High Accuracy Satellite Drag Model Research Articles

Probabilistic Short‐Term Solar Driver Forecasting With Neural Network Ensembles

AbstractSpace weather indices are used to drive forecasts of thermosphere density, which directly affects objects in low‐Earth orbit (LEO) through atmospheric drag force. A set of proxies and indices (drivers), F10.7, S10.7, M10.7, and Y10.7 are used as inputs by the JB2008, (https://doi.org/10.2514/6.2008‐6438) thermosphere density model. The United States Air Force (USAF) operational High Accuracy Satellite Drag Model (HASDM), relies on JB2008, (https://doi.org/10.2514/6.2008‐6438), and forecasts of solar drivers from a linear algorithm. We introduce methods using long short‐term memory (LSTM) model ensembles to improve over the current prediction method as well as a previous univariate approach. We investigate the usage of principal component analysis (PCA) to enhance multivariate forecasting. A novel method, referred to as striped sampling, is created to produce statistically consistent machine learning data sets. We also investigate forecasting performance and uncertainty estimation by varying the training loss function and by investigating novel weighting methods. Results show that stacked neural network model ensembles make multivariate driver forecasts which outperform the operational linear method. When using MV‐MLE (multivariate multi‐lookback ensemble), we see an improvement of RMSE for F10.7, S10.7, M10.7, and Y10.7 of 17.7%, 12.3%, 13.8%, 13.7% respectively, over the operational method. We provide the first probabilistic forecasting method for S10.7, M10.7, and Y10.7. Ensemble approaches are leveraged to provide a distribution of predicted values, allowing an investigation into robustness and reliability (R&R) of uncertainty estimates. Uncertainty was also investigated through the use of calibration error score (CES), with the MV‐MLE providing an average CES of 5.63%, across all drivers.

Predicting global thermospheric neutral density during periods with high geomagnetic activity

Estimating global and multi-level Thermosphere Neutral Density (TND) is important for studying coupling processes within the upper atmosphere, and for applications like orbit prediction. Models are applied for predicting TND changes, however, their performance can be improved by accounting for the simplicity of model structure and the sampling limitations of model inputs. In this study, a simultaneous Calibration and Data Assimilation (C/DA) algorithm is applied to integrate freely available CHAMP, GRACE, and Swarm derived TND measurements into the NRLMSISE-00 model. The improved model, called ‘C/DA-NRLMSISE-00’, and its outputs fit to these measured TNDs, are used to produce global TND fields at arbitrary altitudes (with the same vertical coverage as the NRLMSISE-00). Seven periods, between 2003-2020 that are associated with relatively high geomagnetic activity selected to investigate these fields, within which available models represent difficulties to provide reasonable TND estimates. Independent validations are performed with along-track TNDs that were not used within the C/DA framework, as well as with the outputs of other models such as the Jacchia-Bowman 2008 and the High Accuracy Satellite Drag Model. The numerical results indicate an average 52%, 50%, 56%, 25%, 47%, 54%, and 63% improvement in the Root Mean Squared Errors of the short term TND forecasts of C/DA-NRLMSISE00 compared to the along-track TND estimates of GRACE (2003, altitude 490 km), GRACE (2004, altitude 486 km), CHAMP (2008, altitude 343 km), GOCE (2010, altitude 270 km), Swarm-B (2015, altitude 520 km), Swarm-B (2017, altitude 514 km), and Swarm-B (2020, altitude 512 km), respectively.

Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning

AbstractUnderstanding the variation of the Thermospheric Mass Density (TMD) is important for solar‐terrestrial physics and applications for spacecraft safety. The thermosphere, as an open system, is impacted by various space environment conditions and has complicated temporal and spatial features. Consequently, TMD observations contain a wealth of multi‐scale feature information. How to extract such information from observations is a challenge that requires ongoing research. It is vital to improving our understanding of the TMD features. Deep learning (DL) can learn complex representations directly from raw data, which makes it a compelling feature extraction and modeling tool for providing a novel perspective for TMD modeling. The Residual Network is used in this study to build a DL model with deep network architecture. The observations of CHAllenging Minisatellite Payload are utilized in the training phase, while the Gravity Recovery and Climate Experiment, High Accuracy Satellite Drag Model and Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Extended model are used to evaluate the performance of the DL model. The results reveal that, compared with the shallow model of the typical Multi‐Layer Perceptron, the DL model can better extract multi‐scale features in the observations while retaining generalization capabilities. Controlled simulation experiments allow us to extract the effects of different physical processes, which improves the interpretability of the DL model. It is demonstrated that the DL model can discriminate the physical processes corresponding to the different space environment indices by simulating Equatorial Mass density Anomaly and geomagnetic storms.

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.

MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification

AbstractThe Mass Spectrometer and Incoherent Scatter radar (MSIS) model family has been developed and improved since the early 1970's. The most recent version of MSIS is the Naval Research Laboratory (NRL) MSIS 2.0 empirical atmospheric model. NRLMSIS 2.0 provides species density, mass density, and temperature estimates as function of location and space weather conditions. MSIS models have long been a popular choice of thermosphere model in the research and operations community alike, but—like many models—does not provide uncertainty estimates. In this work, we develop an exospheric temperature model based in machine learning that can be used with NRLMSIS 2.0 to calibrate it relative to high‐fidelity satellite density estimates directly through the exospheric temperature parameter. Instead of providing point estimates, our model (called MSIS‐UQ) outputs a distribution which is assessed using a metric called the calibration error score. We show that MSIS‐UQ debiases NRLMSIS 2.0 resulting in reduced differences between model and satellite density of 25% and is 11% closer to satellite density than the Space Force's High Accuracy Satellite Drag Model. We also show the model's uncertainty estimation capabilities by generating altitude profiles for species density, mass density, and temperature. This explicitly demonstrates how exospheric temperature probabilities affect density and temperature profiles within NRLMSIS 2.0. Another study displays improved post‐storm overcooling capabilities relative to NRLMSIS 2.0 alone, enhancing the phenomena that it can capture.

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.

Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density application

Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth’s upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and computation of probability of collision between two space objects in low Earth orbit (LEO) for decision making in space operations. Even with (assumed) perfect forecast of model drivers, our incomplete knowledge of the system results in often inaccurate thermospheric neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of confidence in predictions. In this work, we propose two techniques to develop nonlinear ML regression models to predict thermospheric density while providing robust and reliable uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance capabilities for models trained on both local and global datasets. We show that the NLPD loss provides similar results for both techniques but the direct probability distribution prediction method has a much lower computational cost. For the global model regressed on the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, we achieve errors of approximately 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAllenging Minisatellite Payload (CHAMP) density dataset, models developed using both techniques provide test error on the order of 13%. The CHAMP models—on validation and test data—are within 2% of perfect calibration for the twenty prediction intervals tested. We show that this model can also be used to obtain global density predictions with uncertainties at a given epoch.

Machine‐Learned HASDM Thermospheric Mass Density Model With Uncertainty Quantification

AbstractA thermospheric neutral mass density model with robust and reliable uncertainty estimates is developed based on the Space Environment Technologies (SET) High Accuracy Satellite Drag Model (HASDM) density database. This database, created by SET, contains 20 years of outputs from the U.S. Space Force's HASDM, which currently represents the state of the art for density and drag modeling. We utilize principal component analysis for dimensionality reduction, which creates the coefficients upon which nonlinear machine‐learned (ML) regression models are trained. These models use three unique loss functions: Mean square error (MSE), negative logarithm of predictive density (NLPD), and continuous ranked probability score. Three input sets are also tested, showing improved performance when introducing time histories for geomagnetic indices. These models leverage Monte Carlo dropout to provide uncertainty estimates, and the use of the NLPD loss function results in well‐calibrated uncertainty estimates while only increasing error by 0.25% (<10% mean absolute error) relative to MSE. By comparing the best HASDM‐ML model to the HASDM database along satellite orbits, we found that the model provides robust and reliable density uncertainties over diverse space weather conditions. A storm‐time comparison shows that HASDM‐ML also supplies meaningful uncertainty estimates during extreme geomagnetic events.

