Search for: All records

Abstract Data assimilation methods often also employ the same discrete dynamical model used to evolve the state estimate in time to propagate an approximation of the state estimation error covariance matrix. Four‐dimensional variational methods, for instance, evolve the covariance matrix implicitly via discrete tangent linear dynamics. Ensemble methods, while not forming this matrix explicitly, approximate its evolution at low rank from the evolution of the ensemble members. Such approximate evolution schemes for the covariance matrix imply an approximate evolution of the estimation error variances along its diagonal. For states that satisfy the advection equation, the continuity equation, or related hyperbolic partial differential equations (PDEs), the estimation error variance itself satisfies a known PDE, so the accuracy of the various approximations to the variances implied by the discrete covariance propagation can be determined directly. Experiments conducted by the atmospheric chemical constituent data assimilation community have indicated that such approximate variance evolution can be highly inaccurate. Through careful analysis and simple numerical experiments, we show that such poor accuracy must be expected, due to the inherent nature of discrete covariance propagation, coupled with a special property of the continuum covariance dynamics for states governed by these types of hyperbolic PDE. The intuitive explanation for this inaccuracy is that discrete covariance propagation involves approximating diagonal elements of the covariance matrix with combinations of off‐diagonal elements, even when there is a discontinuity in the continuum covariance dynamics along the diagonal. Our analysis uncovers the resulting error terms that depend on the ratio of the grid spacing to the correlation length, and these terms become very large when correlation lengths begin to approach the grid scale, for instance, as gradients steepen near the diagonal. We show that inaccurate variance evolution can manifest itself as both spurious loss and gain of variance. 

Abstract Previous studies have shown that Strong Thermal Emission Velocity Enhancement (STEVE) events occur at the end of a prolonged substorm expansion phase. However, the connection between STEVE occurrence and substorms and the global high‐latitude ionospheric electrodynamics associated with the development of STEVE and non‐STEVE substorms are not yet well understood. The focus of this paper is to identify electrodynamics features that are unique to STEVE events through a comprehensive analysis of ionospheric convection patterns estimated from SuperDARN plasma drift and ground‐based magnetometer data using the Assimilative Mapping of Geospace Observations (AMGeO) procedure. Results from AMGeO are further analyzed using principal component analysis and superposed epoch analysis for 32 STEVE and 32 non‐STEVE substorm events. The analysis shows that the magnitude of cross‐polar cap potential drop is generally greater for STEVE events. In contrast to non‐STEVE substorms, the majority of STEVE events investigated are accompanied by with a pronounced extension of the dawn‐cell into the pre‐midnight subauroral latitudes, reminiscent of the Harang reversal convection feature where the eastward electrojet overlaps with the westward electrojet, which tends to prolong over substorm expansion and recovery phases. This is consistent with the presence of an enhanced subauroral electric field confirmed by previous STEVE studies. The global and localized features of high‐latitude ionospheric convection associated with optical STEVE events characterized in this paper provide important insights into cross‐scale magnetosphere‐ionosphere coupling mechanisms that differentiate STEVE events from non‐STEVE substorm events. 

Abstract The largest obstacle to managing satellites in low Earth orbit (LEO) is accurately forecasting the neutral mass densities that appreciably impact atmospheric drag. Empirical thermospheric models are often used to estimate neutral densities but they struggle to forecast neutral densities during geomagnetic storms when they are highly variable. Physics‐based models are thus increasingly turned to for their ability to describe the dynamical evolution of neutral densities. However, these models require observations to constrain dynamical state variables to be able to forecast mass densities with adequate fidelity. The LEO environment has scarce neutral state observations. Here, we demonstrate, in simulated experiments, a reduction in orbit errors and neutral densities using a physics‐based, data assimilation approach with ionospheric observations. Using a coupled thermosphere‐ionosphere model, the Thermosphere Ionosphere Electrodynamics General Circulation Model, we assimilate Constellation Observing System for Meterology, Ionosphere, and Climate electron density profiles (EDPs) derived from radio occultation (RO) observations. We use the EDPs to directly update neutral states, improving errors for neutral temperature by 70% and neutral winds by 20%. Updated neutral temperature and neutral winds additionally improve helium composition errors by 60% and 40%, respectively. Improved neutral density estimates correspond to a reduction in orbit errors of 1.2 km over 2 days, a 70% reduction over a no‐assimilation control, and a 29 km improvement over 9 days. This study builds on the results of our earlier work to further develop and demonstrate the potential of using a vast and growing RO data source, with a physics‐based model, to overcome our limited number of neutral observations. 

