A change in climate is not likely captured from any single instrument, since no single instrument can span decades of time. Therefore, to detect signals of global climate change, observations from many instruments on different platforms have to be concatenated. This requires careful and detailed consideration of instrumental differences such as footprint size, diurnal cycle of observations, and relative biases in the spectral brightness temperatures. Furthermore, a common basic assumption is that the data quality is independent of the observed scene and therefore can be determined using clear scene data. However, as will be demonstrated, this is not necessarily a valid assumption as the globe is mostly cloudy. In this study we highlight challenges in inter-calibration and concatenation of infrared radiances from multiple instruments by focusing on the analysis of deep convective or anvil clouds. TRMM/VIRS is potentially useful instrument to make correction for observational differences in the local time and footprint sizes, and thus could be applied retroactively to vintage instruments such as AIRS, IASI, IRIS, AVHRR, and HIRS. As the first step, in this study, we investigate and discuss to what extent AIRS and VIRS agree in capturing deep cloudy radiances at the same local time. The analysis also includes comparisons with one year observations from CrIS. It was found that the instruments show calibration differences of about 1K under deep cloudy scenes that can vary as a function of land type and local time of observation. The sensitivity of footprint size, view angle, and spectral band-pass differenceartmut h. Aumanns cannot fully explain the observed differences. The observed discrepancies can be considered as a measure of the magnitude of issues which will arise in the comparison of legacy data with current data. Ketwords: Climate, cloudy radiance, AIRS, TRMM,VIRS, diurnal cycle, hyperspectral.