Lidar Methods for Observing Mineral Dust

Lidar methods for observing mineral dust aerosols are reviewed.These methods include Mie scattering lidars,polarization lidars,Raman scattering lidars,high-spectral-resolution lidars,and fluorescence lidars.Some of the lidar systems developed by the authors and the results of the observations and applications are introduced.The largest advantage of the lidar methods is that they can observe vertical distribution of aerosols continuously with high temporal and spatial resolutions.Networks of ground-based lidars provide useful data for understanding the distribution and movement of mineral dust and other aerosols.The lidar network data are actually used for validation and assimilation of dust transport models,which can evaluate emission,transport,and deposition of mineral dust.The lidar methods are also useful for measuring the optical characteristics of aerosols that are essential to assess the radiative effects of aerosols.Evolution of the lidar data analysis methods for aerosol characterization is also reviewed.Observations from space and ground-based networks are two important approaches with the lidar methods in the studies of the effects of mineral dust and other aerosols on climate and the environment.Directions of the researches with lidar methods in the near future are discussed.

High-precision temperature retrieval algorithm and verification for mesospheric airglow spectrum photometer

Photometric technology,characterized by its compact structure and relatively high stability,finds wide application in measuring airglow spectra.This instrumentation is anticipated to assume a pivotal role as the primary equipment for extensive network observations of middle and upper atmospheric temperatures in China,thereby providing crucial support for space environmental monitoring and atmospheric dynamic research.Nevertheless,susceptibility to various factors such as instrument inconsistency,variability in observation conditions,and alterations in the background atmospheric environment across different stations poses a challenge,potentially resulting in data inconsistencies in network observations.In response to these challenges,we propose a multiple-parameter iterative inversion(MPII)algorithm for temperature retrieval based on a mesospheric airglow spectrum photometer(MASP)developed by our research group.This algorithm accurately identifies the center of the image circle,corrects image distortion,and thereby obtains an accurate synthetic spectrum reflective of actual observations.It encompasses five adjustable parameters:sky background light,atmospheric temperature,filter temperature,optical system focal length,and degree of synthetic spectrum modulation.Compared to traditional methods,significant enhancements in the accuracy of the inverted temperature are achieved.To validate the effectiveness of the MPII algorithm,we conducted combined active and passive remote sensing synchronous measurements using MASP in conjunction with a sodium fluorescence Doppler lidar developed by the National Space Science Center.By utilizing the lidar temperature as a reference,atmospheric background radiation is mitigated from the MASP data,and the temperature is inverted using the MPII algorithm.Comparative analysis with the traditional method reveals that temperatures calculated by the MPII algorithm exhibit better consistency than those observed by the lidar.