Seasonal adjustment is the process of removing regular seasonal patterns from a time series. While weather effects can be seasonal, their variation from year to year can be greater than what would normally be the case for a regular seasonal pattern. Failure to account for a weather effect may result in observations that are unfortunately regarded as outliers, so there may be some interpretive value in being able to accommodate weather effects for seasonal adjustment. In this paper, we use weather data to construct regressors for use in modeling series likely to experience weather effects. We wish to consider the following questions: (1) Does the use of weather regressors help improve a model that is estimated without them? (2) How well does their inclusion in a model explain outliers that are identified by models without? (3) To what extent does their inclusion affect a seasonal adjustment? We find that the weather regressors used can result in better models, that they do explain some, but not all, outliers, and that the difference between a seasonally adjusted series and a seasonal and weather adjusted series can be quite pronounced for some months.