra are further averaged to produce a mean PSD per AOI, which is then compared to the theoretical \(k^{-11/3}\) slope predicted by 2D quasi-geostrophic turbulence ([15]), where \(k\) is the wavenumber.
The analysis and results are organised in the following steps, which are detailed in the sections below:
1. Import packages and define parameters for the requests to the CDS
4. Plot SST snapshots on 9th of July 1992, and spectra within each AOI
📈 Analysis and results#
1. Import packages and define parameters for the requests to the CDS#
In the following cell the necessary packages are imported, defining into python dictionaries the AOIs and the CDS request. AOIs are then displayed on a mute map.
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import collections
import matplotlib.pyplot as plt
import scipy.signal
import xarray as xr
from c3s_eqc_automatic_quality_control import download, utils, plot
import numpy as np
import cacholote # Delete me before adapting to the new template
cacholote.config.set(create_engine_kwargs=dict(connect_args={"timeout": 30})) # Same as above
import os
os.environ["CDSAPI_RC"] = os.path.expanduser("~/detoma_vincenzo/.cdsapirc")
plt.style.use("seaborn-v0_8-notebook")
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# Time
year_start = 1982
year_stop = 2016
# Space
regions = {
"Gulf Stream": {"lon_slice": slice(-62, -32), "lat_slice": slice(34, 42)},
"Kuroshio": {"lon_slice": slice(151, 181), "lat_slice": slice(34, 44)},
"Agulhas": {"lon_slice": slice(5, 35), "lat_slice": slice(-45, -35)},
"Brazilian-Malvinas": {"lon_slice": slice(-56, -26), "lat_slice": slice(-47, -37)},
"Tropical-Pacific": {"lon_slice": slice(-155,-125), "lat_slice": slice(-7, 7)},
}
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.io.img_tiles import Stamen
# Set up map with Robinson projection
fig = plt.figure(figsize=(10, 5))
ax = plt.axes(projection=ccrs.Robinson(central_longitude=180))
ax.set_global()
ax.stock_img()
# Add land, ocean and topographic background (Natural Earth)
ax.add_feature(cfeature.LAND, facecolor='lightgray')
ax.add_feature(cfeature.OCEAN, facecolor='lightblue')
ax.add_feature(cfeature.COASTLINE, linewidth=0.5)
ax.add_feature(cfeature.BORDERS, linestyle=':', linewidth=0.5)
ax.add_feature(cfeature.LAKES, facecolor='lightblue', alpha=0.5)
ax.add_feature(cfeature.RIVERS, linewidth=0.5, alpha=0.5)
# Plot regions as black rectangles
for name, coords in regions.items():
lon0, lon1 = coords["lon_slice"].start, coords["lon_slice"].stop
lat0, lat1 = coords["lat_slice"].start, coords["lat_slice"].stop
rect_lon = [lon0, lon1, lon1, lon0, lon0]
rect_lat = [lat0, lat0, lat1, lat1, lat0]
ax.plot(rect_lon, rect_lat, transform=ccrs.PlateCarree(),
color='black', linewidth=1.5)
# Optional: label
ax.text((lon0 + lon1) / 2, (lat0 + lat1) / 2, name,
transform=ccrs.PlateCarree(), fontsize=7, ha='center', va='center',
bbox=dict(facecolor='white', edgecolor='none', alpha=0.6))
plt.title("Oceanographic Regions to calculate Spectra", fontsize=12)
plt.tight_layout()
plt.show()
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request_dicts = {
"ESACCI": {
"collection_id": "satellite-sea-surface-temperature",
"request": {
"processinglevel": "level_4",
"format": "zip",
"variable": "all",
"sensor_on_satellite": "combined_product",
"version": "2_1",
},
"chunks": {"year": 1, "month": 1, "day": 7},
},
"GMPE": {
"collection_id": "satellite-sea-surface-temperature-ensemble-product",
"request": {
"format": "zip",
"variable": "all",
},
"chunks": {"year": 1, "month": 1, "day": 12},
},
}
2. Define functions to cache#
Functions for rechunking to optimise caching of intermediate results, get nan values on land, convert to degree Celsius in the ocean and calculate the trend are defined below.
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def _welch(x, **kwargs):
freqs, psd = scipy.signal.welch(x, **kwargs)
return xr.DataArray(psd, coords={"wavenumber": freqs}, dims=["wavenumber"])
def welch(da, dim, **kwargs):
return xr.apply_ufunc(
_welch,
da.compute(),
input_core_dims=[[dim]],
output_core_dims=[["wavenumber"]],
vectorize=True,
kwargs=kwargs,
keep_attrs=True
)
def compute_spectrum(ds, lon_slice, lat_slice, scaling):
dim = "longitude"
da = utils.regionalise(ds["analysed_sst"], lon_slice, lat_slice)
nperseg = 1 << (da.sizes[dim].bit_length() - 1) # lower power of 2
da = welch(
da,
dim,
window="blackmanharris",
detrend="linear",
fs=1. / da[dim].diff(dim).mean().item(),
nperseg=nperseg,
nfft=2 * nperseg,
scaling=scaling,
)
return da.to_dataset(name="spectrum")
3. Download and transform#
In this step the data are requested, retrieved and processed in one step.
