EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 4. Applicability for other lakes
---
The values from the table presented in *Annex A. LWL Performance indicators* of the [PQAR](https://confluence.ecmwf.int/pages/viewpage.action?pageId=428248112) were used for the analysis in this section.

Load PQAR table (ANNEX A)

```text
Lake name  Dispersion 1992-2023 (cm)  \
0       Albert                        8.0
1        Bagre                       20.0
2       Bankim                       39.0
3      Bogoria                       13.0
4        Fitri                       10.0
..         ...                        ...
246      Sevan                        6.0
247  Srisailam                       68.0
248   Tharthar                        3.0
249   Toktogul                       14.0
250        Van                        6.0

High Frequency variation 1992-2023 (cm)  \
0                                       3.32
1                                       8.65
2                                      11.03
3                                       0.26
4                                       0.32
..                                       ...
246                                     3.40
247                                    12.74
248                                    18.20
249                                     0.00
250                                     3.39

Median Timestep 1992-2023 (days)  Max Timestep 1992-2023 (days)  \
0                               26.47                          76.81
1                                9.98                          99.01
2                                9.41                          50.84
3                               27.00                          55.78
4                               27.00                          70.00
..                                ...                            ...
246                             25.55                         124.39
247                             27.00                          27.00
248                              9.92                          63.87
249                             34.44                        1006.73
250                             12.46                          73.00

Timeseries duration 1992-2023  Dispersion 2014-2023 (cm)  \
0                             28.5                        6.0
1                             15.3                       18.0
2                             15.5                        9.0
3                              7.8                       13.0
4                             10.6                       10.0
..                             ...                        ...
246                           28.5                        5.0
247                            4.8                       68.0
248                           31.2                        2.0
249                           28.5                        8.0
250                           28.6                        5.0

High Frequency variation 2014-2023 (cm)  \
0                                       3.24
1                                       8.71
2                                      12.29
3                                       0.26
4                                       0.33
..                                       ...
246                                     3.25
247                                    12.74
248                                    24.16
249                                     2.86
250                                     3.03

Median Timestep 2014-2023 (days)  Max Timestep 2014-2023 (days)
0                               13.00                          76.81
1                                9.98                          89.25
2                                9.31                          50.84
3                               27.00                          55.78
4                               27.00                          70.00
..                                ...                            ...
246                              6.54                          93.70
247                             27.00                          27.00
248                              9.92                          29.75
249                             27.00                         951.49
250                             10.00                          39.43

[251 rows x 10 columns]
```

Define the columns of interest
Get the row for Lake Titicaca
Compute percentile ranks for Titicaca
Create a DataFrame for display

```text
Titicaca Percentile
Median Timestep 1992-2023 (days)                95.22
Max Timestep 1992-2023 (days)                   76.89
Timeseries duration 1992-2023                   66.53
Median Timestep 2014-2023 (days)                45.42
Max Timestep 2014-2023 (days)                   73.31
```


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 4. Applicability for other lakes
---
2-2023                   66.53
Median Timestep 2014-2023 (days)                45.42
Max Timestep 2014-2023 (days)                   73.31
```

Since the dataset has different median timestep, maximum timestep, and timeseries duration for each lake, it is recommended to consult Annex A: LWL Performance Indicators in the [PQAR](https://confluence.ecmwf.int/pages/viewpage.action?pageId=428248112) for further interpretation when it comes to suitability for IWRM applications.

For Lake Titicaca, the median timestep for the full period (1992–2023) is among the highest, falling in the 95th percentile. This indicates that observations are relatively sparse, many other lakes have more frequent data. In the more recent period (2014–2023), Titicaca's median timestep falls near the 45th percentile, suggesting that data coverage has improved and is closer to the dataset median.

The maximum timestep (which reflects the size of the largest gap in the timeseries) is in the 77th percentile for the full period, indicating that Titicaca has experienced longer gaps than most lakes. Additionally, the timeseries duration places Titicaca in the 67th percentile, meaning it has longer data coverage than average, but still not among the very longest.

This implies that if Lake Titicaca is considered suitable for applications like extreme event detection and seasonality analysis, then most other lakes in the dataset—many of which have higher temporal resolution and fewer gaps—should be equally or more suitable. However, users are encouraged to consult Annex A or rerun the code from previous sections to assess whether the temporal completeness of each individual lake meets their specific requirements.


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > ℹ If you want to know more
---
* Autonomous Binational Authority of the Lake Titicaca, Desaguadero River, Lake Poopó, and Salar de Coipasa Hydrological System (ALT): [Publications IWRM-TDPS](https://alt-perubolivia.org/?page_id=2178)
* National Meteorological and Hydrological Service of Bolivia (SENAMHI Bolivia): [Hydrological Lake Titicaca Level Reports](https://senamhi.gob.bo/index.php/thidrologico)
* National Meteorological and Hydrological Service of Peru (SENAMHI Peru): [Download Meteorological Data](https://www.senamhi.gob.pe/site/descarga-datos/)
* Messager, M., Lehner, B., Grill, G. et al. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat Commun 7, 13603 (2016). [](https://doi.org/10.1038/ncomms13603)


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > ℹ If you want to know more > Key resources
---
Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/)

Dataset documentation:

* [LWL v5.0 and LWL-S v1.0: Product User Guide and Specification (PUGS)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=425328964)
* [LWL v5.0 and LWL-S v1.0: Algorithm Theoretical Basis Document (ATBD)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=425328942)
* [LWL v5.0 and LWL-S v1.0: Product Quality Assessment Report (PQAR)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=428248112)


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.3390/w8040144) Canedo, C., Pillco Zolá, R., & Berndtsson, R. (2016). Role of Hydrological Studies for the Development of the TDPS System. Water, 8(4), 144.

[[2]](https://doi.org/10.1016/j.ejrh.2021.100927) Lima-Quispe, N., Escobar, M., Wickel, A. J., von Kaenel, M., & Purkey, D. (2021). Untangling the effects of climate variability and irrigation management on water levels in Lakes Titicaca and Poopó. Journal of Hydrology: Regional Studies, 37, 100927.

[[3]](https://doi.org/10.1016/j.wace.2024.100710) Gutierrez-Villarreal, R. A., Espinoza, J.-C., Lavado-Casimiro, W., Junquas, C., Molina-Carpio, J., Condom, T., & Marengo, J. A. (2024). The 2022–23 drought in the South American Altiplano: ENSO effects on moisture flux in the western Amazon during the pre-wet season. Weather and Climate Extremes, 43, 100710.

[[4]](https://senamhi.gob.bo/meteorologia/boletines/hidrologico/LagoTiticaca/2025/Agosto/LAGO%20TITICACA%20-%20Informe%20Semanal%20%28Actualizado%2001_08_2025%29.pdf) SENAMHI Bolivia (2025) BOLETÍN SEMANAL LAGO TITICACA ACTUALIZADO AL 01 DE AGOSTO DE 2025. Ministry of Environment and Water of Bolivia.

[[5]](https://doi.org/10.5194/hess-29-655-2025) Lima-Quispe, N., Ruelland, D., Rabatel, A., Lavado-Casimiro, W., & Condom, T. (2025). Modeling Lake Titicaca's water balance: The dominant roles of precipitation and evaporation. Hydrology and Earth System Sciences, 29, 655–675.

[[6]](https://reliefweb.int/report/peru/act-peru-12003-heavy-rainfall) ACT Alliance (2003) ACT Peru 1/2003: Heavy rainfall. ReliefWeb, United Nations Office for the Coordination of Humanitarian Affairs (OCHA).

[[7]](https://reliefweb.int/report/peru/flash-floods-leave-peruvian-farmers-devastated) IFRC (2003) Flash floods leave Peruvian farmers devastated. ReliefWeb, United Nations Office for the Coordination of Humanitarian Affairs (OCHA).

[[8]](https://www.inei.gob.pe/media/MenuRecursivo/boletines/5340.pdf) Instituto Nacional de Estadística e Informática del Perú INE (2004) Estadísticas Ambientales.

[[9]](https://www.copernicus.eu/fr/node/40141) Copernicus (2023) Drought in Lake Titicaca, South America.

[[10]](https://doi.org/10.1029/2002jd002670) Rayner N. A., Parker D. E., Horton E. B., Folland C. K., Alexander L. V., Rowell D. P., Kent E. C., & Kaplan A. (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res., 108 (D14), 4407.


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior
---
Production date: 15-05-2026

Produced by: Victor Couplet (VUB) & Camila Trigoso (VUB)


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Quality assessment question(s)
---
* **Can the C3S satellite lake surface water temperature dataset accurately reproduce summer LSWT observations in Lake Superior, and is it suitable for assessing long-term summer temperature variability and trends?**

Cases of an increasing trend in lake surface water temperature (LSWT) have been observed in numerous lakes across various regions, including the United States, Europe, and other parts of the world [[1]](https://doi.org/10.1038/s41558-024-02122-y) [[2]](https://climate.copernicus.eu/lake-surface-temperatures). For example, Austin and Colman (2008) found that summer LSWT in Lake Superior increased at a rate of (11±6)×10-2 °C/year from 1979 to 2006. They attributed this trend to the earlier retreat of winter ice, which causes the positive overturning period to begin sooner, allowing the lake more time to warm. Their analysis was based on measurements from in-situ buoys [[3]](https://doi.org/10.1029/2006GL029021). Later, the Great Lakes Integrated Sciences and Assessments (GLISA) analyzed the annual mean LSWT of Lake Superior between 1995 and 2021. The increasing long-term trend observed by Austin and Colman was not apparent in the period analyzed by GLISA. However, they noted a shift to higher temperatures after 1998 [[4]](https://glisa.umich.edu/lake-superior-retrospective/). The LSWT data analyzed by GLISA was derived from satellite observations and obtained through the NOAA Great Lakes CoastWatch [[5]](https://coastwatch.glerl.noaa.gov/statistics/average-surface-water-temperature-glsea/). Cannon et al. (2024) investigated summer LSWT of Lake Superior using simulation data over a longer time interval (1980-2021), and found a warming trend of (5.2±3.1)×10-2 °C/year [[6]](https://doi.org/10.1175/JCLI-D-23-0092.1).

The objective of this assessment is to evaluate whether the [satellite-lake-water-temperature](https://cds.climate.copernicus.eu/datasets/satellite-lake-water-temperature?tab=overview) dataset from the Climate Data Store (CDS) is suitable for climate change monitoring, using Lake Superior as a case study. We calculated the trend summer LWST for a period of 28 years, from 1995 to 2023, and validated the data and trend computation against in-situ buoy measurements from the NOAA National Buoy Data Center (NBDC) [[7]](https://www.ndbc.noaa.gov/).


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Quality assessment statement
---
These are the key outcomes of this assessment

* Lake Superior’s summer LSWT trend over 1995–2023, derived from the C3S satellite lake water temperature dataset, is small and not statistically significant. This indicates that no robust long-term warming or cooling signal can be detected over this period.
* The C3S dataset shows good agreement with NOAA in situ buoy observations at selected locations. Differences in summer mean temperatures are partly attributable to the higher temporal sampling of in situ measurements and to differences in measurement depth, with satellites observing skin temperature and buoys measuring subsurface temperature (≈1 m). Additional discrepancies may result from cloud contamination in the satellite record, as well as from limitations in the in situ data, which may include temporal gaps, quality issues, and measurement inconsistencies.
* The dataset is well suited for monitoring interannual variability in lake surface temperature; however, trend analyses require caution. The lack of statistical significance in the estimated trends, combined with strong interannual variability (e.g., elevated temperatures during the 1998 El Niño event), limits the ability to detect long-term changes over this period. Incorporating longer time series, including pre-1995 observations where available, would improve the robustness of trend assessments.
```


