The SSMI is a robust sensor , capable of producing numerous products.
As discussed earlier with equation (1), there is a direct relationship between
the Tb of the SSMI and the actual earth's surface temperature. A general
list of products derived from the SSMI follows:
Any attempt to fully discuss these products ( referred to as Environmental
Data Records, or EDRs) would be quite difficult. The author will attempt
only to give a flavor of these EDRs and how they are used.
One of the other uses of the SSMI is tropical cyclone reconnaissance.
Looking at figure (11), one sees a tropical cyclone in the North Arabian Sea.
The system exhibits a convective Central Dense Overcast (CDO) , blocking
a clear view of the cyclones center. Figure (12) shows the same DMSP pass
with the 85H channel from the SSMI. Note that the center of the cyclone is
37H. The primary channel is the 19V: as wind blows on the water, sea foam
is generated. This foam has a high emissivity, and is detectable at 19V.
One problem remains though: foam can be generated by a passing rainstorm:
a convective rain will certainly stir up the water above its normal level, even if
the winds a reasonbly calm. Thus, the potential for erros exists. Using the
37V/H channels both as a screen and as weighting factors in the wind speed
retrieval algorithm, these errors can be eliminated, reduced, or at least
identified. The primary way that the 37 GHz channels are used to screen the
data is to take the 37 differential (D37 = 37V - 37H) and use it to assign
weighting factors to the wind speed data using this D37. Generally, the higher
values of D37 indicate greater accuracy ( and confidence, statistically) of the
wind speed values. The current algorithm for surface winds is given below
from Grant  in equation (4):
SW = 147.90 + (1.0969) Tb19V - (0.4555) Tb22V
-(1.76)Tb37V + (0.786)Tb37H
For a complete discussion on the surface wind speed algorithm, see the
work of Grant  and Hollinger et al . See figure 13 for an example of
the output associated with this data. Other examples of this data are also
being made available through NGDC on the internet, and also by HQ AFWA
regression models, such as equation (4) . Robust algorithms on a global scale
are difficult to derive, since the surface characteristics of the earth are so
variable. One source of new research is the use of computer neural
networks (NN) in EDR computation. These NN algorithms, though non-linear,
are still regression oriented. At AFGWC, these new algorithms are being
tested. The results are displayed in figure 14. The figure shows several
different experimental surface wind algorithms. Bear in mind that colors of
red are 30 knots or greater: orange, yellow and blue are all less that 30 knots.
Notice the poor performance of the neural network algorithm on the bottom
left. This is meant to demonstrate that regression-type algorithms are simply not
'getting the job done ' on a global scale (note : this same NN algorithm performs
quite well in the extratropics).
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