Estimation of CH<sub>4</sub> emission based on an advanced 4D-LETKF assimilation system
<p>Methane (CH<span class="inline-formula"><sub>4</sub></span>) is the second major greenhouse gas after carbon dioxide (CO<span class="inline-formula"><sub>2</sub></span>) which has substantially increased during recent decad...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-03-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/16/1823/2023/gmd-16-1823-2023.pdf |
| Summary: | <p>Methane (CH<span class="inline-formula"><sub>4</sub></span>) is the second major greenhouse gas after carbon dioxide
(CO<span class="inline-formula"><sub>2</sub></span>) which has substantially increased during recent decades in the
atmosphere, raising serious sustainability and climate change issues. Here,
we develop a data assimilation system for in situ and column-averaged
concentrations using a local ensemble transform Kalman filter (LETKF) to
estimate surface emissions of CH<span class="inline-formula"><sub>4</sub></span>. The data assimilation performance is
tested and optimized based on idealized settings using observation system
simulation experiments (OSSEs), where a known surface emission distribution
(the truth) is retrieved from synthetic observations. We tested three
covariance inflation methods to avoid covariance underestimation in the
emission estimates, namely fixed multiplicative (FM), relaxation-to-prior
spread (RTPS), and adaptive multiplicative. First, we assimilate the
synthetic observations at every grid point at the surface level. In such a
case of dense observational data, the normalized root mean square error
(RMSE) in the analyses over global land regions is smaller by 10 %–15 % in
the case of RTPS covariance inflation method compared to FM. We have shown that
integrated estimated flux seasonal cycles over 15 regions using RTPS
inflation are in reasonable agreement between true and estimated flux, with
0.04 global normalized annual mean bias. We then assimilated the column-averaged CH<span class="inline-formula"><sub>4</sub></span> concentration by sampling the model simulations at Greenhouse Gases Observing
Satellite (GOSAT) observation locations and time for another OSSE. Similar to the
case of dense observational data, the RTPS covariance inflation method performs
better than FM for GOSAT synthetic observation in terms of normalized RMSE
(2 %–3 %) and integrated flux estimation comparison with the true flux. The
annual mean averaged normalized RMSE (normalized mean bias) in LETKF
CH<span class="inline-formula"><sub>4</sub></span> flux estimation in the case of RTPS and FM covariance inflation is
found to be 0.59 (0.18) and 0.61 (0.23), respectively. The <span class="inline-formula"><i>χ</i><sup>2</sup></span> test
performed for GOSAT synthetic observations assimilation suggests high
underestimation of background error covariance in both RTPS and FM
covariance inflation methods; however, the underestimation is much higher
(<span class="inline-formula"><i>></i>100</span> % always) for FM compared to RTPS covariance inflation
method.</p> |
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| ISSN: | 1991-959X 1991-9603 |