# Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns
## 8 Concluding Remarks and Forthcoming Research Directions
To better reveal the complexity of atmospheric turbulence dynamics, the sensor outputs should be differently affected by atmospheric turbulence; e.g., have enhanced sensitivity to the location of turbulence layers, or to specific spatial and/or temporal characteristics of atmospheric refractive index inhomogeneities, or changes in atmospheric refractivity, visibility, etc. From this viewpoint diversity in sensing data that enter a DNN-based signal processing system is highly desired. Although in this paper we focused solely on sensing of the refractive index structure parameter \(C_{n}^{2}\), machine learning-inspired atmospheric sensors could also selectively target different atmospheric turbulence characteristics (e.g., Fried, or Rytov parameters, Strehl ratio, image sharpness, received signal fading duration, etc. [29; 40]) that more directly characterize the performance of one or another electro-optical system. This eliminates the need for computing these characteristics (performance metrics) using theoretical expressions that link performance metrics with the characteristics of the propagation path, optical system parameters and characterized by \(C_{n}^{2}\) turbulence conditions. Note that in many cases such expressions derived from the theory either do not exist or can only be obtained under assumptions that are not always fulfilled. These arguments emphasize another major advantage of the machine learning-enhanced atmospheric sensing approach: it provides assumption-free prediction (with a properly trained DNN) of specific performance metrics and/or those most important for various types of optical systems. Since machine-learning-inspired atmospheric sensing is based on extraction of spatial or temporal (or both) features from the incoming sensing data, rather than on time-averaging of data acquired during the time interval between sequential measurements as in conventional instruments used for turbulence characterization, DNN-enhanced sensors can potentially provide much higher temporal resolution and be capable of real-time detection of the changes occurring in atmosphere under different turbulence conditions. This capability is especially important for prediction and in-situ adjustment of electro-optical systems parameters based on atmospheric sensing information. For example, prediction of coming deep signal fading in a free-space laser communication system could be used to minimize losses in the throughput data. In adaptive optics systems, in-situ prediction of turbulence conditions that may occur within the time scale of a few seconds may provide opportunities to correspondingly adjust wavefront sensing and control system parameters for optimal system performance. Machine learning-enhanced atmospheric sensing may also transform wave-optics numerical modeling and simulation tools from its current supporting role of electro-optical systems performance assessment under various environments into a key role of being an integrated part of the sensing system responsible for the DNN topology, parameter optimization and training. Another important role machine learning could play for a wide range of atmospheric sensing applications is related to calibration of different sensors. \"Agreement\" between different sensor types, for example for \(C_{n}^{2}\) measurements, is currently difficult to achieve even if these sensors operate side-by-side to simultaneously take measurements at the same atmospheric propagation site. This is not a surprise, as the operational principles of different sensors are commonly based on different assumptions and formulas that are derived from atmospheric turbulence theory and link \(C_{n}^{2}\) with the statistical characteristics of measured signals.