EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > Analysis and results > 3. Compute and plot the global variability of seasonal XCH$_4$
---
To show how data coverage varies between years and seasons, in this section we plot the spatial average XCH$_4$ values for seasons and years. To limit the number of plots produced, this analysis is limited to the period 2013-2022. Seasons are defined as: December-February (DJF), March-May (MAM), June-August (JJA), and September-November (SON). The seasonality of the availability of measurements is evident, with values available in the Southern Hemisphere (SH) mid and high latitudes from September to March, whereas values for the Northern Hemisphere (NH) mid and high latitudes are mostly available from April to August.

Analysis of global variability of seasonal XCH4
To invalidate the cache you can pass the argument invalidate_cache=True

```text
0%|          | 0/1 [00:00<?, ?it/s]/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/xarray/core/duck_array_ops.py:237: RuntimeWarning: invalid value encountered in cast
  return data.astype(dtype, **kwargs)
100%|██████████| 1/1 [00:01<00:00,  1.74s/it]
```

```text
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/xarray/core/nputils.py:256: RankWarning: Polyfit may be poorly conditioned
  warn_on_deficient_rank(rank, x.shape[1])
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/xarray/core/nputils.py:256: RankWarning: Polyfit may be poorly conditioned
  warn_on_deficient_rank(rank, x.shape[1])
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/xarray/core/nputils.py:256: RankWarning: Polyfit may be poorly conditioned
  warn_on_deficient_rank(rank, x.shape[1])
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/xarray/core/nputils.py:256: RankWarning: Polyfit may be poorly conditioned
  warn_on_deficient_rank(rank, x.shape[1])
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/xarray/core/nputils.py:256: RankWarning: Polyfit may be poorly conditioned
  warn_on_deficient_rank(rank, x.shape[1])
```

*Global annual variability of seasonal XCH$_4$, over 2013-2022. The different panels represent the individual seasons (column) and years (rows). Seasons are defined as: December-February (DJF), March-May (MAM), June-August (JJA), and September-November (SON).*

(template:section-4)=


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > Analysis and results > 4. Compute global and spatial trends
---
In this section, XCH$_4$ spatial trends are calculated and statistical significance is assessed for land pixels ("land_fraction > 0.5"). Global statistics for individual pixels are also reported as ppb/month. Please note that the low growth rates observed in the tropics are associated with high statistical errors in trend calculations. This is probably due to the frequent presence of clouds resulting in sparse data availability. Over Greenland, however, high retrieval uncertainty may be a factor due to the high surface albedo.

For 2013-2022, the mean global XCH$_4$ growth rate is 9.71 $\pm$ 2.21 ppb/yr ($\pm$ standard error of the trend calculation). Taking into account the associated uncertainties, the different calculation methods and the different vertical representativeness of satellite observations with respect to near-surface measurements, this value is reasonably consistent with the mean absolute increase of 10.20 ppb/yr over the past 10 years, as reported by the WMO's global in-situ observations [[1]](https://library.wmo.int/idurl/4/68532).

Calculation of global and pixel trends
To invalidate the cache you can pass the argument invalidate_cache=True

```text
100%|██████████| 1/1 [00:03<00:00,  3.63s/it]
```

*Trends (upper map) and related standard error (bottom map) of XCH$_4$ given in ppb/month calculated by using linear model (Theil-Sen) and Mann-Kendall test for statistical significance. Over each pixel, trends are calculated for monthly anomalies over land ("land_fraction > 0.5"). In the upper map, any shaded areas indicate pixels that did not pass the Mann-Kendall significance test. Global statistics for individual pixels are shown on the right of the plot.*

(template:section-5)=


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > Analysis and results > 5. Analysis concerning XCH$_4$ uncertainties > Global maps of XCH$_4$ uncertainties
---
In this section, we show the multi-year (for the 2015-2020 period) global maps of the XCH$_4$ reported uncertainties, i.e., the "xch4_stderr" variable. This parameter is defined as the standard error of the average including single sounding noise and potential seasonal and regional biases [[4]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf). The yearly averages are calculated by averaging the monthly values provided by the considered dataset. Throughout all the selected years some features emerge, with higher uncertainties mainly over the Himalayas, South Asia, high latitudes, and the tropical rainforest zone. This is mainly due to the sparseness in sampling because of frequent cloud cover, as well as large solar zenith angles in high latitudes, which are a challenge for accurate XCH$_4$ retrievals [[5]](https://doi.org/10.5194/amt-13-789-2020). It is worth noting that regions characterised by large uncertainties were also affected by spatial trends that deviated from the global average (see the previous figure). Users should therefore exercise caution when deriving long-term trends for regions affected by high uncertainty.

*Multi-year global distribution of XCH$_4$ standard error (from 2015 to 2020), derived from the XCH4_OBS4MIPS dataset (version 4.5). The title indicates the spatial region and the selected variable.*


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > Analysis and results > 5. Analysis concerning XCH$_4$ uncertainties > Time series analysis for the different variables
---
In this section, we show the time series for three XCH$_4$ variables (i.e., "xch4", "xch4_stderr", and "xch4_nobs"), considering the entire dataset. For each variable, each plot displays the monthly global spatial average, along with the corresponding monthly standard deviation (except for "xch4_nobs").
This analysis would provide the user with:
* the global time series of XCH$_4$ values ("xch4"), highlighting the overall positive trend;
* the change over time of the uncertainty associated to the XCH$_4$ values ("xch4_stderr");
* the time series of "xch4_nobs", which denotes the number of individual XCH$_4$ Level 2 observations used to compute the monthly Level 3 data.

The XCH$_4$ values have been nearly constant until 2007, then a positive trend has been detected. This is likely due to a combination of increasing natural (e.g., wetlands) and anthropogenic (e.g., fossil-fuel related) emissions, and possible decreasing sinks, although this is currently under investigation (see [[3]](https://doi.org/10.5194/essd-17-1873-2025) and references therein).

By looking at the uncertainties ("xch4_stderr"), the most evident feature is the sharp decrease after 2009, likely due to the introduction of algorithms based on GOSAT observations used to calculate the median XCH$_4$ data [[6]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf). A further increase in the uncertainties is observed from 2020 onwards, and this is probably linked to the introduction of GOSAT-2 measurements [[6]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf).

The temporal variability of "xch4_nobs" traces the changes over time of the input data availability, and the number of used algorithms to obtain the merged Level 2 data products, from which the Obs4MIPs product is derived (please note that the standard deviations have not been reported for "xch4_nobs" to increase the plot readability). The first time period (up to April 2009) is characterized by a significant number of observations by the SCIAMACHY WFMD product only. The following period (up to 2022) reflects the reduced number of soundings provided by SCIAMACHY with the contributions from GOSAT and GOSAT-2 (from January 2019). For details about the different Level 2 products used as input for the generation of the Level 3 XCH$_4$ data, see [[5]](https://doi.org/10.5194/amt-13-789-2020) and [[6]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf).

Please note that this analysis can be customized by the user to limit it to specific regions, or to include one additional variable (not shown here): "xch4_stddev", which represents the standard deviation of the XCH$_4$ Level 2 observations within each grid box.

If variaable is xco2_nobs, use log scale for y-axis
ax.set_ylabel("n observations")
Imposta tick logaritmici automatici

*Global monthly time series of XCH$_4$ (top panel) and their associated uncertainties (middle panel) for the entire dataset. The blue lines represent the monthly spatial average, while the shaded areas indicate $\pm$1 standard deviation. The bottom panel shows the time series of monthly spatial average of the number of individual XCH$_4$ Level 2 observations used to compute Obs4MIPs (Level 3) data. The main titles indicate the variable shown, while the subtitles specify the corresponding region.*


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > ℹ If you want to know more > Key resources
---
The CDS catalogue entries for the data used were:
* Methane data from 2002 to present derived from satellite observations: https://cds.climate.copernicus.eu/datasets/satellite-methane?tab=overview

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/)

Users interested in obtaining updated figures for methane growth rates and global trends are directed to official C3S sources for precise reporting: [[7]](https://doi.org/10.24381/14j9-s541), [[8]](https://climate.copernicus.eu/global-climate-highlights-2024).

Users interested in near-real time detection of hot-spot locations for methane emissions can consider to use the CAMS Methane Hotspot Explorer: https://atmosphere.copernicus.eu/ghg-services/cams-methane-hotspot-explorer?utm_source=press&utm_medium=referral&utm_campaign=CH4-app-2025


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > ℹ If you want to know more > References
---
[[1]](https://library.wmo.int/idurl/4/68532) World Meteorological Organization (2023). WMO Greenhouse Gas Bulletin, No. 19, ISSN 2078-0796.

[[2]](https://doi.org/10.1073/pnas.0600201103) West, J. J., Fiore, A. M., Horowitz, L. W., and Mauzerall, D. L. (2006). Global health benefits of mitigating ozone pollution with methane emission controls. Proceedings of the National Academy of Sciences USA, 103, 3988–3993.

[[3]](https://doi.org/10.5194/essd-17-1873-2025) Saunois, M., Martinez, A., Poulter, B., Zhang, Z., Raymond, P. A., Regnier, P., Canadell, J. G., Jackson, R. B., Patra, P. K., Bousquet, P., Ciais, P., Dlugokencky, E. J., Lan, X., Allen, G. H., Bastviken, D., Beerling, D. J., Belikov, D. A., Blake, D. R., Castaldi, S., Crippa, M., Deemer, B. R., Dennison, F., Etiope, G., Gedney, N., Höglund-Isaksson, L., Holgerson, M. A., Hopcroft, P. O., Hugelius, G., Ito, A., Jain, A. K., Janardanan, R., Johnson, M. S., Kleinen, T., Krummel, P. B., Lauerwald, R., Li, T., Liu, X., McDonald, K. C., Melton, J. R., Mühle, J., Müller, J., Murguia-Flores, F., Niwa, Y., Noce, S., Pan, S., Parker, R. J., Peng, C., Ramonet, M., Riley, W. J., Rocher-Ros, G., Rosentreter, J. A., Sasakawa, M., Segers, A., Smith, S. J., Stanley, E. H., Thanwerdas, J., Tian, H., Tsuruta, A., Tubiello, F. N., Weber, T. S., van der Werf, G. R., Worthy, D. E. J., Xi, Y., Yoshida, Y., Zhang, W., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q. (2025). Global Methane Budget 2000–2020, Earth System Science Data, 17, 1873–1958.

