Numerous studies have investigated the role of weather on insect species. For mosquitoes, these studies have yielded mixed results. Although it is clear that weather impacts mosquito population dynamics, these investigations have failed to accurately characterize their fluctuations. We use a novel graphical method to examine large numbers of meteorological aggregations of varying lengths and lags simultaneously to establish relationships between these summary variables and mosquito counts, to gain a better understanding of the weather effects. Poisson regression models were developed to characterize the population dynamics of Aedes sollicitans (Walker) by using meteorological data and a 34-yr set of daily mosquito count data. The models accurately characterize mosquito dynamics over time and space. The aggregated meteorological variables included in the model were lowest minimum tides between days 27 and 14 before trapping, total precipitation between days 22 and 9, total precipitation on day 1 and the day of trapping, cooling degree-days on day 0, average minimum relative humidity between days 28 and 9, lowest stream flow from day 11 to day 0, and lowest minimum temperature between days 28 and 13.