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Methodology
---
The analysis and results are organized in the following steps, which are detailed in the sections below:

**[](section-1)**

* Satellite lake surface water temperature (LSWT) data are downloaded for the summer months (July–September) over the period 1995–2023.

**[](section-2)**

* The dataset is filtered based on quality flags and lake identification to retain only high-quality observations for Lake Superior.

**[](section-3)**

* A spatially weighted mean LSWT is computed, followed by the calculation of annual summer mean LSWT values.
* The temporal trend is estimated using the Theil–Sen estimator, and its statistical significance is assessed with the Mann–Kendall test.

**[](section-4)**

* Satellite LSWT data are validated against in-situ measurements from three NOAA-NBDC buoys located in Lake Superior.
* Performance metrics, including mean bias, mean absolute error (MAE), and root mean square error (RMSE), are calculated.
* Buoy observations are resampled to match the less frequent satellite overpass times. Metrics are then recalculated on these time-matched datasets to determine whether differences in summer means arise from sampling frequency or from measurement characteristics (e.g., sensing depth, retrieval uncertainties).
* Finally, LSWT trends derived from buoy data over multiple periods are computed and compared with satellite-based trends and published literature results.


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Analysis and results > 1. Request and download data > Download data
---
```text
100%|██████████| 29/29 [00:06<00:00,  4.25it/s]
```

(section-2)=


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Analysis and results > 2. Data preprocessing
---
We download satellite lake surface temperature data and visualize the water masses over the region spanning 46.30–49.00°N latitude and 92.10–84.80°W longitude. In the dataset, Lake Superior is identified by ID number 2. The locations of the three NOAA buoys used for data comparison are also plotted.


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Analysis and results > 2. Data preprocessing > Plot lakeid
---
Use one time slice for plotting
Unique lake IDs
Map real lake IDs to categorical indices: 2 -> 0, 300000709 -> 1, etc.
------------------------------------------------------------------
Buoy coordinates (lon, lat)
------------------------------------------------------------------
------------------------------------------------------------------
------------------------------------------------------------------
Longitude / latitude ticks
------------------------------------------------------------------
Legend for lake IDs

*Figure 1 : Lake Superior identification in the dataset. Locations of the three NOAA-NBDC buoys used for data comparison are shown as orange crosses.*


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Analysis and results > 2. Data preprocessing > Data filtering
---
Only observations with quality levels 4 (good) and 5 (best) are retained for analysis.

Reindex using lakeids and min_quality_level

(section-3)=


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Analysis and results > 3. Summer mean analysis
---
For each time step, lake surface water temperatures are averaged spatially across Lake Superior, and these values are then averaged over the summer months (July–September). The 1995–2023 trend is estimated using the Theil–Sen estimator, and its statistical significance is assessed with the Mann–Kendall test.


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Analysis and results > 3. Summer mean analysis > Trend estimation
---
Run Theil-Sen + Mann-Kendall
Compute Theil-Sen line for plotting

*Figure 2 : Mean summer LSWT for Lake Superior between 1995 and 2023, and trend estimation.*

The trend line in *Figure 2* shows a slight negative slope of −0.0032 °C yr⁻¹. However, the p-value of 0.81 is well above the common significance threshold of 0.05, indicating that the trend is not statistically significant. This lack of significance may partly reflect the relatively short 28-year period analyzed, as longer time series are typically required to detect meaningful climate trends in large lakes.

At first glance, the absence of a significant warming trend may seem surprising. Previous work [[3]](https://doi.org/10.1029/2006GL029021) reported a significant increase in summer temperatures from 1979 to 2006, and it seems unlikely that summer lake surface temperatures have stabilised since then, given ongoing global warming.

To verify these results, we will compare the satellite data with in-situ buoy measurements.

(section-4)=


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Analysis and results > 4. Comparison with NOAA buoy data
---
The satellite data are compared with in-situ measurements from three NOAA NDBC buoys in Lake Superior [[7]](https://www.ndbc.noaa.gov/). These are the same buoys used in [[3]](https://doi.org/10.1029/2006GL029021), which reported a significant increase in summer temperatures from 1979 to 2006. In-situ data are now available through 2024. The goal of this section is thus to compare satellite and buoy data over 1995–2023 and evaluate whether similar trends are observed in the buoy measurements.

The buoy data are downloaded from the NOAA National Data Buoy Center website [[7]](https://www.ndbc.noaa.gov/), which provides data by station and year. We developed a Python script that automatically downloads the data for buoys 45001, 45004, and 45006 for all available years between 1979 and 2024, and combines them into a single, unified dataset.

Find header: first non-empty line whose first token contains letters
Extract data lines (non-empty, non-comment lines after header)
print(f"Downloading {url} ...", end=" ")
Ensure numeric columns for date/time
Determine proper year
If any 4-digit year present, treat as full year
2-digit year: <50 -> 2000s, >=50 -> 1900s
Ensure minutes exist
Build datetime column
Process WTMP
Build output dataframe and filter invalid/extreme values
print(f" → loaded {len(out)} rows")
Concatenate all data
Set MultiIndex (station, datetime)

```text
Overall progress:  34%|███▍      | 47/138 [00:34<01:21,  1.12it/s, station=45004, year=1980]
```

```text
→ download error: 404 Client Error: Not Found for url: https://www.ndbc.noaa.gov/data/historical/stdmet/45004h1979.txt.gz
```

```text
Overall progress:  67%|██████▋   | 93/138 [01:09<00:46,  1.03s/it, station=45006, year=1980]
```

```text
→ download error: 404 Client Error: Not Found for url: https://www.ndbc.noaa.gov/data/historical/stdmet/45006h1979.txt.gz
```

```text
Overall progress:  68%|██████▊   | 94/138 [01:09<00:34,  1.26it/s, station=45006, year=1981]
```

```text
→ download error: 404 Client Error: Not Found for url: https://www.ndbc.noaa.gov/data/historical/stdmet/45006h1980.txt.gz
```

```text
Overall progress: 100%|██████████| 138/138 [01:42<00:00,  1.34it/s, station=45006, year=2024]
```

```text
Total rows loaded: 958592
```

Although NOAA National Data Buoy Center (NDBC) buoy observations provide an important independent reference for evaluating satellite-derived LSWT, they should not be treated as error-free ground truth. NDBC applies both real-time automated quality control and delayed/manual quality control to its observations. According to the NDBC Handbook of Automated Data Quality Control Checks and Procedures [[8]](https://www.ndbc.noaa.gov/publications/NDBCHandbookofAutomatedDataQualityControl2023.pdf), automated checks are used to detect issues such as transmission errors, missing data, total sensor failure, range-limit exceedances, and unrealistic temporal changes. Observations may be flagged as good, suspect, failed, or missing, and some failed observations are not released in real time. Manual quality control can also be applied after data are stored, including manual failure of data from sensors judged to be unreliable.

Therefore, discrepancies between satellite and buoy LSWT should not automatically be interpreted as satellite retrieval errors. They may also reflect limitations in the in situ record, including temporal gaps, sensor problems, incomplete quality control, or unrealistic variability.

The buoy data are averaged over the summer months, and each buoy is compared to the summer mean temperature of the nearest satellite pixel corresponding to its location (see *Figure 3*).