[[4]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf) Buchwitz, M. (2024). Product User Guide and Specification (PUGS) – Main document for Greenhouse Gas (GHG: CO$_2$ & CH$_4$) data set CDR7 (01.2003-12.2022), C3S project 2021/C3S2_312a_Lot2_DLR/SC1, v7.3.


EQC Quality Assessment: "Methane satellite observations uncertainty assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty assessment for greenhouse gas monitoring > ℹ If you want to know more > References
---
2003-12.2022), C3S project 2021/C3S2_312a_Lot2_DLR/SC1, v7.3.

[[5]](https://doi.org/10.5194/amt-13-789-2020) Reuter, M., Buchwitz, M., Schneising, O., Noël, S., Bovensmann, H., Burrows, J. P., Boesch, H., Di Noia, A., Anand, J., Parker, R. J., Somkuti, P., Wu, L., Hasekamp, O. P., Aben, I., Kuze, A., Suto, H., Shiomi, K., Yoshida, Y., Morino, I., Crisp, D., O'Dell, C. W., Notholt, J., Petri, C., Warneke, T., Velazco, V. A., Deutscher, N. M., Griffith, D. W. T., Kivi, R., Pollard, D. F., Hase, F., Sussmann, R., Té, Y. V., Strong, K., Roche, S., Sha, M. K., De Mazière, M., Feist, D. G., Iraci, L. T., Roehl, C. M., Retscher, C., and Schepers, D. (2020). Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications, Atmospheric Measurement Techniques, 13, 789–819.

[[6]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf) Reuter,M. and Buchwitz, M. (2024). Algorithm Theoretical Basis Document (ATBD) – ANNEX D for products XCO2_EMMA, XCH4_EMMA, XCO2_OBS4MIPS, XCH4_OBS4MIPS (v4.5, CDR7, 2003-2022), C3S project 2021/C3S2_312a_Lot2_DLR/SC1, v7.1b.

[[7]](https://doi.org/10.24381/14j9-s541) Copernicus Climate Change Service (C3S) and World Meteorological Organization (WMO). (2025). European State of the Climate 2024. https://doi.org/10.24381/14j9-s541

[[8]](https://climate.copernicus.eu/global-climate-highlights-2024) Copernicus Climate Change Service (C3S). (2025). Global Climate Highlights 2024.


EQC Quality Assessment: "Methane satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q04  |  Category: Satellite_ECVs
Section: Methane satellite observations completeness assessment for greenhouse gas monitoring
---
Production date: 11-02-2026

Produced by: Davide Putero, Paolo Cristofanelli (CNR)

Please note that this assessment also refers to the deprecated Level 2 XCH$_4$ data products which won't be extended after 2022.

## 🌍 Use case: Using satellite column-averaged XCH$_4$ observations for investigating global and zonal methane growth rates

## ❓ Quality assessment question
* **Is the spatial coverage of XCH$_4$ EMMA Level 2 merged dataset suitable to calculate the methane annual global and zonal growth rates?**

Methane (CH$_4$) is the second most important anthropogenic greenhouse gas after carbon dioxide (CO$_2$), representing about 19% of the total radiative forcing by long-lived greenhouse gases [[1]](https://library.wmo.int/idurl/4/68532). Atmospheric CH$_4$ also adversely affects human health as a precursor of tropospheric ozone [[2]](https://doi.org/10.1073/pnas.0600201103). Monitoring the long-term CH$_4$ variability is therefore crucial for tracking the emission reductions [[3]](https://doi.org/10.5194/essd-17-1873-2025). Calculating growth rates is particularly important, as they provide a direct measure of how atmospheric CH$_4$ responds to changing emissions and sinks, and help disentangle interannual variability from long-term trends ([[4]](https://doi.org/10.5194/acp-24-577-2024), [[5]](https://doi.org/10.1038/s41558-023-01629-0), [[6]](https://doi.org/10.5194/amt-13-789-2020)).

In this assessment, atmospheric CH$_4$ global and zonal growth rates are derived from the XCH$_4$ merged Ensemble Median Algorithm (EMMA) Level 2 dataset (version 4.5), by adopting an approach similar to [[4]](https://doi.org/10.5194/acp-24-577-2024).

The EMMA Level 2 data product comprise individual soundings retrieved by algorithms that can change from grid box to grid box and from month to month. For more details see [[7]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf). By analyzing an ensemble of different Level 2 datasets from various retrieval algorithms, the EMMA algorithm generates a dataset containing XCH$_4$ from individual retrievals. In particular, for each month and 10°$\times$10° grid box, the algorithm with the grid box mean closest to the median is selected. The list of the considered retrieval algorithms is available from [[7]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf). The various retrieval algorithms are optimized for different instruments (SCIAMACHY, GOSAT, GOSAT-2), which measure backscattered solar radiation in the near-infrared for O$_2$ as well as the absorption bands of CO$_2$ or CH$_4$.

The code is included for transparency and learning purposes, giving users the chance to adapt it for their own analyses. Users should always refer to the official reports for scientific assessments related to atmospheric CH$_4$ variability (e.g., [[9]](https://doi.org/10.24381/14j9-s541), [[10]](https://climate.copernicus.eu/global-climate-highlights-2024)).

## 📢 Quality assessment statements

These are the key outcomes of this assessment


EQC Quality Assessment: "Methane satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q04  |  Category: Satellite_ECVs
Section: Methane satellite observations completeness assessment for greenhouse gas monitoring
---
541), [[10]](https://climate.copernicus.eu/global-climate-highlights-2024)).

## 📢 Quality assessment statements

These are the key outcomes of this assessment

* The dataset "Methane data from 2002 to present derived from satellite observations" (deprecated EMMA Level 2) can be used to evaluate XCH$_4$ global and zonal growth rates.
* For the high-latitude zonal bands ([70 °N-90 °N] and  [90 °S-70 °S]), the few available data prevent a solid quantification of annual growth rates. In addition, for the latitude bands [70 °S-50 °S] and [50 °N-70 °N], the monthly growth rates before 2009 should be treated with extreme caution, as they are affected by non-uniform data coverage and high uncertainties in the data.
* Before 2009, the global annual growth rates are characterised by higher standard deviations and thus must be considered with caution.
* The different averaging methods do not particularly affect the growth rate distributions and growth rate anomalies, both globally and zonally.
* As the EMMA Level 2 XCH$_4$ data product will not be updated in the future, users can use the Level 3 XCH4_OBS4MIPS and MTCH4_OBS4MIPS data products to investigate global and zonal methane growth rates.
```

## 📋 Methodology

In this notebook, we investigate whether the merged EMMA Level 2 product is suitable for calculating XCH$_4$ growth rates, over 2003-2022. The input data are the XCH$_4$ merged EMMA Level 2 files (version 4.5, see [[7]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)). For the calculations, and following the approach presented in [[4]](https://doi.org/10.5194/acp-24-577-2024), the merged EMMA Level 2 data were first regridded onto a daily regular 2°$\times$2° grid, using spatially weighted mean. For the years 2003 and 2022, the annual growth rates were calculated over six-month periods (June–December and January–June, respectively), as the calculation method required rolling operations over a 12-month time window.

In this analysis, to estimate XCH$_4$ global increases and zonal growth rates (in 20° latitude bands), we calculated the monthly growth rates as the centered rolling difference between the XCH$_4$ value of month $i$ and the XCH$_4$ value of month $i$-12, following the approach presented in [[11]](https://doi.org/10.5194/acp-18-17355-2018) and also further used in [[6]](https://doi.org/10.5194/amt-13-789-2020). As done in [[4]](https://doi.org/10.5194/acp-24-577-2024), we also compare two different methods for calculating monthly averages, i.e.:
* the "standard averaging", defined as the area-weighted mean of all grid cells in the region of interest;
* the "zonal-first averaging", which is computed by first evaluating the $m$ averages for all 2° latitude bands, and then averaging these $m$ zonal averages. $m$ can be equal to 90 for global data, or, e.g., 10 for a 20° zonal band.

The zonal-first average should ensure a consistent weighting of all the latitudes, regardless of their individual coverage ([[4]](https://doi.org/10.5194/acp-24-577-2024)).

For better investigating the differences between the hemispheres concerning growth rates, we also computed growth rate anomalies, defined as the difference between the zonal and the global growth rates.

The analysis and results are organized in the following steps, which are detailed in the sections below:

**[](template:section-1)**
 * Import all the relevant packages.
 * Choose the temporal coverage for the analysis.
 * Cache needed functions.

**[](template:section-2)**
 * In this section, we define the data request to CDS.

**[](template:section-3)**

This section presents several results for the XCH$_4$ growth rates, i.e.:
 * the calculation of global XCH$_4$ increases
 * the calculation of the zonal XCH$_4$ growth rates (into 20° latitude bands), to provide spatial information on the global CH$_4$ increases
 * the computation of the zonal growth rate anomalies, defined as the differences between zonal and global growth rates, for the time periods 2003-2022 and 2018-2022


EQC Quality Assessment: "Methane satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q04  |  Category: Satellite_ECVs
Section: Methane satellite observations completeness assessment for greenhouse gas monitoring
---
information on the global CH$_4$ increases
 * the computation of the zonal growth rate anomalies, defined as the differences between zonal and global growth rates, for the time periods 2003-2022 and 2018-2022

## 📈 Analysis and results

(template:section-1)=
### 1. Choose the data to use and set-up the code

#### Import all relevant packages
In this section, we import all the relevant packages needed for running the notebook and we define the style for plot appearance ("seaborn-v0_8-notebook").

#### Choose the temporal coverage
In this section, we define the temporal period of the analysis, that can be customized by the user. For this notebook, we selected the 2003-2022 period.

#### Cache needed functions
In this section, we cached a list of functions used in the analyses.

- The functions `spatial_weighted_mean` and `spatial_weighted_std`compute the spatial XCH$_4$ mean and standard deviation, respectively, over the selected domain. Through the function named `weight_dataset`, they use spatial weighting to account for the latitudinal dependence of the grid size in the lon/lat grids. This is a common approach, e.g., in the treatment of reanalysis and forecast model data.

- The function `regrid` regrids the XCH$_4$ Level 2 data onto a regular $m \times n$ grid, where $m$ and $n$ can be customized by the user. It uses `spatial_weighted_mean` for grouping data onto the regular grid.

- The function `daily_regrid` uses the aforementioned `regrid` function for regridding the XCH$_4$ data onto a regular $m$°$\times$$n$° grid, with a daily time resolution. Again, $m$ and $n$ can be customized by the user.

- The function `monthly_regrid_in_bands` resamples the input dataset into $n$° latitude bands, by using the `regrid` function and a monthly time resolution. $n$ is a parameter that can be specified by the user within the function. If the option `zonal_first` is selected, then the zonal-first averaging is calculated, following the methodology presented in [[4]](https://doi.org/10.5194/acp-24-577-2024) and described above.

- The function `compute_growth_rate` calculates the monthly resolved annual growth rates of XCH$_4$, computed as the centered rolling difference of the XCH$_4$ value of month $i$ minus the XCH$_4$ value of month $i$-12.

- The function `band_from_central_latitude` is used for explicitly specifying the latitude extremes rather than the central latitude for each band.

(template:section-2)=
### 2. Retrieve XCH$_4$ data (EMMA, Level 2)
In this section, we define the data request to CDS (merged EMMA data product, Level 2, version 4.5, XCH$_4$) and download the dataset.

