[A-CG41] Satellite Earth Environment Observation

[ACG41-03] A comparative study of methane emission estimates by high-resolution inversion of GOSAT and GOSAT-2 observations during 2019-2022.

*Rajesh Janardanan¹, Shamil Maksyutov¹, Fenjuan Wang¹, Lorna Raja Nayagam¹, Yukio Yoshida¹, Xin Lan^2,3, Tsuneo Matsunaga¹ (1.Satellite Observation Center, National Institute for Environmental Studies, Japan, 2.Cooperative Institute for Research in Environmental Sciences, Univ. of Colorado Boulder, USA, 3.NOAA Global Monitoring Laboratory, USA)

Keywords:Anthropogenic methane emissions, inverse modeling, GOSAT, GOSAT-2

We present a comparison of high-resolution inverse model estimates of sectoral methane emissions, integrating observations from the GOSAT-2 satellite for the first time, along with observations from the global surface observation network against an inversion of GOSAT. The objective of this study is to evaluate the two inversion results for consistency and identify potential reasons for any existing regional inconsistencies. 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 Saunois et al. (2020) outlined. Our analysis reveals a general agreement between total methane emissions estimates from GOSAT and GOSAT-2. However, on a sectoral level, we found notable regional differences in the flux estimates. These inconsistencies are found in regions where there are significant differences between XCH₄ Level-2 data from the two satellites, such as East Asia and North America, tropical South America, and tropical Africa. The regional biases in the XCH₄ products originate from their respective retrieval processes but persist due to limited representative surface reference sites for Level-2 bias correction. Along with this, the relative difference in the satellite data volume over regions devoid of surface observations introduces biases in the flux estimates. Flux estimates over regions with large seasonal amplitude in XCH₄ and seasonal cloud cover such as in Asia can have a product-dependent bias when the quality filtering of Level 2 data may leave very few GOSAT observations during summer than GOSAT-2. Therefore, while the two inverse fluxes are generally consistent, eliminating remaining biases in the Level-2 products is important to ensure uniform inversion results.