Abstract Far ultraviolet (FUV) imaging of the aurora from space provides great insight into the dynamic coupling of the atmosphere, ionosphere, and magnetosphere on global scales. To gain a quantitative understanding of these coupling processes, the global distribution of auroral energy flux is required, but the inversion of FUV emission to derive precipitating auroral particles' energy flux is not straightforward. Furthermore, the spatial coverage of FUV imaging from Low Earth Orbit (LEO) altitudes is often insufficient to achieve global mapping of this important parameter. This study seeks to fill these gaps left by the current geospace observing system using a combination of data assimilation and machine learning techniques. Specifically, this paper presents a new data‐driven modeling approach to create instantaneous, global assimilative mappings of auroral electron total energy flux from Lyman‐Birge‐Hopfield (LBH) emission data from the Defense Meteorological System Program (DMSP) Special Sensor Ultraviolet Spectrographic Imager (SSUSI). We take a two‐step approach; the creation of assimilative maps of LBH emission using optimal interpolation, followed by the conversion to energy flux using a neural network model trained with conjunction observations of in‐situ auroral particles and LBH emission from the DMSP Special Sensor J and SSUSI instruments. The paper demonstrates the feasibility of this approach with a model prototype built with DMSP data from 17 February 2014 to 23 February 2014. This study serves as a blueprint for a future comprehensive data‐driven model of auroral energy flux that is complementary to traditional inversion techniques to take advantage of FUV imaging from LEO platforms for global assimilative mapping of auroral energy flux. 

Abstract The dayside equatorial ionospheric electrodynamics exhibit strong variability driven simultaneously by highly changeable external forcings that originate from the solar extreme ultraviolet (EUV), magnetosphere, and lower atmosphere. We investigate this variability by carrying out comprehensive data‐driven ensemble modeling using a coupled model of the thermosphere and ionosphere, with the focus on the verticalE×Bdrift variability during a solar minimum and minor storm period. The variability of verticalE×Bdrift in response to the changes and uncertainty of primary forcings (i.e., solar EUV, high‐latitude plasma convection and auroral particle precipitation, and lower‐atmospheric tide and wave forcing) is investigated by ensemble forcing sensitivity experiments that incorporate data‐driven stochastic perturbations of these forcings into the model. Second, the impact of assimilating FORMOsa SATellite‐3/Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT‐3/COSMIC) electron density profiles (EDPs) on the reduction of uncertainty of the modeled verticalE×Bdrift variability resulting from inadequately specified external forcing is revealed. The Communication and Navigation Outage Forecasting System (C/NOFS) ion drift velocity observations are used for validation. The validation results support the importance of the use of a data‐driven forcing perturbation methods in ensemble modeling and data assimilation. In conclusion, the solar EUV dominates the global‐scale day‐to‐day variability, while the lower atmosphere tide and wave forcing is critical to determining the regional variability. The modeled verticalE×Bdrift is also sensitive to the magnetospheric forcing. The ensemble data assimilation of FORMOSAT‐3/COSMIC EDPs helps to reduce the uncertainty and improves agreement of the modeled verticalE×Bdrifts with C/NOFS observations. 

