Empirical Density Model Research Articles

Satellite observations and modeling of the plasmapause structure and dynamics

We investigated the structure and dynamics of the plasmapause using electron density data from the Arase (ERG) satellite between 2017 and 2019. By fitting the electron density profile to a hyperbolic function using the least-squares method, 1891 plasmapause events were identified. The hyperbolic function’s fitting parameters were used to estimate the plasmapause’s location and thickness. The plasmapause location and thickness were mapped onto the geomagnetic equator plane. We examined the dependence of the plasmapause location and thickness on the geomagnetic disturbance. The median plasmapause location on the geomagnetic equator plane was 32,089 km from Earth’s center. Plasmapause locations depend on geomagnetic disturbances that vary between four and six radii of the Earth.In contrast, the median plasmapause thickness obtained by fitting a hyperbolic function on the geomagnetic equator plane was 2472 km. The thickness changed from ∼1500 km at night to ∼ 3000 km during the daytime. The thickness was several times larger than the ring current H+ Larmor radius, suggesting an association with plasma instability in the plasmapause due to the penetration of magnetospheric electric fields.Additionally, we developed a three-dimensional empirical electron density model with historical effects associated with refilling the plasmasphere, plasmapause, and magnetosphere. The International Reference Ionosphere (IRI) model can be enhanced to incorporate the electron density model of the plasmasphere and estimate the total electron content (TECp) in the plasmasphere using the empirical model. Our analysis provides valuable insights into plasmapause and contributes to a better understanding of the inner magnetosphere.

Improving Thermospheric Density Predictions in Low‐Earth Orbit With Machine Learning

AbstractThermospheric density is one of the main sources of uncertainty in the estimation of satellites' position and velocity in low‐Earth orbit. This has negative consequences in several space domains, including space traffic management, collision avoidance, re‐entry predictions, orbital lifetime analysis, and space object cataloging. In this paper, we investigate the prediction accuracy of empirical density models (e.g., NRLMSISE‐00 and JB‐08) against black‐box machine learning (ML) models trained on precise orbit determination‐derived thermospheric density data (from CHAMP, GOCE, GRACE, SWARM‐A/B satellites). We show that by using the same inputs, the ML models we designed are capable of consistently improving the predictions with respect to state‐of‐the‐art empirical models by reducing the mean absolute percentage error (MAPE) in the thermospheric density estimation from the range of 40%–60% to approximately 20%. As a result of this work, we introduce Karman: an open‐source Python software package developed during this study. Karman provides functionalities to ingest and preprocess thermospheric density, solar irradiance, and geomagnetic input data for ML readiness. Additionally, it facilitates developing and training ML models on the aforementioned data and benchmarking their performance at different altitudes, geographic locations, times, and solar activity conditions. Through this contribution, we offer the scientific community a comprehensive tool for comparing and enhancing thermospheric density models using ML techniques.

Speciation of La3+-Cl- Complexes in Hydrothermal Fluids from Deep Potential Molecular Dynamics.

The stability of rare earth element (REE) complexes plays a crucial role in quantitatively assessing their hydrothermal migration and transformation. However, reliable data are lacking under high-temperature hydrothermal conditions, which hampers our understanding of the association behavior of REE. Here a deep learning potential model for the LaCl3-H2O system in hydrothermal fluids is developed based on the first-principles density functional theory calculations. The model accurately predicts the radial distribution functions compared to ab initio molecular dynamics (AIMD) simulations. Furthermore, species of La-Cl complexes, the dissociation pathway of the La-Cl complexes dissociation process, and the potential of mean forces and corresponding association constants (logK) for LaCln3-n (n = 1-4) are extensively investigated under a wide range of temperatures and pressures. Empirical density models for logK calculation are fitted with these data and can accurately predict logK data from both experimental results and AIMD simulations. The distribution of La-Cl species is also evaluated across a wide range of temperatures, pressures, and initial chloride concentration conditions. The results show that La-Cl complexes are prone to forming in a low-density solution, and the number of bonded Cl- ions increases with rising temperature. In contrast, in a high-density solution, La3+ dominates and becomes the more prevalent species.