A Framework to Estimate Local Atmospheric Densities With Reduced Drag‐Coefficient Biases

AbstractAn accurate estimation of upper atmospheric densities is crucial for precise orbit determination (POD), prediction of low Earth orbit satellites, and scientific studies of the Earth's atmosphere. But densities estimated using satellite tracking data are always uncertain up to the drag‐coefficient assumed in the inversion method. This work develops a new framework to simultaneously estimate the density and drag‐coefficient for satellites with a time‐varying attitude. We do so by leveraging Fourier drag‐coefficient models, previously developed by the authors, and physical models of the drag‐coefficient. The method is tested with synthetic data for different geomagnetic activities, altitude levels, and errors in the gas‐surface interaction parameters. We report an improvement of up to 70% in density estimates for the simulations. Finally, POD data from Spire satellites are used for validation. An improvement of around 29% is obtained in the filter density estimates over NRLMSISE‐00 and 49% over JB2008 compared to the High Accuracy Satellite Drag Model densities.

Comparison of a Neutral Density Model With the SET HASDM Density Database

AbstractThe EXospheric TEMperatures on a PoLyhedrAl gRid (EXTEMPLAR) method predicts the neutral densities in the thermosphere. The performance of this model has been evaluated through a comparison with the Air Force High Accuracy Satellite Drag Model (HASDM). The Space Environment Technologies (SET) HASDM database that was used for this test spans the 20 years 2000 through 2019, containing densities at 3 hr time intervals at 25 km altitude steps, and a spatial resolution of 10° latitude by 15° longitude. The upgraded EXTEMPLAR that was tested uses the newer Naval Research Laboratory MSIS 2.0 model to convert global exospheric temperature values to neutral density as a function of altitude. The revision also incorporated time delays that varied as a function of location, between the total Poynting flux in the polar regions and the exospheric temperature response. The density values from both models were integrated on spherical shells at altitudes ranging from 200 to 800 km. These sums were compared as a function of time. The results show an excellent agreement at temporal scales ranging from hours to years. The EXTEMPLAR model performs best at altitudes of 400 km and above, where geomagnetic storms produce the largest relative changes in neutral density. In addition to providing an effective method to compare models that have very different spatial resolutions, the use of density totals at various altitudes presents a useful illustration of how the thermosphere behaves at different altitudes, on time scales ranging from hours to complete solar cycles.

Thermospheric Mass Density Disturbances Due to Magnetospheric Forcing From 2014–2020 CASSIOPE Precise Orbits

AbstractLong‐term thermospheric mass density disturbances due to magnetospheric forcing are not clear due to a lack of measurement data and imprecise models. In this study, the Global Navigation Satellite System (GNSS) precise orbits of CAScade SmallSat and IOnospheric Polar Explorer (CASSIOPE) are used to infer high‐resolution thermospheric mass densities between the years 2014 and 2020. The CASSIOPE densities are comprehensively validated at altitudes from 325 to 425 km at intervals of 25 km with the High Accuracy Satellite Drag Model (HASDM) density database, and further compared with the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter Radar Exosphere/2000 (NRLMSISE‐00) and the Jacchia‐Bowman/2008 (JB2008) empirical models. The CASSIOPE densities are very similar to the HASDM and JB2008 densities, while the NRLMSISE‐00 largely overestimates (∼150%) during low solar‐flux conditions. For density values above ∼10−12 kg/m3, the correlation of CASSIOPE with HASDM is ∼5% better than the models, and the standard deviation is within 10% of the background density. For density values below ∼10−12 kg/m3, systematic errors have shown to reduce the precision of the CASSIOPE densities. By setting geomagnetic contributions to zero in the models, the density disturbances due to magnetospheric forcing are isolated from the CASSIOPE time‐series, allowing investigation into the correlations and time‐delay responses to the models and to the merging electric field (Em). A new linear dependence of the time delay to the Emwas found and then parameterized in this study. Time delays occurred at the 4–7 h range during geomagnetic storms, and at 9–11 h during quiet conditions; neither had significant dependence on altitude. The results represent the validation of the first high‐resolution thermospheric mass density estimates inferred from commercial‐off‐the‐shelf GNSS receivers.