```text
100%|██████████| 20/20 [00:19<00:00,  1.01it/s]
```

(template:section-3)=
### 3. Data analysis

#### Global annual XCH$_4$ increases
In this section, we present the global annual growth rates of XCH$_4$, defined as the average of all monthly growth rates within each calendar year. Please note that the annual growth rate for 2022 is obtained by averaging the available monthly values for January to June, as the data for the first six months of 2023 are not available. For the calculation of growth rates, two different averaging methods were applied to the regridded (onto a daily regular 2°$\times$2° grid) Level 2 data, following the methodology presented in [[4]](https://doi.org/10.5194/acp-24-577-2024). The "standard averaging" is defined as the spatially weighted average of all grid cells in a given region. This was compared to the so-called "zonal-first averaging", which is computed as follows: (i) first, the 90 averages for all 2° latitude bands are computed; then (ii) the average of these 90 zonal averages is calculated, yielding each monthly value of the global time series. As shown in [[4]](https://doi.org/10.5194/acp-24-577-2024), the zonal-first averaging has the advantage of taking into account the inhomogeneous sampling at each latitude, which is influenced by both the distribution of land mass and the seasonal coverage of measurements.


EQC Quality Assessment: "Methane satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q04  |  Category: Satellite_ECVs
Section: Methane satellite observations completeness assessment for greenhouse gas monitoring
---
p-24-577-2024), the zonal-first averaging has the advantage of taking into account the inhomogeneous sampling at each latitude, which is influenced by both the distribution of land mass and the seasonal coverage of measurements.

In agreement with recent studies (e.g., [[12]](https://doi.org/10.5194/acp-23-4863-2023)), the behavior of the global XCH$_4$ growth rate has shown a near-zero growth until 2007, followed by a clear acceleration in subsequent years due to increased emissions from wetlands in the tropics and from anthropogenic sources at the mid-latitudes of the Northern Hemisphere ([[1]](https://library.wmo.int/idurl/4/68532)), with maxima observed in the last three years of the time series. The highest global annual XCH$_4$ increases were recorded in 2021, followed by 2022 and 2020. According to [[12]](https://doi.org/10.5194/acp-23-4863-2023), most of the observed increase in atmospheric XCH$_4$ during 2020 and 2021 was due to increased emissions, with a significant contribution from reduced levels of OH. A recent study ([[13]](https://doi.org/10.5194/acp-25-6757-2025)) suggests a prominent role of biogenic emissions in Asian regions for the persistent high growth rate observed during 2020–2022. The results obtained using the two different averaging methods are comparable for all years, indicating that the choice of the averaging method does not significantly affect the estimated growth rates.

The global growth rates obtained by the EMMA data product are in very good agreement with those provided by the global network of in-situ observations coordinated by the Global Atmosphere Watch (GAW) programme by the World Meteorological Organization (WMO)[[1]](https://library.wmo.int/idurl/4/68532). Here, for comparison, we provided a plot of the WMO/GAW global growth rates as obtained by averaging the monthly values provided by the World Data Center for Greenhouse Gases (WDCGG)[[14]](https://gaw.kishou.go.jp/publications/global_mean_mole_fractions). The general variability of the annual CH$_4$ growth rates is generally consistent between the two datasets. The mean differences are 0.20 ppb/yr and 0.23 ppb/yr for the "standard" and "zonal-first" averaging methods, respectively. For the "standard averaging" method, only four years and for the "zonal-first averaging" method, only five years, reported differences exceeding ±2 ppb/yr. We should keep in mind that perfect agreement is not to be expected, as the EMMA product is column-averaged CH$_4$, whereas WMO/GAW used near-surface data.

In the XCH$_4$ plot, also uncertainties are reported, and these are computed as the annual standard deviation of all monthly growth rates within each calendar year. The most evident feature is the overall decrease in the uncertainties after 2009, likely due to the introduction of algorithms based on GOSAT observations used to calculate the median XCH$_4$ data [[8]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf).

*Global annual XCH$_4$ increases over 2003-2022. The colors differentiate between the standard averaging (orange) and the zonal-first averaging (blue) methods. Bars indicate the uncertainties in the annual increases, computed as $\pm$1 standard deviation. Please note that the annual growth rates in 2003 and 2022 were obtained by averaging only six monthly values (June-December and January-June, respectively).*

![GR_CH4_WDCGG_GLOBAL_REV.png](attachment:110aaca5-28c5-4237-aad0-4e987ef7c0e2.png)

*Global annual CH$_4$ increases over 2003-2022 as obtained by the global in-situ dataset provided by the World Data Center for Greenhouse Gases [[13]](https://doi.org/10.5194/acp-25-6757-2025). Please note that the annual growth rates in 2003 and 2022 were obtained by averaging only six monthly values (June-December and January-June, respectively). The error bars show the standard deviation of the monthly growth rate values.*

#### Zonal growth rates of XCH$_4$ (2003-2022)
In this section, we calculate and report the monthly growth rates of XCH$_4$, divided into 20° latitude bands, to provide spatial context for the global growth rates presented above.


EQC Quality Assessment: "Methane satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q04  |  Category: Satellite_ECVs
Section: Methane satellite observations completeness assessment for greenhouse gas monitoring
---
$_4$ (2003-2022)
In this section, we calculate and report the monthly growth rates of XCH$_4$, divided into 20° latitude bands, to provide spatial context for the global growth rates presented above.

The figure shows the zonal growth rates for the nine 20° latitude bands, using the two different averaging methods described previously. Please note that the limits on the y-axis can be customized by the user.

For most latitude bands, the behavior is similar to that observed for the global growth rates. The [70 °N-90 °N] band, however, is characterized by almost no clear tendency throughout the years, and this is because the high-latitude regions are characterized by sparse sampling and higher uncertainties in the data. Data below 60 °S are only available until 2005, and this is the cause for almost completely missing data in the [70 °S-90 °S] latitude band ([[7]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)). For the latitude bands [70 °S-50 °S] and [50 °N-70 °N], the monthly growth rates are characterised by a higher temporal variability before March 2009, as data over land is only available from the SCIAMACHY WFMD product in this period ([[7]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)).

The two different averaging methods show better agreement after 2009, with nearly identical results across all zonal bands. Before 2009, the growth rates exhibit higher variability, with larger differences between standard and zonal-first averaging. The improvement of the agreement between the two calculation methods, likely reflects the introduction, after 2009, of algorithms incorporating GOSAT observations to calculate median XCH$_4$ values [[8]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf). This highlights that caution should be exercised when using the EMMA data product to derive zonal growth rates prior to 2009 and that results are sensitive to the calculation method adopted.

*Zonal growth rates of XCH$_4$, over 2003-2022. The panels indicate the 20° latitude bands selected for the analysis. The colors differentiate between the standard averaging (orange) and the zonal-first averaging (blue) methods.*

#### Zonal growth rates of XCH$_4$ (2018-2022)
To better focus on the more recent years, characterised by the highest CH$_4$ growth rates, in the following we provide a zoom on the monthly growth rates of XCH$_4$ from 2018 to 2022.
Interestingly, as also reported in [[4]](https://doi.org/10.5194/acp-24-577-2024), the reduction in global XCH$_4$ growth in 2022 with respect to 2021 can be attributed to decreased growth rates in the Northern Hemisphere (particularly evident in the [10 °N-30 °N] and [30 °N-50 °N] latitude bands), while growth rates in the Southern Hemisphere remain high.

*Zonal growth rates of XCH$_4$, over 2018-2022. The panels indicate the 20° latitude bands selected for the analysis. The colors differentiate between the standard averaging (orange) and the zonal-first averaging (blue) methods.*

#### Zonal growth rate anomalies of XCH$_4$
In this section, we calculate and report the zonal growth rate anomalies, defined as the difference between the zonal and global growth rates for each of the 20° latitude bands presented above. As also indicated in [[4]](https://doi.org/10.5194/acp-24-577-2024), the zonal growth rate anomalies can help in better visualizing differences between the hemispheres. Moreover, the anomalies, together with the zonal growth rates, are important for better interpreting the changes in the global methane increases.

As already observed for the zonal growth rates presented above, the choice of the averaging method does not significantly affect the calculated anomalies.


EQC Quality Assessment: "Methane satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q04  |  Category: Satellite_ECVs
Section: Methane satellite observations completeness assessment for greenhouse gas monitoring
---
onal growth rates, are important for better interpreting the changes in the global methane increases.

As already observed for the zonal growth rates presented above, the choice of the averaging method does not significantly affect the calculated anomalies.

From the heatmaps of the anomalies, several features are consistent with previous studies (e.g., [[4]](https://doi.org/10.5194/acp-24-577-2024)), indicating that the EMMA Level 2 dataset can be used to evaluate the time evolution of CH$_4$ zonal growth rates. In 2019, a slowdown in growth rates was observed in the Southern Hemisphere, while growth rates in the Northern Hemisphere remained positive. This was followed by predominantly positive anomalies until 2022. In that year, as already noted for the zonal growth rates, the Northern Hemisphere showed a marked slowdown in XCH$_4$ increases, as reflected by the negative anomalies.