Data‐driven prediction of room‐temperature density for multicomponent silicate‐based glasses

AbstractDensity is one of the most commonly measured or estimated material properties, especially for glasses and melts that are of significant interest to many fields, including metallurgy, geology, materials science, and sustainable cements. Here, three types of machine learning models (i.e., random forest [RF], artificial neural network [ANN], and Gaussian process regression [GPR]) have been developed to predict the room‐temperature density of glasses in the compositional space of CaO–MgO–Al2O3–SiO2–TiO2–FeO–Fe2O3–Na2O–K2O–MnO (CMASTFNKM), based on ∼2100 data points mined from ∼140 literature studies. The results show that the RF, ANN, and GPR models give accurate predictions of glass density with R2 values, root mean square error (RMSE), and mean absolute percentage error (MAPE) of ∼0.95–0.97, ∼0.03–0.04 g/cm3 and ∼0.63%–0.83%, respectively, for the 15% testing set, which are more accurate compared with empirical density models based on ionic packing ratio (with R2 values, RMSE, and MAPE of ∼0.28–0.91, ∼0.05–0.15 g/cm3, and ∼1.40%–4.61%, respectively). Furthermore, glass density is shown to be a reliable reactivity indicator for a range of CaO–Al2O3–SiO2 (CAS) and volcanic glasses due to its strong correlation (R2 values above ∼0.90) with the average metal–oxygen dissociation energy (a structural descriptor) of these glasses. Analysis of the predicted density–composition relationships from these models (for selected compositional subspaces) suggests that the single‐layer ANN and GPR models exhibit a certain level of transferability (i.e., ability to extrapolate to compositional spaces not [or less] covered in the database) and capture some known features, including the mixed alkaline earth (or alkali) effects.

Using Van Allen Probes and Arase Observations to Develop an Empirical Plasma Density Model in the Inner Zone

AbstractA new empirical density model is developed for the inner zone between 1 < L < 3 using plasma densities inferred from the upper hybrid resonance on Arase, and hiss‐inferred density values from Van Allen Probes. The Van Allen Probes hiss‐inferred densities are first recalibrated and validated against Arase observations, using both a conjunction event and statistical analyses. The newly developed density model includes dependencies on L, magnetic latitude, and magnetic local time (MLT). Between 1.5 < L < 3.0, the equatorial density variation with L is shown to be equivalent to that of the Ozhogin et al. (2012, https://doi.org/10.1029/2011JA017330) model. However, for L < 1.5, this dependence changes as the plasma density increases at a faster rate with decreasing L. The latitudinal dependence of the plasma density is shown to present a flatter profile than previous models, meaning lower densities extend to higher latitudes. This dependence is well‐modeled by updated fitting coefficients. A clear MLT dependence of the plasma density is identified, which was not found or included in some previous models. This variation is consistent with the diurnal variation of the ionosphere, peaking near MLT = 14 and becoming larger in amplitude with decreasing L. A function describing this MLT dependence is presented. Overall, the new L, latitude, and MLT‐dependent empirical model can provide density values in areas outside the validity region of many previous models, making it a useful resource for accurately determining diffusion coefficients and predicting electron dynamics and their lifetimes in the inner radiation belt.

Modeling of the cold electron plasma density for radiation belt physics

This review focusses strictly on existing plasma density models, including ionospheric source models, empirical density models, physics-based and machine-learning density models. This review is framed in the context of radiation belt physics and space weather codes. The review is limited to the most commonly used models or to models recently developed and promising. A great variety of conditions is considered such as the magnetic local time variation, geomagnetic conditions, ionospheric source regions, radial and latitudinal dependence, and collisional vs. collisionless conditions. These models can serve to complement satellite observations of the electron plasma density when data are lacking, are for most of them commonly used in radiation belt physics simulations, and can improve our understanding of the plasmasphere dynamics.

