Data Leap Frogging

Remote Sensing Systems (RSS) is one of the groups that use the Mircowave Sounding Units (MSU) on board satellites to develop atmospheric temperature products. While the MSU data has its issues, in general it is the best source of atmospheric temperature information we have. In order to build their data sets, the have to weigh layers of the atmosphere using filters on the data. The chart above show the weighing for the four different products.

Starting at the surface, I will call the products A,B,C and D. As you can see, there is considerable overlap between A and B, B and C, C and D, which would tend to suppress the information when the adjacent layers are compared. Leap Frogging, would compare A to C, B to D and A to D, to reduce the signal suppression. Not a very complicated thing to do, right?

Then B could be compared to A-C, A-D and B-D to determine the best approximation for a direct comparison to A or C. While a little complicated, it would improve the confidence in the values for each layer.

To take advantage of this leaf frogging, I have recommend that a Bucky Ball shaped model of concentric spheres be use. Again, a little complicated, but by using the center of the Earth as a distance vector and Bucky shaped areas, much like the sections of a soccer ball as target areas, a three dimensional model of the common thermodynamic boundary layers of the Earth climate system can be made to better determine the energy flows between layers and sections.

Using the modified Kimoto equation adapted for what I call learning mode, the fungible characteristic of energy flux can be used to more accurately track the various energy flows and energy lost to heat in transit from any point in the model.

Constructed in this manner, the model would be comparable to the Relativistic Heat Equations as the relative velocity of energy flow could be roughly determined between adjacent thermodynamic boundary layers. A simple concept, not so simple to develop fully, but it should be capable of continuous modification and more of the relationships between boundary layers is learned. Kinda like complex modeling for dummies.