Effective Benchmarks for Optical Turbulence Modeling

Abstract

Optical turbulence poses a significant challenge for communication, directed energy, and imaging systems, particularly in the atmospheric boundary layer. Effective modeling of optical turbulence is crucial for the development and deployment of these systems, yet the lack of standardized evaluation tools and benchmark data sets hinders the development and adoption of machine learning to address these challenges. We introduce the otbench Python package, a comprehensive framework for rigorous development and evaluation of optical turbulence strength prediction models. This package provides a consistent interface for testing models across diverse data sets and tasks, including a novel, long-term data set collected over two years at the United States Naval Academy. otbench incorporates a range of baseline models (statistical, data-driven, and deep learning), enabling researchers to assess the relative quality of their approaches and identify areas for improvement. Our analysis reveals the applicability of various models across different environments, highlighting the importance of long-term data sets for robust model evaluation. By promoting standardized benchmarking and facilitating model comparison, otbench empowers researchers to accelerate the adoption of machine learning techniques for optical turbulence modeling.