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.

Benchmarking Forecasting Models for Space Weather Drivers

AbstractSpace weather indices are commonly used to drive operational forecasts of various geospace systems, including the thermosphere for mass density and satellite drag. The drivers serve as proxies for various processes that cause energy flow and deposition in the geospace system. Forecasts of neutral mass density are a major uncertainty in operational orbit prediction and collision avoidance for objects in low Earth orbit (LEO). For the strongly driven system, accuracy of space weather driver forecasts is crucial for operations. The High Accuracy Satellite Drag Model (HASDM) currently employed by the U.S. Air Force in an operational environment is driven by four solar and two geomagnetic proxies. Space Environment Technologies (SET) is contracted by the space command to provide forecasts for the drivers. This work performs a comprehensive assessment for the performance of the driver forecast models. The goal is to provide a benchmark for future improvements of the forecast models. Using an archived data set spanning 6 years and 15,000 forecasts across Solar Cycle 24, we quantify the temporal statistics of the model performance.

A Partially Orthogonal EnKF approach to atmospheric density estimation using orbital debris

A key requirement for accurate trajectory prediction and space situational awareness is knowledge of how non-conservative forces affect space object motion. These forces vary temporally and spatially, and are driven by the underlying behavior of space weather particularly in Low Earth Orbit (LEO). Existing trajectory prediction algorithms adjust space weather models based on calibration satellite observations. However, lack of sufficient data and mismodeling of non-conservative forces cause inaccuracies in space object motion prediction, especially for uncontrolled debris objects. The uncontrolled nature of debris objects makes them particularly sensitive to the variations in space weather. Our research takes advantage of this behavior by utilizing observations of debris objects to infer the space environment parameters influencing their motion.The hypothesis of this research is that it is possible to utilize debris objects as passive, indirect sensors of the space environment. We focus on estimating atmospheric density and its spatial variability to allow for more precise prediction of LEO object motion. The estimated density is parameterized as a grid of values, distributed by latitude and local sidereal time over a spherical shell encompassing Earth at a fixed altitude of 400 km. The position and velocity of each debris object are also estimated. A Partially Orthogonal Ensemble Kalman Filter (POEnKF) is used for assimilation of space object measurements to estimate density.For performance comparison, the scenario characteristics (number of objects, measurement cadence, etc.) are based on a sensor tasking campaign executed for the High Accuracy Satellite Drag Model project. The POEnKF analysis details spatial comparisons between the true and estimated density fields, and quantifies the improved accuracy in debris object motion predictions due to more accurate drag force models from density estimates. It is shown that there is an advantage to utilizing multiple debris objects instead of just one object. Although the work presented here explores the POEnKF performance when using information from only 16 debris objects, the research vision is to utilize information from all routinely observed debris objects. Overall, the filter demonstrates the ability to estimate density to within a threshold of accuracy dependent on measurement/sensor error. In the case of a geomagnetic storm, the filter is able to track the storm and provide more accurate density estimates than would be achieved using a simple exponential atmospheric density model or MSIS Atmospheric Model (when calm conditions are assumed).

Analysis and Evaluation of High Accuracy Satellite Drag Model

分析Jacchia70（J70）热层模式原理、美国空军高精度卫星拖曳模型（HASDM）的修正方法及选取球面调和函数的原因.推导模式密度对球谐系数（SH）的偏导数，给出利用模式密度泰勒展开进行线性化处理、迭代求解球谐系数的具体过程.针对2003年10月29日大磁暴事件，基于CHAMP和GRACE A/B卫星加速度计实测密度，进行修正方法的性能评估.统计相对误差的均值及标准差变化：改进前分别为-81.7%，74.4%；改进后分别为-5.9%，53.1%，验证了改进算法的有效性.从热层上下边界温度角度，详细分析了热层模式动态修正原理，研究结果为类HASDM修正模式的工程应用提供了理论基础.

