This paper consists in a new step toward the integration of the effects of inclement weather into traffic management strategies. It is well recognized that adverse weather conditions are a critical factor impacting traffic operations and safety. In a previous work (1), a methodology for the analysis of the rain impact has been put forward and this impact on key traffic indicators (e.g. free-flow speed, capacity) has been quantified. Thanks to these quantification studies, a first parameterization of the fundamental diagram according to the rain intensity is proposed. Next, since the fundamental diagram represents the basis of many simulation tools, the goal is to develop weather-responsive traffic state estimation tools, which 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. Here, 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 Lyon's ring road section (8sensors from a homogeneous section). The results from different scenarios show the benefits of the integration of the rain impact for traffic state estimation. Strategies to detect a rain event in time and in space are also suggested.