This study addresses the question whether fuzzy logic is feasible for modeling air temperature error processes. The study focuses on developing a fuzzy rule-based model to predict air temperature errors. The fuzzy model uses qualitative relationships to describe the effects of the solar radiation, wind speed, and ground surface albedo among the ASOS, MMTS, Gill, CRS, ASP-ES, and NON-ASP-ES temperature shields. The predictions of air temperature errors compared well with statistical modeling for both summer time and winter time, as well as the snow-covered surface conditions. The results indicate that the fuzzy logic may provide another possibility to model air temperature errors and generate more homogeneous air temperature data in weather data applications.