Dataset of the study titled Improved Dust Microphysical Retrieval from Lidar by Introducing a priori Constraints from In Situ and Laboratory Measurements

This dataset contains simulation results of a study titled: “Improved dust microphysical retrieval from lidar by introducing a priori constraints from in situ measurements”. Dust aerosols have significant impacts on the atmospheric radiation, ecosystem, and human activities. In order to better understand dust effects, information on spatiotemporal distribution of dust microphysical properties, including the size distribution and complex refractive index, is crucial and can be derived from active lidar remote sensing technique through some retrieval methods. This study discusses potential retrieval bias of an aerosol-lidar retrieval method when it is applied to dust retrieval and proposes a modified method aiming to improve the retrieval accuracy. The basic idea is to introduce complementary measurements to provide additional information on dust microphysical properties. We apply the original and modified methods to both simulated and real lidar measurements of dust aerosols. The results show that compared to the original method, the proposed method greatly improves the retrieval quality, although faint degradation is also found in certain circumstances. We conclude that the proposed method can be used to improve dust microphysical retrieval from lidar measurements, which further facilitates our understanding of the impacts of mineral dust on the Earth system. This uploaded dataset consists of three NetCDF4 files, recording the results of retrieval simulation of error-free optical data, error-perturbed optical data, and the results of retrieval standard deviation calculation, respectively. A README file provides more details about the structure of the dataset.