12.4 Icing Beyond Plain Vanilla

Thursday, 4 August 2011: 9:45 AM
Imperial Suite ABC (Los Angeles Airport Marriott)
Marcia K. Politovich, NCAR, Boulder, CO

Icing diagnosis and forecasting have come a long way in the past decade. The automated Current and Forecast Icing Algorithms have reached operational status and do a pretty good job at what they're meant to do: provide hourly diagnoses and 12-h forecasts of icing on a 20-km scale across the CONUS with a broad-brush icing severity estimation. You might call this the “plain vanilla” version of icing forecasting.

What else is needed? Users have expressed their needs and wants for forecasting; higher resolution and better estimates of severity applicable for their specific aircraft usually head the list. Changing regulatory requirements will also demand more specific forecasts of the amount of liquid and the sizes of drops – along with characterizations of mixed-phase conditions. These needs call for operational versions of high-resolution numerical weather prediction models with vastly improved microphysics, better use of information from remote and in situ sensors, and additional research in aircraft response to icing conditions. Research in instrumentation designed specifically for icing detection has languished in recent years. Efforts to more effectively incorporate existing operational sensors such as NexRad, GOES and POES are making slow progress. Resource limitations necessarily result in tough choices, and if those choices are not well coordinated we tend to end up with incomplete programs, unmet requirements, and unhappy people.

Thus, the icing community must prioritize needs and means to meet those needs. We must arrive at a consensus by balancing user desires, regulatory requirements, technical feasibility, NextGen timelines, and finally that magical and sometimes elusive ingredient -- funding. This talk will propose a set of research priorities as a starting point for future discussion and will show some highlights of recent research that look promising for improving icing diagnoses and forecasts.

This research is in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA.

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