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ERDC/CRREL TR-02-13
Though the oldest and most simple forecast techniques use only radiosonde
information to predict icing conditions, newer models embellish this information
by considering the location of pressure systems and fronts, and topography. One
model, in use by the FAA, uses neural network derived statistical relationships
between standard atmospheric measurements, such as made by a radiosonde, and
pilot reports of icing, to make predictions. However, the most sophisticated
model available, developed by NCAR and in operational use by the National
Weather Service, is the Current Icing Potential (CIP), which combines output
from a mesoscale forecast model, MM5, surface observations, satellite imagery,
and NEXt generation RADar (NEXRAD) radar to determine the probable loca-
tion and timing of icing. NCAR is developing the Forecast Icing Potential (FIP),
which can use principally MM5 output, and potentially satellite and radar infor-
mation from any location, to predict icing. FIP should be valuable for OCONUS
military operations.
Icing forecast techniques are steadily improving, and provide forecasts today
of higher spatial and temporal resolution than only a few years ago. However,
they can be improved substantially as computing power allows model physics to
be implemented on finer spatial and temporal scales, as the ability to interpret
satellite observations improves, and as the understanding of cloud physics
matures. At the very least, satellite imagery can tell us where there are no clouds,
and thus no icing. However, despite needed improvements in spatial and tempo-
ral resolution, even more progress is needed to predict icing intensity, a difficult
problem because it requires the amount of supercooled liquid water at a location
to be predicted.
As a result of forecasting shortcomings, the National Aeronautics and Space
Administration (NASA), the Federal Aviation Administration (FAA), the
National Atmospheric and Oceanic Administration, (NOAA), the National
Center for Atmospheric Research (NCAR), and the U.S. Army Corps of
Engineers Engineer Research and Development Center Cold Regions Research
and Engineering Laboratory (ERDCCRREL) are developing technology for
remotely detecting icing conditions ahead of aircraft in-flight (Ryerson et al.
2001). Remote sensing systems should provide more timely and detailed infor-
mation about icing spatial extent and intensity.
Airframe icing typically does not occur until aircraft enter cloud or precipita-
tion conditions containing supercooled drops. Remote sensing systems, therefore,
must detect cloud microphysical conditions, such as droplets and their size and
temperature, rather than ice. Remote sensing systems and information retrieval
algorithms are being developed to allow radars or microwave radiometers located
either near airfields on the ground, or on aircraft, to detect and map icing poten-
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