Abstract
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 en-compasses 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.
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