Compare buoy and satellite summer mean temperatures
Extract nearest-neighbor satellite temperature at each buoy position,
convert from Kelvin to Celsius, and compute summer annual means
Nearest satellite pixel to buoy location
Convert from Kelvin to Celsius
Restrict to summer months
Compute annual summer mean
Concatenate all stations into a single reusable table
Plot comparison


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Analysis and results > 4. Comparison with NOAA buoy data
---
,
convert from Kelvin to Celsius, and compute summer annual means
Nearest satellite pixel to buoy location
Convert from Kelvin to Celsius
Restrict to summer months
Compute annual summer mean
Concatenate all stations into a single reusable table
Plot comparison

*Figure 3: Mean summer LSWT for three Lake Superior buoys, compared to the mean summer LSWT from satellite data at the nearest pixel locations.*

We observe that the satellite data are generally close to the buoy measurements; for instance, the 1998 peak in summer LSWT is well captured. To further quantify the differences between satellite and buoy data, we compute the mean bias, mean absolute error (MAE), and root mean square error (RMSE).

---- Helper to compute metrics for any subset ----
---- Per-station metrics ----
---- Overall metrics (all stations pooled) ----

```text
station  n_samples  bias  MAE  RMSE
0   45001         29  0.32 1.23  1.88
1   45004         28 -0.12 1.20  1.58
2   45006         28  0.21 0.95  1.18
3     ALL         85  0.14 1.13  1.58
```

Overall, the satellite data exhibit a small warm bias of 0.14 °C. However, the MAE and RMSE are relatively large, at 1.13 °C and 1.58 °C, respectively. A likely source of error is the difference in sampling frequency: the satellite dataset provides roughly one measurement per day, whereas the buoys record about 30 measurements per day. In addition, some days are missing in the satellite data, possibly due to cloud cover. To explore this further, we focus on summer 2016 for buoy 45001 (*see Figure 4*).

We observe that only two satellite observations are available between late July and mid-August, a particularly warm period, whereas the buoy provides continuous coverage throughout the three summer months (*Figure 4, top pannel*). This limited satellite data availability likely contributes to the satellite-derived mean summer LSWT being approximately 2 °C lower than the buoy-based mean. Indeed, when restricting the buoy data to the 31 measurements closest in time to the 31 satellite observations during summer 2016, the resulting mean LSWT is very similar to the satellite-derived value (*Figure 4, bottom pannel*).

Flatten buoy data and add year/month
Satellite at buoy location
Buoy summer series
Match satellite and buoy points year by year

--------------------------------------------------
--------------------------------------------------
Station coordinates
--------------------------------------------------
1. All points (summer) for the station/year
--------------------------------------------------
Buoy: all summer points
Satellite: all summer points at gridcell
Means for all points
--------------------------------------------------
2. Matched points from df_matched
--------------------------------------------------
--------------------------------------------------
3. Shared y-axis limits
--------------------------------------------------
--------------------------------------------------
--------------------------------------------------
---- Date formatting ----
-------------------------
Top subplot: all summer points
-------------------------
-------------------------
Bottom subplot: matched points only
-------------------------
---- Shared x-axis formatting ----

*Figure 4 : Comparison of buoy and satellite measurements used to compute the 2016 mean summer LSWT at the location of buoy 45001.*

This time-matching procedure is repeated for each buoy and year. Specifically, each satellite observation is paired with the nearest buoy measurement in time, provided that the time difference is less than the specified tolerance (default: 1 hour).

The mean summer LSWT computed from the successfully time-matched satellite–buoy pairs are compared in *Figure 5*. To further quantify differences between the time-matched satellite and buoy data, we compute the mean bias, mean absolute error (MAE), and root mean square error (RMSE).

Compute summer mean per year for each station

Plot buoy data with cross marker
Plot satellite data with circle marker

*Figure 5: Mean summer LSWT from satellite data compared to buoy data using only successfully time-matched satellite–buoy pairs.*

```text
station  n_samples  bias  MAE  RMSE
0   45001         29  0.30 0.43  0.50
1   45004         28  0.10 0.33  0.39
2   45006         28  0.26 0.41  0.53
3     ALL         85  0.22 0.39  0.48
```


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Analysis and results > 4. Comparison with NOAA buoy data
---
2   45006         28  0.26 0.41  0.53
3     ALL         85  0.22 0.39  0.48
```

Overall, the satellite data exhibit a small warm bias of 0.22 °C relative to the time-matched buoy observations, slightly larger than the bias obtained when using all available data to compute summer means (0.14 °C). In contrast, the MAE and RMSE are substantially reduced, at 0.39 °C and 0.48 °C, respectively (compared to 1.13 °C and 1.58 °C). This indicates that a large fraction of the difference between the LSWT means shown in *Figure 3* (computed using all data) is attributable to the lower sampling frequency and limited temporal availability of the satellite observations, while individual satellite measurements are comparatively precise. The remaining discrepancies with buoy data are likely due to fundamental measurement differences: satellites measure the lake skin temperature, whereas buoys measure subsurface temperature (typically at ~1 m depth). Depending on atmospheric and lake conditions (e.g., wind and stratification), these measurements can differ. In addition, satellite retrievals may be affected by cloud contamination.


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Analysis and results > 4. Comparison with NOAA buoy data > Estimation of linear trends
---
Using all the available buoy data, we estimate linear trends for three periods: 1979–2006, 1995–2023, and 1979–2023 (Figure 6). These are compared with trends derived from satellite data and with previously published results.

For 1979–2006, the three buoys show warming trends of 0.108 °C/year, 0.098 °C/year, and 0.100 °C/year. Unsurprisingly, these values are consistent with the 0.11 ± 0.06 °C/year estimate reported in [[3]](https://doi.org/10.1029/2006GL029021), which used the same buoy dataset. The associated p-values are relatively small (0.10, 0.30, and 0.07) but fall short of the conventional 0.05 significance threshold, likely due to the limited number of observations.

For 1995-2023, the three buoys show diverging trends of -0.013 °C/year, 0.035 °C/year, and 0.014 °C/year, with large associated p-values ($\geq$0.8), indicating that none of these trends are significant. Satellite-derived trends at the corresponding buoy locations (0.003 °C/year, −0.032 °C/year, and −0.004 °C/year) display the same qualitative behavior: diverging and non-significant. These results are consistent with those in *Figure 2* based on spatially averaged satellite data, and with previous analyses [[4]](https://glisa.umich.edu/lake-superior-retrospective/), confirming the absence of a significant summer LSWT warming trend over this period.

For 1979–2023, the three buoys show warming trends of 0.048 °C/year, 0.061 °C/year, and 0.044 °C/year, with p-values just above the conventional 0.05 significance threshold. These results are consistent with [[6]](https://doi.org/10.1175/JCLI-D-23-0092.1), which reported a warming trend of 0.052±0.031°C/year for 1980-2021 based on simulation data.

--------------------------------------------------
Adjustable line transparency
--------------------------------------------------
Buoy trend periods
Satellite trend period
--------------------------------------------------
Helper function to plot series + trend
--------------------------------------------------
Add trends if periods provided
--------------------------------------------------
--------------------------------------------------
---- Buoy summer means ----
---- Satellite summer means ----
---- Axes settings ----

*Figure 6: Time series of mean summer LSWT for buoy and satellite observations at each buoy location. Semi-transparent lines show summer means, and overlaid trend lines (dotted, dashed, or solid) indicate linear trends for different periods, with slope and p-values reported. Trends were estimated using the Theil-Sen method  in combination with the Mann-Kendall test for statistical significance.*


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > Analysis and results > 4. Comparison with NOAA buoy data > Discussion
---
Lake Superior’s summer LSWT trend over 1995–2023, derived from the Copernicus [satellite-lake-water-temperature](https://cds.climate.copernicus.eu/datasets/satellite-lake-water-temperature?tab=overview) dataset, is slightly negative but not statistically significant. Although this initially seemed surprising given sustained global warming, the result was confirmed using in-situ buoy data.

Overall, the satellite data show good agreement with the quality-controlled and time-matched buoy measurements, although the buoy records themselves may contain gaps or suspect observations and should not be interpreted as error-free ground truth. Differences in computed summer means primarily arise from the lower temporal sampling and occasional missing satellite observations, but the systematic bias is small. This indicates that the satellite-lake-water-temperature dataset is suitable for analyzing summer LSWT trends in Lake Superior. However, no significant warming trend is observed over the 1995–2023 period.

One possible explanation is the unusually high summer temperatures in 1998, associated with a strong El Niño event early in the satellite record. This may have contributed to the relatively large warming trend reported for 1979–2006 [[3]](https://doi.org/10.1029/2006GL029021) and simultaneously obscured longer-term warming signals in the 1995–2023 period. Incorporating pre-1995 in-situ observations, where available, would improve assessments of long-term trends. Indeed, when considering the full buoy record, a near-significant warming trend of ~0.05 °C/year is observed over 1979–2023.


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > ℹ If you want to know more
---
* Farina, G., Thiem, H. (2024) [Several Great Lakes experience record-warm water temperatures heading into winter](https://www.climate.gov/news-features/event-tracker/several-great-lakes-experience-record-warm-water-temperatures-heading#). Climate.gov
*  [NOAA CoastWatch: Great Lakes Node](https://coastwatch.glerl.noaa.gov/statistics/average-surface-water-temperature-glsea/)


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > ℹ If you want to know more > Key resources
---
Dataset documentation:
* [LSWT v4.5: Product User Guide and Specification (PUGS)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=348800177)

Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`, prepared by [B-Open](https://www.bopen.eu/)


EQC Quality Assessment: "Satellite-Derived Monitoring of Summer LSWT in Lake Superior"
Dataset: satellite-lake-water-temperature [CDS]
Aspect: validation_q02  |  Category: Satellite_ECVs
Section: Satellite-Derived Monitoring of Summer LSWT in Lake Superior > ℹ If you want to know more > References
---
* [[1]](https://doi.org/10.1038/s41558-024-02122-y) Wang, X., Shi, K., Qin, B. et al. (2024). Disproportionate impact of atmospheric heat events on lake surface water temperature increases. Nat. Clim. Chang.
* [[2]](https://climate.copernicus.eu/lake-surface-temperatures) Copernicus (2018). Lake surface temperatures. Climate Change Service.
* [[3]](https://doi.org/10.1029/2006GL029021) Jay A. Austin, Steven M. Colman (2007). Lake Superior summer water temperatures are increasing more rapidly than regional air temperatures: A positive ice-albedo feedback. Geophysical Research Letters Volume 34, Issue 6.
* [[4]](https://glisa.umich.edu/lake-superior-retrospective/) GLISA (2024). Lake Superior Retrospective.
* [[5]](https://coastwatch.glerl.noaa.gov/statistics/average-surface-water-temperature-glsea/) NOAA CoastWatch Great Lakes Regional Node (2024). Average Surface Water Temperature (GLSEA).
* [[6]](https://doi.org/10.1175/JCLI-D-23-0092.1)  Cannon, D., Wang, J., Fujisaki-Manome, A., Kessler, J., Ruberg, S., & Constant, S. (2024). Investigating Multidecadal Trends in Ice Cover and Subsurface Temperatures in the Laurentian Great Lakes Using a Coupled Hydrodynamic–Ice Model. Journal of Climate, 37(4), 1249-1276.
* [[7]](https://www.ndbc.noaa.gov/) NOAA National Buoy Data Centre.
* [[8]](https://www.ndbc.noaa.gov/publications/NDBCHandbookofAutomatedDataQualityControl2023.pdf) National Data Buoy Center (NDBC) (2023). Handbook of Automated Data Quality Control Checks and Procedures. NDBC Technical Document T80-10. National Oceanic and Atmospheric Administration (NOAA), National Weather Service, Stennis Space Center, Mississippi, USA.


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > Quality assessment question(s)
---
**Is satellite-derived soil moisture data sufficiently complete, consistent, and temporally representative to reliably detect and monitor anomalies in drought conditions at continental and regional scales?**

This assessment evaluates the suitability of satellite-derived soil moisture data for drought monitoring in Europe, with a specific focus on data completeness in the context of anomaly-based analysis. Completeness is assessed in terms of the availability of valid observations required to construct a 30-year climatological baseline (1991–2020) and to support dekadal (10-day) anomaly calculations. The analysis does not aim to provide a comprehensive evaluation of all quality dimensions, but rather to examine whether the dataset provides sufficient temporal and spatial support for this specific use case. In this context, completeness is considered a critical factor influencing the reliability of standardized soil moisture anomalies (SSMA), particularly given the uneven temporal distribution of observations across the record. The objective of this assessment is to determine to what extent the dataset provides adequate baseline support to reliably detect and monitor drought conditions at continental and regional scales, using the 2023 European drought as a case study.


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > Quality assessment statement
---
These are the key outcomes of this assessment

* The dataset provides adequate spatial and temporal consistency to capture the main large-scale drought patterns across Europe during the 2023 growing season.
* Data completeness varies significantly across regions and seasons, with lower availability in areas affected by snow cover, complex terrain, and dense vegetation.
* Applying stricter baseline completeness thresholds improves the reliability of anomaly estimates but reduces spatial coverage, particularly in regions with historically sparse observations.
* The dataset is suitable for qualitative drought monitoring and regional-scale analysis, but limitations in baseline representativeness and surface sensitivity should be considered for quantitative applications.

