The Impact of Clarus Data on Weather and Road Weather Forecasts

In 2004, the U.S. Department of Transportation (USDOT) established the Clarus initiative. A primary objective of the Clarus Initiative is to help public transportation agencies make better road management decisions by taking full advantage of investments in Road Weather Information Systems (RWIS). The Clarus System is the physical (hardware and software) portion of the initiative and is currently a functioning prototype data management system that collects, quality checks, and disseminates public agency-sponsored surface transportation weather data from across many U.S. states and a few Canadian provinces. The goal is to have all public transportation agencies provide their atmospheric and pavement data and metadata into Clarus which would benefit both the transportation and weather communities. In 2009, the FHWA sponsored two Clarus Regional Demonstrations intended to foster innovative use of Clarus data. These two demonstrations support different combinations of five Clarus-enabled services. One service, titled “Enhanced Road Weather Forecasting Enabled by Claru”, used Clarus-based RWIS data to enhance atmospheric and pavement forecasts for surface transportation. The FHWA also sponsored an evaluation of the impact that Clarus data on the several types of forecasts produced by the Clarus Regional Demonstrations, including: • Traditional atmospheric weather model forecasts. The Clarus data was integrated with other data sources to initialize atmospheric weather models. • Pavement Precipitation Accumulation Estimation System (PPAES) forecasts. The Clarus data was used as a source of precipitation observations to supplement radar observations and model forecasts to provide improved estimates of surface precipitation. • Pavement condition forecasts. The Clarus data was used in a pavement condition model to provide initial pavement condition data and improved atmospheric weather forecasts at sensor stations. For each of these forecast types, two versions of the models were run in parallel, one including the Clarus data and one excluding it. The results from the two model runs were archived and analyzed to assess the impacts of the Clarus data on those forecasts. Differences in the atmospheric forecasts (with and without Clarus data were identified) and compared to surface observations. PPAES estimates, made with and without Clarus data, were compared to surface observations. Pavement temperature forecasts were compared to pavement temperature observations. Results of those comparisons between forecasts and estimates made with and without Clarus data will be described.