Assessing the influence of assimilating Clarus data into road and atmospheric forecasts

Under the Clarus initiative, the Federal Highway Administration is coordinating the development a nationwide network of Road Weather Information Systems (RWIS). These RWIS collect a suite of atmospheric and surface data that offer the promise of improving road and weather forecasts. In this presentation, we assess the influence of assimilating Clarus data into a numerical weather model (WRF) by examining forecast error when the Clarus data are included or left out of the model simulations. Five historical high-impact weather case studies, focused on Iowa, Indiana, and Illinois, are used for the assessment. The case studies include a precipitation event (2008 Midwest Flood), high winds (remnants of Hurricane Ike), a squall line, and hot and cold weather. The Model Evaluation Tools (MET) verification software is used for determining the prediction skill of the numerical model surface variables. Changes in forecast values of precipitation, winds, temperature, and relative humidity due to data assimilation are examined.