Advances in retrieving XCH<sub>4</sub> and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm
<p>The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite enables the accurate determination of atmospheric methane (<span class="inline-formula">CH<sub>4</sub></span>) and carbon monoxide (<span class="inline-form...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-02-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/16/669/2023/amt-16-669-2023.pdf |
| Summary: | <p>The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite enables
the accurate determination of atmospheric methane (<span class="inline-formula">CH<sub>4</sub></span>) and carbon monoxide (<span class="inline-formula">CO</span>)
abundances at high spatial resolution and global daily sampling. Due to its wide swath and
sampling, the global distribution of both gases can be determined in unprecedented detail. The
scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption
Spectroscopy (WFMD) has proven valuable in simultaneously retrieving the atmospheric
column-averaged dry-air mole fractions <span class="inline-formula">XCH<sub>4</sub></span> and <span class="inline-formula">XCO</span> from TROPOMI's radiance
measurements in the shortwave infrared (SWIR) spectral range.</p>
<p>Here we present recent improvements of the algorithm which have been incorporated into the current
version (v1.8) of the TROPOMI/WFMD product. This includes processing adjustments such as increasing
the polynomial degree to 3 in the fitting procedure to better account for possible spectral albedo
variations within the fitting window and updating the digital elevation model to minimise topography-related biases. In the post-processing, the machine-learning-based quality filter has
been refined using additional data when training the random forest classifier to further reduce
scenes with residual cloudiness that are incorrectly classified as good. In particular, the cloud
filtering over the Arctic ocean is considerably improved. Furthermore, the machine learning
calibration, addressing systematic errors due to simplifications in the forward model or
instrumental issues, has been optimised. By including an additional feature associated with the
fitted polynomial when training the corresponding random forest regressor, spectral albedo
variations are better accounted for. To remove vertical stripes in the <span class="inline-formula">XCH<sub>4</sub></span> and <span class="inline-formula">XCO</span>
data, an efficient orbit-wise destriping filter based on combined wavelet–Fourier filtering has been
implemented, while optimally preserving the original spatial trace gas features. The temporal
coverage of the data records has been extended to the end of April 2022, covering a total length of 4.5 years since the start of the mission, and will be further extended in the future.</p>
<p>Validation with the ground-based Total Carbon Column Observing Network (TCCON) demonstrates
that the implemented improvements reduce the pseudo-noise component of the products, resulting in an
improved random error. The <span class="inline-formula">XCH<sub>4</sub></span> and <span class="inline-formula">XCO</span> products have similar spatial coverage from
year to year including high latitudes and the oceans. The analysis of annual growth rates reveals
accelerated growth of atmospheric methane during the covered period, in line with observations
at marine surface sites of the Global Monitoring Division of NOAA's Earth System Research
Laboratory, which reported consecutive annual record increases over the past 2 years of 2020 and 2021.</p> |
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| ISSN: | 1867-1381 1867-8548 |