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A multiparameter aerosol classification method and its application to retrievals from spaceborne polarimetry

Classifying observed aerosols into types (e.g., urban-industrial, biomass burning, mineral dust, maritime) helps to understand aerosol sources, transformations, effects, and feedback mechanisms; to improve accuracy of satellite retrievals; and to quantify aerosol radiative impacts on climate. The number of aerosol parameters retrieved from spaceborne sensors has been growing, from the initial aerosol optical depth (AOD) at one or a few wavelengths to a list that now includes AOD, complex refractive index, single scattering albedo (SSA), and depolarization of backscatter, each at several wavelengths, plus several particle size and shape parameters. Making optimal use of these varied data products requires objective, multidimensional analysis methods. We describe such a method, which makes explicit use of uncertainties in input parameters. It treats an N-parameter retrieved data point and its N-dimensional uncertainty as an extended data point, E. It then uses a modified Mahalanobis distance, DEC, to assign an observation to the class (cluster) C that has minimum DEC from the point. We use parameters retrieved from the Aerosol Robotic Network (AERONET) to define seven prespecified clusters (pure dust, polluted dust, urban-industrial/developed economy, urban-industrial/developing economy, dark biomass smoke, light biomass smoke, and pure marine), and we demonstrate application of the method to a 5 year record of retrievals from the spaceborne Polarization and Directionality of the Earths Reflectances 3 (POLDER 3) polarimeter over the island of Crete, Greece. Results show changes of aerosol type at this location in the eastern Mediterranean Sea, which is influenced by a wide variety of aerosol sources.

Remote sensing of above cloud aerosols

The direct and indirect radiative effects of aerosols suspended in the atmosphere above clouds (ACA) are a highly uncertain component of both regional and global climate. Much of this uncertainty is observational in nature most orbital remote sensing algorithms were not designed to simultaneously retrieve aerosol and cloud optical properties in the same vertical column.

Creating Aerosol Types from CHemistry (CATCH): A New Algorithm to Extend the Link Between Remote Sensing and Models

Current remote sensing methods can identify aerosol types within an atmospheric column, presenting an opportunity to incrementally bridge the gap between remote sensing and models. Here a new algorithm was designed for Creating Aerosol Types from CHemistry (CATCH). CATCH-derived aerosol types—dusty mix, maritime, urban, smoke, and fresh smoke—are based on first-generation airborne High Spectral Resolution Lidar (HSRL-1) retrievals during the Ship-Aircraft Bio-Optical Research (SABOR) campaign, July/August 2014. CATCH is designed to derive aerosol types from model output of chemical composition. CATCH-derived aerosol types are determined by multivariate clustering of model-calculated variables that have been trained using retrievals of aerosol types from HSRL-1. CATCH-derived aerosol types (with the exception of smoke) compare well with HSRL-1 retrievals during SABOR with an average difference in aerosol optical depth (AOD) <0.03. Data analysis shows that episodic free tropospheric transport of smoke is underpredicted by the Goddard Earth Observing System- with Chemistry (GEOS-Chem) model. Spatial distributions of CATCH-derived aerosol types for the North American model domain during July/August 2014 show that aerosol type-specific AOD values occurred over representative locations: urban over areas with large population, maritime over oceans, smoke, and fresh smoke over typical biomass burning regions. This study demonstrates that model-generated information on aerosol chemical composition can be translated into aerosol types analogous to those retrieved from remote sensing methods. In the future, spaceborne HSRL-1 and CATCH can be used to gain insight into chemical composition of aerosol types, reducing uncertainties in estimates of aerosol radiative forcing.

Aerosol Analysis and Forecast in the ECMWF Integrated Forecast System: Evaluation by Means of Case Studies

A near real-time assimilation and forecast system of aerosols has been developed by integration in the ECMWF IFS code within the GEMS project. The GEMS aerosol modeling system is novel as it is the first aerosol model fully coupled to a NWP model with data assimilation. Aerosol optical depth (AOD) data of the MODIS instrument on Terra and Aqua satellites was assimilated. The performance of the aerosol model was evaluated by the means of case studies. The assimilation of MODIS AOD improved the subsequent aerosol predictions when compared with observations, in particular concerning correlations and AOD peak values. The assimilation is less effective in correcting a positive or a negative bias.