William L. Smith

Transformation of contrails into cirrus during SUCCESS

Three contrail systems were analyzed with geostationary satellite data to document the conversion of the contrails to cirrus clouds. Two unique contrails, a pair of figure eights and a NASA DC-8 oval, were tracked for more than 7 hours. A cluster of contrails from commercial aircraft lasted over 17 hours. The figure eights produced a cirrus cloud having a maximum extent of 12,000 km²; the commercial cluster reached an area of ∼35,000 km². The contrail-cirrus were thin with optical depths between 0.2 and 0.5. In all cases, cloud particle size increased as the contrails developed into cirrus clouds. The climatic impact of contrails will be greater than would be estimated if only linear contrails, those typically observed in satellite imagery, are considered. Additional research is required to obtain reliable statistics on contrail growth and lifetime.

Cloud and radiative fields derived from GOES‐8 during SUCCESS and the ARM‐UAV spring 1996 flight series

Cloud and radiative properties were derived for the southern Great Plains for April 9 - May 9, 1996 from GOES-8 to provide a large-scale context for SUCCESS and a dataset for mesoscale modeling of cirrus processes. The analyses indicate that cloudiness over the domain was below normal during April 1996. Thin cirrus with optical depths less than 6 was the predominant cloud type over much of the domain, however, providing a considerable number of measurement opportunities for the suite of SUCCESS instruments. Diurnal variation of cloudiness was substantial over the ARM southern Great Plains central facility. Total albedos were below normal because of the diminished thick cloudiness, while surface temperature and total longwave fluxes were relatively high. The data are on a 0.5° grid and are available electronically.

Detection of single and multilayer clouds in an artificial neural network approach

Determining whether a scene observed with a satellite imager is composed of a thin cirrus over a water cloud or thick cirrus contiguous with underlying layers of ice and water clouds is often difficult because of similarities in the observed radiance values. In this paper an artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels and the retrieved total cloud visible optical depth, is trained to detect multilayer ice-over-water cloud systems as identified by matched April 2009 CloudSat and CALIPSO (CC) data. The CC lidar and radar profiles provide the vertical structure that serves as output truth for a multilayer ANN, or MLANN, algorithm. Applying the trained MLANN to independent July 2008 MODIS data resulted in a combined ML and single layer hit rate of 75% (72%) for nonpolar regions during the day (night). The results are comparable to or more accurate than currently available methods. Areas of improvement are identified and will be addressed in future versions of the MLANN.