The SET HASDM density database is available for scientific studies through a SQL database with open community access. The information in the SET HASDM density database covers the period from January 1, 2000 through December 31, 2019. Data records exist every 3 hours during solar cycles 23 and 24. The database has a grid size of 10° × 15° (latitude, longitude) with 25 km altitude steps between 175-825 km. A description of the source of the database, its validation, its information content, and its accessibility are provided.
Space Situational Awareness is a major focus of space agencies and private defense/technology companies worldwide. With the number of objects in low-Earth orbit (LEO) continuously growing, knowledge of future satellite/ debris positions is becoming increasingly important (Radtke et al., 2017). While there are numerous perturbations affecting the trajectories of objects, atmospheric drag is the largest source of uncertainty in the LEO region (Emmert et al., 2017;Storz et al., 2005). Our current understanding of the thermosphere is incomplete, resulting in imperfect modeling of neutral mass density. Over the past several decades, researchers have developed increasingly accurate models and made improvements to existing ones. This has come from a combination of the incorporation of new measurements, refinements of the underlying physics, and improvements to satellite geometry modeling (
Space 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.
covering almost two solar cycles. 11• HASDM models the movement of lighter species during solar minimum condi-12 tions.
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.
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