Deriving Predictive Turbulence Data Models

摘要

We present a novel algorithm taking measurements of time, solar irradiance, wind speed, peak wind speed, temperature gradient, and relative humidity to derive a predictive differential equation for mean C_n~2. Our method derives individual control terms and forcing functions by modeling macro-structure, micro-structure, and fine structure terms independently. The final model is suitable for analysis and able to be used as a baseline expectation model for in situ battlefield use for predictive optical correction or slewing, and possibly for mitigating the effects of wind shear on artillery shells downrange.