Tuesday, 8 January 2013: 9:15 AM
Room 11AB (Austin Convention Center)
Handout (3.2 MB) Handout (4.9 MB)
Forecast systems provide decision support for end-users ranging from the solar energy industry to municipalities concerned with winter road maintenance. The racing community also relies on racetrack pavement temperature forecast systems because tire friction decreases as temperature increases, affecting vehicle performance. Race crews perform vehicle maintenance (e.g., tire pressure and suspension adjustments) to maximize traction given a forecasted racetrack temperature. Many forecast systems suffer from inaccurate radiation forecasts resulting in part from a lack of information relating radiation to cloud types. This research seeks to improve the forecasts by determining how cloud type impacts the amount of solar radiation reaching the surface. Cloud type information was obtained from the Naval Research Laboratory Cloud Classifier algorithm and radiation data were obtained from a Davis Weather Station. A theoretical maximum solar radiation distribution was calculated. Cloud type-radiation distribution analyses from Salisbury, North Carolina during May-June 2012 indicated that low clouds allowed approximately 20% of the maximum possible radiation to reach the surface, mid level clouds 32%, high clouds 40% and cumuliform types 34%. A categorical regression analysis revealed 33% of the variation in solar radiation on cloudy case days can be explained by cloud type. Inclusion of clear case days with apparent variability lowered this to 7% suggesting another influence on radiation. A similar bulk statistical analysis is in progress on a much larger data set obtained from the Oklahoma Mesonet. This work lays the foundation for use of satellite cloud type information in order to improve the output of forecast systems.
Supplementary URL: http://www.soars.ucar.edu/people/proteges/Curtis_Walker.php
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