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 datasets 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 datasets and tasks, including a novel, long-term dataset collected over 2 years at the United States Naval Academy. The otbench Python package 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 datasets 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. Significance Statement The accurate prediction and forecasting of optical turbulence are vital for the successful operation of laser-based systems in operational environments. However, the lack of standardized benchmark datasets and evaluation tools has hindered progress in this field, particularly in the application of advanced machine learning techniques. The otbench Python package addresses this critical gap by providing a comprehensive framework for developing, evaluating, and comparing optical turbulence models. By incorporating a novel, long-term dataset and a range of baseline models, otbench enables rigorous model assessment and facilitates the development of next-generation machine learning solutions that can significantly enhance the performance and reliability of laser-based systems in real-world conditions. This package not only contributes to the scientific understanding of optical turbulence but also holds significant potential to drive innovation in the design and optimization of machine learning applications in laser-based technologies across various domains, including communication, directed energy, and imaging.