A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases
<p>Satellite observations of dry-column methane mixing ratios (XCH<span class="inline-formula"><sub>4</sub></span>) from shortwave infrared (SWIR) solar backscatter radiation provide a powerful resource to quantify methane emissions in service of climate actio...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-08-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/16/3787/2023/amt-16-3787-2023.pdf |
| Summary: | <p>Satellite observations of dry-column methane mixing
ratios (XCH<span class="inline-formula"><sub>4</sub></span>) from shortwave infrared (SWIR) solar backscatter
radiation provide a powerful resource to quantify methane emissions in
service of climate action. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017,
provides global daily coverage at a 5.5 <span class="inline-formula">×</span> 7 km<span class="inline-formula"><sup>2</sup></span> (nadir) pixel
resolution, but its methane retrievals can suffer from biases associated with
SWIR surface albedo, scattering from aerosols and cirrus clouds, and
across-track variability (striping). The Greenhouse gases Observing SATellite (GOSAT) instrument, launched in 2009,
has better spectral characteristics and its methane retrieval is much less
subject to biases, but its data density is 250 times sparser than TROPOMI.
Here, we present a blended TROPOMI<span class="inline-formula">+</span>GOSAT methane product obtained by
training a machine learning (ML) model to predict the difference between
TROPOMI and GOSAT co-located measurements, using only predictor variables
included in the TROPOMI retrieval, and then applying the correction to the
complete TROPOMI record from April 2018 to present. We find that the largest corrections are associated with coarse aerosol particles, high SWIR surface
albedo, and across-track pixel index. Our blended product corrects a
systematic difference between TROPOMI and GOSAT over water, and it features
corrections exceeding 10 ppb over arid land, persistently cloudy regions,
and high northern latitudes. It reduces the TROPOMI spatially variable bias
over land (referenced to GOSAT data) from 14.3 to 10.4 ppb at a
0.25<span class="inline-formula"><sup>∘</sup></span> <span class="inline-formula">×</span> 0.3125<span class="inline-formula"><sup>∘</sup></span> resolution. Validation
with Total Carbon Column Observing Network (TCCON) ground-based column measurements shows reductions in variable
bias compared with the original TROPOMI data from 4.7 to 4.4 ppb and in
single-retrieval precision from 14.5 to 11.9 ppb. TCCON data are all in
locations with a SWIR surface albedo below 0.4 (where TROPOMI biases tend to be
relatively low), but they confirm the dependence of TROPOMI biases on SWIR
surface albedo and coarse aerosol particles, as well as the reduction of
these biases in the blended product. Fine-scale inspection of the Arabian
Peninsula shows that a number of hotspots in the original TROPOMI data are
removed as artifacts in the blended product. The blended product also
corrects striping and aerosol/cloud biases in single-orbit TROPOMI data,
enabling better detection and quantification of ultra-emitters. Residual
coastal biases can be removed by applying additional filters. The ML method
presented here can be applied more generally to validate and correct data
from any new satellite instrument by reference to a more established
instrument.</p> |
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| ISSN: | 1867-1381 1867-8548 |