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 uncertainty parameters in a theoretical model

作　　者：任德馨 雷久侯[1] 陈雪涛 党童 刘宇 Ren Dexin;Lei Jiuhou;Chen Xuetao;Dang Tong;Liu Yu(School of Earth and Space Sciences,University of Science and Technology of China,Hefei 230026,China)

机构地区：[1]中国科学技术大学地球和空间科学学院,合肥230026

出　　处：《地球与行星物理论评(中英文)》2025年第3期352-359,共8页Reviews of Geophysics and Planetary Physics

基　　金：国家重点研发计划资助项目(2022YFF0503702)。

摘　　要：中间层位于地表上方约50~90 km处,是大气波动的重要传播介质,也是地球大气能量收支的关键区域.因此,有必要开展中间层大气数据同化研究,通过融合有限的观测资料与数值模式模拟,实现中间层大气状态的更准确模拟和数值预报.然而,受限于中间层观测资料缺乏,其数据同化的研究仍处于相对初期阶段.因此,本研究关注中间层大气数据同化与数值预报,利用低轨卫星探测的中间层大气温度、密度和气压驱动智能优化粒子滤波算法,优化理论模式内部不确定性参数.结果显示,尽管同化前后大气温度的统计误差相当,同化后中间层密度和气压误差相较于原有的理论模式模拟得到了显著改善.此外,智能优化算法通过对模式内部不确定参数的调整,还提升了同化区域上部的低热层非同化区域大气模拟精度.同时,本研究结果还表明,通过使用智能优化粒子滤波算法调整理论模式不确定参数,可以使中间层大气预报误差在多天内维持稳定.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 forecasting 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 parameters 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 Broadband Emission Radiometry)onboard the TIMED(Thermosphere Ionosphere Mesosphere Energetics and Dynamics)satellite,were injected into the data assimilation model.The comparison results demonstrated that the statistical 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 assimilation 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 optimization 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 

分 类 号：P351[天文地球—空间物理学]