Physics‐Based Approach to Thermospheric Density Estimation Using CubeSat GPS Data

AbstractIn Low Earth Orbit (LEO), atmospheric drag is the largest contributor to trajectory prediction error. The current thermospheric density model used in operations, the High Accuracy Satellite Drag Model (HASDM), applies corrections to an empirical density model every 3 hr using observations of 75+ calibration satellites. This work aims to improve global thermospheric density estimation by utilizing a physics‐based space environment model and precise GPS‐based orbit estimates of LEO CubeSats. The data assimilation approach presented here estimates drivers of the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIE‐GCM) every 1.5 hr using CubeSat GPS information. In this work, Spire Global CubeSat data are used to demonstrate the method using only 10 satellites; the true strength of the method is its potential to exploit data already collected on large LEO constellations (hundreds of CubeSats). Precise Orbit Determination (POD) information from 10 CubeSats over 12 days is used to sense a global density field when Kp historical data show a minor and moderate geomagnetic storm in succession. This paper provides a direct comparison of estimated density, derived by our new method, to HASDM and Swarm mission derived density. A propagation analysis is also executed by comparing the CubeSat POD data to orbits propagated using our estimated density versus HASDM density. The analyses show that the estimated density is within 35% of HASDM during storm‐time conditions, and that the propagation using the estimated density yields an improvement of 26% over NRLMSISE‐00 compared to HASDM, while outperforming HASDM during the second storm peak.

Improving the estimation of thermospheric neutral density via two-step assimilation of in situ neutral density into a numerical model

AbstractNeutral thermospheric density is an essential quantity required for precise orbit determination of satellites, collision avoidance of satellites, re-entry prediction of satellites or space debris, and satellite lifetime assessments. Empirical models of the thermosphere fail to provide sufficient estimates of neutral thermospheric density along the orbits of satellites by reason of approximations, assumptions and a limited temporal resolution. At high solar activity these estimates can be off by 70% when comparing to observations at 12-hourly averages. In recent decades, neutral density is regularly observed with satellite accelerometers on board of low Earth orbiting satellites like CHAMP, GOCE, GRACE, GRACE-FO, or Swarm. When assimilating such along-track information into global models of thermosphere–ionosphere dynamics, it has been often observed that only a very local sub-domain of the model grid around the satellite’s position is updated. To extend the impact to the entire model domain we suggest a new two-step approach: we use accelerometer-derived neutral densities from the CHAMP mission in a first step to calibrate an empirical thermosphere density model (NRLMSIS 2.0). In a second step, we assimilate—for the first time—densities predicted for a regular three-dimensional grid into the TIE-GCM (Thermosphere Ionosphere Electrodynamics General Circulation Model). Data assimilation is performed using the Local Error-Subspace Transform Kalman Filter provided by the Parallel Data Assimilation Framework (PDAF). We test the new approach using a 2-week-long period containing the 5 April 2010 Geomagnetic storm. Accelerometer-derived neutral densities from the GRACE mission are used for additional evaluation. We demonstrate that the two-step approach globally improves the simulation of thermospheric density. We could significantly improve the density prediction for CHAMP and GRACE. In fact, the offset between the accelerometer-derived densities and the model prediction is reduced by 45% for CHAMP and 20% for GRACE when applying the two-step approach. The implication is that our approach allows one to much better ’transplant’ the precise CHAMP thermospheric density measurements to satellites flying at a similar altitude. Graphical Abstract

Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).

Science Through Machine Learning: Quantification of Post‐Storm Thermospheric Cooling

AbstractMachine learning (ML) models are universal function approximators and—if used correctly—can summarize the information content of observational data sets in a functional form for scientific and engineering applications. A benefit to ML over parametric models is that there are no a priori assumptions about particular basis functions which can potentially limit the phenomena that can be modeled. In this work, we develop ML models on three data sets: the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, a spatiotemporally matched data set of outputs from the Jacchia‐Bowman 2008 Empirical Thermospheric Density Model (JB2008), and an accelerometer‐derived density data set from CHAllenging Minisatellite Payload (CHAMP). These ML models are compared to the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar (NRLMSIS 2.0) model to study the presence of post‐storm cooling in the middle‐thermosphere. We find that both NRLMSIS 2.0 and JB2008‐ML do not account for post‐storm cooling and consequently perform poorly in periods following strong geomagnetic storms (e.g., the 2003 Halloween storms). Conversely, HASDM‐ML and CHAMP‐ML do show evidence of post‐storm cooling indicating that this phenomenon is present in the original data sets. Results show that density reductions up to 40% can occur 1–3 days post‐storm depending on the location and strength of the storm.