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time_means = collections.defaultdict(dict)
lat_means = collections.defaultdict(dict)
for product, request_kwargs in request_dicts.items():
requests = []
for year in range(year_start, year_stop + 1):
requests.extend(
download.update_request_date(
request_kwargs["request"],
start=f"{year - 1}-12",
stop=f"{year}-11",
stringify_dates=True,
)
)
for region, region_kwargs in regions.items():
ds = download.download_and_transform(
request_kwargs["collection_id"],
requests,
chunks=request_kwargs["chunks"],
transform_func=compute_spectrum,
transform_func_kwargs=region_kwargs | {"scaling": "density"},
quiet=True
)
time_means[product][region] = ds.mean("time", keep_attrs=True).compute()
lat_means[product][region] = ds.mean("latitude", keep_attrs=True).compute()
time_means = dict(time_means)
lat_means = dict(lat_means)
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product='ESACCI' region='Brazilian-Malvinas'
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product='GMPE' region='Gulf Stream'
0%| | 0/1260 [00:00<?, ?it/s]
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4. Plot SST snapshots on 9th of July 1992, and spectra within each AOI#
Visual inspection of the two SST datasets under evaluation is provided for the four AOIs on a sample date: July 9th, 1992.
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snapshots = collections.defaultdict(dict)
for product, request_kwargs in request_dicts.items():
for region, region_kwargs in regions.items():
snapshots[product][region] = download.download_and_transform(
request_kwargs["collection_id"],
request_kwargs["request"] | {"year": "1992", "month": "07", "day": "09"},
chunks=request_kwargs["chunks"],
transform_func=utils.regionalise,
transform_func_kwargs=region_kwargs,
quiet=True
)
nperseg = 1 << (snapshots[product][region].sizes["longitude"].bit_length() - 1) # lower power of 2
freqs, _ = scipy.signal.welch(np.zeros(snapshots[product][region].sizes["longitude"]),
window="blackmanharris",
detrend="linear",
fs=1. / snapshots[product][region]["longitude"].diff('longitude').mean().item(),
nperseg=nperseg,
nfft=2 * nperseg,
scaling='density'
)
time_means[product][region] = time_means[product][region].assign_coords({'wavenumber': freqs})
lat_means[product][region] = lat_means[product][region].assign_coords({'wavenumber': freqs})
snapshots = dict(snapshots)
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product='GMPE' region='Tropical-Pacific'
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import matplotlib.gridspec as gridspec
fig = plt.figure(1, figsize=(20,5))
fig.suptitle('SST on 9th July 1992')
gs = gridspec.GridSpec(len(snapshots), len(snapshots['ESACCI'])) # First subplot 3x taller than second
for i, (product, request_kwargs) in enumerate(request_dicts.items()):
for j, (region, region_kwargs) in enumerate(regions.items()):
ax = fig.add_subplot(gs[i,j])
p = snapshots[product][region]['analysed_sst'].plot(ax=ax, cmap='nipy_spectral', add_colorbar=False)
ax.set_title('')
ax.text(region_kwargs['lon_slice'].stop-6,region_kwargs['lat_slice'].stop-1, region, fontsize=7, ha='center', va='center',
bbox=dict(facecolor='white', edgecolor='none', alpha=0.8))
if i==0:
ax.set_xlabel('')
ax.set_xticklabels([])
if j>0:
ax.set_ylabel('')
if j==len(snapshots['ESACCI'])-1:
ax.yaxis.set_label_position("right")
ax.set_ylabel(f'{product}', position='right')
cf = p # the filled contour set
# Add a new colorbar axis manually (adjust [left, bottom, width, height] as needed)
cbar_ax = fig.add_axes([0.125, -0.07, 0.75, 0.05]) # right-side vertical bar
cbar = fig.colorbar(cf, cax=cbar_ax, orientation='horizontal', label=r'SST [k]')
Both L4 SSTs exhibit similar patterns of upper ocean mesoscale dynamics, as confirmed by the presence of eddies and filaments that create several sharp SST transitions. ESA CCI SST clearly provides a sharper description of frontal features, primarily due to its higher nominal spatial resolution (\(\sim 5\, km\)) compared to that of GMPE (\(\sim 25 \, km\)). However, local maxima in ESA CCI SST are less intense than the ones in GMPE SST, as in the south-western section of Gulf Stream and Kuroshio, and north-western part of Brazilian-Malvinas and Tropical Pacific regions.
The inter-comparison of feature resolution is further quantified via spectral analyses based on FFT, whose results are showcased below.