```


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > Methodology
---
The C3S COMBINED satellite soil moisture dataset was downloaded for the period 1991–2023 and spatially subsetted to Europe. The analysis focuses on the volumetric surface soil moisture variable aggregated at dekadal (10-day) resolution. A land mask based on European boundaries was applied to exclude ocean pixels from all analyses.

To assess the suitability of the dataset for anomaly-based drought monitoring, completeness was evaluated in terms of the availability of valid observations required to construct a 30-year climatological baseline (1991–2020). The number of valid years per pixel and dekad was quantified, and different completeness thresholds were applied (no threshold, ≥50%, ≥70%) to evaluate their impact on anomaly calculations.

SSMA for 2023 were computed at the dekadal scale using the baseline mean and standard deviation for each pixel and day-of-year. The analysis focuses on the March–August period to capture the onset, development, and peak of the 2023 European drought.

Spatial maps were generated for selected dekads to compare anomaly patterns under different completeness thresholds, and predefined regions were used to analyse regional behaviour. For each region, the distribution of SSMA across land pixels was summarized using the median and interquartile range (IQR), and data completeness was quantified as the percentage of valid pixels contributing to each dekad.

The methodology is designed to evaluate how data availability influences the reliability of anomaly-based drought indicators, rather than to provide a full validation against reference datasets.

**[](section-1)**
 * Import packages
 * Define dataset request and parameters
 * Download and transform soil moisture data
 * Spatially subset to Europe
 * Apply land mask to exclude ocean pixels

**[](section-2)**
 * Compute number of valid years per pixel and dekad (1991–2020)
 * Visualize spatial distribution of baseline support
 * Analyse completeness patterns across regions and seasons
 * Identify areas with limited baseline representativeness

**[](section-3)**
 * Evaluate completeness across years and dekads (1991–2023)
 * Analyse seasonal and interannual variability in data availability
 * Relate completeness patterns to known limitations

**[](section-4)**
 * Compute climatological mean and standard deviation per dekad
 * Apply completeness thresholds (no threshold, ≥50%, ≥70%)
 * Calculate standardized soil moisture anomalies (SSMA) for 2023
 * Mask pixels not meeting threshold criteria

**[](section-5)**
 * Generate maps of SSMA for selected dekads (April–July 3rd dekads)
 * Compare spatial patterns across thresholds
 * Relate patterns to drought evolution reported in literature

**[](section-6)**
 * Define regions of interest
 * Compute median SSMA and IQR over land pixels
 * Quantify completeness (% valid pixels) per dekad
 * Compare temporal evolution across thresholds
 * Interpret results in the context of drought development


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > Analysis and results > 1. Request and download data > Download data
---
Administrative boundaries were obtained from the Eurostat GISCO database, ensuring consistency with official European statistical geographies [[1]](https://ec.europa.eu/eurostat/web/gisco/geodata/administrative-units).

```text
100%|██████████| 31/31 [00:07<00:00,  4.30it/s]
```


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > Analysis and results > 1. Request and download data > Spatially subset to Europe
---
Select soil mouisture variable

Filter the shapefile to keep only Europe
EU + nearby Europe (adjust if needed)
Ensure soil_moisture has CRS information compatible with europe_shape (e.g., EPSG:4326)
Clip soil moisture data to the Europe shape

(section-2)=


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > Analysis and results > 2. Baseline completeness assessment
---
To assess whether the dataset provides sufficient baseline support for computing standardized soil moisture anomalies (SSMA), the number of valid years available for each pixel and dekad was quantified over the 1991–2020 reference period, which corresponds to the standard 30-year climatological baseline recommended by the World Meteorological Organization (WMO)[[2]](https://community.wmo.int/site/knowledge-hub/programmes-and-initiatives/climate-services/wmo-climatological-normals). The most recent years (2021–2022) were not included to ensure consistency with established climatological reference periods and to avoid mixing the baseline with the period of analysis. In addition, recent years—particularly 2022—were characterized by pronounced drought conditions in Europe [[3]](https://doi.org/10.2760/998985); including such anomalous years in the baseline could bias the climatological mean and standard deviation, thereby reducing the sensitivity of the anomaly calculation.

The soil moisture time series was first subset to the baseline period and grouped by dekad of the year (36 dekads per year). For each dekad and pixel, the number of years containing a valid soil moisture value was counted. This results in a spatially explicit estimate of baseline sample size, ranging from 0 to 30 years, which represents the number of observations available to compute the climatological mean and standard deviation for that dekad. The resulting maps show the distribution of baseline support across selected dekads of the year.

This analysis is performed to evaluate whether the available baseline data are sufficient to support robust anomaly calculations. Although the dataset is provided as dekadal aggregates, the effective sample size underlying the climatology can vary substantially across space and time. In some regions and dekads, only a limited number of valid years are available, which may reduce the reliability of the derived anomalies. Therefore, assessing baseline support is a useful step to interpret the SSMA results within the scope of this drought monitoring use case.

This assessment focuses on the availability of valid dekadal values across years, rather than the number of daily observations contributing to each dekadal aggregate, as the anomaly calculation depends primarily on the robustness of the baseline climatology.

Keep only the baseline period
Build a dekad-of-year index from the timestamp
day 1  -> dekad 1 of month
day 11 -> dekad 2 of month
day 21 -> dekad 3 of month
Then combine months into 1..36 across the year
Attach year and dekad_of_year as coordinates
Count valid observations across years for each dekad-of-year and pixel
Result dims: dekad_of_year, latitude, longitude
Ensure integer type for plotting / storage

Representative dekads through the year
Optional labels
Optional: overlay borders if world_shape already exists
Europe-focused extent; adjust if needed
Hide any unused axes

*Figure 1. Number of valid baseline years per pixel (1991–2020) for selected dekads of the year.*

(section-3)=


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > Analysis and results > 3. Temporal completeness analysis
---
Completeness was calculated for each year–dekad combination between 1991 and 2023 as the percentage of valid land pixels within the study domain. A land mask derived from the European land geometry was applied so that only inland pixels were included. The resulting heatmap shows how data availability changes through both the seasonal cycle and the observational record, and helps identify whether low completeness is concentrated in specific years or dekads.

Land mask from first timestep
Keep only land pixels
Convert dekad number (1..36) to month and dekad-in-month

*Figure 2. Heatmap of completeness over European land pixels by year and dekad (1991–2023), expressed as the percentage of valid observations.*

The results presented in Figures 1 and 2 are consistent with the spatial and temporal completeness assessment described in the Product Quality Assessment Report [(PQAR)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=445290728).