*Zonal growth rate anomalies of XCH$_4$, for the 20° latitude bands presented above, over 2003-2022. The anomalies are defined as the differences between zonal and global growth rates. The top panel is for the standard averaging, while the bottom panel is for the zonal-first averaging.*

## ℹ If you want to know more

### Key resources

The CDS catalogue entries for the data used were:
* Methane data from 2002 to present derived from satellite observations: https://cds.climate.copernicus.eu/datasets/satellite-methane?tab=overview

Users interested in near-real time detection of hot-spot locations for methane emissions can consider to use the CAMS Methane Hotspot Explorer: https://atmosphere.copernicus.eu/ghg-services/cams-methane-hotspot-explorer?utm_source=press&utm_medium=referral&utm_campaign=CH4-app-2025

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/)

### References
[[1]](https://library.wmo.int/idurl/4/68532) World Meteorological Organization (2023). WMO Greenhouse Gas Bulletin, No. 19, ISSN 2078-0796.

[[2]](https://doi.org/10.1073/pnas.0600201103) West, J. J., Fiore, A. M., Horowitz, L. W., and Mauzerall, D. L. (2006). Global health benefits of mitigating ozone pollution with methane emission controls, Proceedings of the National Academy of Sciences USA, 103, 3988–3993.


EQC Quality Assessment: "Methane satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q04  |  Category: Satellite_ECVs
Section: Methane satellite observations completeness assessment for greenhouse gas monitoring
---
ating ozone pollution with methane emission controls, Proceedings of the National Academy of Sciences USA, 103, 3988–3993.

[[3]](https://doi.org/10.5194/essd-17-1873-2025) Saunois, M., Martinez, A., Poulter, B., Zhang, Z., Raymond, P. A., Regnier, P., Canadell, J. G., Jackson, R. B., Patra, P. K., Bousquet, P., Ciais, P., Dlugokencky, E. J., Lan, X., Allen, G. H., Bastviken, D., Beerling, D. J., Belikov, D. A., Blake, D. R., Castaldi, S., Crippa, M., Deemer, B. R., Dennison, F., Etiope, G., Gedney, N., Höglund-Isaksson, L., Holgerson, M. A., Hopcroft, P. O., Hugelius, G., Ito, A., Jain, A. K., Janardanan, R., Johnson, M. S., Kleinen, T., Krummel, P. B., Lauerwald, R., Li, T., Liu, X., McDonald, K. C., Melton, J. R., Mühle, J., Müller, J., Murguia-Flores, F., Niwa, Y., Noce, S., Pan, S., Parker, R. J., Peng, C., Ramonet, M., Riley, W. J., Rocher-Ros, G., Rosentreter, J. A., Sasakawa, M., Segers, A., Smith, S. J., Stanley, E. H., Thanwerdas, J., Tian, H., Tsuruta, A., Tubiello, F. N., Weber, T. S., van der Werf, G. R., Worthy, D. E. J., Xi, Y., Yoshida, Y., Zhang, W., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q. (2025). Global Methane Budget 2000–2020, Earth System Science Data, 17, 1873-1958.

[[4]](https://doi.org/10.5194/acp-24-577-2024) Hachmeister, J., Schneising, O., Buchwitz, M., Burrows, J. P., Notholt, J., and Buschmann, M. (2024). Zonal variability of methane trends derived from satellite data, Atmospheric Chemistry and Physics, 24, 577–595.

[[5]](https://doi.org/10.1038/s41558-023-01629-0) Zhang, Z., Poulter, B., Feldman, A.F., Ying, Q., Ciais, P., Peng, S., and Li, X. (2023). Recent intensification of wetland methane feedback, Nature Climate Change, 13, 430–433.

[[6]](https://doi.org/10.5194/amt-13-789-2020) Reuter, M., Buchwitz, M., Schneising, O., Noël, S., Bovensmann, H., Burrows, J. P., Boesch, H., Di Noia, A., Anand, J., Parker, R. J., Somkuti, P., Wu, L., Hasekamp, O. P., Aben, I., Kuze, A., Suto, H., Shiomi, K., Yoshida, Y., Morino, I., Crisp, D., O'Dell, C. W., Notholt, J., Petri, C., Warneke, T., Velazco, V. A., Deutscher, N. M., Griffith, D. W. T., Kivi, R., Pollard, D. F., Hase, F., Sussmann, R., Te, Y. V., Strong, K., Roche, S., Sha, M. K., De Maziere, M., Feist, D. G., Iraci, L. T., Roehl, C. M., Retscher, C., and  Schepers, D. (2020). Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003-2018) for carbon and climate applications, Atmospheric Measurement Techniques, 13, 789-819.


EQC Quality Assessment: "Methane satellite observations completeness assessment for greenhouse gas monitoring"
Dataset: satellite-methane [CDS]
Aspect: completeness_q04  |  Category: Satellite_ECVs
Section: Methane satellite observations completeness assessment for greenhouse gas monitoring
---
(2020). Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003-2018) for carbon and climate applications, Atmospheric Measurement Techniques, 13, 789-819.

[[7]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf) Buchwitz, M. (2024). Product User Guide and Specification (PUGS) – Main document for Greenhouse Gas (GHG: CO$_2$ & CH$_4$) data set CDR7 (01.2003-12.2022), C3S project 2021/C3S2_312a_Lot2_DLR/SC1, v7.3.

[[8]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_D_latest.pdf) Reuter, M., and Buchwitz, M. (2024). Algorithm Theoretical Basis Document (ATBD) – ANNEX D for products XCO2_EMMA, XCH4_EMMA, XCO2_OBS4MIPS, XCH4_OBS4MIPS (v4.5, CDR7, 2003-2022), C3S project 2021/C3S2_312a_Lot2_DLR/SC1, v7.1b.

[[9]](https://doi.org/10.24381/14j9-s541) Copernicus Climate Change Service (C3S) and World Meteorological Organization (WMO). (2025). European State of the Climate 2024.

[[10]](https://climate.copernicus.eu/global-climate-highlights-2024) Copernicus Climate Change Service (C3S). (2025). Global Climate Highlights 2024.

[[11]](https://doi.org/10.5194/acp-18-17355-2018) Buchwitz, M., Reuter, M., Schneising, O., Noël, S., Gier, B., Bovensmann, H., Burrows, J. P., Boesch, H., Anand, J., Parker, R. J., Somkuti, P., Detmers, R. G., Hasekamp, O. P., Aben, I., Butz, A., Kuze, A., Suto, H., Yoshida, Y., Crisp, D., and O'Dell, C. (2018). Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003-2016, Atmospheric Chemistry and Physics, 18, 17355-17370.

[[12]](https://doi.org/10.5194/acp-23-4863-2023) Feng, L., Palmer, P. I., Parker, R. J., Lunt, M. F., and Bösch, H. (2023). Methane emissions are predominantly responsible for record-breaking atmospheric methane growth rates in 2020 and 2021, Atmospheric Chemistry and Physics, 23, 4863-4880.

[[13]](https://doi.org/10.5194/acp-25-6757-2025) Niwa, Y., Tohjima, Y., Terao, Y., Saeki, T., Ito, A., Umezawa, T., Yamada, K., Sasakawa, M., Machida, T., Nakaoka, S.-I., Nara, H., Tanimoto, H., Mukai, H., Yoshida, Y., Morimoto, S., Takatsuji, S., Tsuboi, K., Sawa, Y., Matsueda, H., Ishijima, K., Fujita, R., Goto, D., Lan, X., Schuldt, K., Heliasz, M., Biermann, T., Chmura, L., Necki, J., Xueref-Remy, I., and Sferlazzo, D. (2025). Multi-observational estimation of regional and sectoral emission contributions to the persistent high growth rate of atmospheric CH$_4$ for 2020–2022, Atmospheric Chemistry and Physics, 25, 6757–6785.

[[14]](https://gaw.kishou.go.jp/publications/global_mean_mole_fractions) World Meteorological Organization (2025). WMO Greenhouse Gas Bulletin, No. 21, 2025 (web site, last access: 16 January 2026).


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > Quality assessment question
---
* **Are methane satellite observations suitable for detecting atmospheric signals associated with extreme emission releases?**
* **How well, in terms of completeness, do Level 2 mid-tropospheric CH$_4$ columns capture the spatial variability of methane associated with large release events?**

Methane (CH$_4$) is the second most important anthropogenic greenhouse gas after carbon dioxide (CO$_2$), representing about 19% of the total radiative forcing by long-lived greenhouse gases [[1]](https://wmo.int/publication-series/wmo-greenhouse-gas-bulletin-no-19). Atmospheric CH$_4$ also adversely affects human health as a precursor of tropospheric ozone [[2]](https://doi.org/10.1073/pnas.0600201103). Thus, sudden large release of CH$_4$ into the atmosphere could have significant consequences in terms of climate change and health.

On 26 September 2022, multiple gas leaks were detected from the Nord Stream, an offshore submerged pipeline system that carries natural gas from Russian facilities into Western Europe ([[3]](https://doi.org/10.5194/acp-24-10639-2024), [[4]](https://www.unep.org/news-and-stories/story/pipeline-blasts-released-record-shattering-amount-methane-unep-study)).

In this application, the Level-2 mid-tropospheric CH$_4$ columns from the IASI instruments (dataset version 10.2) is evaluated. A Level 2 dataset corresponds to data from a specific combination of satellite sensor and retrivial algorithm for each individual satellite footprint along the orbit tracks (see [[5]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)).

Although the CH$_4$ satellite observations provided by the Climate Data Store are not specifically designed to identify sudden large release events, this application explores the possibility that mid-tropospheric satellite IASI CH$_4$ (v10.2) data can be used to identify the atmospheric signal of emissions as those associated with the Nord Stream event.


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > Quality assessment statement
---
These are the key outcomes of this assessment

* The IASI CH$_4$ (v10.2) mid-tropospheric product was only able to partially detect the atmospheric signal of massive CH$_4$ release associated with the Nord Stream leak on September 2022.
* In terms of completeness, the ability of IASI CH$_4$ (v10.2) mid-tropospheric product to detect the atmospheric signal of the CH$_4$ plume was mostly limited by the cloud coverage and users need to be aware about the strict cloud screening adopted for the production of this dataset.
* As the vertical sensitivity of CH$_4$ can affect the quantification of the atmospheric CH$_4$ signal associated with surface releases, users must be aware of the vertical sensitivity of the product. They should consult the the Documentation (e.g., [[7]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_E_latest.pdf)) for information about the altitude sensitivity of the retrieved CH$_4$.
* The use of this data product to investigate CH$_4$ fluxes occurring at the Earth's surface is recommended in conjunction with appropriate (inversion) modelling.
* Since this dataset is updated on a yearly basis, users interested in near real time applications should consider other Copernicus resources (e.g., [[11]](https://atmosphere.copernicus.eu/ghg-services/cams-methane-hotspot-explorer?utm_source=press&utm_medium=referral&utm_campaign=CH4-app-2025)).
```

