The extensive global climate observing system (GCOS) reference upper-air network (GRUAN) datasets provide a chance to validate newly released Atmospheric Infrared Sounder (AIRS) version 7 (v7) products over the Arctic. This manuscript reports on the analysis performed to evaluate errors from AIRS version 6 (v6) and v7 temperature profiles and to characterize the derived low-level temperature inversion (LLI) representativeness in the Arctic region. The AIRS averaging kernel, representing the AIRS measurement sensitivity, is applied to reduce the vertical resolution of the radiosonde profiles for comparison. Due to improved retrieval algorithms, v7 produces smaller biases in the troposphere and suppresses the cold bias in v6. Nevertheless, the profile-averaged root mean square error (RMSE) increased by over 30% in v7, particularly in the winter half-year when v7 showed a larger RMSE below 800 hPa. The AIRS temperature retrieval accuracy is primarily sensitive to surface type and cloud fraction. Compared to v6, v7 has less bias over frozen land and sea ice in different cloud fraction conditions. However, the RMSEs of v7 are more sensitive to the effective cloud fraction (ECF) and are highly influenced by a more significant contribution from nonfrozen land samples. Compared to the kernel-averaged radiosonde profiles, more than 80% of the temperature profiles from v6 and v7 accurately detect LLIs. The discreteness of the AIRS’s predefined pressure level results is consistent with the radiosondes only 65% of the time for LLI depth calculation. In contrast, the AIRS can obtain LLI intensity with a relatively high correlation (>0.9). With the AIRS temperature retrieval in the boundary layer further improved, it has the potential to be used as an independent LLI detector in the Arctic region.

Rural settlement is the main vehicle for the existence and development of the countryside. The functions of rural settlements vary across different regions, influencing land use patterns. This study conducted multi-function evaluations of rural settlements by selecting three representative villages from different locations in Hebei Province, China. This was achieved through the establishment of an evaluation indicator system and the adoption of methods such as coordination degree, dominance degree, and obstacle factor diagnosis. This paper also used the Markov and CLUE-S models to predict future changes in land use within these settlements. The results showed that the closer the relationship between rural settlements and towns, the more obvious the settlement’s overall function becomes. Suburban settlements showed the highest multi-function coordination degree, with a prominent living function, but lagged in production and ecological functions. These villages should prioritize areas for commercial, landscape, and greening land to better serve the urban areas. Exurban villages excel in production but fall short in ecological and residential aspects. These areas should allocate land for environmental and infrastructure development to support a larger peasant population. Remote villages showed good multi-functionality, with a strong focus on eco-friendliness. However, they lacked in production and living function. Future plans should include converting residential areas to commercial use and enhancing public services and infrastructure to raise the living standards of villagers.