An Investigation of Non‐Spherical Smoke Particles Using CATS Lidar

Abstract

AbstractSmoke 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.