![Figure1.jpg](attachment:c54ae6d5-d20f-46bf-b1c3-0cfa38b58afb.jpg)

*The figure shows mid-tropospheric IASI column average CH$_4$ (ppb) for 26–28 September 2022 as provided by [[3]](https://doi.org/10.5194/acp-24-10639-2024) under CC-BY license. Turquoise boxes show regions characterised by the presence of the methane plume. AM (PM) indicate the morning (evening) satellite overpasses of each day*


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > Methodology
---
This notebook has three main objectives:
* To compare the spatial fields of mid-tropospheric column-averaged methane (CH$_4$) mixing ratios from IASI sensors with independent analyses [[3]](https://doi.org/10.5194/acp-24-10639-2024) over the geographical region and time frame (26-28 September 2022) affected by the Nord Stream emissions.
* To attribute the observed differences as a function of the cloud cover over the observation scene and the adopted averaging kernels (describing the vertical sensitivity of the retrieved products).
* To guide the user in the correct use of the evaluated data sets.

The analysis methodology is divided into the following steps:

**[](section-1)**
 * Define the libraries to be used.
 * Define notebook setup parameters and data requests.
 * Define the functions used in the analysis.

**[](section-2)**
 * Download mid-tropospheric CH$_4$ and cloud cover data for the time period and spatial region of interest.

**[](section-3)**
 * Spatial and temporal analysis of mid-tropospheric CH$_4$.
 * Spatial and temporal analysis of cloud cover.
 * Analysis of the altitude sensitivity of the retrieved mid-tropospheric CH$_4$.
 * Temporal variability of mid-tropospheric CH4 over the region.

Note that prior to the analyses, the original Level 2 mid-tropospheric CH$_4$ data were regridded to a regular 1° x 1° grid and the cloud cover data were retrieved from the Climate Data Store dataset [[6]](https://cds.climate.copernicus.eu/datasets/satellite-cloud-properties?tab=overview) "Cloud properties global gridded monthly and daily data from 1979 to present derived from satellite observations" (CLARA-A3).


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > Analysis and results > 1. Choose the data to use and setup the code > Define notebook setting parameters and data request
---
In this step, we define:
* the list of sensors to be used for getting methane data.
* the region of interest.
* the variables to be retrieved by the datasets.
* the time frame of data to be analysed.
* the range of legend variability to plot CH4 values in the spatial map.
* the list of Climate Data Store datasets to be downloaded.

Region to plot
Variable to plot
List of days to be considered (format 'YYYY-MM-DD')
To define the range of map legend


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > Analysis and results > 1. Choose the data to use and setup the code > Define functions used in the analysis
---
In this step we define the functions to be used in the analysis:
* Function to regrid the data to a regular spatial grid.
* Function to extract mean kernel values and associated pressure levels from the mid-tropospheric IASI CH4 (v10.2) dataset and perform spatial aggregation.

(section-2)=


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > Analysis and results > 2. Data retrieval > Download and transform data
---
In this step, mid-tropospheric IASI CH4 (v10.2) and cloud cover data (CLARA-A3) are downloaded from the Climate Data Store; functions are applied to the datasets.

```text
sensor = 'iasi_metop_b_nlis'
```

```text
100%|██████████| 1/1 [00:00<00:00,  1.63it/s]
```

```text
sensor = 'iasi_metop_c_nlis'
```

```text
100%|██████████| 1/1 [00:00<00:00,  5.96it/s]
```

```text
cloud cover
```

```text
100%|██████████| 1/1 [00:00<00:00,  1.60it/s]
```

(section-3)=


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > Analysis and results > 3. Analyses and plotting > Spatial and temporal analysis of mid-tropospheric CH4
---
Here we have plotted the mid-tropospheric CH$_4$ data from IASI-B and IASI-C in the spatial domain and temporal framework defined by [[3]](https://doi.org/10.5194/acp-24-10639-2024).
As described by [[3]](https://doi.org/10.5194/acp-24-10639-2024), the CH$_4$ released from the Nordstream pipeline moved from the Baltic Sea northwards and eastwards from the point of emission during the first day, while during the following two days it was transported westwards across the Scandinavian peninsula and the North Sea.

The area of high CH$_4$ values identified by [[3]](https://doi.org/10.5194/acp-24-10639-2024) as affected by the Nord Stream release over [0°-5° E, 60° - 65°N] is well visible in the IASI-C data on 28 September 2022 and partially detected by IASI-B over the western Scandinavian coasts. However, despite [[3]](https://doi.org/10.5194/acp-24-10639-2024), the Climate Data Store mid-tropospheric CH$_4$ data from the IASI-B and IASI-C were not able to detect the Nord Stream plume over the Baltic Sea on 26 September 2022 and west of 0°E on 28 September 2022. This is related to the fact that the Climate Data Store mid-tropospheric CH$_4$ columns have a lower spatial occurrence of valid data, due to the different algorithm and quality control screening applied in the dataset production.

To plot CH4 data for sensors and selected days
Select days of interest

*The figure shows mid-tropospheric CH$_4$ (expressed in ppb) for 26-28 September 2022 as provided by the IASI-B (upper plots) and IASI-C (lower plots) datasets (v10.2).*


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > Analysis and results > 3. Analyses and plotting > Spatial and temporal analysis of cloud cover
---
To attribute differences in the data availability between mid-tropospheric CH$_4$ data from Climate Data Store and that reported by [[3]](https://doi.org/10.5194/acp-24-10639-2024), we inspected the daily values of cloud cover fraction provided by the Interim Climate Data Record [[6]](https://cds.climate.copernicus.eu/datasets/satellite-cloud-properties?tab=overview) "Cloud properties global gridded monthly and daily data from 1979 to present derived from satellite observations" (product family: CLARA-A3).

It is clear that the the presence of high values of cloud cover fraction over the most part of the observation scene prevented the detection of the Nord Stream plume on 26 -  27 September 202 by the Climate Data Store IASI mid-tropospheric CH$_4$ dataset. According to the Documentation [[7]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_E_latest.pdf), the Climate Data Store mid-tropospheric satellite IASI CH4 (v10.2) dataset is retrieved when no cloud or aerosol is detected. On the other hand, for the dataset used by [[3]](https://doi.org/10.5194/acp-24-10639-2024), only scenes strongly affected by clouds are not processed [[8]](https://doi.org/10.5281/zenodo.5873645).

Filter data for selected days
Setting plot view
Plotting cloud cover data

*The figure shows the total cloud coverage fraction (espressed as %) for 26-28 September 2022 as provided by the Climate Data Store Interim Climate Data Record “Cloud properties global gridded monthly and daily data from 1979 to present derived from satellite observations” (product family: CLARA-A3)*


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > Analysis and results > 3. Analyses and plotting > Analysis of the altitude sensitivity of the retrieved mid-tropospheric CH4
---
The vertical sensitivity of the IASI CH$_4$ mid-tropospheric product may affect the identification and the quantification of the plume from the Nord Stream pipelines. The averaging kernel (AK), which is provided as variable in the Climate Data Store data, accounts for the vertical sensitivity of the derived CH$_4$ values from the IASI retrievals, as described in [[7]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_E_latest.pdf).

For the Climate Data Store mid-tropospheric CH$_4$ product, the normalised column AK values averaged over the spatial domain showed a maximum sensitivity in the mid-upper troposphere (from 100 to 600 hPa) during the time period considered. According to [[3]] (https://doi.org/10.5194/acp-24-10639-2024), the southern part of the Nord Stream plume [59-63° N; 0-7° E], extending from the west coast of Scandinavia, was diffuse above 500 hPa, favouring the plume detection by the Climate Data Store mid-tropospheric CH$_4$ IASI data products.

Users interested in further information on the use of the AKs (as an example for comparison with model simulations) are encouraged to consult the dataset documentation [[9]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_E_latest.pdf) and [[5]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf).

Setting plot view

*IASI-B (top plot) and IASI-C (bottom plot) for CH$_4$ normalised column averaging kernel computed over the analysed region on 28 September 2022.*


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > Analysis and results > 3. Analyses and plotting > Temporal variability of mid-tropospheric CH$_4$ over the region
---
As clearly stated in the dataset Documentation [[5]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf), the dataset "Methane data from 2002 to present derived from satellite observations" should be used them in combination with appropriate modelling to obtain information on surface CH$_4$ fluxes.

To illustrate that the use of this dataset without appropriate modelling can lead to misleading conclusions, we analysed the temporal variability of the mid-tropospheric CH$_4$ column over the study region. In particular, we calculated the distribution of CH$_4$ values for each single day during September 2022 for both IASI-B and IASI-C. The box and wiskers plot shows the percentiles of the data distribution, the median (green line) as well as the outliers.
It can be seen that despite the occurrence of the Nord Stream leak and the effective detection of the plume signal by IASI-C on 29 September 2022, the CH$_4$ values did not deviate from the monthly variability. This shows that even if the atmospheric data provided by the mid-tropospheric IASI CH$_4$ (v10.2) dataset contain the signal of the CH$_4$ release from the Nord Stream pipelines, it can hardly be detected without the use of appropriate modelling, as adopted by [[3]](https://doi.org/10.5194/acp-24-10639-2024) or [[10]](https://climate.copernicus.eu/esotc/2023/greenhouse-gas-fluxes).

