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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/acp-2020-550
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-550
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Sep 2020

03 Sep 2020

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This preprint is currently under review for the journal ACP.

Systematic detection of local CH4 emissions anomalies combining satellite measurements and high-resolution forecasts

Jérôme Barré1, Ilse Aben2, Anna Agustí-Panareda1, Gianpaolo Balsamo1, Nicolas Bousserez1, Peter Dueben1, Richard Engelen1, Antje Inness1, Alba Lorente2, Joe McNorton1, Vincent-Henri Peuch1, Gabor Radnoti1, and Roberto Ribas1 Jérôme Barré et al.
  • 1ECMWF, European Centre for Medium Range Weather Forecasts, Shinfield Park, Reading, United Kingdom
  • 2SRON, Netherlands Institute for Space Research, Utrecht, the Netherlands

Abstract. In this study we present a novel monitoring methodology to detect local CH4 concentration anomalies worldwide that are related to rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget. The method uses high resolution (7 km × 7 km) retrievals of total column CH4 from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel 5 Precursor satellite. Observations are combined with high resolution CH4 forecasts (~ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) close to the native satellite resolution at appropriate time. Investigating the departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the large-scale biases on both forecasts and satellite observations. We then use a simple classification on the filtered departures to detect anomalies and plumes coming from CAMS emissions that are missing (e.g. pipeline or facility leaks), under-reported or over-reported (e.g. depleted drilling fields). Additionally, the classification helps to detect local satellite retrieval errors due to land surface albedo issues.

Jérôme Barré et al.

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Jérôme Barré et al.

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Short summary
This study shows new capability to detect systematically anomalous local CH4 concentration anomalies worldwide that are related with rapidly changing anthropogenic emissions combining satellite measurements and model simulations. Novel data analysis (such as filtering and classification) can automatically detect globally anomalous emissions coming from point sources and small areas, such as oil and gas drilling sites, pipeline or facility leaks, etc.
This study shows new capability to detect systematically anomalous local CH4 concentration...
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