An Investigation On Thermospheric Responses To Solar Flux Changes And Data Assimilation-based Thermospheric Prediction

The near-Earth space environment has been increasingly busy and congested in recent years owing to the fast growth in the number of satellites orbiting the Earth.In this respect,the application of the Earth's upper atmosphere has rapidly increased.It is thus desired to understand and forecast the variation of the upper atmosphere.This paper firstly focused on the characteristics and mechanisms of the thermospheric response to the periodic variations of solar activity,and its effects on the ionospheric variations.Then,we developed a data assimilation system for the thermosphere based on a theoretical model,and constructed a thermospheric forecasting model by specifying the uncertain parameters in a physics-based model using an intelligent optimized particle filtering algorithm.The main results are given as follows.I.Characteristics and Mechanisms of Thermospheric Responses to the 27-day EUV Flux Changes,and their Roles on the Time Delay of the Ionosphere(1)Different Responses of Neutral Temperature and Species in the Thermosphere to the Periodic Variations of Solar RadiationThe thermosphere has been found to lag the 27-day variation of solar extreme ultraviolet(EUV)radiation based on the decades of observational and theoretical studies.However,the physical mechanisms that are responsible for the delayed response of the thermosphere to solar radiation variations are not yet understood.In this study,we focused on the physical mechanisms of thermospheric temperature and neutral species responses to the 27-day variation of solar EUV radiation.The Thermosphere Ionosphere Electrodynamic General Circulation Model(TIEGCM)was used to diagnostically analyze the physical mechanisms responsible for their different delayed responses.It was found that the time delay of the thermospheric temperature response to the 27-day solar EUV flux variation is about 0.5-0.8 days,which corresponds to the time when the total heating rate is balanced by the total cooling rate.This time is slightly later than the times of the peaks of both the heating and cooling rates.The global circulation in the upper atmosphere plays a significant role in the delayed response of thermospheric temperature to solar EUV flux variations.The peak response time of atomic oxygen(O)or molecular nitrogen(N2)mass density corresponds to the time of the equilibrium between the contributions from the barometric effect and the change in its abundance.The peak response time of O is shorter than that of thermospheric temperature Tn,due to a dynamic change in the circulation that acts to cancel out the contribution from the barometric process prior to the peak of Tn.On the contrary,the change of N2 abundance contributes further to a decrease of N2 mass density on a constant pressure surface when the thermosphere is expanding.The change of chemical loss leads to a longer peak response time of N2 abundance than that due to barometric motion.Therefore,an equilibrium is reached after the barometric effect turns from expansion(contraction)to contraction(expansion),so that the peak response time of N2 is longer than that of Tn and O.Moreover,the meridional circulation in the thermosphere modulates the latitudinal dependence of the peak response time of thermospheric neutral species.Additionally,we found that the time delay of thermospheric temperature and neutral species varies slightly with different periods and amplitudes of solar EUV flux variations.(2)Characteristics of the Time Delay of Thermospheric Mass Density Response to the 27-day Variation of EUV FluxThe characteristics of the delayed responses of temperature and neutral species to the periodic variations of solar EUV radiation are difficult to validate from direct observations owing to the lack of sufficient measurements.As expected,the time delay of thermospheric mass density is controlled by the delayed responses of both temperature and neutral species.Consequently,the characteristics of the delayed responses of temperature and neutral species can be inferred from the characteristics of the time delay of the observed thermospheric mass density.In this study,the mass densities from Challenging Minisatellite Payload(CHAMP)and Gravity Recovery and Climate Experiment(GRACE)satellites and the simulation results from the Thermosphere Ionosphere Electrodynamics General Circulation Model(TIEGCM)have been used to systematically explore the peak response time(or time delay hereafter)of thermospheric mass density to the 27-day solar EUV flux variation.The TIEGCM can generally reproduce the observed time delay of thermospheric mass density response to the 27-day solar EUV flux changes.The simulation results suggest that the delay of the peak of thermospheric mass density to that of the 27-day solar EUV flux variation is about 0.9 days.However,geomagnetic activity can significantly affect the derivation of the time delay of thermospheric mass density due only to solar EUV flux change.Additionally,the delay of thermospheric mass density to the 27-day solar EUV flux changes with altitude,latitude,and local time according to the different delays of temperature,abundance of atomic and molecular species in the thermosphere.