*Daily percentiles and outliers for mid-tropospheric CH4 (expressed in ppb) during September 2022 as provided by the IASI-B (upper plots) and IASI-C (lower plots) datasets (v10.2).*


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > ℹ If you want to know more > Key resources
---
The CDS catalogue entries for the data used were:
* Methane data from 2002 to present derived from satellite observations: https://cds.climate.copernicus.eu/datasets/satellite-methane?tab=overview
* Cloud properties global gridded monthly and daily data from 1979 to present derived from satellite observations: https://cds.climate.copernicus.eu/datasets/satellite-cloud-properties?tab=overview

More information on the Nord Stream leak event can be found in [[4]](https://www.unep.org/news-and-stories/story/pipeline-blasts-released-record-shattering-amount-methane-unep-study).

Users interested in near-ral time detection of hot-spot locations for methane emissions can consider to use the CAMS Methane Hotspot Explorer [[11]](https://atmosphere.copernicus.eu/ghg-services/cams-methane-hotspot-explorer?utm_source=press&utm_medium=referral&utm_campaign=CH4-app-2025).

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/)

https://www.unep.org/news-and-stories/story/pipeline-blasts-released-record-shattering-amount-methane-unep-study


EQC Quality Assessment: "Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases"
Dataset: satellite-methane [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases > ℹ If you want to know more > References
---
[[1]](https://wmo.int/publication-series/wmo-greenhouse-gas-bulletin-no-19) World Meteorological Organization (2023). WMO Greenhouse Gas Bulletin, No. 19, ISSN 2078-0796.

[[2]](https://doi.org/10.1073/pnas.0600201103) West, J. J., Fiore, A. M., Horowitz, L. W., and Mauzerall, D. L. (2006). Global health benefits of mitigating ozone pollution with methane emission controls. P. Natl. Acad. Sci. USA, 103, 3988–3993.

[[3]](https://doi.org/10.5194/acp-24-10639-2024) Wilson, C., Kerridge, B. J., Siddans, R., Moore, D. P., Ventress, L. J., Dowd, E., Feng, W., Chipperfield, M. P., and Remedios, J. J. (2024). Quantifying large methane emissions from the Nord Stream pipeline gas leak of September 2022 using IASI satellite observations and inverse modelling. Atmos. Chem. Phys., 24, 10639–10653.

[[4]](https://www.unep.org/news-and-stories/story/pipeline-blasts-released-record-shattering-amount-methane-unep-study) United Nations Environmental Programme (UNEP), (2025). Pipeline blasts released record-shattering amount of methane: UNEP study. https://www.unep.org/news-and-stories/story/pipeline-blasts-released-record-shattering-amount-methane-unep-study

[[5]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf) Buchwitz, M. (2024). Product User Guide and Specification (PUGS) – Main document for Greenhouse Gas (GHG: CO2 & CH4) data set CDR7 (01.2003-12.2022).

[[6]](https://cds.climate.copernicus.eu/datasets/satellite-cloud-properties?tab=overview) Copernicus Climate Change Service (C3S), Climate Data Store (CDS). (2022). Cloud properties global gridded monthly and daily data from 1982 to present derived from satellite observations. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.68653055 (Accessed on 19-Mar-2025).

[[7]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_ATBD_GHG_E_latest.pdf) Crevoisier, C. (2024). Algorithm Theoretical Basis Document (ATBD) – ANNEX E for IASI CO2 (v10.1) and CH4 (v10.2) and AIRS CO2 mid-tropospheric products.

[[8]](https://doi.org/10.5281/zenodo.5873645). Ventress, L., Siddans, R., Knappett, D. (2021). RAL IASI Methane Retrieval (ATDB).

[[9]](https://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_E_latest.pdf) Crevoisier, C. (2023). Product User Guide and Specification (PUGS) – ANNEX E for IASI CO2 (10.1) and CH4 (v10.2) and AIRS CO2 mid-tropospheric products.

[[10]](https://climate.copernicus.eu/esotc/2023/greenhouse-gas-fluxes) Copernicus Climate Change Service (C3S). (2024). European State of the Climate 2023, Web site.

[[11]](https://atmosphere.copernicus.eu/ghg-services/cams-methane-hotspot-explorer?utm_source=press&utm_medium=referral&utm_campaign=CH4-app-2025) Copernicus Atmosphere Monitoring Service (CAMS). (2025). Copernicus: Tool to routinely identify and track methane emissions and leaks goes operational, Web site.


EQC Quality Assessment: "Methane satellite observations uncertainty and completeness assessment for carbon cycle"
Dataset: satellite-methane [CDS]
Aspect: uncertainty_q01  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty and completeness assessment for carbon cycle
---
Production date: 02-09-2024

Produced by: Consiglio Nazionale delle Ricerche ([CNR](https://www.cnr.it/en))


EQC Quality Assessment: "Methane satellite observations uncertainty and completeness assessment for carbon cycle"
Dataset: satellite-methane [CDS]
Aspect: uncertainty_q01  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty and completeness assessment for carbon cycle > Quality assessment questions
---
* **Is the uncertainty of satellite-based observations sufficiently low to assess year-to-year anomalies in methane emissions of wetlands?**
* **Is the spatial and temporal resolution/coverage of satellite-based observations sufficient to assess year-to-year anomalies in methane emissions of wetlands?**

Methane (CH4) is the second most important anthropogenic greenhouse gas, representing about 19% of the total radiative forcing by long living greenhouse gases [[1]](https://library.wmo.int/viewer/68532/?offset=#page=4&viewer=picture&o=bookmarks&n=0&q=).  Natural wetlands account for up to 30% of global methane (CH4) emissions, but a large uncertainty (up to 65%) still affects tropical wetland CH4 emission estimates [[2]](https://doi.org/10.5194/essd-12-1561-2020). The positive response of wetland CH4 emissions to climate change is an important feedback that can amplify atmospheric CH4 values. This response can be related to the effect of rising temperatures on microbial activities (e.g., methanogenesis) and to the expansion of wetlands with increased total precipitations. Intensified wetland CH4 emissions have been reported during 2000–2021 ([[3]](https://doi.org/10.1038/s41558-023-01629-0)), highlighting the need for sustained monitoring and observations of global wetland CH4 fluxes. Moreover, future projections of wetland CH4 emissions suggested sustained emission increase under different climate change scenarios ([[3]](https://doi.org/10.1038/s41558-023-01629-0) and [[4]](https://doi.org/10.1126/sciadv.aay4444)). Satellite observations can represent a powerful tool for contributing to enhance the knowledge about CH4 sources and sinks. While these data are commonly used in inverse modelling system (e.g., [[5]](https://ads.atmosphere.copernicus.eu/datasets/cams-global-greenhouse-gas-inversion?tab=overview)), this assessment explores the possibility to directly use satellite CH4 data for investigate year-to-year anomalies in CH4 emissions of tropical wetlands.

attachment:2b185ba0-efe5-4f85-9b1f-19651082b442.png
---
height: 500px
---
The figure shows temporal trends and variations in wetland CH4 emissions during 2000–2021 relative to the baseline of 2000–2006 level in comparison to future projections. Black lines report variations over 2000-2021 as derived from simulations based on two climate forcing datasets: a ground-based dataset from the CRU at the University of East Anglia (continuous line) and a reanalysis-based MERRA2 (dashed line). Coloured lines represent the ensemble mean of each future projection RCP scenario, while shaded areas represent the 1σ range of the future estimates. **The estimated increase in emissions over 2000-2021 appears to be higher than under any of the RCP scenarios.** Image reproduced from [[3]](https://doi.org/10.1038/s41558-023-01629-0) under CC-BY license.
```


EQC Quality Assessment: "Methane satellite observations uncertainty and completeness assessment for carbon cycle"
Dataset: satellite-methane [CDS]
Aspect: uncertainty_q01  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty and completeness assessment for carbon cycle > Quality assessment statement
---
These are the key outcomes of this assessment

* In terms of uncertainty, Level 2 XCH4 data products appear appropriate for investigating inter-annual XCH4 variability associated with anomalies in tropical wetland emissions
* In terms of spatial/temporal resolution, Level 2 XCH4 data products appear appropriate for investigating inter-annual XCH4 variability associated with anomalies in tropical wetland emissions
* In terms of spatial/temporal coverage, users would use data products derived by algorithms (such as CH4_GOS_OCPR, CH4_GO2_SRPR or XCH4_EMMA) that optimise data availability over regions frequently affected by cloud cover or high aerosol loading.
* Users should consult the documentation as well as the uncertainty and quality flags in the data files as appropriate for their applications. It should also be emphasised that the use of the XCH4 data provided by satellite is not trivial, and interpretation of these products typically requires appropriate modelling [[6]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf).
* Users will need to consult product user guides ([[6]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)) for guidance on how to use the averaging kernels when using satellite CH4 data, especially for comparison with model outputs

## 📋 Methodology

To assess if the uncertainty, spatial/temporal resolution and coverage of satellite-based observations are sufficient to investigate year-to-year anomalies in CH4 emissions of tropical wetlands, we inspected the documentation of the CDS dataset “Methane data from 2002 to present derived from satellite observations” ([[7]](https://cds.climate.copernicus.eu/datasets/satellite-methane?tab=overview), [[6]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf), [[8]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PQAR_GHG_main_latest.pdf)) as well as a case study available from scientific literature ([[9]](https://doi.org/10.1016/j.rse.2018.02.011)). Please note that to analyse annual anomalies in wetland methane emissions, [[9]](https://doi.org/10.1016/j.rse.2018.02.011) used an earlier version (v7) of Level 2 data from the CH4_GOS_OCPR data product.

attachment:88fcf479-6fcf-4d60-834f-18e8ca2c2bb4.png
---
height: 500px
---
Map showing the location of the individual wetland regions identified using the Sustainable Wetlands Adaptation and Mitigation Program (SWAMP) data from the Center for International Forestry Research (CIFOR) and timeseries of detrended XCH4 seasonal cycle over the Pantanal region, including the CH4_GOS_OCFP dataset (“GOSAT”) and model simulations utilising the different wetland emission. Image reproduced from [[9]](https://doi.org/10.1016/j.rse.2018.02.011) under CC-BY license.
```

The analysis and results are organised in the following steps, which are detailed in the sections below:

**[](template:section-1)**
 * We inspected the uncertainty related to the data product used by [[9]](https://doi.org/10.1016/j.rse.2018.02.011) and we compared with that reported by the product quality assessment report related to "Methane data from 2002 to present derived from satellite observations" ([[8]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PQAR_GHG_main_latest.pdf)).

