Studying Clouds and Aerosols with Lidar Depolarization Ratio and Backscatter Relationships



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This dissertation consists of three parts, each devoted to a particular issue of significant importance for CALIPSO lidar observation of depolarization ratio (delta) and backscatter (gamma?) to improve current understanding of the microphysical properties of clouds and aerosols. The relationships between depolarization ratio and backscatter allow us to retrieve particle thermodynamic phase and shape and/or orientation of aerosols and clouds.

The first part is devoted to the investigation of the relationships between lidar backscatter and the corresponding depolarization ratio for different cloud classifications and aerosol types. For each cloud and aerosol types, layer-averaged backscatter and backscattering depolarization ratio from the CALIPSO measurements are discussed. The present results demonstrate the unique capabilities of the CALIPSO lidar instrument for determining cloud phase and aerosols subtypes.

In the second part, we evaluate the MODIS IR cloud phase with the CALIPSO cloud products. The three possible misclassifications of MODIS IR cloud phasealgorithm, which are studied by Nasiri and Kahn (2008) with radiative transfer modeling, are tested by comparing between MODIS IR phase and CALIOP observations. The current results support their hypotheses, which is that the MODIS phase algorithm may tend to classify thin cirrus clouds as water clouds or mixed phase clouds or unknown, and classify midlevel and/or mid-temperature clouds as mixed or unknown phase.

In the third part, we present a comparison of mineral dust aerosol retrievals from two instruments, MODIS and CALIPSO lidar. And, we implement and evaluate a new mineral dust detection algorithm based on the analysis of thin dust radiative signature. In comparison, three commonly used visible and IR mineral dust detection algorithms, including BTD procedure, D parameter method, and multi-channel image algorithm, are evaluated with CALIPSO aerosol classification. The comparison reveals that those dust detection algorithms are not effective for optically thin dust layers, but for thick dust storm. The new algorithm using discriminant analysis with CALIPSO observation is much better in detecting thin dust layer of optical thickness between 0.1 and 2.