Abstract Far ultraviolet observations of Earth's dayglow from the National Aeronautics and Space Administration (NASA) Global‐scale Observations of the Limb and Disk (GOLD) mission presents an unparalleled opportunity for upper atmosphere radiance data assimilation. Assimilation of the Lyman‐Birge‐Hopfield (LBH) band emissions can be formulated in a similar fashion to lower atmosphere radiance data assimilation approaches. To provide a proof‐of‐concept for such an approach, this paper presents assimilation experiments of simulated LBH emission data using an ensemble filter measurement update step implemented with National Oceanic and Atmospheric Administration (NOAA)'s Whole Atmosphere Model (WAM) and National Center for Atmospheric Research (NCAR)'s Global Airglow (GLOW) model. Primary findings from observing system simulation experiments (OSSEs), wherein “truth” atmospheric conditions simulated by NCAR's Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM) are used to generate synthetic GOLD data, are as follows: (1) Assimilation of GOLD LBH disk emission data can reduce the bias in model temperature specification (ensemble mean) by 60% under both geomagnetically quiet conditions and disturbed conditions. (2) The reduction in model uncertainty (ensemble spread) as a result of assimilation is about 20% in the lower thermosphere and 30% in the upper thermosphere for both conditions. These OSSEs demonstrate the potential for far ultraviolet radiance data assimilation to dramatically reduce the model biases in thermospheric temperature specification and to extend the utility of GOLD observations by helping to resolve the altitude‐dependent global‐scale response of the thermosphere to geomagnetic storms. 

Abstract The FORMOSAT‐7/COSMIC‐2 (F7/C2) satellite mission was launched on 25 June 2019 with six low‐Earth‐orbit satellites and can provide thousands of daily radio occultation (RO) soundings in the low‐latitude and midlatitude regions. This study shows the preliminary results of space weather data products based on F7/C2 RO sounding: global ionospheric specification (GIS) electron density and Ne‐aided Abel and Abel electron density profiles. GIS is the ionospheric data assimilation product based on the Gauss‐Markov Kalman filter, assimilating the ground‐based Global Positioning System and space‐based F7/C2 RO slant total electron content, providing continuous global three‐dimensional electron density distribution. The Ne‐aided Abel inversion implements four‐dimensional climatological electron density constructed from previous RO observations, which has the advantage of providing altitudinal information on the horizontal gradient to reduce the retrieval error due to the spherical symmetry assumption of the Abel inversion. The comparisons show that climatological structures are consistent with each other above 300 km altitude. Both the Abel electron density profiles and GIS detect electron density variations during a minor geomagnetic storm that occurred within the study period. Moreover, GIS is further capable of reconstructing the variation of equatorial ionization anomaly crests. Detailed validations of all the three products are carried out using manually scaled digisondeN_mF₂(h_mF₂), yielding correlation coefficients of 0.885 (0.885) for both Abel inversions and 0.903 (0.862) for GIS. The results show that both GIS and Ne‐aided Abel are reliable products in studying ionosphere climatology, with the additional advantage of GIS for space weather research and day‐to‐day variations. 

Low Earth orbit (LEO) radio occultation|radio occultations (RO) constellations can provide global electron density profiles (EDPs) to better specify and forecast the ionosphere‐thermosphere (I‐T) system. To inform future RO constellation design, this study uses comprehensive Observing System Simulation Experiments (OSSEs) to assess the ionospheric specification impact of assimilating synthetic EDPs into a coupled I‐T model. These OSSEs use 10 different sets of RO constellation configurations containing 6 or 12 LEO satellites with base orbit parameter combinations of 520 or 800 km altitude, and 24° or 72° inclination. The OSSEs are performed using the Ensemble Adjustment Kalman Filter implemented in the data assimilation (DA) Research Testbed and the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIEGCM). A different I‐T model is used for the nature run, the Whole Atmosphere Model‐Ionosphere Plasmasphere Electrodynamics (WAM‐IPE), to simulate the period of interest is the St. Patrick's Day storm on March 13–18, 2015. Errors from models and EDP retrieval are realistically accounted for in this study through distinct I‐T models and by retrieving synthetic EDPs through an extension Abel inversion algorithm. OSSE assessment, using multiple metrics, finds that greater EDP spatial coverage leading to improved specification at altitudes 300 km and above, with the 520 km altitude constellations performing best due to yielding the highest observation counts. A potential performance limit is suggested with two 6‐satellite constellations. Lastly, close examination of Abel inversion error impacts highlights major EDP limitations at altitudes below 200 km and dayside equatorial regions with large horizontal gradients and low electron density magnitudes. 