Modeling the thermosphere as a driven-dissipative thermodynamic system

Thermospheric density impacts satellite position and lifetime through atmospheric drag. More accurate specification of thermospheric temperature, a key input to current models such as the High Accuracy Satellite Drag Model, can decrease model density errors. This paper improves the model of Burke et al. (2009) to model thermospheric temperatures using the magnetospheric convective electric field as a driver. In better alignment with Air Force satellite tracking operations, we model the arithmetic mean temperature, T1/2, defined by the Jacchia (1977) model as the mean of the daytime maximum and nighttime minimum exospheric temperatures occurring in opposite hemispheres at a given time, instead of the exospheric temperature used by Burke et al. (2009). Two methods of treating the solar ultraviolet (UV) contribution to T1/2 are tested. Two model parameters, the coupling and relaxation constants, are optimized for 38 storms from 2002 to 2008. Observed T1/2 values are derived from densities and heights measured by the Gravity Recovery and Climate Experiment satellite. The coupling and relaxation constants were found to vary over the solar cycle and are fit as functions of F10.7a, the 162 day average of the F10.7 index. Model results show that allowing temporal UV variation decreased model T1/2 errors for storms with decreasing UV over the storm period but increased T1/2 errors for storms with increasing UV. Model accuracy was found to be improved by separating storms by type (coronal mass ejection or co-rotating interaction region). The model parameter fits established will be useful for improving satellite drag forecasts.

Thermospheric density variations: Observability using precision satellite orbits and effects on orbit propagation

AbstractThis paper examines atmospheric density estimated using precision orbit ephemerides (POE) from the CHAMP and GRACE satellites during short periods of greater atmospheric density variability. The results of the calibration of CHAMP densities derived using POEs with those derived using accelerometers are examined for three different types of density perturbations, [traveling atmospheric disturbances (TADs), geomagnetic cusp phenomena, and midnight density maxima] in order to determine the temporal resolution of POE solutions. In addition, the densities are compared to High‐Accuracy Satellite Drag Model (HASDM) densities to compare temporal resolution for both types of corrections. The resolution for these models of thermospheric density was found to be inadequate to sufficiently characterize the short‐term density variations examined here. Also examined in this paper is the effect of differing density estimation schemes by propagating an initial orbit state forward in time and examining induced errors. The propagated POE‐derived densities incurred errors of a smaller magnitude than the empirical models and errors on the same scale or better than those incurred using the HASDM model.

Thermospheric basis functions for improved dynamic calibration of semi‐empirical models

State‐of‐the‐art satellite drag models require upgrades to meet operational Precision Orbit Determination requirements for collision avoidance, reentry predictions and catalog maintenance. However, accurate model representations of thermospheric density are not currently possible without the use of data assimilation, or model calibration. Furthermore, due to sparse data sampling in the thermosphere, such calibration has only been successfully demonstrated by fitting the available data to a low‐dimensional model. The High Accuracy Satellite Drag Model (HASDM), used operationally by the Space Surveillance Network to aid in the tracking of low‐earth orbiting satellites, compensates for errors in the Jacchia‐70 static diffusion model by fitting a truncated set of spherical harmonics to a subset of recent satellite tracking data. We present a technique to derive a set of basis functions better suited to capturing the spatial variability and response of the thermosphere. By comparing the uncompensated Jacchia‐70 model with the Thermosphere‐Ionosphere‐Electrodynamic General Circulation Model (TIEGCM), we create a new set of orthogonal basis functions that can be used to calibrate semi‐empirical models such as HASDM with increased accuracy in the presence of sparse data. An initial analysis of the new approach, driven by synthetic data, shows a 32.9% improvement in the RMS error over the current implementation of HASDM.

