ACPD Atmospheric Chemistry and Physics Discussions ACPD 1680-7375 Copernicus GmbH Göttingen, Germany 10.5194/acpd-8-18385-2008 Relating observations of contrail persistence to numerical weather analysis output Duda D. P. 1 Palikonda R. 2 Minnis P. 3 National Institute of Aerospace, Hampton, VA, USA Science Systems and Applications, Inc., Hampton, VA, USA Science Directorate, NASA Langley Research Center, Hampton, VA, USA 21 10 2008 8 5 18385 18407 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys-discuss.net/8/18385/2008/acpd-8-18385-2008.html The full text article is available as a PDF file from http://www.atmos-chem-phys-discuss.net/8/18385/2008/acpd-8-18385-2008.pdf

The potential for using high-resolution meteorological data from two operational numerical weather analyses (NWA) to diagnose and predict persistent contrail formation is evaluated using two independent contrail observation databases. Contrail occurrence statistics derived from surface and satellite observations between April 2004 and June 2005 are matched to the humidity, vertical velocity, wind shear and atmospheric stability derived from analyses from the Rapid Update Cycle (RUC) and the Advanced Regional Prediction System (ARPS) models. The relationships between contrail occurrence and the NWA-derived statistics are analyzed to determine under which atmospheric conditions persistent contrail formation is favored within NWAs. Humidity is the most important factor determining whether contrails are short-lived or persistent, and persistent contrails are more likely to appear when vertical velocities are positive, and more likely to spread when the atmosphere is less stable. Although artificial upper limits on upper tropospheric humidity within the NWAs prevent a direct quantitative agreement of model data with contrail formation theory, logistic regression or similar statistical methods may improve the prediction of contrail occurrence.

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