Figure 1 shows that completeness is lower in regions affected by seasonal soil moisture freeze–thaw processes, particularly at higher latitudes and during the winter season. Reduced completeness is also observed in mountainous areas, as well as in some urban regions (e.g. around London), which may be influenced by radio-frequency interference (RFI).

Figure 2 indicates a clear seasonal signal, with generally higher completeness across Europe (approximately between 35°N and 70°N) during the May–September period. It can also be observed that completeness during the spring–summer months is lower in earlier years of the record, particularly prior to 2007. This is consistent with Figure 4 of the PQAR, which highlights that the addition of AMSR-E (from 2002) and ASCAT (from 2007) significantly improved spatial coverage. This improvement is reflected in Figure 2, where a noticeable increase in completeness is observed following the introduction of ASCAT in 2007.

(section-4)=


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > Analysis and results > 4. SSMA computation under different thresholds
---
The SSMA was calculated following a similar approach as the one explained by the European Drought Observatory (EDO) [[4]](https://drought.emergency.copernicus.eu/data/factsheets/factsheet_soilmoisture.pdf). Surface soil moisture values for each pixel and each dekad of the year 2023 were compared to the long-term climatological period (1991–2020, 30 years). The anomaly was computed using the following equation:

$\text{SMA} = \frac{\text{SMI}_t - \overline{\text{SMI}}}{\delta_{\text{SMI}}}$

where:

$\text{SMI}_t$ = Surface soil moisture 2023 for a specific dekad for that pixel

$\overline{\text{SMI}}$ = Mean surface soil moisture 1991-2020 for that same dekad for that pixel

$\delta_{\text{SMI}}$ = Standard deviation surface soil moisture 1991-2020 for that same dekad for that pixel

Furthermore, based on the completeness analysis presented in the previous section, a minimum baseline-availability threshold was introduced in the SSMA calculation. For each pixel and each dekad, the number of years within the 1991–2020 baseline period containing a valid soil moisture value was counted. A threshold was then applied requiring that a minimum fraction of these 30 years be available in order to compute the climatological mean and standard deviation.

Specifically, thresholds of 50% and 70% were tested, corresponding to at least 15 and 21 valid years, respectively, for a given pixel–dekad combination. If the number of valid years did not meet the selected threshold, the anomaly for that pixel and dekad was masked and not included in the analysis. A no-threshold case was also retained for comparison, where anomalies are computed regardless of baseline availability.

The threshold is applied at the dekadal level across years and does not account for the number of daily observations contributing to each dekadal aggregate. This is consistent with common practice in drought monitoring applications, where anomalies are typically computed from dekadal products, as also adopted in Joint Research Centre (JRC) Global Drought Observatory (GDO) analyses [[3]](https://doi.org/10.2760/998985) [[5]](https://doi.org/10.2760/575433) [[6]](https://doi.org/10.2760/928418). Given the focus of this use case on dekadal-scale drought conditions, this approach was considered appropriate.

Select long-term (baseline) and target period
Group baseline by day-of-year
Count valid (non-NaN) years for each DOY at each pixel
Total number of years in the baseline
Build completeness mask
Compute long-term mean and std
Avoid division by zero
Group target year by DOY
Compute standardized anomaly
Apply completeness mask
Remove inf values

(section-5)=


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > Analysis and results > 5. Spatial analysis of anomalies
---
To support the interpretation of the SSMA results, spatial maps were generated for the third dekad of April, May, June, and July 2023. These four dekads were selected to enable direct comparison with the corresponding multi-panel figure presented in the JRC August 2023 drought report [[6]](https://doi.org/10.2760/928418), which shows the evolution of soil moisture anomaly conditions during late spring and early summer. The standardized anomalies were mapped over Europe using a consistent color scale, and predefined regions of interest were delineated to guide the subsequent regional analysis. The same figure layout was produced for different baseline-availability thresholds in order to visually compare their effect on the spatial patterns of the anomalies.

The comparison with JRC drought products should be interpreted as a qualitative assessment of pattern consistency rather than a strict validation. The C3S COMBINED dataset represents surface soil moisture, while the JRC Soil Moisture Anomaly is derived from LISFLOOD simulations of root-zone soil moisture [[4]](https://drought.emergency.copernicus.eu/data/factsheets/factsheet_soilmoisture.pdf). These differences in depth and methodology imply distinct temporal responses and spatial characteristics, and therefore some discrepancies are expected.

Define custom colormap and boundaries
Create colormap and norm

Third dekad of April, May, June, July
Lat/lon ticks
Manual spacing instead of tight_layout
Colorbar placed lower
Main title + year

*Figure 3. SSMA over Europe for the third dekad of April, May, June, and July 2023. Anomalies are computed without applying a baseline completeness threshold. Boxes indicate the regions selected for subsequent regional analysis.*

*Figure 4. SSMA over Europe for the third dekad of April, May, June, and July 2023. Anomalies are computed using a baseline completeness threshold of ≥50%. Boxes indicate the regions selected for subsequent regional analysis.*

*Figure 5. SSMA over Europe for the third dekad of April, May, June, and July 2023. Anomalies are computed using a baseline completeness threshold of ≥70%. Boxes indicate the regions selected for subsequent regional analysis.*

![JRGaugust23.jpg](attachment:004a20bf-a633-43eb-b74a-9cbebdc4ce91.jpg)

*Figure 6. Soil Moisture Anomaly from April to July 2023. Extracted from: JRC (2023)[[6]](https://doi.org/10.2760/928418)*

The application of a baseline completeness threshold has a clear impact on the spatial coverage of the SSMA maps. Under the ≥70% threshold, missing values become more prominent in regions where long-term data availability is limited, particularly in areas with complex terrain such as the Pyrenees, Alps, Apennines, Dinaric Alps, Balkan Mountains, and Carpathians. Reduced coverage is also observed in parts of northern Spain and southern UK, potentially reflecting a combination of retrieval limitations over heterogeneous terrain and urban areas.

Compared to the no-threshold and ≥50% cases, the ≥70% threshold leads to a noticeable reduction in spatial coverage over southern and southeastern Europe, especially across Italy and the Balkans. In these regions, areas that appear as neutral or positive anomalies in the lower-threshold cases are masked out when stricter baseline requirements are applied. This highlights the sensitivity of the spatial patterns to baseline data availability.

Despite these differences, several large-scale features remain consistent with the patterns reported in the JRC drought analyses. In particular, persistent negative anomalies are observed across Scandinavia, and a transition towards more neutral or slightly positive anomalies is visible over parts of the British Isles in late July. This suggests that, even with reduced baseline support in some regions, the main spatial signals are broadly captured.

The largest discrepancies with respect to the JRC results are observed over central-western Europe (e.g. Benelux, Germany, and surrounding areas), where the satellite-based anomalies show weaker or more spatially fragmented drought signals. These differences may reflect both the sensitivity of surface soil moisture to short-term variability and the impact of data completeness on the anomaly calculation.

(section-6)=


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > Analysis and results > 6. Regional SSMA analysis
---
The following section computes and visualizes regional SSMA statistics for selected European subregions. For each region, SSMA values are spatially subsetted to the corresponding bounding box and restricted to land pixels using a geographic mask. The analysis focuses on the period from March to August 2023, using dekadal time steps.

This period was selected to capture the onset, development, and peak of the 2023 European drought, while maintaining consistency with the temporal coverage of the JRC drought reports, which provide assessments for March [[3]](https://doi.org/10.2760/998985), June [[5]](https://doi.org/10.2760/575433), and August [[6]](https://doi.org/10.2760/928418). Focusing on this interval allows for a targeted evaluation of the dataset’s ability to represent the main phases of the drought event, while avoiding the inclusion of less relevant periods outside the core drought season.

Within each region and dekad, the distribution of SSMA across all land pixels is summarized using the median and interquartile range (IQR; 25th–75th percentiles), providing a robust representation of central tendency and spatial variability. In parallel, data completeness is quantified as the percentage of valid (non-missing) land pixels contributing to each dekad.

These statistics are computed separately for different baseline completeness thresholds (no threshold, ≥50%, ≥70%) to assess the sensitivity of the SSMA signal to data availability. The resulting time series are displayed as multi-panel figures, allowing direct comparison of SSMA behaviour and completeness across thresholds and regions.

Subset region
Keep only land pixels
Stack spatial dims
Completeness over land only
Left axis: median + IQR
Right axis: completeness
X ticks on dekad starts
Only one combined legend, placed in first panel

*Figure 7. Mean SSMA over the Iberian Peninsula from spring to summer 2023, together with the corresponding data completeness (%). Panels (left to right) show results for different completeness thresholds: no threshold, ≥50%, and ≥70%.*

Over the Iberian Peninsula, SSMA shows a clear seasonal evolution, with the most negative values occurring in mid-May, followed by a rapid transition towards positive anomalies in early June and a gradual stabilization towards near-neutral conditions during July and August. This pattern reflects the transition from peak drought conditions in late spring to partial recovery in early summer.

The temporal evolution of the median SSMA is highly consistent across all completeness thresholds, with only minor differences in magnitude. This indicates that the regional drought signal is robust to the choice of baseline filtering.

Data completeness remains high throughout the period. For the no-threshold and ≥50% cases, completeness exceeds 90% across most dekads, indicating strong spatial support. The ≥70% threshold results in slightly lower completeness (around 80%), but still maintains sufficient coverage to support the analysis.

Overall, the strong agreement across thresholds, combined with high data availability, suggests that the SSMA estimates for the Iberian Peninsula are both stable and well-supported by the underlying data.

