  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}.

Atmospheric and Oceanic Physics;Machine Learning