Methane (CH4) is a potent greenhouse gas with high radiative forcing and a relatively short atmospheric lifetime of around a decade. We used a decade-long dataset (2011-2022) from the Fourier transform spectrometer at the California Laboratory for Atmospheric Remote Sensing (CLARS-FTS) to quantify a dramatic increase in methane observed in 2020. We report an increase of 1.13 ppb/month starting in 2020 until the end of 2021, compared to a growth rate of 0.345 ppb/month from 2016 to 2019. The observed increase in methane concentrations in 2020 is of significant concern due to its potential contribution to global warming. The Total Carbon Column Observing Network (TCCON) is then used to examine the global geospatial variability of the increase in methane. The results suggest an approximately uniform rise in methane globally. Finally, results from a two-box model used to simulate atmospheric chemical processes of methane production and loss indicate that changes in OH alone are insufficient to explain the rise in atmospheric methane. Encouragingly, recent data from 2022 suggest a deceleration in the methane growth rate, indicating a potential slowdown in the methane increase observed in 2020.

TanSat is the 1st Chinese carbon dioxide (CO) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8 order Fourier series. The model and a priori is developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O A band retrieval. Accordingly, we extend the previous TanSat single CO weak band retrieval to a combined O A and CO weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the stronger correlation with the XCO retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO retrieval. We show that our new approach produces a significant improvement on the XCO retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of -0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO processing.