Precision Orbit Derived Total Density

O PERATIONAL orbit determination and orbit prediction experience suggests that the extreme upper atmosphere is more variable than the factors included in atmospheric density models commonly used in orbit propagation. Unmodeled atmospheric density variations can greatly impact the orbit determination process and add kilometers of error to orbit predictions. The motivation for this research is to improve orbit determination and prediction by improving density models and to better measure and understand thermospheric and exospheric density variations, especially variations with time scales shorter than those of the empirical models typically used for atmospheric drag calculations. This paper represents afirst step toward those long-term goals, which will process precision orbit data from multiple satellites simultaneously. Atmospheric density modeling has long been one of the greatest uncertainties in the dynamics of low-Earth-orbit satellites. Accurate density calculations are required to provide meaningful estimates of the atmospheric drag perturbing satellite motion. These effects increase with lower altitude orbits, higher effective area, and lower mass satellites. McLaughlin [1] gave an introduction to the neutral atmosphere and the time varying effects on the density of the thermosphere and exosphere. The effects include diurnal variations, solar rotation, the solar cycle, winds and tides, gravity waves, long-term climate change, and magnetic storms and substorms. The authors hope to increase understanding of the variations in neutral density that are important for satellite drag. Vallado [2] gave an introduction to the basic density variations and to the density models most commonly used in orbit determination. A more comprehensive introduction to the space environment and the neutral atmosphere can be found in Hargreaves [3]. Sabol and Luu [4] gave a summary of the drivers of atmospheric density variations and some of the problems associated with the temporal resolution of various proxies used in empirical density models. Marcos et al. [5] presented an overview of ongoing research to address the inaccuracies in satellite drag modeling. There are two major types of research ongoing to address these problems. The first is dynamic calibration of the atmosphere (DCA) and the second is use of satellites with accelerometers to measure the nonconservative accelerations, including drag. DCA involves estimating density corrections to a given atmospheric density model based upon the observed motion of satellites. The work of Storz et al. [6] and Bowman et al. [7,8] on the High Accuracy Satellite Drag Model (HASDM), Cefola et al. [9], Yurasov et al. [10,11], andWilkins et al. [12,13] are all examples of approaches to provide corrections to atmospheric density models. In each case, the observations from a group of satellites are used to estimate large-scale corrections to an existing atmospheric density model. The approaches have shown the ability to provide a general improvement to a baseline atmospheric density model. The DCA approaches have several disadvantages, however. First, the approaches are designed to run internal to a particular orbit determination scheme. This means that users of other orbit determination schemes have to rely on that system to provide atmospheric density correction updates. In addition, the atmospheric density corrections are only applicable to a certain point in time. Thus, one must have access to the entire archive of density corrections applicable to a given problem. A second limitation of DCA approaches to date is that the corrections have limited spatial and temporal resolution. The corrections do allow the models to better represent effects with temporal resolution of several hours to days, but not temporal effects with shorter time scales.Most dynamic atmospheric density models use a daily solar flux and averaged 3 h geomagnetic indices as input values to address solar and geomagnetic activity. Using these values limits the ability of the models to represent changes in the atmosphere that occur within the averaging interval of the input data. Although the original Russian DCAwork used radar observations, most current DCA approaches use two-line element sets of a large number of low-Earth-orbit objects as observations to develop atmospheric corrections in a DCA scheme. Unfortunately, relying on two-line element sets reduces the accuracy of the corrections and provides limited temporal resolution. HASDM [6–8] relies on the actual radar observations of low-Earthorbit satellites, but even this accuracy is lower than that available from precision orbit ephemerides (POEs) or satellite laser ranging (SLR). In addition, the radar observations are not generally available. The second major type of research related to improving atmospheric density knowledge is using satellites with accelerometers to measure nonconservative forces, which can then be used to estimate density. This represents the opposite extreme from using two-line element sets in terms of accuracy and total data availability. The accelerometer data provide a way to separate the gravitational forces from the nonconservative forces such as drag, solar radiation pressure, and Earth radiation pressure. Then by using accurate radiation pressure models, the drag acceleration can be determined Presented as Paper 2009-6951 at theAIAA/AASAstrodynamics Specialist Conference, Honolulu, HI, 18–21 August 2008; received 12 October 2009; revision received 22 June 2010; accepted for publication 31 July 2010. Copyright © 2010 by Craig A. McLaughlin. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0022-4650/11 and $10.00 in correspondence with the CCC. Assistant Professor, Department of Aerospace Engineering, 2120 Learned Hall, 1530 West 15th Street. Senior Member AIAA. Graduate Research Assistant, Department of Aerospace Engineering; currently Ph.D. Student, University of Alabama in Huntsville. Member AIAA. Graduate Research Assistant, Department of Aerospace Engineering, 2120 Learned Hall, 1530 West 15th Street. Member AIAA. JOURNAL OF SPACECRAFT AND ROCKETS Vol. 48, No. 1, January–February 2011

