Date of this Version
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, yet direct relationship exists between tire and pavement temperatures. Literature has shown that as tire temperature increases, friction decreases which 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. A preliminary simple radiative transfer model sensitivity study was conducted to determine the relative magnitude of cloud radiative forcing. 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. Distribution and correlation analyses during the daytime in June 2011 revealed that the median pavement temperatures for clear conditions are 40.9 °C, followed by cumuliform clouds at 34.9 °C, low clouds at 31.5 °C, high clouds at 27.9 °C and the coolest group is middle clouds at 23.7 °C. Similarly, distribution analyses identified maximum median observed surface radiation for clear conditions is 919.0 Wm-2, followed by cumuliform clouds at 800.0 Wm-2, low clouds at 750.0 Wm-2, high clouds at 675.1 Wm-2 and the least amount of radiation is associated with middle clouds at 388.4 Wm-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. These relationships will be used to develop future model sensitivity analyses to assimilate cloud information directly into pavement temperature energy balance models.
Adviser: Mark R. Anderson