According to FAA guidelines, aircraft must circumnavigate thunderstorms by wide margins both horizontally and vertically to mitigate the risk of encountering the dangerous turbulence the storms may generate. In order to provide pilots, dispatchers and air traffic managers a more precise assessment of the turbulence location and severity--and minimize unnecessary disruptions to air traffic--an automated convectively-induced turbulence (CIT) diagnosis product, called DCIT, is being developed under FAA and NASA sponsorship. DCIT combines information from a number of sources using a Takagi-Sugeno fuzzy logic expert system to produce high-resolution, rapid-update, 3D probabilistic assessments of light, moderate, and severe turbulence. NEXRAD Turbulence Detection Algorithm (NTDA) data provide remote measurements of in-cloud turbulence; near-cloud CIT is inferred based on identifiable features and conceptual models derived from NTDA, NEXRAD reflectivity, GOES satellite, lightning, and RUC model data. This paper presents an overview of the DCIT algorithm, shows output from a real-time prototype for several case studies, and evaluates the prototype's skill based on statistical comparisons with in-situ eddy dissipation rate (EDR) reports from commercial aircraft. DCIT is scheduled to be incorporated into the comprehensive Graphical Turbulence Guidance Nowcast (GTGN) for dissemination via the Aviation Weather Center's Aviation Digital Data Service, and may provide input to the FAA's Consolidated Storm Prediction for Aviation (CoSPA) product. Ultimately, DCIT will contribute to the Joint Planning and Development Office's (JPDO's) vision of a comprehensive weather information database for all aviation users to support the next-generation air transportation system (NextGen).