This work introduces a new, compactly supported correlation function that can be inhomogeneous over Euclidean three‐space, anisotropic when restricted to the sphere, and compactly supported on regions other than spheres of fixed radius. This function, which we call the Generalized Gaspari–Cohn (GenGC) correlation function, is a generalization of the compactly supported, piecewise rational approximation to a Gaussian introduced by Gaspari and Cohn in 1999 and its subsequent extension by Gaspariet alin 2006. The GenGC correlation function is a parametric correlation function that allows two parameters and to vary, as functions, over space, whereas the earlier formulations either keep both and fixed or only allow to vary. Like these earlier formulations, GenGC is a sixth‐order piecewise rational function (fifth‐order near the origin), while the coefficients now depend explicitly on the values of both and at each pair of points being correlated. We show that, by allowing both and to vary, the correlation length of GenGC also varies over space and introduces inhomogeneous and anisotropic features that may be useful in data assimilation applications. Covariances produced using GenGC are computationally tractable due to their compact support and have the added flexibility of generating compact support regions that adapt to the input field. These features can be useful for covariance modeling and covariance tapering applications in data assimilation. We derive the GenGC correlation function using convolutions, discuss continuity properties relating to and and its correlation length, and provide one‐ and two‐dimensional examples that highlight its anisotropy and variable regions of compact support. 

Daedalus MASE (Mission Assessment through Simulation Exercise) is an open-source package of scientific analysis tools aimed at research in the Lower Thermosphere-Ionosphere (LTI). It was created with the purpose to assess the performance and demonstrate closure of the mission objectives of Daedalus, a mission concept targeting to performin-situmeasurements in the LTI. However, through its successful usage as a mission-simulator toolset, Daedalus MASE has evolved to encompass numerous capabilities related to LTI science and modeling. Inputs are geophysical observables in the LTI, which can be obtained either throughin-situmeasurements from spacecraft and rockets, or through Global Circulation Models (GCM). These include ion, neutral and electron densities, ion and neutral composition, ion, electron and neutral temperatures, ion drifts, neutral winds, electric field, and magnetic field. In the examples presented, these geophysical observables are obtained through NCAR’s Thermosphere-Ionosphere-Electrodynamics General Circulation Model. Capabilities of Daedalus MASE include: 1) Calculations of products that are derived from the above geophysical observables, such as Joule heating, energy transfer rates between species, electrical currents, electrical conductivity, ion-neutral collision frequencies between all combinations of species, as well as height-integrations of derived products. 2) Calculation and cross-comparison of collision frequencies and estimates of the effect of using different models of collision frequencies into derived products. 3) Calculation of the uncertainties of derived products based on the uncertainties of the geophysical observables, due to instrument errors or to uncertainties in measurement techniques. 4) Routines for the along-orbit interpolation within gridded datasets of GCMs. 5) Routines for the calculation of the global coverage of anin situmission in regions of interest and for various conditions of solar and geomagnetic activity. 6) Calculations of the statistical significance of obtaining the primary and derived products throughout anin situmission’s lifetime. 7) Routines for the visualization of 3D datasets of GCMs and of measurements along orbit. Daedalus MASE code is accompanied by a set of Jupyter Notebooks, incorporating all required theory, references, codes and plotting in a user-friendly environment. Daedalus MASE is developed and maintained at the Department for Electrical and Computer Engineering of the Democritus University of Thrace, with key contributions from several partner institutions.