Qualitative and Quantitative Assessment of the SET HASDM Database

AbstractThe High Accuracy Satellite Drag Model (HASDM) is the operational thermospheric density model used by the US Space Force Combined Space Operations Center. By using real‐time data assimilation, HASDM can provide density estimates with increased accuracy over other empirical models. With historical HASDM density data being released publicly for the first time, we can analyze the data to compare dominant modes of variability in the upper atmosphere as modeled by HASDM and the Jacchia‐Bowman 2008 Empirical Thermospheric Density Model (JB2008), a Jacchia family model upon which density corrections are made as a part of the HASDM framework. This model comparison is conducted through the use of principal component analysis (PCA) which shows the increased variability of the HASDM dataset. We highlight HASDM's ability to capture the movement of lighter species during solar minimum conditions, unlike many empirical models. We then compare density from both models to the CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) accelerometer‐derived density estimates. This comparison shows that HASDM more closely matches the accelerometer‐derived densities with mean absolute differences of compared to CHAMP and GRACE‐A, respectively. The comparison also reveals improved representation of cooling mechanisms due to NO and CO2 by the HASDM database.

An Empirical Atmospheric Density Calibration Model Based on Long Short-Term Memory Neural Network

The accuracy of the atmospheric mass density is one of the most important factors affecting the orbital precision of spacecraft at low Earth orbits (LEO). Although there are a number of empirical density models available to use in the orbit determination and prediction of LEO spacecraft, all of them suffer from errors of various degrees. A practical way to reduce the error of a particular model is to calibrate the model using precise density data or tracking data. In this paper, a long short-term memory (LSTM) neural network is proposed to calibrate the NRLMSISE-00 density model, in which the densities derived from spaceborne accelerometer data are the main input. The resulted LSTM-NRL model, calibrated using the accelerometer data from Challenging Minisatellite Payload (CHAMP) satellite, is extensively experimented to evaluate the calibration performance. With data in one month to train the LSTM-NRL model, the model is shown to effectively reduce the root mean square error of the model densities outside the training window by more than 40% in various time spans and space weather environment. The LSTM-NRL model is also shown to have remarkable transferring performance when it is applied along the GRACE satellite orbits.

Radial Distributions of Coronal Electron Temperatures: Specificities of the DYN Model

This paper is a follow up of the article where Lemaire and Stegen (2016) introduced their DYN method to calculate coronal temperature profiles from given radial distributions of the coronal and solar wind (SW) electron densities. Several such temperature profiles are calculated and presented corresponding to a set of given empirical density models derived from eclipse observations and in-situ measurements of the electron density and bulk velocity at 1 AU. The DYN temperature profiles obtained for the equatorial and polar regions of the corona challenge the results deduced since 1958 from singular hydrodynamical models of the SW. In these models - where the expansion velocity transits through a singular saddle point - the maximum coronal temperature is predicted to be located at the base of the corona, while in all DYN models the altitude of the maximum temperature is found at significantly higher altitudes in the mid-corona. Furthermore, the maximum of the DYN-estimated temperatures is found at much higher altitudes over the polar regions and coronal holes, than over the equator. However, at low altitudes, in the inner corona, the DYN temperatures are always smaller at high latitudes, than at low equatorial latitudes. This appears well in agreement with existing coronal hole observations. These findings have serious implications on the open questions: what is the actual source of the coronal heating, and where is the maximum energy deposited within the solar corona?

Adaptation of the NeQuick2 model for GNSS wide-area ionospheric delay correction in China and the surrounding areas

As an empirical three-dimensional ionospheric electron density model, NeQuick2 can generally represent the temporal and spatial characterization of ionospheric Total Electron Content (TEC) at a global scale. However, mismodeling errors usually exist between NeQuick2 representation and actual state of short-term ionospheric variation in small scales. In this contribution, an adaptation method is proposed for the NeQuick2 model based on data ingestion with GNSS-derived ionospheric TEC data. The relationship between the background NeQuick2 Slant TEC (STEC) estimations and real STEC measurements can be properly modelled with a ‘scaling factor’ in a highly efficient manner. In order to verify the feasibility and usefulness of the algorithm, validation experiments are carried out with GNSS tracking stations in China and the surrounding areas under different levels of solar activity. The results show that the bias of NeQuick2 corrections after adaptation are mostly within 1.0 TECu, suggesting that the systematic bias of the original NeQuick2 model can be significantly reduced. In comparison with about 50%-70% STEC RMS errors corrected by the original NeQuick2 model, the NeQuick2 model after adaptation can generally correct about 80% − 90% STEC RMS errors, which is also better than the CODE final GIM product as it has smaller residual bias and uses fewer reference stations. Therefore, the proposed algorithm can be satisfactorily applied in wide area GNSS ionospheric corrections.

