Because weather has the potential to confound or modify the pollution-mortality relationship, researchers have developed several approaches for controlling it in estimating the independent effect of air pollution on mortality. This report considers the consequences of using alternative approaches to controlling for weather and explores modification of air pollution effects by weather, as weather patterns could plausibly alter air pollution's effect on health. We analyzed 1973-1980 total mortality data for Philadelphia using four weather models and compared estimates of the effects of TSP and SO2 on mortality using a Poisson regression model. Two synoptic categories developed by Kalkstein were selected--the Temporal Synoptic Index (TSI) and the Spatial Synoptic Classification (SSC)--and compared with (1) descriptive models developed by Schwartz and Dockery (S-D); and (2) LOESS, a non-parametric function of the previous day's temperature and dew point. We considered model fit using Akaike's Information Criterion (AIC) and changes in the estimated effects of TSP and SO2. In the full-year analysis, S-D is better than LOESS at predicting mortality, and S-D and LOESS are better than TSI, as measured by AIC. When TSP or SO2 was fit alone, the results were qualitatively similar, regardless of how weather was controlled; when TSP and SO2 were fit simultaneously, the S-D and LOESS models give qualitatively different results than TSI, which attributes more of the pollution effect to SO2 than to TSP. Model fit is substantially poorer with TSI. This pattern was repeated in analyses of summer and winter months, which included SSC. In summary, using synoptic weather categories in regression models does not meaningfully change the association between mortality and air pollution indexes. We also found little evidence that weather conditions modified the effect of pollution, regardless of the approach used to represent weather.