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fig = plt.figure(1, figsize=(18,10))
for i, r in enumerate(regions.keys()):
ax = fig.add_subplot(int(len(regions)/2),int(len(regions)/2)+1,i+1)
da_cci = lat_means['ESACCI'][r]['spectrum']
da_gmpe = lat_means['GMPE'][r]['spectrum']
p = da_cci.plot(ax=ax, hue='time', xscale='log', yscale='log', color='grey', alpha=0.002, add_legend=False)
p2 = da_cci.mean('time').plot(ax=ax, hue='time', xscale='log', yscale='log', color='black', alpha=1, label='ESACCI')
p = da_gmpe.plot(ax=ax, hue='time', xscale='log', yscale='log', color='orange', alpha=0.008, add_legend=False)
p3 = da_gmpe.mean('time').plot(ax=ax, hue='time', xscale='log', yscale='log', color='red', alpha=1, label='GMPE')
xref = np.array(da_cci['wavenumber'])
yref = xref**(-11/3)
ax.plot(xref, yref, 'k--', label=r'$k^{-11/3}$')
ax.legend(loc='best')
ax.set_ylabel(r'PSD [$K^2/deg^{-1}$]')
ax.set_ylim(3*10**-7,3*10**7)
ax.set_xlabel(r'Wavenumber [$deg^{-1}$]')
ax.set_title(f'SST Spectra \n in {r} \n (Shading is time)')
ax.grid(ls=':')
fig.subplots_adjust(hspace=0.45)
/data/wp5/.tmp/ipykernel_2129238/1834464309.py:11: RuntimeWarning: divide by zero encountered in power
yref = xref**(-11/3)
/data/wp5/.tmp/ipykernel_2129238/1834464309.py:11: RuntimeWarning: divide by zero encountered in power
yref = xref**(-11/3)
/data/wp5/.tmp/ipykernel_2129238/1834464309.py:11: RuntimeWarning: divide by zero encountered in power
yref = xref**(-11/3)
/data/wp5/.tmp/ipykernel_2129238/1834464309.py:11: RuntimeWarning: divide by zero encountered in power
yref = xref**(-11/3)
/data/wp5/.tmp/ipykernel_2129238/1834464309.py:11: RuntimeWarning: divide by zero encountered in power
yref = xref**(-11/3)
Overall, the PSDs of the SST datasets exhibit three regimes: i) flat at large scale; ii) decreasing with different slopes at wavenumbers larger than \(0.1 \, deg^{-1}\); iii) with a tendency to flatten at the smallest scales allowed by the dataset’s resolution.
For our analyses, we focus on the PSD behaviour over the wavenumber range 0.3 to 10 \(deg^{-1}\), which corresponds to spatial scales from approximately 300 \(km\) down to 10 \(km\) across all selected AOIs. Note that the average length scale of 1 \(deg^{-1}\) is about 85 \(km\) in all AOIs, except in the tropical Pacific, where it is closer to 110 \(km\). This wavenumber range thus captures dynamics from the large mesoscale to the small mesoscale and submesoscale, based on the Rossby deformation radii characteristic of our study regions [16].
For GMPE, being provided on a coarser resolution grid, the analysis will be restricted to the 0.3 to 2 \(deg^{-1}\) range. In all the selected AOIs, the GMPE SST exhibits a PSD cascade in agreement with predictions of geostrophic turbulence (same slope as the \(k^{-11/3}\) line) across all wavenumbers within the previously specified range. Conversely, the ESA CCI SST, shows two different regimes: i) In the 0.3 to 2 \(deg^{-1}\) range the spectra show fair agreement with the \(k^{-11/3}\) slope, but exhibit higher PSDs values, suggesting a higher spatial variance in the ~800 to 40 \(km\) scale range (1000 to 50 \(km\) scale range in the Tropical Pacific AOI). This quantitative result is in full agreement with the visual inspection performed on July 9th, 1992, confirming an enhanced feature resolution of the oceanic mesoscale for the ESA CCI SST; ii) at wavenumbers higher than 2 \(deg^{-1}\) (scales smaller than \(\sim\)40 \(km\)), the ESA CCI SST is still able to provide information on the ocean features, but PSDs starts to deviate significantly from the \(k^{-11/3}\) slope, suggesting that the turbulent cascade is not fully represented at those scales. Eventually, the PSDs become flat and exhibit an unphysical oscillatory behaviour, suggesting that information on feature resolution is lost, and the L4 field is assumed as noise.
ℹ️ If you want to know more#
Key resources#
SST CCI Climate Assessment Report (CAR): https://climate.esa.int/media/documents/SST_CCI_D5.1_CAR_v1.1-signed.pdf
GHRSST Website: https://www.ghrsst.org/
Additional sources for SST ensemble intercomparison statistics: https://ghrsst-pp.metoffice.gov.uk/ostia-website/gmpe-monitoring.html
Code libraries used:
C3S EQC custom functions,
c3s_eqc_automatic_quality_control, prepared by B-Openpython packages: matplotlib, xarray, cartopy, numpy, tqdm
References#
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