**[](template:section-2)**
 * We inspected the spatial and temporal resolution of the data product used by [[9]](https://doi.org/10.1016/j.rse.2018.02.011) and we compared with that reported by the Level 2 of the dataset "Methane data from 2002 to present derived from satellite observations" ([[6]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)).


EQC Quality Assessment: "Methane satellite observations uncertainty and completeness assessment for carbon cycle"
Dataset: satellite-methane [CDS]
Aspect: uncertainty_q01  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty and completeness assessment for carbon cycle > Quality assessment statement
---
to present derived from satellite observations" ([[6]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)).

**[](template:section-3)**
 * [[9]](https://doi.org/10.1016/j.rse.2018.02.011) used a 'proxy (PR)' data product (CH4_GOS_OCPR) to take advantage of the higher data availability compared to the 'full physics (FP)' product (CH4_GOS_OCFP) in regions such as the tropics, which are characterised by the prevalence of clouds and high aerosol loading. PR products typically contain more data points and better coverage compared to FP: PR products suffer less from potential biases and therefore require less strict quality filtering resulting in more data points with quality flag “good” in the final product files ([[6]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)).

## 📈 Analysis and results

(template:section-1)=
### 1. Uncertainty assessment

The data product used by [[9]](https://doi.org/10.1016/j.rse.2018.02.011) was characterised by a random error of 13 ppb and a relative spatial bias of 2 ppb with respect to TCCON co-located observations ([[10]](https://doi.org/10.5194/acp-21-4339-2021)). According with [[8]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PQAR_GHG_main_latest.pdf), the **Level 2 XCH4 products** of the dataset "Methane data from 2002 to present derived from satellite observations" have **uncertainties comparable to those of the dataset used by [[9]](https://doi.org/10.1016/j.rse.2018.02.011)**: individual measurement accuracies are typically less than 20 ppb (13 ppb for the latest version of CH4_GOS_OCPR), while the relative spatial bias is less than 6.2 ppb (4 ppb for CH4_GOS_OCPR). Moreover, **except for the SCIAMACHY XCH4 products**, the single measurements random errors are close to the "breakthrough" requirement (< 17 ppb) defined by the  Climate Research Group (CRG) of the ESA GHG-CCI project for the usability of satellite measurements to derive information on regional scale sources and sink of methane [[11]](https://www.iup.uni-bremen.de/carbon_ghg/docs/GHG-CCIplus/URD/URDv3.0_GHG-CCIp_Final.pdf).

Thus, **the Level 2 XCH4 products of the dataset "Methane data from 2002 to present derived from satellite observations" have uncertainties sufficiently low to assess year-to-year anomalies in XCH4 related to wetland emissions in tropical regions.**

(template:section-2)=
### 2. Spatial resolution assessment

The Level 2 XCH4 products of the dataset "Methane data from 2002 to present derived from satellite observations" are an updated version of the dataset used by [[10]](https://doi.org/10.5194/acp-21-4339-2021) and **they have a spatial resolution characterised by the native satellite footprint** (i.e., with a 10 km diameter and single observations typically on the order of 100 km apart for GOSAT and GOSAT-2).

Thus, **the Level 2 XCH4 products of the dataset "Methane data from 2002 to present derived from satellite observations" have spatial resolution sufficiently high to assess year-to-year anomalies in XCH4 related to wetland emissions in tropical regions.**

(template:section-3)=
### 3. Spatial and temporal coverage

The product user guide related to "Methane data from 2002 to present derived from satellite observations" ([[6]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf)) was inspected. In terms of spatial/temporal coverage users would use data products derived by algorithms (such as CH4_GOS_OCPR, CH4_GO2_SRPR or XCH4_EMMA) that **optimise data availability over regions frequently affected by cloud cover or high aerosol loading**. The temporal coverage of algorithms using GOSAT and GOSAT-2 observations (as done by [[9]](https://doi.org/10.1016/j.rse.2018.02.011)) ranges from 2009 to 2022.


EQC Quality Assessment: "Methane satellite observations uncertainty and completeness assessment for carbon cycle"
Dataset: satellite-methane [CDS]
Aspect: uncertainty_q01  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty and completeness assessment for carbon cycle > Quality assessment statement
---
algorithms using GOSAT and GOSAT-2 observations (as done by [[9]](https://doi.org/10.1016/j.rse.2018.02.011)) ranges from 2009 to 2022.

Thus, **the level 2 XCH4 products of the dataset "Methane data from 2002 to present derived from satellite observations" have sufficient spatial and temporal coverage to assess year-to-year anomalies in XCH4 related to wetland emissions in tropical regions.** In any case, users should consult the documentation as well as the uncertainty and quality flags in the data files as appropriate for their applications.

attachment:d0fa8c88-b432-41af-8f69-37e786fcb898.png
---
height: 500px
---
Figure showing a comparison between FP and PR Level 2 XCH4 products from the University of Leicester GOSAT products from the CDS for January to June 2021 (top) and July to December 2021 (bottom). Right: “CH4_GOS_OCFP algorithm”. Left: “CH4_GOS_OCPR algorithm”. Image adapted from [[6]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf) under CC-BY license.
```

## ℹ If you want to know more

### Key resources

The CDS catalogue entries for the data used were:
* Methane data from 2002 to present derived from satellite observations: https://cds.climate.copernicus.eu/datasets/satellite-methane?tab=overview

More about wetlands and C3S products that assist wetland conservation:
* https://climate.copernicus.eu/working-our-wetlands

More about greenhouse gases by the World Meteorological Organization (WMO):
* https://wmo.int/publication-series/greenhouse-gas-bulletin

### References

[[1]](https://library.wmo.int/viewer/68532/?offset=#page=4&viewer=picture&o=bookmarks&n=0&q=) World Meteorological Organization (2023). WMO Greenhouse Gas Bulletin, No. 19, ISSN 2078-0796.

[[2]](https://doi.org/10.5194/essd-12-1561-2020) Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., et al. (2020). The Global Methane Budget 2000–2017. Earth System Science Data, 12, 1561–1623.

[[3]](https://doi.org/10.1038/s41558-023-01629-0) Zhang, Z., Poulter, B., Feldman, A.F. et al. (2023). Recent intensification of wetland methane feedback. Nature Climate Change, 13, 430–433.

[[4]](https://doi.org/10.1126/sciadv.aay4444) Ernest N. Koffi et al. (2020). An observation-constrained assessment of the climate sensitivity and future trajectories of wetland methane emissions. Science Advances, 6,eaay4444.

[[5]](https://ads.atmosphere.copernicus.eu/datasets/cams-global-greenhouse-gas-inversion?tab=overview) Copernicus Atmosphere Monitoring Service (2020). CAMS global inversion-optimised greenhouse gas fluxes and concentrations.

[[6]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PUGS_GHG_main_latest.pdf) Buchwitz, M. (2023). Product User Guide and Specification (PUGS) – Main document for Greenhouse Gas (GHG: CO2 & CH4) data set CDR6 (01.2003-12.2021).

[[7]](https://cds.climate.copernicus.eu/datasets/satellite-methane?tab=overview) Copernicus Climate Change Service, Climate Data Store, (2018): Methane data from 2002 to present derived from satellite observations. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.b25419f8

[[8]](http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/C3S2_312a_Lot2_PQAR_GHG_main_latest.pdf) Buchwitz, M. (2023). Product Quality Assessment Report (PQAR) – Main document for Greenhouse Gas (GHG: CO2 & CH4) data set CDR6 (01.2003-12.2021).


EQC Quality Assessment: "Methane satellite observations uncertainty and completeness assessment for carbon cycle"
Dataset: satellite-methane [CDS]
Aspect: uncertainty_q01  |  Category: Satellite_ECVs
Section: Methane satellite observations uncertainty and completeness assessment for carbon cycle > Quality assessment statement
---
itz, M. (2023). Product Quality Assessment Report (PQAR) – Main document for Greenhouse Gas (GHG: CO2 & CH4) data set CDR6 (01.2003-12.2021).

[[9]](https://doi.org/10.1016/j.rse.2018.02.011) Parker, R.J., Boesch, H., McNorton, J., Comyn-Platt, E., Gloor, M., Wilson, C., Chipperfield, M.P., Hayman, G.D., Bloom, A.A. (2018). Evaluating year-to-year anomalies in tropical wetland methane emissions using satellite CH4 observations. Remote Sensing of Environment, 211, 261-275.

[[10]](https://doi.org/10.5194/acp-21-4339-2021) Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J., Zhang, Y., Lu, X., Bloom, A. A., Bowman, K. W., Worden, J. R., and Parker, R. J. (2021). 2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane. Atmospheric Chemistry and Physics, 21, 4339–4356.

[[11]](https://www.iup.uni-bremen.de/carbon_ghg/docs/GHG-CCIplus/URD/URDv3.0_GHG-CCIp_Final.pdf) Chevallier, F. et al. (2020). User Requirements Document (URD), ESA Climate Change Initiative (CCI) GHG-CCI project, Version 3.0, pp.42.


EQC Quality Assessment: "Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies"
Dataset: satellite-ozone-v1 [CDS]
Aspect: climate-monitoring_q01  |  Category: Satellite_ECVs
Section: Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies
---
Production date: 06-06-2024

Produced by: Sandro Calmanti (ENEA), Chiara Volta (ENEA), Irene Cionni (ENEA)


EQC Quality Assessment: "Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies"
Dataset: satellite-ozone-v1 [CDS]
Aspect: climate-monitoring_q01  |  Category: Satellite_ECVs
Section: Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies > Quality assessment questions
---
* **How well, in terms of completeness, does the longest Level-3 atmospheric ozone content data from merged satellite missions represent the variability of the ozone layer over time?**
* **How well data from merged satellite missions capture extreme events in ozone concentration?**

Assessing the return of the ozone layer to historical levels and the full recovery from ozone-depleting substances (ODS) under the 1987 Montreal Protocol is a critical task. The Montreal Protocol required the international scientific community to provide governments with regular updates on the latest scientific knowledge about the ozone layer. Since 1987, ten reports called "Scientific Assessment of Ozone Depletion" have been produced. These regular assessments have guided policymakers in strengthening the provisions of the Montreal Protocol.

Satellite data undergoes several levels of processing before it can be used to provide decision-relevant information.

In this application, the Level-3 MERGED-UV dataset version v2000 is evaluated. A Level 3 dataset corresponds to data from a specific satellite sensor that has been quality tagged, converted to the relevant environmental variable, and aggregated on a uniform time-space grid. Level 3 datasets therefore represent a relatively low level of processing to produce usable data for decision making.

The MERGED-UV dataset combines total column ozone measurements at a 1°x1° spatial resolution from 5 UV nadir satellite sensors (i.e., GOME, SCIAMACHY, GOME-2A/B and OMI) [[1]](https://dast.copernicus-climate.eu/documents/satellite-ozone/C3S2_312a_Lot2_PUGS_O3_latest.pdf) , resulting in the longest L3 satellite product currently available (1995 to present).


EQC Quality Assessment: "Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies"
Dataset: satellite-ozone-v1 [CDS]
Aspect: climate-monitoring_q01  |  Category: Satellite_ECVs
Section: Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies > Quality assessment statements
---
These are the key outcomes of this assessment

* The MERGED-UV v2000 provides a long-term, consistent record of total column ozone concentration and is therefore valuable for providing insight into the evolution of the ozone layer, although data prior to 2004 should be carefully evaluated.
* The MERGED-UV v2000 does not include measurements during the polar night at latitudes higher than 57.5° in both hemispheres and therefore cannot be used to describe the ozone climatology and trends over these regions.
* The lack of data in high-latitude regions during the polar night hinders the use of the MERGED-UV v2000 dataset to monitor the evolution of alarming ozone depletion at the poles.
* Data gaps in the polar region are a direct consequence of the decision to produce a long-term consistent ozone data set suitable for trend analysis. If data coverage is to be preferred to consistency, a better choice would be to use the ozone multi-sensor reanalysis (MSR) [[2]](https://doi.org/10.5194/amt-8-3021-2015) available from 1970 to present.
* Despite the data gaps, the MERGED-UV v2000 dataset reproduces the major ozone variations over time described in the literature.
* MERGED-UV v2000 captures episodes of extremely low regional total column ozone associated with the impact of volcanic eruptions.
```


