## 4 Summary and Conclusions  
During its 33 months of operation on the ISS, CATS both extended and enhanced the global climate record of aerosol vertical profiles by providing aerosol heights and types with comprehensive coverage of the tropics and mid-latitudes at local times not observed by sun-synchronous NASA sensors. Aerosol-typing algorithms use depolarization to identify dust aerosols and invoke model data to help differentiate smoke and polluted continental aerosols to mitigate the impacts of set thresholds (e.g., surface type) on aerosol typing. The CATS aerosol-typing processing algorithms leveraged the heritage of the CALIPSO algorithms, and despite relying primarily on 1064 nm observations in Mode 7.2, regional and seasonal maps of CATS aerosol types show areas dominated by marine aerosols, dust, and smoke that are very consistent with well-known aerosol source regions, CALIPSO aerosol types, and MERRA-2 data. Additionally, the \(51^{\circ}\) inclination angle of the ISS orbit enables estimates of the diurnal and vertical variability of aerosols in regions such as Southeast Asia, where smoke from biomass burning tracks the daily convective cycle. This paper has demonstrated that the CATS aerosol algorithms and data products have a wide range of applications such as aerosol typing and transport, aerosol particle sphericity, and diurnal variability. CATS data users should consider limitations when analyzing CATS aerosol data, such as the mistyping of smoke and polluted continental aerosols off the west coast of South America or dust aerosols that may actually be non-spherical smoke in Southern Africa due to the burning of dry savannah in intense high-temperature fires. Furthermore, day-night SNR contrasts may hinder the ability to determine the diurnal variability of aerosols in regions with weaker aerosol loading. Future algorithms for spaceborne backscatter lidars have the potential to evolve beyond using hard threshold values for determining aerosol types, as used in the operational CALIPSO and CATS aerosol-typing algorithms, by using machine learning [23] and other techniques that utilize spatial correlations instead of determining aerosol type for a single profile independently. Finally, the next generation of space-based backscatter lidars should include multiple wavelengths of the depolarization ratio and a better SNR, specifically more similar day-to-night SNRs if in an inclined orbit, to further improve our understanding of the diurnal, seasonal, and regional variability of aerosol type, height, and extinction. The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, Figure S1: Suomi/NPP Deep Blue Aerosol Type on 6 August 2016; Figure S2: HYSPLIT back trajectories for a northeastern point of the CATS profile featured in Figure 3; Figure S3: HYSPLIT back trajectories for a southwestern point of the CATS profile featured in Figure 3; Figure S4: Observed diurnal cycle in extinction weighted altitudes for CATS and MPLNET for July, August, September, October, and November for the Southeast Asian region denoted in Figure 6. Shaded region denotes \(1\sigma^{\circ}\). Writing--original draft, E.P.N., K.E.C., J.E.Y., N.M., Z.L. and J.W. ; Writing--review & editing, E.P.N., K.E.C., J.E.Y., M.J.M., N.M., Z.L. and J.W. ; Investigation, M.J.M. ; Data curation, P.A.S. and S.V.S. All authors have read and agreed to the published version of the manuscript. The funding sources for this paper are: (1) the ISS NASA Research Office (NRO) for funding the CATS instrument, (2) the NASA Science Mission Directorate (SMD) for funding the operational CATS data products and processing algorithms, and (3) the NASA Earth Science Technology Office (ESTO) for funding the development of the machine learning algorithms presented in this paper. Not applicable. We acknowledge the ISS NASA Research Office (NRO) for funding the CATS instrument and the NASA Science Mission Directorate (SMD) for funding the operational CATS data products and processing algorithms.