Day 4

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Paper title Detection of locally elevated methane concentrations by analyzing Sentinel-5 Precursor satellite data
Authors
  1. Steffen Vanselow Institute of Environmental Physics, University Bremen Speaker
  2. oliver schneising Institut für Umweltphysik, Universität Bremen
  3. Michael Buchwitz University of Bremen
  4. Heinrich Bovensmann Institut für Umweltphysik, Universität Bremen
  5. John P. Burrows Institute of Environmental Physics (IUP), University of Bremen, Germany
Form of presentation Poster
Topics
  • A1. Atmosphere
    • A1.04 Greenhouse Gases
Abstract text Methane (CH₄) is an important anthropogenic greenhouse gas and its rising concentration in the atmosphere contributes significantly to global warming. Satellite measurements of the column-averaged dry-air mole fraction of atmospheric methane, denoted as XCH₄, can be used to detect and quantify the emissions of methane sources. This is important since emissions from many methane sources have a high uncertainty and some emission sources are unknown. In addition, sufficiently accurate long-term satellite measurements provide information on emission trends and other characteristics of the sources, which can help to improve emission inventories and review policies to mitigate climate change.
The Sentinel-5 Precursor (S5P) satellite with the TROPOspheric Monitoring Instrument (TROPOMI) onboard was launched in October 2017 into a sun-synchronous orbit with an equator crossing time of 13:30. TROPOMI measures reflected solar radiation in different wavelength bands to generate various data products and combines daily global coverage with high spatial resolution. TROPOMI's observations in the shortwave infrared (SWIR) spectral range yield methane with a horizontal resolution of typically 7x7km².
We use a monthly XCH₄ data set (2018-2020) generated with the WFM-DOAS retrieval algorithm, developed at the University of Bremen, to detect locally enhanced methane concentrations originating from emission sources.
Our detection algorithm consists of several steps. At first, we apply a spatial high-pass filter to our data set to filter out the large-scale methane fluctuations. The resulting anomaly ∆XCH₄ maps show the difference of the local XCH₄ values compared to its surroundings. We then use these monthly maps to identify regions with local methane enhancements by utilizing different filter criteria, such as the number of months in which the local methane anomalies ∆XCH₄ of a possible hot spot region must exceed a certain threshold value. In the last step, we calculate some properties of the detected hot spot regions like the monthly averaged methane enhancement and attribute the hot spots to potential emission sources by comparing them with inventories of anthropogenic methane emissions.
In this presentation, the algorithm and initial results concerning the detection of local methane enhancements by spatially localized methane sources (e.g. wetlands, coal mining areas, oil and gas fields) are presented.