Abstract
Forecast systems provide decision support for end users ranging from the solar energy industry to municipalities concerned with road safety. Pavement temperature is an important variable when considering vehicle response to various weather conditions. A complex relationship exists between tire and pavement temperatures that affects vehicle performance. Many forecast systems suffer from inaccurate radiation forecasts resulting in part from the inability to model different types of clouds and their influence on radiation. This research focuses on forecast improvement by determining how cloud type impacts pavement temperature and the amount of shortwave radiation reaching the surface. The study region is the Great Plains where surface radiation data were obtained from the High Plains Regional Climate Center’s Automated Weather Data Network stations. Pavement temperature data were obtained from the Meteorological Assimilation Data Ingest System. Cloud-type identification was possible via the Naval Research Laboratory Cloud Classification algorithm, and clouds were subsequently sorted into five distinct groups: clear conditions, low clouds, middle clouds, high clouds, and cumuliform clouds. Statistical analyses during the daytime in June 2011 revealed that cloud cover lowered pavement temperatures by up to approximately 10°C and dampened downwelling shortwave radiation by up to 400 W m−2. These pavement temperatures and surface radiation observations were strongly correlated, with a maximum correlation coefficient of 0.83. A comparison between cloud-type group identified and cloud cover observed from satellite images provided a measure of confidence in the results and identified cautions with using satellite-based cloud detection.
