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.
Plain Language Summary
One aspect of space weather is a magnetic signature across the surface of the Earth. The creation of this signal involves nonlinear interactions of electromagnetic forces on charged particles and can therefore be difficult to predict. The perturbations that space storms and other activity causes in some observation sets, however, are fairly regular in their pattern. Some of these measurements have been compiled together into a single value, a geomagnetic index. Several such indices exist, providing a global estimate of the activity in different parts of geospace. Models have been developed to predict the time series of these indices, and various statistical methods are used to assess their performance at reproducing the original index. Existing studies of geomagnetic indices, however, use different approaches to quantify the performance of the model. This document defines a standardized set of statistical analyses as a baseline set of comparison tools that are recommended to assess geomagnetic index prediction models. It also discusses best practices, limitations, uncertainties, and caveats to consider when conducting a model assessment.
Key Points
We review existing practices for assessing geomagnetic index prediction models and recommend a “standard set” of metrics
Along with fit performance metrics that use all data‐model pairs in their formulas, event detection performance metrics are recommended
Other aspects of metrics assessment best practices, limitations, uncertainties, and geomagnetic index caveats are also discussed
We describe a lightweight, accurate nowcasting model for electron flux levels measured by the Van Allen probes. Largely motivated by Rigler et al. (, https://doi.org/10.1029/2003SW000036), we turn to ...a time‐varying linear filter of previous flux levels and Kp. We train and test this model on data gathered from the 2.10 MeV channel of the Relativistic Electron‐Proton Telescope sensor onboard the Van Allen probes. Dynamic linear models are a specific case of state space models and can be made flexible enough to emulate the nonlinear behavior of particle fluxes within the radiation belts. Real‐time estimation of the parameters of the model is done using a Kalman filter, where the state of the model is exactly the parameters. Nowcast performance is assessed against several baseline interpolation schemes. Our model demonstrates significant improvements in performance over persistence nowcasting. In particular, during times of high geomagnetic activity, our model is able to attain performance substantially better than a persistence model. In addition, residual analysis is conducted in order to assess model fit and to suggest future improvements to the model.
Key Points
A lightweight, accurate, and scalable framework for energetic electron flux prediction has been developed
A Kalman filter model demonstrated notable improvements over a persistence scheme and can be tuned for optimal predictive accuracy
Performance is best during geomagnetically disturbed periods, relative to other baseline models
Very low frequency (VLF) waves (about 3–30 kHz) in the Earth’s magnetosphere interact strongly with energetic electrons and are a key element in controlling dynamics of the Van Allen radiation belts. ...Bistatic very low frequency (VLF) transmission experiments have recently been conducted in the magnetosphere using the high-power VLF transmitter on the Air Force Research Laboratory’s Demonstration and Science Experiments (DSX) spacecraft and an electric field receiver onboard the Japan Aerospace Exploration Agency’s Arase (ERG) spacecraft. On 4 September 2019, the spacecraft came within 410 km of each other and were in geomagnetic alignment. During this time, VLF signals were successfully transmitted from DSX to Arase, marking the first successful reception of a space-to-space VLF signal. Arase measurements were consistent with field-aligned propagation as expected from linear cold plasma theory. Details of the transmission event and comparison to VLF propagation model predictions are presented. The capability to directly inject VLF waves into near-Earth space provides a new way to study the dynamics of the radiation belts, ushering in a new era of space experimentation.
Graphical Abstract
An algorithm has been developed for specifying>2MeV electron flux everywhere along geosynchronous orbit for use in operational products. The statistics of integrated electron fluxes from four GOESs ...for more than a solar cycle clearly indicate that the local time variation can be represented by a Gaussian distribution as a function of geomagnetic Kp index, which empirically determines the center and the half width of the Gaussian distribution. Using the most current estimated 3h Kp value as an input, the prediction scheme requires the most recent electron flux measurements from available GOES(s) to determine the maximum and minimum for a Gaussian fit and to provide estimated electron fluxes at geosynchronous orbit with the time resolution of the instrument. In balancing between sufficient data for statistics and the change of geomagnetic configuration, the optimal length of data accumulation time for nowcasting is 6h when one or two satellites are available. The prediction efficiency (PE) is independent of local time and solar cycle. We found that the PE values are greater than 0.5 when Kp<5 and independent of Kp at low and moderate values; however, PE decreases dramatically with increasing Kp when Kp≥5. Although the PE varies from year to year and with the choice of the test satellite, our finding resulted in a PE>0.6 in 67.6% of the cases and PE>0.8 more than 23.5% of the time based on our analysis from four GOESs between 1998 and 2009. Moreover, skill scores from our newly developed algorithm are ~90% of the time better than those resulting from a simpler algorithm based on a table provided by O'Brien (2009), indicating a dramatic improvement in predictive capability. Key Points Skill scores from our newly developed algorithm are ~90% better A dramatic improvement in predictive capability PE is independent of local time and solar cycle
The space physics community continues to grow and become both more interdisciplinary and more intertwined with commercial and government operations. This has created a need for a framework to easily ...identify what projects can be used for specific applications and how close the tool is to routine autonomous or on-demand implementation and operation. We propose the Application Usability Level (AUL) framework and publicizing AULs to help the community quantify the progress of successful applications, metrics, and validation efforts. This framework will also aid the scientific community by supplying the type of information needed to build off of previously published work and publicizing the applications and requirements needed by the user communities. In this paper, we define the AUL framework, outline the milestones required for progression to higher AULs, and provide example projects utilizing the AUL framework. This work has been completed as part of the activities of the Assessment of Understanding and Quantifying Progress working group which is part of the International Forum for Space Weather Capabilities Assessment.
