A new approach to the integration of the effects of inclement weather into traffic management strategies is presented. Adverse weather conditions are a critical factor affecting traffic operations and safety. Previously, a methodology for the analysis of the impact of rain has been addressed, and this impact on key traffic indicators (e.g., free-flow speed, capacity) has been quantified. As a result of these quantification studies, a first parameterization of the fundamental diagram according to rain intensity has been proposed. Since the fundamental diagram represents the basis of many simulation tools, the goal is to develop weather-responsive traffic state estimation tools that can be useful for control applications and traffic management. More precisely, the online traffic state estimation takes place within a Bayesian framework with particle-filtering techniques (i.e., sequential Monte Carlo simulations) in combination with a parameterized first-order macroscopic model. This approach has already been validated for sensor diagnosis and accident detection. In this paper the goal is to show how the integration of the weather effects can improve this efficient tool. The approach is validated with real-world data from the ring road section in Lyon, France (eight sensors from a homogeneous section). The results from different scenarios show the benefits of integrating the impact of rain into traffic state estimation. Strategies to detect a rain event in time and space are also suggested.