Abstract. Atmospheric Methane (CH4) is generated as a standard product in recent version of the hyperspectral Atmospheric Infrared Sounder (AIRS-V6) aboard NASA's Aqua satellite at the NASA Goddard Earth Sciences Data and Information Services Center (NASA/GES/DISC). Significant improvements in AIRS-V6 was expected but without a thorough validation. This paper first introduced the improvements of CH4 retrieval in AIRS-V6 and some characterizations, then presented the results of validation using ~ 1000 aircraft profiles from several campaigns spread over a couple of years and in different regions. It was found the mean biases of AIRS CH4 at layers 343–441 and 441–575 hPa are −0.76 and −0.05 % and the RMS errors are 1.56 and 1.16 %, respectively. Further analysis demonstrates that the errors in the spring and in the high northern latitudes are larger than in other seasons or regions. The error is correlated with Degree of Freedoms (DOFs), particularly in the tropics or in the summer, and cloud amount, suggesting that the "observed" spatiotemporal variation of CH4 by AIRS is imbedded with some artificial impact from the retrieval sensitivity in addition to its variation in reality, so the variation of information content in the retrievals needs to be taken into account in data analysis of the retrieval products. Some additional filtering (i.e. rejection of profiles with obvious oscillation as well as those deviating greatly from the norm) for quality control is recommended for the users to better utilize AIRS-V6 CH4, and their implementation in the future versions of the AIRS retrieval algorithm is under consideration.

Abstract. The Community Radiative Transfer Model (CRTM), a sensor-based radiative transfer model, has been used within the Gridpoint Statistical Interpolation (GSI) system for directly assimilating radiances from infrared and microwave sensors. We conducted numerical experiments to illustrate how including aerosol radiative effects in CRTM calculations changes the GSI analysis. Compared to the default aerosol-blind calculations, the aerosol influences reduced simulated brightness temperature (BT) in thermal window channels, particularly over dust-dominant regions. A case study is presented, which illustrates how failing to correct for aerosol transmittance effects leads to errors in meteorological analyses that assimilate radiances from satellite infrared sensors. In particular, the case study shows that assimilating aerosol-affected BTs significantly affects analyzed temperatures in the lower atmosphere across several regions of the globe. Consequently, a fully cycled aerosol-aware experiment improves 1–5 d forecasts of wind, temperature, and geopotential height in the tropical troposphere and Northern Hemisphere stratosphere. Whilst both GSI and CRTM are well documented with online user guides, tutorials, and code repositories, this article is intended to provide a joined-up documentation for aerosol absorption and scattering calculations in the CRTM and GSI. It also provides guidance for prospective users of the CRTM aerosol option and GSI aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are briefly discussed.