The monthly and hourly climatology of Saharan dust layer height over the Atlantic, at a spatial resolution of ∼10 km, is obtained for the first time, via a passive remote sensing technique. The technique is applied to multiple years of Earth Polychromatic Imaging Camera (EPIC) data collected at the Lagrange-1 point, generating a climate data record (CDR) of aerosol optical centroid height (AOCH). This CDR offers unprecedented spatial coverage and diurnal sampling compared to spaceborne lidars (CALIOP and CATS). Our results show high correspondence with CALIOP data in domain-averaged monthly variations and with CATS data in diurnal variations, respectively. A principal component analysis (PCA) reveals the dominant role of dust transport in regulating AOCH variation, whereas the impact of the boundary layer is less significant. MERRA-2 and satellite retrievals respectively display zero and 200–1,000 m of diurnal variation of AOCH, highlighting the uniqueness of EPIC AOCH CDR in constraining climate models. Plain Language Summary The vertical distribution of atmospheric dust particles affects the Earth's radiative balance, air quality prediction, and remote sensing of greenhouse gases and aerosols but is poorly represented in model reanalysis, such as MERRA-2. In this study, the monthly mean heights of the Saharan dust plume are characterized based on a retrieval of Earth Polychromatic Imaging Camera (EPIC) measurements using 4 years of data. The unique position of the EPIC instrument at the Lagrange-1 point, between the Earth and the sun, enables the observation of the sunlit side of the Earth every 1–2 hr. From these observations, the hourly mean climatology of Saharan dust plume height can be calculated. We find that the EPIC-retrieved dust layer heights agree well with spaceborne lidar data. Our analysis shows that trans-Atlantic dust transport dominates the variability in the dust layer heights observed by EPIC. This study is among the first to demonstrate the strong synergy between the passive sensing featuring large spatial coverage and active sensing with detailed profiling for characterizing the diurnal variation of aerosol vertical distribution and processes as well as the need of such synergy for the climate modeling studies.