地球与行星物理论评（中英文）2025，Vol.56Issue(3)：352-359,8.DOI:10.19975/j.dqyxx.2024-045
基于理论模式参数智能优化的中间层数据同化
Mesosphere data assimilation based on the intelligent optimization of the uncer-tainty parameters in a theoretical model
摘要
Abstract
The mesosphere,which is located approximately 50-90 km above the Earth's surface,is a crucial part of the Earth's atmosphere.Data assimilation in the mesosphere is essential for accurately simulating and fore-casting its state.However,the lack of sufficient observations results in this field being relatively underdeveloped.In this study,we conducted an intelligent optimization particle filtering algorithm to optimize the uncertainty parame-ters in a physics-based model,which was used to simulate the terrestrial mesosphere.This algorithm was employed to improve the accuracy of mesospheric state simulation via the injection of sparse observations.The mesospheric temperature,density,and pressure profiles,measured by the SABER(Sounding of the Atmosphere using Broad-band Emission Radiometry)onboard the TIMED(Thermosphere Ionosphere Mesosphere Energetics and Dyna-mics)satellite,were injected into the data assimilation model.The comparison results demonstrated that the statisti-cal error in the mesospheric temperature simulation from the data assimilation model is comparable to that from the theoretical model.However,owing to the improved accuracy in simulating individual temperature profile,the as-similation model significantly improved the accuracy of the mesospheric pressure and density estimation.Notably,our model also improved the simulation accuracy of the lower thermosphere,where none of the measurements were injected.Moreover,the results indicated that fine-tuning the uncertainty parameters in the physics-based model can contribute to maintaining the level of forecasting accuracy for the mesosphere over several days'lead time,which is essential for long-term mesospheric prediction capabilities.This study highlights the effectiveness of intelligent op-timization of the uncertainty parameters in a theoretical model in improving model accuracy and extending forecast reliability within the mesosphere.关键词
中间层/数据同化/数值预报/智能优化Key words
mesosphere/data assimilation/forecasting/intelligent optimization分类
地球科学引用本文复制引用
任德馨,雷久侯,陈雪涛,党童,刘宇..基于理论模式参数智能优化的中间层数据同化[J].地球与行星物理论评（中英文）,2025,56(3):352-359,8.基金项目
国家重点研发计划资助项目(2022YFF0503702)Supported by the National Key R&D Program of China(Grant No.2022YFF0503702) （2022YFF0503702）