A Simple Method for Correcting Empirical Model Densities During Geomagnetic Storms Using Satellite Orbit Data

AbstractEmpirical models of the thermospheric density are routinely used to perform orbit maintenance, satellite collision avoidance, and estimate time and location of re‐entry for spacecraft. These models have characteristic errors in the thermospheric density below 10% during geomagnetic quiet time but are unable to reproduce the significant increase and subsequent recovery in the density observed during geomagnetic storms. Underestimation of the density during these conditions translates to errors in orbit propagation that reduce the accuracy of any resulting orbit predictions. These drawbacks risk the safety of astronauts and orbiting spacecraft and also limit understanding of the physics of thermospheric density enhancements. Numerous CubeSats with publicly available ephemeris in the form of two‐line element (TLEs) sets orbit in this region. We present the Multifaceted Optimization Algorithm (MOA), a method to estimate the thermospheric density by minimizing the error between a modeled trajectory and a set of TLEs. The algorithm first estimates a representative cross‐sectional area for several reference CubeSats during the quiet time 3 weeks prior to the storm, and then estimates modifications to the inputs of the NRLMSISE‐00 empirical density model in order to minimize the difference between the modeled and TLE‐provided semimajor axis of the CubeSats. For validation, the median value of the modifications across all CubeSats are applied along the Swarm spacecraft orbits. This results in orbit‐averaged empirical densities below 10% error in magnitude during a geomagnetic storm, compared to errors in excess of 25% for uncalibrated NLRMSISE‐00 when compared to Swarm GPS‐derived densities.

Inward Propagation of Flow‐Generated Pi2 Waves From the Plasma Sheet to the Inner Magnetosphere

AbstractTo understand the inward propagation of the Pi2 waves generated by bursty bulk flows (BBFs) from the plasma sheet to the inner magnetosphere, we statistically investigate 137 midnight conjunction events with one satellite of the Time History of Events and Macroscale Interactions during Substorms (THEMIS) in the plasma sheet and one satellite of Geostationary Operational Environmental Satellite (GOES) in the inner magnetosphere. All events have Pi2 wave enhancements associated with BBFs at THEMIS. In response, larger Pi2 wave enhancements at GOES are correlated with larger dipolarization associated with each BBF at THEMIS but not with stronger waves and flows at THEMIS. The larger wave enhancements at GOES occur preferentially under stronger solar wind driving. We discuss an explanation considering that BBFs are an earthward moving plasma sheet bubble having relatively lower flux tube entropy compared to that of the background plasma and that Pi2 waves are generated by the slowing down of the bubble. The stronger dipolarization of BBFs suggests a bubble of smaller entropy, while the stronger solar wind driving leads to higher background entropy in the inner magnetosphere, both favorable for a bubble to penetrate deeper, and thus, Pi2 waves generated by the bubble are more likely to propagate to the GOES location with less damping to result in larger wave enhancements. In addition, our analyses using empirical density and magnetic field models suggest that the strong Pi2 wave enhancements at GOES are less likely to be a cavity resonance driven inside the plasmasphere.

The application of a gravimetric forward modelling of the lithospheric structure for an estimate of the average density of the upper asthenosphere

The average density of 3300 kg m−3 is often attributed for the asthenosphere. In this study, we inspect this value by estimating the average value of the (upper) asthenosphere based on applying the gravimetric forward modelling of major known lithospheric density structures. The LITHO1.0 global seismic model of the lithospheric density structure is used for this purpose, while considering that the lithosphere-asthenosphere boundary (LAB) is rheological, conventionally taken at the 1300 °C isotherm, above which the mantle behaves in a rigid fashion and below which it behaves in a ductile fashion. According to our result, the average density of the upper asthenosphere is roughly 3400 kg m−3. This density value closely agrees with the corresponding average value 3371 kg m−3 computed based on an empirical density model provided in the Preliminary Reference Earth Model (PREM), while using the LITHO1.0 LAB depth data. We also demonstrate that the sub-lithospheric mantle gravity map exhibits mainly a thermal signature. The most prominent features in this gravity map are mid-oceanic spearing ridges marked by gravity lows, while oceanic subductions in the West Pacific are characterized by the most pronounced gravity highs.