This evolution is consistent with the progression of drought conditions reported for the western Mediterranean in spring 2023, followed by partial recovery in early summer [[3]](https://doi.org/10.2760/998985), [[5]](https://doi.org/10.2760/575433), [[6]](https://doi.org/10.2760/928418), [[7]](https://joint-research-centre.ec.europa.eu/jrc-news-and-updates/severe-drought-western-mediterranean-faces-low-river-flows-and-crop-yields-earlier-ever-2023-06-13_en), [[8]](https://climate.copernicus.eu/precipitation-relative-humidity-and-soil-moisture-may-2023), and with studies highlighting the role of persistent soil moisture deficits in amplifying the record-breaking spring heatwave in the region [[9]](https://doi.org/10.1038/s41612-024-00569-6).

*Figure 8. Mean SSMA over Great Britain and Ireland from spring to summer 2023, together with the corresponding data completeness (%). Panels (left to right) show results for different completeness thresholds: no threshold, ≥50%, and ≥70%.*

Over Great Britain and Ireland, SSMA exhibits a clear minimum in June, indicating the peak of dry conditions, followed by a gradual recovery towards neutral and slight possitive anomalies conditions in July and August. The magnitude of the negative anomaly is slightly stronger when applying the ≥70% threshold, suggesting that stricter filtering removes pixels with limited baseline support, which can reduce noise and result in slightly stronger anomaly signals.


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > Analysis and results > 6. Regional SSMA analysis
---
possitive anomalies conditions in July and August. The magnitude of the negative anomaly is slightly stronger when applying the ≥70% threshold, suggesting that stricter filtering removes pixels with limited baseline support, which can reduce noise and result in slightly stronger anomaly signals.

Data completeness remains high (>80%) for both the no-threshold and ≥50% cases, indicating robust spatial support. In contrast, the ≥70% threshold leads to a substantial reduction in completeness (approximately 40–60%), reflecting limited baseline availability in parts of the region. Despite this reduction, the temporal evolution of SSMA remains consistent across thresholds, indicating that the main drought signal is stable.

*Figure 9. Mean SSMA over the Southern Balkans and Italy from spring to summer 2023, together with the corresponding data completeness (%). Panels (left to right) show results for different completeness thresholds: no threshold, ≥50%, and ≥70%.*

In the Southern Balkans and Italy, SSMA is predominantly positive throughout the analysis period, with peaks in mid-May and mid-June. This behaviour contrasts with drought-affected regions and is consistent with spatial patterns observed in the maps. This positive anomaly is consistent with reported rainfall surpluses in May and June over Italy, as well as parts of Slovakia, Hungary, Croatia, and Slovenia, which led to waterlogging of soils and increased pest pressure on crops [[10]](https://doi.org/10.2760/82821).

Completeness shows a stronger dependence on the applied threshold. While the no-threshold case increases from ~80% to near-complete coverage over time, the ≥50% threshold introduces moderate variability around ~80%. The ≥70% threshold results in lower and more stable completeness (approximately 40–60%), with slightly higher values in early spring.

Despite these differences in data availability, the SSMA signal remains largely unchanged across thresholds, indicating that the positive anomaly pattern is robust.

*Figure 10. Mean SSMA over the Nordic and Baltic region from spring to summer 2023, together with the corresponding data completeness (%). Panels (left to right) show results for different completeness thresholds: no threshold, ≥50%, and ≥70%.*

The Nordic and Baltic region shows a pronounced seasonal evolution, with the lowest SSMA values occurring in June and a transition to slight positive anomalies by early August. The magnitude of negative anomalies is slightly enhanced under the ≥70% threshold.

Completeness exhibits a strong seasonal increase, rising from very low values (<20%) in early spring to high values (>80%) in summer. This pattern is consistent across thresholds, although the ≥70% threshold delays the point at which sufficient coverage is reached (crossing ~60% only in early July). This behaviour reflects known limitations in satellite soil moisture retrievals under snow and frozen soil conditions.

Despite the strong variation in completeness, the temporal SSMA pattern remains consistent across thresholds once sufficient data coverage is achieved.

*Figure 11. Mean SSMA over the Germany–Benelux–Poland area from spring to summer 2023, together with the corresponding data completeness (%). Panels (left to right) show results for different completeness thresholds: no threshold, ≥50%, and ≥70%.*

In the Germany–Benelux–Poland region, SSMA reaches its minimum in early June and transitions to positive anomalies by early August. Compared to other regions, this area shows greater sensitivity to threshold selection, particularly in early spring.

Completeness is near-total (≈100%) in the no-threshold case and remains high (>90%) for the ≥50% threshold. Under the ≥70% threshold, completeness is initially low (~40% in early March) but increases rapidly, stabilizing around ~80% for the remainder of the period.

Across all regions, the median SSMA and its temporal evolution are broadly consistent across the different completeness thresholds, despite substantial differences in data availability. This indicates that the main drought signal is relatively robust to the choice of threshold. However, stricter thresholds (e.g. ≥70%) significantly reduce spatial coverage in several regions, particularly where baseline data availability is limited.

The close agreement between the no-threshold and ≥50% cases indicates that applying a 50% completeness requirement does not materially change the baseline composition. This is likely due to the uneven temporal distribution of valid observations, with a higher density of data in more recent years following the introduction of additional sensors (e.g. AMSR-E and ASCAT). Consequently, the ≥50% threshold effectively selects pixels for which a sufficient number of valid years is already concentrated in the later part of the record, leading to an overrepresentation of recent conditions in the baseline and a reduced contribution from earlier, less complete years.


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > ℹ If you want to know more
---
* Markonis, Y., Kumar, R., Hanel, M., Rakovec, O., Máca, P., & AghaKouchak, A. (2021). The rise of compound warm-season droughts in Europe. Science Advances, 7(6), eabb9668. [](https://doi.org/10.1126/sciadv.abb9668)
* Ministerio para la Transición Ecológica y el Reto Demográfico (2023). El 14,6% del territorio está en emergencia por escasez de agua y el 27,4%, en alerta. Nota de prensa. [](https://www.miteco.gob.es/es/prensa/ultimas-noticias/2023/09/el-14-6--del-territorio-esta-en-emergencia-por-escasez-de-agua-y.html)
* Laguardia, G. & Niemeyer, S. (2008). On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates. Hydrol. Earth Syst. Sci., 12, 1339–1351. [](https://hess.copernicus.org/articles/12/1339/2008/)


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > ℹ If you want to know more > Key resources
---
Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/)

Dataset documentation:

* [SM v202212: Product User Guide and Specification (PUGS)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=355349314)

* [SM v202312: Algorithm Theoretical Basis Document (ATBD)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=453676750)

* [SM v202312: Product Quality Assurance Document (PQAD)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=445290971)

* [SM v202312: Product Quality Assessment Report (PQAR)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=445290728#SMv202312:ProductQualityAssessmentReport(PQAR)-Spatialandtemporalcompletenesss2.1)


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > ℹ If you want to know more > References
---
[[1]](https://ec.europa.eu/eurostat/web/gisco/geodata/administrative-units) Eurostat (Accessed on 2016). GISCO. Administrative units.

[[2]](https://community.wmo.int/site/knowledge-hub/programmes-and-initiatives/climate-services/wmo-climatological-normals)) World Meteorological Organization. (Accessed on 2016). WMO Climatological Normals.

[[3]](https://doi.org/10.2760/998985) Toreti, A., Bavera, D., Acosta Navarro, J., Arias Muñoz, C., Avanzi, F., Barbosa, P., de Jager, A., Di Ciollo, C., Ferraris, L., Fioravanti, G., Gabellani, S., Grimaldi, S., Hrast Essenfelder, A., Isabellon, M., Jonas, T., Maetens, W., Magni, D., Masante, D., Mazzeschi, M., McCormick, N., Meroni, M., Rossi, L., Salamon, P., Spinoni, J. (2023). Drought in Europe March 2023. Publications Office of the European Union, Luxembourg, doi:10.2760/998985, JRC133025.

[[4]](https://drought.emergency.copernicus.eu/data/factsheets/factsheet_soilmoisture.pdf) European Commission (2019). EDO INDICATOR FACTSHEET Soil Moisture Anomaly (SMA). European Drought Observatory.

[[5]](https://doi.org/10.2760/575433) Toreti, A., Bavera, D., Acosta Navarro, J., Arias Muñoz, C., Avanzi, F., Barbosa, P., de Jager, A., Di Ciollo, C., Ferraris, L., Fioravanti, G., Gabellani, S., Grimaldi, S., Hrast Essenfelder, A., Isabellon, M., Jonas, T., Maetens, W., Magni, D., Masante, D., Mazzeschi, M., McCormick, N., Rossi, L., Salamon, P. (2023). Drought in Europe June 2023, Publications Office of the European Union, Luxembourg, doi:10.2760/575433, JRC134492.

[[6]](https://doi.org/10.2760/928418) Toreti, A., Bavera, D., Acosta Navarro, J., Arias Muñoz, C., Barbosa, P., de Jager, A., Di Ciollo, C., Fioravanti, G., , Grimaldi, S., Hrast Essenfelder, A., Maetens, W., Magni, D., Masante, D., Mazzeschi, M., McCormick, N., Salamon, P. (2023). Drought in Europe - August 2023, Publications Office of the European Union, Luxembourg, doi:10.2760/928418, JRC135032.

[[7]](https://joint-research-centre.ec.europa.eu/jrc-news-and-updates/severe-drought-western-mediterranean-faces-low-river-flows-and-crop-yields-earlier-ever-2023-06-13_en) European Commission (2023). Severe drought: western Mediterranean faces low river flows and crop yields earlier than ever. European Drought Observatory. The Joint Research Centre: EU Science Hub.

[[8]](https://climate.copernicus.eu/precipitation-relative-humidity-and-soil-moisture-may-2023) European Commission (2023). Precipitation, relative humidity and soil moisture for May 2023.

