An Investigation of Non-Spherical Smoke Particles Using CATS Lidar

Smoke particles originating from biomass burning events are typically assumed to be spherical, yet non-spherical smoke particles are also reported from in situ observations. The spatial and temporal distributions of non-spherical smoke particles, which could have impacts on passive- and active-based satellite aerosol retrievals, are not yet well understood. In this analysis, using NASA's Cloud Aerosol Transport System (CATS) lidar data during the biomass burning season over Africa and South America from 2015 to 2017, we studied the frequency and distribution of non-spherical smoke particles. A supplemental smoke aerosol typing algorithm was developed to identify aerosol layers containing non-spherical smoke particles which could otherwise be misclassified as dust or dust mixture using the CATS standard aerosol typing algorithm. Approximately 30% of smoke layers over Africa and South America are non-spherical (depolarization ratio >0.1) and align with dry biomes of low soil moisture values. Conversely, spherical smoke layers (depolarization ratio <0.1) are in moist regions. The modified algorithm with improved discrimination of non-spherical smoke detection using CATS depolarization ratio was further verified with the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory model, Aerosol Robotic Network Ångström exponent retrievals, and National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis soil moisture data. This study highlights the limitations of current aerosol typing algorithms and the potential of algorithms employing ancillary data to improve aerosol typing such as multi-wavelength volume depolarization ratio measurements or synergy with passive sensors to further discriminate between aerosol types from spaceborne elastic backscatter lidar. Plain Language Summary Biomass burning over Africa and South America releases large amounts of anthropogenic aerosols in the form of smoke particles. Non-spherical smoke particles have been found in previous studies which impact the accuracy of passive and active sensor retrievals relying on assumptions of spherical smoke aerosol. In this study, a supplemental smoke aerosol typing algorithm is applied to 3 years of NASA CATS lidar data over Africa and South America to determine the frequency and spatial distribution of non-spherical smoke which is typically misclassified as dust or dust mixture by the standard aerosol typing algorithm. While approximately 70% of smoke layers identified by the algorithm are considered spherical, 30% of layers are non-spherical as determined by increased values in lidar volume depolarization ratio (VDR). Regions with high values of depolarization ratio, indicative of non-spherical particles, are dry biomes with low soil moisture, while spherical particles are found in moist regions with higher values of soil moisture. The findings of this analysis highlight the need for improved aerosol typing algorithms which may be possible through multiple wavelengths of VDR retrievals in future spaceborne elastic backscatter lidar.