A fundamental challenge in boundary-layer meteorology is how turbulence is dissipated; inaccuracies in the balance of turbulence production and dissipation undermine our ability to simulate atmospheric flows where turbulence is important. With support from NSF via Lundquist’s CAREER award, we have developed and demonstrated techniques using lidar to estimate dissipation rate in flat terrain (Bodini, Lundquist, and Newsom 2018 AMT), in complex terrain (Bodini, Lundquist, Krishnamurthy et al. 2019 ACP), in complex terrain with wind turbine wakes (Wildmann et al. 2019 AMT) and offshore (Bodini, Lundquist, and Kirincich 2019 GRL). We used complex terrain datasets to test a machine learning approach for approximating dissipation rate (Bodini, Optis, and Lundquist 2020 GMD). We then expanded this approach for a different complex terrain dataset and other lidar instruments (Sanchez Gomez and Lundquist 2021 ESSD). To better represent turbulence dissipation in numerical models, we are testing existing models (Muñoz-Esparza, Sharman, and Lundquist 2017 MWR) and new modeling approaches (Arthur et al. 2022 JAMC, Rybchuk et al. 2022, in review at WES).