Over the past decade there have been significant improvements in the availability, volume, and quality of the sensors and technology utilized to both capture the current state of the atmosphere and generate weather forecasts. New radar systems, automated surface observing systems, satellites and advanced numerical models have all contributed to these advances. However, the practical application of this new technology for transportation decision makers has been primarily limited to aviation. Surface transportation operators, like air traffic operators, require tailored weather products and alerts and guidance on recommended remedial action (e.g. applying chemicals or adjusting traffic flow). Recognizing this deficiency, the FHWA (Federal Highway Administration) has been working to define the weather related needs and operational requirements of the surface transportation community since October 1999. A primary focus of the FHWA baseline user needs and requirements has been winter road maintenance personnel (Pisano, 2001). A key finding of the requirements process was that state DOTs (Departments of Transportation) were in need of a weather forecast system that provided them both an integrated view of their weather, road and crew operations and advanced guidance on what course of action might be required to keep traffic flowing safely. As a result, the FHWA funded a small project (~$900K/year) involving a consortium of national laboratories to aggressively research and develop a prototype integrated Maintenance Decision Support System (MDSS). The prototype MDSS uses state-of-the-art weather and road condition forecast technology and integrates it with FHWA anti-icing guidelines to provide guidance to State DOTs in planning and managing winter storm events (Mahoney, 2003). The overall flow of the MDSS is shown in Figure 1. Basic meteorological data and advanced models are ingested into the Road Weather Forecast System (RWFS). The RWFS, developed by the National Center for Atmospheric Research (NCAR), dynamically weights the ingested model and station data to produce ambient weather forecasts (temperature, precipitation, wind, etc.). More details on the RWFS system can be found in (Myers, 2002). Next, the RCTM (Road Condition Treatment Module) ingests the forecasted weather conditions from the RWFS, calculates the predicted road conditions (snow depth, pavement temperature), Once a treatment plan has been determined, the recommendations are presented in map and table form through the MDSS display. The display also allows users to examine specific road and weather parameters, and to override the algorithm recommended treatments with a user-specified plan. A brief test of the MDSS system was performed in Minnesota during the spring of 2002. Further refinements were made and an initial version of the MDSS was released by the FHWA in September 2002. While this basic system is not yet complete, it does ingest all the necessary weather data and produce an integrated view of the road conditions and recommended treatments. This paper details the RCTM algorithm and its’ components, including the current and potential capabilities of the system.