Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
High-Resolution Inversion of GOSAT-2 Retrievals for Sectoral Methane Emission Estimates During 2019–2022: A Consistency Analysis with GOSAT Inversion
by
Maksyutov, Shamil
, Yoshida, Yukio
, Nayagam, Lorna
, Matsunaga, Tsuneo
, Janardanan, Rajesh
, Lan, Xin
, Wang, Fenjuan
in
Aircraft
/ anthropogenic
/ Bias
/ Constraints
/ Datasets
/ Emissions
/ Environmental studies
/ Estimates
/ Fluctuations
/ Fossil fuels
/ Geology
/ GOSAT
/ GOSAT-2
/ Greenhouse gases
/ High resolution
/ Information management
/ inverse model
/ Inversions
/ Methane
/ methane emissions
/ Satellites
/ sectoral methane emissions
/ Wetlands
2025
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
High-Resolution Inversion of GOSAT-2 Retrievals for Sectoral Methane Emission Estimates During 2019–2022: A Consistency Analysis with GOSAT Inversion
by
Maksyutov, Shamil
, Yoshida, Yukio
, Nayagam, Lorna
, Matsunaga, Tsuneo
, Janardanan, Rajesh
, Lan, Xin
, Wang, Fenjuan
in
Aircraft
/ anthropogenic
/ Bias
/ Constraints
/ Datasets
/ Emissions
/ Environmental studies
/ Estimates
/ Fluctuations
/ Fossil fuels
/ Geology
/ GOSAT
/ GOSAT-2
/ Greenhouse gases
/ High resolution
/ Information management
/ inverse model
/ Inversions
/ Methane
/ methane emissions
/ Satellites
/ sectoral methane emissions
/ Wetlands
2025
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
High-Resolution Inversion of GOSAT-2 Retrievals for Sectoral Methane Emission Estimates During 2019–2022: A Consistency Analysis with GOSAT Inversion
by
Maksyutov, Shamil
, Yoshida, Yukio
, Nayagam, Lorna
, Matsunaga, Tsuneo
, Janardanan, Rajesh
, Lan, Xin
, Wang, Fenjuan
in
Aircraft
/ anthropogenic
/ Bias
/ Constraints
/ Datasets
/ Emissions
/ Environmental studies
/ Estimates
/ Fluctuations
/ Fossil fuels
/ Geology
/ GOSAT
/ GOSAT-2
/ Greenhouse gases
/ High resolution
/ Information management
/ inverse model
/ Inversions
/ Methane
/ methane emissions
/ Satellites
/ sectoral methane emissions
/ Wetlands
2025
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
High-Resolution Inversion of GOSAT-2 Retrievals for Sectoral Methane Emission Estimates During 2019–2022: A Consistency Analysis with GOSAT Inversion
Journal Article
High-Resolution Inversion of GOSAT-2 Retrievals for Sectoral Methane Emission Estimates During 2019–2022: A Consistency Analysis with GOSAT Inversion
2025
Request Book From Autostore
and Choose the Collection Method
Overview
We employed a global high-resolution inverse model to estimate sectoral methane emissions, integrating observations from the GOSAT-2 satellite for the first time, along with observations from the surface observation network. A similar set of inversions using GOSAT observations was carried out to evaluate the consistency between emissions estimates derived from these two satellites and to ensure that GOSAT-2 data could seamlessly integrate with the existing data series without disrupting the continuity of flux estimates. This analysis, covering the period from 2019 to 2022, utilized prior anthropogenic emissions data mainly from EDGAR v6 and incorporated additional natural sources and sinks as outlined by global methane budget, 2020. Our analysis reveals a general agreement between total methane emissions estimates from GOSAT and GOSAT-2. However, on a sectoral basis, we found notable regional differences in the flux estimates. While GOSAT inversion estimates ~8 Tg a−1 more anthropogenic emissions for China and around 4 Tg a−1 more wetland emissions for Brazil and Indonesia, the posterior error distribution suggests that GOSAT-2 inversion is closer to surface observations over Asia. These discrepancies are found in regions with significant differences in XCH4 data from the two satellites, such as East Asia and North America, tropical South America, and tropical Africa. These regional biases persist due to limited representative surface reference sites for Level 2 bias correction. The relatively lower data volume from GOSAT also introduces seasonal biases in the flux estimates when the quality filtering of Level 2 data persistently reduces usable observations during certain seasons, resulting in inadequate representation of the seasonal cycle in regions such as East Asia. Similarly, in tropical South America, where the model is relatively under-constrained by the limited surface observations, the lower data volume of GOSAT-2 suffers. While the two inversions exhibit consistent overall performance across North America and Europe, the GOSAT-2-based inversion demonstrates a better performance over East Asia. Therefore, while the two satellite datasets are broadly consistent, considering the fact that the biases in the XCH4 data overlap with regions under-constrained by surface observations, establishing additional surface reference measurement sites is desirable to ensure consistent inversion results.
MBRLCatalogueRelatedBooks
Related Items
Related Items
This website uses cookies to ensure you get the best experience on our website.