[[9]](https://doi.org/10.1038/s41612-024-00569-6) Lemus-Canovas, M., Insua-Costa, D., Trigo, R. & Miralles, D. (2024). Record-shattering 2023 Spring heatwave in western Mediterranean amplified by long-term drought. npj Clim Atmos Sci 7, 25.


EQC Quality Assessment: "Satellite soil moisture for drought monitoring in Europe (2023 case study)"
Dataset: satellite-soil-moisture [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Satellite soil moisture for drought monitoring in Europe (2023 case study) > ℹ If you want to know more > References
---
., Trigo, R. & Miralles, D. (2024). Record-shattering 2023 Spring heatwave in western Mediterranean amplified by long-term drought. npj Clim Atmos Sci 7, 25.

[[10]](https://doi.org/10.2760/82821) Baruth, B., Ben Aoun, W., Biavetti, I., Bratu, M., Bussay, A., Cerrani, I., Chemin, Y., Claverie, M., De Palma, P., Fumagalli, D., Manfron, G., Morel, J., Nisini, L., Panarello, L., Rossi, M., Tarnavsky, E., Van Den Berg, M., Zajac, Z. and Zucchini, A., JRC MARS Bulletin - Crop monitoring in Europe - June 2023 - Vol. 31 No 6, Van Den Berg, M., Niemeyer, S. and Baruth, B. editor(s), Publications Office of the European Union, Luxembourg, 2023, doi:10.2760/82821, JRC133186.

[[11]](https://www.ncei.noaa.gov/access/monitoring/monthly-report/global-drought/202306) NOAA National Centers for Environmental Information (2023). Monthly Climate Reports, Global Drought Narrative, June 2023.


EQC Quality Assessment: "Completeness of ocean colour observations for biogeochemical models"
Dataset: satellite-ocean-colour [CDS]
Aspect: completeness_q01  |  Category: Satellite_ECVs
Section: Completeness of ocean colour observations for biogeochemical models > Quality assessment question
---
**Is chlorophyll-a data sufficiently complete in time and space for integration into biogeochemical models?**

The Ocean Colour dataset version 6.0, as produced for the Copernicus Climate Change Service (C3S), includes the two variables: mass concentration of chlorophyll-a and remote sensing reflectance (Rrs) for six wavelengths from October 1997 to present [[1]](https://dast.copernicus-climate.eu/documents/satellite-ocean-colour/v6.0/WP2-FDDP-2022-04_C3S2-Lot3_PUGS-of-v6.0-OceanColour-product_v1.1_FINAL.pdf).
Chlorophyll-a concentration data, which are derived through specific algorithms using remote sensing reflectance (Rrs) [[2]](https://dast.copernicus-climate.eu/documents/satellite-ocean-colour/v6.0/WP2-FDDP-2022-04_C3S2-Lot3_ATBD-of-v6.0-OceanColour-product_v1.1_FINAL.pdf), are indispensable in biogeochemical modelling as they provide inputs for models initialisation and references to calibrate biogeochemical parameters and validate models results (e.g., [[3]](https://doi.org/10.1016/j.jmarsys.2013.02.007) [[4]](https://doi.org/10.3390/rs10101666) [[5]](https://doi.org/10.1038/s41467-019-08457-x)). Moreover, chlorophyll-a data can be assimilated into models, enabling continuous updates with observed conditions and significantly improve their accuracy (e.g., [[6]](https://doi.org/10.1029/2018JC014329)).
The dataset merges measurements carried out by six satellite sensors [[1]](https://dast.copernicus-climate.eu/documents/satellite-ocean-colour/v6.0/WP2-FDDP-2022-04_C3S2-Lot3_PUGS-of-v6.0-OceanColour-product_v1.1_FINAL.pdf): SeaWiFS, MERIS, MODIS-Aqua, VIIRS, OLCI-3A and OLCI-3B. Each sensor has specific design characteristics and viewing geometry, and no sensor was operational over the whole temporal coverage of the datasets [[1]](https://dast.copernicus-climate.eu/documents/satellite-ocean-colour/v6.0/WP2-FDDP-2022-04_C3S2-Lot3_PUGS-of-v6.0-OceanColour-product_v1.1_FINAL.pdf) [[2]](https://dast.copernicus-climate.eu/documents/satellite-ocean-colour/v6.0/WP2-FDDP-2022-04_C3S2-Lot3_ATBD-of-v6.0-OceanColour-product_v1.1_FINAL.pdf). Currently, only OLCI-3A and OLCI-3B are operational.
Here, the goal is to assess the completeness of the chlorophyll-a dataset at both spatial and temporal scales in the Southern Ocean.


EQC Quality Assessment: "Completeness of ocean colour observations for biogeochemical models"
Dataset: satellite-ocean-colour [CDS]
Aspect: completeness_q01  |  Category: Satellite_ECVs
Section: Completeness of ocean colour observations for biogeochemical models > Quality assessment statement
---
These are the key outcomes of this assessment

The Ocean Colour dataset version 6.0:
* provides a limited representation of the spatial distribution of chlorophyll-a concentrations in the oceanic region below latitude 45.5°S, based on less than 8% of valid pixels.
* offers an acceptable representation of the spatial distribution of chlorophyll-a concentrations off the southeast coast of South America, based on more than 30% of valid pixels.
* requires filtering based on the availability of valid pixels when analysing chlorophyll-a temporal distribution, to reduce potential bias due to limited sampling, especially during the austral winter (May-August).
* allows for the calculation of monthly chlorophyll-a trends in the Southern Ocean, although these trends should be interpreted with caution as based on less than 15% of valid pixels.
* can be used for calibrating, assimilating and validating biogeochemical models in the Southern Ocean from September to April, when valid observations are more abundant.

## 📋 Methodology

This notebook provides an assessment of the ability of the Ocean Colour dataset version 6.0 to represent the spatial distribution and temporal variability of chlorophyll-a concentrations in the Indian, Pacific, and Atlantic Ocean sectors of the Permanent Open Ocean Zone in the Southern Ocean by analysing the number of valid pixels available.

The analysis and results are detailed in the sections below:

[](section-1)
   * Import required packages
   * Define parameters to be analysed (time period, variables, regions) and data request

[](section-2)
   * Define transform functions
   * Retrieve data for the selected variables, time period and regions

[](section-3)
   * Define mapping function
   * Compute trends

[](section-4)
   * Display results
   * Discussion

## 📈 Analysis and results
(section-1)=
### 1. Choose the data to use and set up the code
#### Import required packages
Besides the standard libraries used to manage and analyse multidimensional arrays, the [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control) `c3s_eqc_automatic_quality_control`is imported to download data and calculate statistics.

#### Define parameters to be analysed (time period, variables, regions) and data request
The analysis performed in this notebook examines the spatial and temporal distribution of chlorophyll-a data, and corresponding valid pixels, over a 21-year period (January 2003 - December 2023), which accounts for previous EQC information (https://cds.climate.copernicus.eu/datasets/satellite-ocean-colour?tab=quality_assurance_tab) and includes only complete years. The analysis is performed over three sectors within the oceanic region in the Southern Ocean located between latitudes 47.5°S and 63.5°S (i.e., the Permanent Open Ocean Zone, POOZ) [[7]](https://doi.org/10.1016/0967-0637%2895%2900021-W). The three POOZ sectors analysed are: the Indian Ocean sector (IO_POOZ), the Pacific Ocean sector (PO_POOZ) and the Atlantic Ocean sector (AO_POOZ), which extend between longitudes 20°E and 150°E, 150°E and 70°W, and 70°W and 20°E, respectively [[8]](https://doi.org/10.1029/2019GL083163).

Define data request

(section-2)=
### 2. Transform functions and data retrieval
#### Define transform functions
The `monthly_weighted_log_mean` function is defined to calculate temporal averages of chlorophyll-a concentration (mg m-3) and the percentage of corresponding valid pixels for each POOZ sector by month and year, while the `weighted_log_map` function is defined to compute spatial averages across the entire POOZ.
Both functions account for the varying surface area at different latitudes. Averages for chlorophyll-a concentration are computed using log-transformed daily data, and then back-transformed [[9]](https://doi.org/10.1029/95JC00458). Daily chlorophyll-a concentrations outside the range 0.01-100 mg m-3 are excluded from the analysis [[10]](https://doi.org/10.3390/s19194285).

#### Data retrieval
The `monthly_weighted_log_mean` and `weighted_log_map` functions are applied to the chlorophyll-a dataset in the selected regions, and temporal and spatial averages are downloaded as two separated arrays.


EQC Quality Assessment: "Completeness of ocean colour observations for biogeochemical models"
Dataset: satellite-ocean-colour [CDS]
Aspect: completeness_q01  |  Category: Satellite_ECVs
Section: Completeness of ocean colour observations for biogeochemical models > Quality assessment statement
---
19194285).

#### Data retrieval
The `monthly_weighted_log_mean` and `weighted_log_map` functions are applied to the chlorophyll-a dataset in the selected regions, and temporal and spatial averages are downloaded as two separated arrays.

(section-3)=
### 3. Mapping function and trends calculation
#### Define mapping function
The `plot_map` function is defined to visualise the maps of average chlorophyll-a concentrations and corresponding percentages of valid pixels across the POOZ.

#### Compute trends
The `calculate_mk_trends` function is defined to calculate chlorophyll-a trends and their statistical significance for each individual month over the 21-year period analysed here in the three POOZ sectors.

