A sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources
<p>Sensitive and accurate detection of sulfur dioxide (SO<span class="inline-formula"><sub>2</sub></span>) from space is important for monitoring and estimating global sulfur emissions. Inspired by detection methods applied in the thermal infrared, we present...
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/e95768eaa5b34689a1188007083db0dd |
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| Sumario: | <p>Sensitive and accurate detection of sulfur dioxide (SO<span class="inline-formula"><sub>2</sub></span>) from space is
important for monitoring and estimating global sulfur emissions. Inspired by
detection methods applied in the thermal infrared, we present here a new
scheme to retrieve SO<span class="inline-formula"><sub>2</sub></span> columns from satellite observations of
ultraviolet back-scattered radiances. The retrieval is based on a
measurement error covariance matrix to fully represent the SO<span class="inline-formula"><sub>2</sub></span>-free
radiance variability, so that the SO<span class="inline-formula"><sub>2</sub></span> slant column density is the only
retrieved parameter of the algorithm. We demonstrate this approach, named
COBRA, on measurements from the TROPOspheric Monitoring Instrument (TROPOMI)
aboard the Sentinel-5 Precursor (S-5P) satellite. We show that the method
reduces significantly both the noise and biases present in the current
TROPOMI operational DOAS SO<span class="inline-formula"><sub>2</sub></span> retrievals. The performance of
this technique is also benchmarked against that of the principal component
algorithm (PCA) approach. We find that the quality of the data is similar
and even slightly better with the proposed COBRA approach. The ability of
the algorithm to retrieve SO<span class="inline-formula"><sub>2</sub></span> accurately is further supported
by comparison with ground-based observations. We illustrate the great
sensitivity of the method with a high-resolution global SO<span class="inline-formula"><sub>2</sub></span> map,
considering 2.5 years of TROPOMI data. In addition to the known
sources, we detect many new SO<span class="inline-formula"><sub>2</sub></span> emission hotspots worldwide.
For the largest sources, we use the COBRA data to estimate SO<span class="inline-formula"><sub>2</sub></span> emission
rates. Results are comparable to other recently published TROPOMI-based
SO<span class="inline-formula"><sub>2</sub></span> emissions estimates, but the associated uncertainties are
significantly lower than with the operational data. Next, for a limited
number of weak sources, we demonstrate the potential of our data for
quantifying SO<span class="inline-formula"><sub>2</sub></span> emissions with a detection limit of about 8 kt yr<span class="inline-formula"><sup>−1</sup></span>, a factor of 4 better than the emissions derived from the Ozone
Monitoring Instrument (OMI). We anticipate that the systematic use of our
TROPOMI COBRA SO<span class="inline-formula"><sub>2</sub></span> column data set at a global scale will allow missing sources to be identified and quantified and help improve SO<span class="inline-formula"><sub>2</sub></span>
emission inventories.</p> |
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