Shortwave minus longwave IR:
The discussion concerning Shortwave IR images only scratched the surface
of the importance of the shortwave IR channel data. It was stated that
the shortwave IR channel is sensitive to both reflected AND emitted IR energy
at about 3.9 microns. While the sun is illuminating clouds, a very large
portion of the measurements MAY be contributed by reflected energy. I use
the word may because it depends on many things. The amount of reflected energy
depends upon the relative angle between the sun, clouds, and satellite. It also
depends upon the reflecting material: ocean, land, sandy deserts, snow-covered
ground, ice particles and water droplets in clouds. It is these
last 3 items which we care about most. These data have potential to
discriminate clouds composed of water versus those composed of ice particles.
A general rule of thumb: clouds composed of water REFLECT much more shortwave
IR energy than clouds composed of ice or snow-covered ground. The amount of
reflection is dependent upon the size of the water drops and ice particles.
At night, reflection is not contributing to the measurements and any
differences between water and ice/snow are purely emission. The water drops
EMIT less energy at 3.9 microns than at 11 microns whereas ice particles EMIT
nearly the same amount at the two wavelengths. Therefore subtracting the
shortwave IR data from the longwave IR data produces negative values for
clouds composed of water and near-zero values for ice/snow clouds. So what?
Well, if you work on aircraft icing problems like we do, this data can be a
goldmine. These data are also useful in detecting low stratus clouds and
fog.
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Aircraft Icing product:
This product is created with all of the data mentioned in the above bullets as
well as the visible and longwave IR channel data. With all of the above
data and products, a data fusion is performed to determine areas of supercooled
liquid water (clouds composed of water drops which are below 0°C).
These areas are highlighted in blue and represent a possible icing hazard to
aircraft (possible because different planes may or may not be affected by the
supercooled droplets). When all of the pre-set conditions are not met for the
Icing Product, the standard visible or IR data is shown in its place depending
on whether it is daytime (visible) or nighttime (IR). Three statements of
WARNING are necessary: 1) the technique is experimental and further
testing continues. 2) the technique will not work well for multi-level cloud
decks. This is not surprising since weather satellites cannot "see" through
clouds. They "see" only the tops of most clouds. When a layer of cirrus or
altostratus (and those clouds are composed of ice and/or snow) exists above a
layer of water-filled stratus, the satellite data only contain the information
of the top layer. Therefore it would be impossible for this product to display
an icing hazard for the lower cloud deck in this case. 3) because of the strong
dependence on the angle between the sun, clouds, and satellite, the images are
not effective in regions near where the sun is rising/setting. Disregard data
in these regions (displayed on the images as a very distinct, nearly vertical
line close to sunrise/sunset. NCAR/RAP has done extensive testing and
verification of this product and we think this product has great potential
when combined with other available data.
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References:
- Allen, R.C., P.A. Durkee, and C.H. Wash, 1990: Snow-cloud discrimination
with multispectral satellite images. J. Appl. Meteor., 29, 994-1004.
- Arking, A., and J.D. Childs, 1985: Retrieval of cloud cover parameters
from multispectral satellite images. J. Clim. Appl. Meteor., 24, 322-333.
- Coakley, J.A., Bernstein, R.L., and P.A. Durkee, 1987: Effect of ship-stack
effluents on cloud reflectivity. Science, 237, 1020-1022.
- Kleespies, T.J., 1995: The retrieval of marine stratiform cloud properties
from multiple observations in the 3.9 micron window under conditions of
varying solar illumination. J. Appl. Meteor., 34, 1512-1524.
- Menzel, W.P., and J.F.W. Purdom, 1994: Introducing GOES-I: The first
of a new generation of geostationary operational environmental satellites.
Bull. Amer. Meteor. Soc., 75, 757-781.
- Stamnes, K., S.C. Tsay, W. Wiscombe, and K. Jayaweera, 1988:
Numerically stable algorithm for discrete ordinate method radiative transfer
in multiple scattering and emitting layered media. Appl. Optics,
27, 2502-2509.
- Thompson, G., T. Lee, and J. Vivekanandan, 1997: Comparisons of
satellite-based aircraft icing diagnoses. Preprints,
7th Conf. on Aviation, Range and Aerospace Meteorology,
Long Beach, CA, 2-7 Feb 1997.
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