(section-4)=
### 4. Display and discuss results
#### Display results
Maps showing the spatial distribution of valid pixel percentages and derived chlorophyll-a concentration in the POOZ are provided. Two sets of heatmaps are generated: one for the monthly percentage of valid chlorophyll-a pixels by POOZ sector, and another for the monthly chlorophyll-a concentrations. Here, chlorophyll-a data are displayed only when data coverage meets or exceeds the 21-year mean percentage of valid pixels across the POOZ (≈8%; see Discussion), and are referred to as filtered chlorophyll-a. Monthly climatologies of valid pixels and filtered chlorophyll-a concentrations are also shown. Trends in filtered chlorophyll-a and their statistical significance, calculated for each individual month over the 21-year period analysed, are summarised in a table, where statistically significant trends (p>0.05) are highlighted in bold and na indicates not enough data for trends calculations.

Display maps
Filtering chlorophyll-a based on valid pixel coverage
Display heatmaps
Display climatologies
Display mk results

#### Discussion

**Chlorophyll-a spatial distribution:**

The mean availability of valid chlorophyll-a  pixels over the 21-year period analysed is approximately 8% over the entire POOZ, indicating limited dataset coverage that may not fully capture the spatial variability of chlorophyll-a distribution in this region. A small difference in the number of valid pixels is observed across the three POOZ sectors: the PO_POOZ exhibits the greatest availability (9.5%), followed by the AO_POOZ (8.3%) and the IO_POOZ (7.3%). A significant number of valid pixels (above 30%) are detected off the southeastern coasts of South America. Therefore, the representation of the spatial distribution of chlorophyll-a in this area can be assumed to be more reliable than elsewhere in the POOZ.
Different average chlorophyll-a concentrations are observed in the three POOZ sectors: the AO_POOZ sector is the less oligotrophic with an average value of about 0.2 mg m-3, whereas the IO_POOZ and PO_POOZ are characterised by lower averages (0.12 and 0.14 mg m-3, respectively), confirming what reported in previous studies (e.g., [[8]](https://doi.org/10.1029/2019GL083163)). While the subregional variability in terms of valid pixels likely depends on differences in sea ice extent and cloud coverage across the three POOZ sectors, differences in their average chlorophyll-a concentration are due to several key factors, such as nutrient supply through Patagonian dust deposition and/or upwelling (e.g., [[11]](https://doi.org/10.1029/2006jc004072) [[12]](https://doi.org/10.5670/oceanog.2018.408) [[13]](https://doi.org/10.3389/fmars.2024.1363088)).

**Chlorophyll-a temporal distribution:**


EQC Quality Assessment: "Completeness of ocean colour observations for biogeochemical models"
Dataset: satellite-ocean-colour [CDS]
Aspect: completeness_q01  |  Category: Satellite_ECVs
Section: Completeness of ocean colour observations for biogeochemical models > Quality assessment statement
---
8.408) [[13]](https://doi.org/10.3389/fmars.2024.1363088)).

**Chlorophyll-a temporal distribution:**

The monthly climatological distribution of valid chlorophyll-a pixels is in line with expectations based on their dependence on the solar zenith angle: the highest percentage (up to about 15%) is observed between November and February, when the solar zenith angle is low, then it decreases to reach its minimum (almost 0%) during the polar night months (i.e. May-July) and gradually increases again towards the end of the year. It is interesting to note that, as shown in the valid pixel heatmap, the valid percentages increased between 2017 and 2019, reaching a maximum in 2018 (up to about 24%), when the highest number of observing systems were operational (i.e., MODIS-Aqua, VIIRS, OLCI-3A, and OLCI-3B)
([[1]](https://dast.copernicus-climate.eu/documents/satellite-ocean-colour/v6.0/WP2-FDDP-2022-04_C3S2-Lot3_PUGS-of-v6.0-OceanColour-product_v1.1_FINAL.pdf)).
Filtering chlorophyll-a data based on the availability of valid pixels allows to reduce the bias resulting from the lack of sampling at high latitudes, which leads to artefacts in the apparent seasonal cycle from ocean colour sensors [[14]](https://doi.org/10.1016/j.rse.2007.03.008). However, the temporal distribution for the filtered chlorophyll-a data alignes with typical observations in the Southern Ocean, where chlorophyll-a concentrations decrease during the austral winter (e.g., [[15]](https://doi.org/10.1038/s41467-020-19157-2)).
Linear trend analysis for all months in all three POOZ sectors yields to results summarised in the table, indicating an overall chlorophyll-a increase in the POOZ, consistent with previous studies (e.g., [[8]](https://doi.org/10.1029/2019GL083163); [[16]](https://doi.org/10.1016/j.marenvres.2024.106856)). However, these trends should be interpreted with caution, as they are based on a low percentage of valid pixels (i.e., 8-15%). Including data prior to 2003 in the analysis would not change the sign of the significant chlorophyll-a trends but could affect their statistical significance.
Overall, the results indicate that the dataset remains valuable for biogeochemical calibration and validation in the Southern Ocean during months with reliable observations (September to April). In contrast, using data from the local winter (May-August), when satellite-derived chlorophyll-a concentrations have been shown to be overestimated [[14]](https://doi.org/10.1016/j.rse.2007.03.008), could lead to biased model parameterisation. This in turn may result in inflated estimates of primary production and carbon fluxes, as well as a misrepresentation of the ecosystem functioning, including nutrient cycling and grazing pressure. To address these issues, chlorophyll-a data should be filtered based on the availability of valid pixels used for their retrieval and corrected using in situ measurements (e.g., Argo floats, ship-based observations), or through assimilation techniques that incorporate satellite chlorophyll-a data from reliable seasons, enabling the model to estimate missing data based on physical and biogeochemical constraints.

## ℹ If you want to know more

### Key resources
* CDS catalogue entry used in this notebook is the [Ocean colour daily data from 1997 to present derived from satellite observations](https://cds.climate.copernicus.eu/datasets/satellite-ocean-colour?tab=overview)
* Data download is from [CDS](https://cds.climate.copernicus.eu/datasets/satellite-ocean-colour?tab=form)
* Product Documentation is available on the [CDS](https://cds.climate.copernicus.eu/datasets/satellite-ocean-colour?tab=documentation) website

Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`, prepared by [B-Open](https://www.bopen.eu/)


EQC Quality Assessment: "Completeness of ocean colour observations for biogeochemical models"
Dataset: satellite-ocean-colour [CDS]
Aspect: completeness_q01  |  Category: Satellite_ECVs
Section: Completeness of ocean colour observations for biogeochemical models > Quality assessment statement
---
open/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`, prepared by [B-Open](https://www.bopen.eu/)

Further readings:
* Ocean Colour Monitoring Portals: [CMEMS](https://marine.copernicus.eu/), [ESA-GlobColour Project](https://hermes.acri.fr), [ESA-OC-CCI](https://climate.esa.int/en/projects/ocean-colour/), [ESA-Sentinel 3](https://sentiwiki.copernicus.eu/web/s3-mission), [IMOS](https://imos.org.au/facility/satellite-remote-sensing/ocean-colour), [NASA](https://oceancolor.gsfc.nasa.gov/#), [NOAA CoastWatch](https://coastwatch.noaa.gov/cwn/index.html)
* Use cases: [EUMETSAT](https://user.eumetsat.int/search-view?sort=startDate%20desc&facets=%7B%22theme%22:%5B%22Marine%7COcean%20biogeochemistry%22%5D,%22contentTypes%22:%5B%22Resources%7CCase%20studies%22%5D%7D), [C3S](https://climate.copernicus.eu/ocean)
* Groom, S., et al. (2019). [Satellite Ocean Colour: Current Status and Future Perspective](https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2019.00485/full). Frontiers in Marine Science, 6.
* [International Ocean Colour Coordinating Group (ICCG)](https://ioccg.org/)
* [Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM)](https://soccom.princeton.edu)

### References

[[1]](https://dast.copernicus-climate.eu/documents/satellite-ocean-colour/v6.0/WP2-FDDP-2022-04_C3S2-Lot3_PUGS-of-v6.0-OceanColour-product_v1.1_FINAL.pdf) Jackson, T., et al. (2023). C3S Ocean Colour Version 6.0: Product User Guide and Specification. Issue 1.1. E.U. Copernicus Climate Change Service. Document ref. WP2-FDDP-2022-04_C3S2-Lot3_PUGS-of-v6.0-OceanColour-product.

[[2]](https://dast.copernicus-climate.eu/documents/satellite-ocean-colour/v6.0/WP2-FDDP-2022-04_C3S2-Lot3_ATBD-of-v6.0-OceanColour-product_v1.1_FINAL.pdf) Jackson, T., et al. (2022). C3S Ocean Colour Version 6.0: Algorithm Theoretical Basis Document. Issue 1.1. E.U. Copernicus Climate Change Service. Document ref. WP2-FDDP-2022-04_C3S2-Lot3_ATBD-of-v6.0-OceanColour-product.

[[3]](https://doi.org/10.1016/j.jmarsys.2013.02.007) Doron, M., Brasseur, P., Brankart, J.-M., Losa, S.N., & Melet, A. (2013). Stochastic estimation of biogeochemical parameters from Globcolour ocean colour satellite data in a North Atlantic 3D ocean coupled physical–biogeochemical model. Journal of Marine Systems, 117-118, 91-95.

[[4]](https://doi.org/10.3390/rs10101666) Sammartino, M., Marullo, S., Santoleri, R., & Scardi, M. (2018). Modelling the Vertical Distribution of Phytoplankton Biomass in the Mediterranean Sea from Satellite Data: A Neural Network Approach. Remote Sensing, 10(10), 1666.

[[5]](https://doi.org/10.1038/s41467-019-08457-x) Dutkiewicz, S., Hickman, A.E., Jahn, O., Henson, S., Beaulieu, C., & Monier, E. (2019) Ocean colour signature of climate change. Nature Communications, 10, 578.