The SSMI is a robust sensor , capable of producing numerous products.

As discussed earlier with equation (1),  there is a direct relationship between

the T_b 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

clearly evident.

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 [1991] in equation (4):
 

SW = 147.90 + (1.0969) Tb19V  - (0.4555) Tb22V             (4)
            -(1.76)Tb37V + (0.786)Tb37H
           

For a complete discussion on the surface wind speed algorithm,  see the

work of Grant [1991] and Hollinger et al [1989]. 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

(see http://afwin.afwa.af.mil:443).
 

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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