EQC Quality Assessment: "Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies"
Dataset: satellite-ozone-v1 [CDS]
Aspect: climate-monitoring_q01  |  Category: Satellite_ECVs
Section: Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies > Methodology
---
This notebook has two main goals:

- to show how the basic data coverage has changed over time and to highlight the potential impact on the accuracy of the final product;
 - to illustrate how the satellite orbit affects the seasonal data coverage and the resulting impact on the monitoring of total ozone over critical regions.

The methodology adopted for the analysis is split into the following steps:

[](sec1)
* Define data request and other parameters.
* Define required functions

[](sec2)
* Unit conversion
* Computation of time series

[](sec3)
* Discussion of data coverage
* Analysis of ozone distribution and its seasonal cycle
* Time series of total column ozone (TCO)


EQC Quality Assessment: "Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies"
Dataset: satellite-ozone-v1 [CDS]
Aspect: climate-monitoring_q01  |  Category: Satellite_ECVs
Section: Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies > Analysis and results > 1. Choose the data to use and setup the code > Define data request and other parameters
---
The analysis performed in this notebook focuses on the time series of the MERGED-UV total column ozone (TCO) over a period of 26 years (January 1996 - December 2022).

Additionally, spatially weighted means and climatological monthly averages are computed at the global scale and over 5 latitudinal bands: polar (latitudes > 60°) and mid-latitude (30°<latitude<30°) regions in the Northern and Southern hemispheres, and the tropics (25°S<latitude<25°N).

Import packages
Time frame for the analysis
Variable to show


EQC Quality Assessment: "Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies"
Dataset: satellite-ozone-v1 [CDS]
Aspect: climate-monitoring_q01  |  Category: Satellite_ECVs
Section: Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies > Analysis and results > 1. Choose the data to use and setup the code > Define required functions
---
For this dataset it is necessary to handle the time dimension carefully with a simple dedicated function like `add_time_dim`.
The function `spatial_weighted_mean` is used to calculate ozone averages over the regions of interest.

(sec2)=


EQC Quality Assessment: "Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies"
Dataset: satellite-ozone-v1 [CDS]
Aspect: climate-monitoring_q01  |  Category: Satellite_ECVs
Section: Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies > Analysis and results > 2. Data retrieval
---
The full dataset is retrieved, including information on the number of observations used to compute the monthly TCO at each grid point. All TCO is then converted in Dobson Unit (DU) by multiplying ozone data (in mol m$^{-2}$) by the conversion coefficient provided in the dataset as a metadata attribute.

The computation of the time series of TCO over the regions of interest is performed by applying a weighted mean to the gridded data.

Full dataset

```text
100%|██████████| 27/27 [00:00<00:00, 59.02it/s]
```

```text
region='global'
region='tropics'
region='NH mid-latitudes'
region='SH mid-latitudes'
region='NH polar'
region='SH polar'
```

(sec3)=


EQC Quality Assessment: "Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies"
Dataset: satellite-ozone-v1 [CDS]
Aspect: climate-monitoring_q01  |  Category: Satellite_ECVs
Section: Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies > Analysis and results > 3. Plot and describe the results > Data Coverage
---
The global map of data coverage shows that the number of observations decreases from 57.5° poleward in both hemispheres. As shown in the latitudinal time series, this reduction is due to the fact that the dataset does not include measurements during the polar night (i.e., winter and summer months in the northern and the southern hemisphere, respectively). The time series also shows reduced observations between 1996 and 2002, a period during which GOME was the only active sensor. Since 2002, the number of observations slightly increases due to the activation of SCIAMACHY. In 2004, the activation of a third sensor (OMI) strongly enhanced the data coverage. Since then, the data coverage has been guaranteed over time by using at least three sensors simultaneously [[1]](https://dast.copernicus-climate.eu/documents/satellite-ozone/C3S2_312a_Lot2_PUGS_O3_latest.pdf). Small, non-periodic reductions in the number of observations are visible after 2004 and are likely to depend on differences in the sensors and satellites used over time, such as their orbital period and swath [[3]](https://dast.copernicus-climate.eu/documents/satellite-ozone/C3S2_312a_Lot2_ATBD_O3_latest.pdf). Small, but periodic reductions are due to differences in the length of the months (e.g., February).

A significant impact of data coverage on the accuracy of the total ozone content can be derived as a by-product of the data assimilation process for the elaboration of the multi-sensor reanalysis [[2]](https://doi.org/10.5194/amt-8-3021-2015), which is also available on the CDS as the MSR dataset. Increasing the number of measurements used to derive the mean total ozone column by about an order of magnitude (e.g., between 2000 and 2020) corresponds to reducing the absolute error from about 20 DU to about 7 DU, which corresponds to reducing the expected error from about 6% to about 2% [[2]](https://doi.org/10.5194/amt-8-3021-2015).


EQC Quality Assessment: "Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies"
Dataset: satellite-ozone-v1 [CDS]
Aspect: climate-monitoring_q01  |  Category: Satellite_ECVs
Section: Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies > Analysis and results > 3. Plot and describe the results > Total column ozone distribution and seasonal cycle
---
The global map of total column ozone (TCO) shows that TCO varies strongly with latitude. The highest TCO values are observed at latitudes around 60°S and above 30°N due to the poleward transport of tropical ozone by stratospheric air circulation [[4]](https://doi.org/10.1002/2013RG000448). As a result, tropical regions become ozone-depleted. Low TCO values are also observed below 60°S, where the Antarctic ozone hole is located [[5]](https://doi.org/10.1038/315207a0) [[6]](https://doi.org/10.1126/science.aae0061).

The Hövmoller plot of the climatological monthly TCO means shows that the seasonal variability of ozone levels is small in the tropics and larger above 35° in both hemispheres. The dataset MERGED-UV is able to reproduce the TCO climatology in the tropics and mid-latitudes, with an expected maximum over eastern Asia at about 60°N and the largest TCO values over the northern polar region during winter with TCO above 410 DU [[7]](https://doi.org/10.5194/acp-11-9237-2011). However it fails to reproduce the full climatology in polar regions due to the unavailability of data during the polar night from  57.5° poleward in both hemispheres. In particular, the dataset partially captures the TCO depletion typically observed in early autumn and during the austral spring above and below 60° N and S, respectively, but cannot reproduce the low TCO levels typically observed in early austral autumn [[8]](https://doi.org/10.1029/1999RG900008) [[9]](https://ozone.unep.org/sites/default/files/2023-05/Final_20Qs%202022%20full%20document_26April2023_digital%20version-reduced_0.pdf).


EQC Quality Assessment: "Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies"
Dataset: satellite-ozone-v1 [CDS]
Aspect: climate-monitoring_q01  |  Category: Satellite_ECVs
Section: Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies > Analysis and results > 3. Plot and describe the results > Time series
---
The deseasonalized monthly time series show the limited inter-annual variability at the global scale and over the tropics and mid-latitudes, compared to the larger variability observed over the polar regions. The time series show that the dataset is able to detect documented large variations, such as the large TCO depletion in the Arctic in 2011 and 2020, and the re-emergence of the large, long-lived ozone hole over Antarctica since 2020 due to anomalous circulation patterns [[10]](https://doi.org/10.1038/s41467-023-42637-0) [[11]](https://doi.org/10.1029/2022JD037581). The unique split in the ozone hole that occurred in the second half of 2002 is clearly visible, with anomalies above 50 DU compared to the expected seasonal cycle, consistent with more detailed analysis presented in dedicated studies [[12]](https://doi.org/10.1175/JAS-3338.1). Extremely low TCO levels, triggered by the Calbuco eruption, are also observed below 30°S in 2015 [[6]](https://doi.org/10.1126/science.aae0061). In 2010, high TCO levels are observed at mid-latitudes in the Northern Hemisphere due to simultaneous negative phases of the Arctic and North Atlantic Oscillation [[13]](https://doi.org/10.1029/2010GL046634). High TCO levels, induced by the stratospheric warming over Antarctica, are also observed in the southernmost region in 2019 [[14]](https://doi.org/10.1029/2020GL087810). Furthermore, the time series show that the dataset is able to reproduce the quasi-biennial oscillation in tropical ozone [[15]](https://doi.org/10.1029/2002JD002170).