Operational Space Weather Entering a New Era

U.S. operational space weather maturing because of two competing factors. On one hand, directed agency funding at about $1 billion for model development over the past decade has brought modeling maturity to !ve broad Sun-toEarth domains, i.e., Sun, heliosphere, magnetosphere, ionosphere, and thermosphere. On the other hand, agency funding for transitioning these models into operations has been a small fraction of the level provided for model development. is situation has le# implementation of operational space weather largely unfunded and woefully undirected, with the exception of a few U.S. Air Force Weather Agency projects. A new vision needed so that operational space weather can help solve 21st-century challenges. e current paradigm for operational space weather began in the 1990s, when the idea was to build complete systems at single facilities. ese rapid prototyping centers were envisioned as a means for implementing new operational systems, but this path was unsuccessful. Although increased funding for operational space weather would have bene!ted the community, this did not happen, and in the lopsided funding pro!le, operational space weather was forced to develop an unusual but robust architecture. is architecture consists of distributed networks—automated systems of models, data streams, and algorithms at multiple, geographically dispersed facilities linked by operational servers. A central server manages the network and uses a database enabling remote nodes to deposit output data and access input data at their geophysical or measurement cadences and latencies. e emergence of distributed networks represents a paradigm shi# from 1995, when the National Space Weather Program (NSWP) strategic plan was !rst published. At that time, very few models existed that could be even considered for use in operations. Model development increased due to the funding surge, and by 2000 seventy-three models characterizing 15 space environment domains were listed in the NSWP implementation plan (2nd edition). During the past decade some of these models were implemented operationally and became important components of distributed networks. Early examples were the Magnetospheric Spec!cation Model (MSM; 2000), SOLAR2000 (S2K; 2001), and High Accuracy Satellite Drag Model (HASDM; 2004). More recently, systems of coupled models and data streams have been created, including Global Assimilation of Ionospheric Measurements (GAIM; 2006), HakamadaAkasofuFry (HAF; 2007), and Communication Alert and Prediction System (CAPS; 2008). Coupled model/ data systems that are currently being implemented include the JacchiaBowman 2008 (JB2008), the Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS), U.S. Geological Survey Dst, ENLIL with Cone, and Ionosphere Operational System (IONS; i.e., the commercial version of GAIM at the Utah Science Technology and Research (USTAR) Center for Space Weather). Of added note that the Space Weather Modeling Framework (SWMF) and the Center for Integrated Space Weather Modeling (CISM) have developed multi domain systems to represent the entire Sunto-Earth environment, in which some of the models have been running for years. e need to test coupled systems existed even without funding, and the lack of funds drove small businesses, universities, and the multiagency Community Coordinated Modeling Center to create innovative, cost-e$ective solutions. Basically, these organizations had to !nd cheaper ways to accomplish testing and operational implementation. e result was the development of distributed networks to close the Technology Readiness Level (TRL) 7–9 gap. TRL the concept used by agencies and companies to assess the maturity of evolving technologies. TRL 7 models and data can operate in stand-alone mode within the context of an operational environment. TRL 8 signi!es the completion of a fully functional system prototype, and TRL 9 describes a fully delivered, operational system. e distributed network systems at TRL 7–9 are linked in a way that allows developers to maintain versioning and proprietary control over their models, permits data streams to be integrated at their own measurement cadence, results in testing %exibility, enables rapid