Coastalregionsaresusceptibletomultiplecomplexdynamicandchemicalmechanismsandemission sources that lead to frequently observed large tropospheric ozone variations. These large ozone variations occur on a mesoscale and have proven to be arduous to simulate using chemical transport models (CTMs). We present a clustering analysis of multi-dimensional measurements from ozone lidar in conjunction with both an offline GEOS-Chem chemical-transport model (CTM) simulation and the online GEOS-Chem simulation GEOS-CF, to investigate the vertical and temporal variability of coastal ozone during three recent air quality campaigns: 2017 Ozone Water-Land Environmental Transition Study (OWLETS)-1, 2018 OWLETS-2, and 2018 Long Is- land Sound Tropospheric Ozone Study (LISTOS). We developed and tested a clustering method that resulted in five ozone profile curtain clusters. The established five clusters all varied significantly in ozone magnitude verti- cally and temporally, which allowed us to characterize the coastal ozone behavior. The lidar clusters provided a simplified way to evaluate the two CTMs for their performance of diverse coastal ozone cases. An overall evalu- ation of the models reveals good agreement (R ≈ 0.70) in the low-level altitude range (0 to 2000 m), with a low and unsystematic bias for GEOS-Chem and a high systemic positive bias for GEOS-CF. The mid-level (2000– 4000 m) performances show a high systematic negative bias for GEOS-Chem and an overall low unsystematic bias for GEOS-CF and a generally weak agreement to the lidar observations (R = 0.12 and 0.22, respectively). Evaluating cluster-by-cluster model performance reveals additional model insight that is overlooked in the over- all model performance. Utilizing the full vertical and diurnal ozone distribution information specific to lidar measurements, this work provides new insights on model proficiency in complex coastal regions.