(3)Role of the Thermosphere in the Time Dealy of the Ionospheric Responses to the Periodic Variations of Solar Flux ChangesThe ionosphere and thermosphere are tightly coupled.As a result,the terrestrial ionosphere also has a?27-day variation owing to the solar rotation.However,it is very controversial for ionospheric time delay in previous studies which is reported to vary from about 0 to 3 days or even longer.It is thus interesting to address how the ionosphere responses to the periodic variations of solar EUV flux,and what are the contributions of thermospheric temperature and neutral species to the time delay of the ionospheric response.In this study,the physical mechanisms that afre responsible for the time delay of the ionospheric response to the 27-day variation of solar EUV flux were investigated by using the in-situ electron density from CHAMP,EUV radiation from the TIMED satellite,and numerical simulations from the TIEGCM.The role of the thermosphere in the delayed response of the ionosphere was further discussed.The time delays of in-situ electron density changes obtained from the CHAMP satellite in response to 27-day solar EUV flux changes vary from 0 to about 3 days.Meanwhile,the TIEGCM simulations driven by the measured EUV flux and the actual geomagnetic activity show similar time delays as those observed in the CHAMP measurements.Further simulations reveal that geomagnetic activity greatly affects the determination of the ionospheric time delay to the 27-day solar EUV flux variations.The solar zenith angle change within the solar rotation interval can cause large latitudinal differences in the time delay.The ionospheric time delay due only to the 27-day solar EUV flux variation is less than 1 day and slightly increases with latitude,when geomagnetic activity and seasonal variations are eliminated in the simulation.The simulation results further suggest that the ionospheric response time is associated with the photochemical,dynamic and electrodynamic processes in the ionosphere-thermosphere system.?.Development of the Theoretical Model Based Data Assimilation Method and Intelligent Optimized Algorithm Based Forecasting System for the Thermosphere(4)Data Assimilation for the Thermosphere Based on the Theoretical ModelingAccurate forecast of the thermospheric density is critical to the space community.The data assimilation approach that is based on a self-consistent upper-atmosphere model may provide a better predictive capability of the coupled thermosphere ionosphere system.In this study,a physics-based assimilation system(PIDA)that is based on the Thermosphere-Ionosphere-Electrodynamics General Circulation Model(TIEGCM)was used to validate the capability of reproducing the evolution of the global thermosphere state.The effective solar and geophysical drivers were estimated by ingesting neutral density from a single satellite into the PIDA.It was found that the PIDA can reproduce the temporal variation of the global thermospheric density near the altitude where the orbit density was ingested.Furthermore,the PIDA is also capable of capturing the temporal evolution of the thermospheric density at various altitudes.However,a systematic bias,depending on altitude,is seen in the modeled neutral densities by the PIDA.Moreover,this systematic bias in the predicted thermospheric density is likely ascribed to the overestimation of the density in the lower thermosphere.Consequently,the spatial and temporal evolution of the lower thermosphere under various conditions should be considered carefully in the physics-based data assimilation system.Additionally,the assessments of the obtained results suggest that the observations of multiple parameters at different altitudes are required in the assimilation model to accurately predicte thermospheric densities.(5)Development of a Forecasting System for the Thermosphere Based on the Intelligent Optimized AlgorithmThe uncertainties associated with the variations in the thermosphere are responsible for the inaccurate prediction of the orbit decay of the low Earth orbiting space objects due to the drag force.Accurate forecasting of the thermosphere is urgently required to avoid satellite collisions,which is a potential threat to the rapid growth of spacecraft applications.However,the realization of reliable long-range forecasting of the thermosphere would still not be achieved even if accurate predictions of solar and geomagnetic forcings were achieved.In this study,we constructed a novel methodology to forecast the thermosphere tens of days in advance by specifying the uncertain parameters in a physics-based model using an intelligent optimized particle filtering algorithm.A comparison of the results suggests that this method has the capability of providing a more reliable forecast with more than 30-days leading time for the thermospheric density than the existing ones.Moreover,a reliable prediction under extreme solar and geomagnetic forcing was achieved.The method provides a new pathway to the forecast of the nonlinearly-coupled complex upper atmosphere system.