Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.
The Automated Radiation Measurements for Aerospace Safety (ARMAS) program has successfully deployed a fleet of six instruments measuring the ambient radiation environment at commercial aircraft altitudes. ARMAS transmits real‐time data to the ground and provides quality, tissue‐relevant ambient dose equivalent rates with 5 min latency for dose rates on 213 flights up to 17.3 km (56,700 ft). We show five cases from different aircraft; the source particles are dominated by galactic cosmic rays but include particle fluxes for minor radiation periods and geomagnetically disturbed conditions. The measurements from 2013 to 2016 do not cover a period of time to quantify galactic cosmic rays' dependence on solar cycle variation and their effect on aviation radiation. However, we report on small radiation “clouds” in specific magnetic latitude regions and note that active geomagnetic, variable space weather conditions may sufficiently modify the magnetospheric magnetic field that can enhance the radiation environment, particularly at high altitudes and middle to high latitudes. When there is no significant space weather, high‐latitude flights produce a dose rate analogous to a chest X‐ray every 12.5 h, every 25 h for midlatitudes, and every 100 h for equatorial latitudes at typical commercial flight altitudes of 37,000 ft (~11 km). The dose rate doubles every 2 km altitude increase, suggesting a radiation event management strategy for pilots or air traffic control; i.e., where event‐driven radiation regions can be identified, they can be treated like volcanic ash clouds to achieve radiation safety goals with slightly lower flight altitudes or more equatorial flight paths.
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 (
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