About NCAR-RAP multi-spectral satellite products
Brightness Temperature Difference:
The subtraction of channel 5 data from channel 4 is referred to as Brightness Temperature Difference. Channel 5 is referred to as the Split-Window IR channel and is centered at approximately 12 microns in wavelength. Channel 4 is the more standard IR channel and is centered at approximately 11 microns. Optically thin clouds emit different amounts of energy at these two wavelengths. Optically opaque (optical depth = 1.0) clouds emit the same amount of energy at these wavelengths. By subtracting these two channels, regions of thin clouds (usually high, thin cirrus) can be displayed very prominently. Also, because of the chemical composition of effluents from volcanic eruptions as well as the emitting characteristics of ash clouds, these data can be quite useful at locating plumes of ash from volcanic eruptions.
Shortwave minus longwave IR:
The discussion on the previous page 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.
Shortwave IR reflectance product:
This is a product created by a multispectral analysis program which uses radiative transfer theory to derive a new product from other data. This is not one of the GOES imager channels but is a product of a combination of raw data obtained from the imager channels. A quick summary is described here. A more detailed text can be found on a page written by Joe Turk at the Naval Research Lab and in the journal references he cites. The goal of this product is to enhance the difference between shortwave and longwave IR data by isolating the solar reflectance at 3.9 microns (shortwave IR) and eliminating the emitted component. Again, this is valuable to discriminate between water and ice/snow clouds. An image of this product can highlight areas of water clouds that may represent stratus and/or fog and when used in conjunction with temperature data can indicate regions of supercooled liquid water (hence a possible aircraft icing hazard).
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.
References:
  1. Allen, R.C., P.A. Durkee, and C.H. Wash, 1990: Snow-cloud discrimination with multispectral satellite images. J. Appl. Meteor., 29, 994-1004.
  2. Arking, A., and J.D. Childs, 1985: Retrieval of cloud cover parameters from multispectral satellite images. J. Clim. Appl. Meteor., 24, 322-333.
  3. Coakley, J.A., Bernstein, R.L., and P.A. Durkee, 1987: Effect of ship-stack effluents on cloud reflectivity. Science, 237, 1020-1022.
  4. 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.
  5. 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.
  6. 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.
  7. 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.