Systematic detection of local CH<sub>4</sub> anomalies by combining satellite measurements with high-resolution forecasts
<p>In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local <span class="inline-formula">CH<sub>4</sub></span> concentration anomalies worldwide. These anomalies are caused by rapidly changing an...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2021-04-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/21/5117/2021/acp-21-5117-2021.pdf |
| Summary: | <p>In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local <span class="inline-formula">CH<sub>4</sub></span> concentration
anomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the <span class="inline-formula">CH<sub>4</sub></span> atmospheric
budget and by biases in the satellite retrieval data. The method uses high-resolution (7 km <span class="inline-formula">×</span> 7 km) retrievals of
total column <span class="inline-formula">CH<sub>4</sub></span> from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are
combined with high-resolution <span class="inline-formula">CH<sub>4</sub></span> forecasts (<span class="inline-formula">∼</span> 9 <span class="inline-formula">km</span>) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide
departures (observations minus forecasts) at close to the satellite's native resolution at appropriate time. Investigating these 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
synoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered
departures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or
overreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to
local satellite retrieval biases linked to albedo and scattering issues.</p> |
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| ISSN: | 1680-7316 1680-7324 |