Effective Benchmarks for Optical Turbulence Modeling

TL;DR

This paper introduces the otbench Python package, which standardizes the evaluation of optical turbulence models using diverse benchmarks, datasets, and metrics to improve model comparison and development.

Contribution

The paper presents otbench, a comprehensive toolkit for consistent evaluation of optical turbulence models, including baseline models and support for new data and metrics.

Findings

Provides a unified interface for model evaluation

Includes various baseline models for comparison

Supports addition of new datasets and metrics

Abstract

Optical turbulence presents a significant challenge for communication, directed energy, and imaging systems, especially in the atmospheric boundary layer. Effective modeling of optical turbulence strength is critical for the development and deployment of these systems. The lack of standard evaluation tools, especially long-term data sets, modeling tasks, metrics, and baseline models, prevent effective comparisons between approaches and models. This reduces the ease of reproducing results and contributes to over-fitting on local micro-climates. Performance characterized using evaluation metrics provides some insight into the applicability of a model for predicting the strength of optical turbulence. However, these metrics are not sufficient for understanding the relative quality of a model. We introduce the \texttt{otbench} package, a Python package for rigorous development and evaluation of optical turbulence strength prediction models. The package provides a consistent interface for evaluating optical turbulence models on a variety of benchmark tasks and data sets. The \texttt{otbench} package includes a range of baseline models, including statistical, data-driven, and deep learning models, to provide a sense of relative model quality. \texttt{otbench} also provides support for adding new data sets, tasks, and evaluation metrics. The package is available at \url{https://github.com/cdjellen/otbench}.