Dimensional analysis and modelling of energy density of lithium-ion battery

Abstract A number of literature studies have shown that the energy density of lithium ion battery depends majorly on the particle radius, diffusivity, electric conductivity and thickness of the electrode. However, since the discovery of these major parameters, there has been no significant breakthrough in the present design technology to achieve successful design application in the Electric Vehicle industry. The energy density still ranges around 250Wh/kg on improvement and is insignificant compared to the energy produced by the internal combustion engine. Therefore, in this paper dimensional analysis is applied to lithium ion battery’s energy density in order to obtain the sets of parameters that influence the performance with cathode material as reference. Five different cathode materials including; LiMn2O4, LiFePO4, LiCoO2, LiV6O13, and LiTiS2 were used and the ranges for all material properties was selected based on reported data from literature. In order to bridge the gap in literature towards predicting the amount of energy obtainable from a specific electrode design, an empirical energy density model is proposed. Result showed that the specific modulus of the battery’s electrode is a dominant factor for improving the energy density of the lithium ion battery compared to the particle radius, diffusivity and electrode thickness. Thus, in order to achieve a significant breakthrough, electrodes with high specific modulus are required. Designing an electrode with the characteristics of very low density, high compressibility factor, and high young modulus is necessary for an improved system performance. Thus, material combination of LiCoO2/Aqueous-Lithium-air could give a practical breakthrough in achieving very high specific modulus and consequently high energy density. It is therefore suggested that focus on the mechanical/elastic property should not be geared only to its durability but also towards the energy density.

Comparison of accelerometer data calibration methods used in thermospheric neutral density estimation

Abstract. Ultra-sensitive space-borne accelerometers on board of low Earth orbit (LEO) satellites are used to measure non-gravitational forces acting on the surface of these satellites. These forces consist of the Earth radiation pressure, the solar radiation pressure and the atmospheric drag, where the first two are caused by the radiation emitted from the Earth and the Sun, respectively, and the latter is related to the thermospheric density. On-board accelerometer measurements contain systematic errors, which need to be mitigated by applying a calibration before their use in gravity recovery or thermospheric neutral density estimations. Therefore, we improve, apply and compare three calibration procedures: (1) a multi-step numerical estimation approach, which is based on the numerical differentiation of the kinematic orbits of LEO satellites; (2) a calibration of accelerometer observations within the dynamic precise orbit determination procedure and (3) a comparison of observed to modeled forces acting on the surface of LEO satellites. Here, accelerometer measurements obtained by the Gravity Recovery And Climate Experiment (GRACE) are used. Time series of bias and scale factor derived from the three calibration procedures are found to be different in timescales of a few days to months. Results are more similar (statistically significant) when considering longer timescales, from which the results of approach (1) and (2) show better agreement to those of approach (3) during medium and high solar activity. Calibrated accelerometer observations are then applied to estimate thermospheric neutral densities. Differences between accelerometer-based density estimations and those from empirical neutral density models, e.g., NRLMSISE-00, are observed to be significant during quiet periods, on average 22 % of the simulated densities (during low solar activity), and up to 28 % during high solar activity. Therefore, daily corrections are estimated for neutral densities derived from NRLMSISE-00. Our results indicate that these corrections improve model-based density simulations in order to provide density estimates at locations outside the vicinity of the GRACE satellites, in particular during the period of high solar/magnetic activity, e.g., during the St. Patrick's Day storm on 17 March 2015.

Analysis of cross sections of (n,t) nuclear reaction using different empirical formulae and level density models

This paper aims the calculations of cross section of the 27Al(n,t)25Mg, 50Cr(n,t)48V, 56Fe(n,t)54Mn, 58Ni(n,t)56Co, 64Zn(n,t)62Cu, 70Ge(n,t)68Ga, 92Mo(n,t)90Nb, 106Cd(n,t)104Ag and 114Cd(n,t)112Ag nuclear reactions. These cross section calculations were obtained using the different level density models in ALICE/ASH code. Besides, the cross sections of the investigated reactions at incident energies near 14.6 MeV were predicted by using the empirical formulae suggested in our previous paper and in the literature. Therefore, the predicted nuclear data are compared with the other data taken from the literature.