An algorithm has been developed for specifying > 2 MeV electron flux everywhere along geosynchronous orbit for use in operational products. The statistics of integrated electron fluxes from four ...GOESs for more than a solar cycle clearly indicate that the local time variation can be represented by a Gaussian distribution as a function of geomagnetic Kp index, which empirically determines the center and the half width of the Gaussian distribution. Using the most current estimated 3 h Kp value as an input, the prediction scheme requires the most recent electron flux measurements from available GOES(s) to determine the maximum and minimum for a Gaussian fit and to provide estimated electron fluxes at geosynchronous orbit with the time resolution of the instrument. In balancing between sufficient data for statistics and the change of geomagnetic configuration, the optimal length of data accumulation time for nowcasting is 6 h when one or two satellites are available. The prediction efficiency (PE) is independent of local time and solar cycle. We found that the PE values are greater than 0.5 when Kp < 5 and independent of Kp at low and moderate values; however, PE decreases dramatically with increasing Kp when Kp ≥ 5. Although the PE varies from year to year and with the choice of the test satellite, our finding resulted in a PE > 0.6 in 67.6% of the cases and PE > 0.8 more than 23.5% of the time based on our analysis from four GOESs between 1998 and 2009. Moreover, skill scores from our newly developed algorithm are ~90% of the time better than those resulting from a simpler algorithm based on a table provided by O'Brien (2009), indicating a dramatic improvement in predictive capability.
Key Points
Skill scores from our newly developed algorithm are ~90% better
A dramatic improvement in predictive capability
PE is independent of local time and solar cycle
To determine the accuracy and sensitivity for dual-energy computed tomography (DECT) discrimination of uric acid (UA) stones from other (non-UA) renal stones in a commercially implemented product.
...Forty human renal stones comprising uric acid (n=16), hydroxyapatite (n=8), calcium oxalate (n=8), and cystine (n=8) were inserted in four porcine kidneys (10 each) and placed inside a 32-cm water tank anterior to a cadaver spine. Spiral dual-energy scans were obtained on a dual-source, 64-slice computed tomography (CT) system using a clinical protocol and automatic exposure control. Scanning was performed at two different collimations (0.6 mm and 1.2 mm) and within three phantom sizes (medium, large, and extra large) resulting in a total of six image datasets. These datasets were analyzed using the dual-energy software tool available on the CT system for both accuracy (number of stones correctly classified as either UA or non-UA) and sensitivity (for UA stones). Stone characterization was correlated with micro-CT.
For the medium and large phantom sizes, the DECT technique demonstrated 100% accuracy (40/40), regardless of collimation. For the extra large phantom size and the 0.6-mm collimation (resulting in the noisiest dataset), three (two cystine and one small UA) stones could not be classified (93% accuracy and 94% sensitivity). For the extra large phantom size and the 1.2-mm collimation, the dual-energy tool failed to identify two small UA stones (95% accuracy and 88% sensitivity).
In an anthropomorphic phantom model, dual-energy CT can accurately discriminate uric acid stones from other stone types.
This special report aims to inform the medical community about the many challenges involved in managing radiation exposure in a way that maximizes the benefit-risk ratio. The report discusses the ...state of current knowledge and key questions in regard to sources of medical imaging radiation exposure, radiation risk estimation, dose reduction strategies, and regulatory options.
The space physics community continues to grow and become both more interdisciplinary and more intertwined with commercial and government operations. This has created a need for a framework to easily ...identify what projects can be used for specific applications and how close the tool is to routine autonomous or on-demand implementation and operation. We propose the Application Usability Level (AUL) framework and publicizing AULs to help the community quantify the progress of successful applications, metrics, and validation efforts. This framework will also aid the scientific community by supplying the type of information needed to build off of previously published work and publicizing the applications and requirements needed by the user communities. In this paper, we define the AUL framework, outline the milestones required for progression to higher AULs, and provide example projects utilizing the AUL framework. This work has been completed as part of the activities of the Assessment of Understanding and Quantifying Progress working group which is part of the International Forum for Space Weather Capabilities Assessment.
In a population‐based, cross‐sectional study, we assessed age‐ and sex‐specific changes in bone structure by QCT. Over life, the cross‐sectional area of the vertebrae and proximal femur increased by ...∼15% in both sexes, whereas vBMD at these sites decreased by 39–55% and 34–46%, respectively, with greater decreases in women than in men.
Introduction: The changes in bone structure and density with aging that lead to fragility fractures are still unclear.
Materials and Methods: In an age‐ and sex‐stratified population sample of 373 women and 323 men (age, 20–97 years), we assessed bone geometry and volumetric BMD (vBMD) by QCT at the lumbar spine, femoral neck, distal radius, and distal tibia.
Results: In young adulthood, men had 35–42% larger bone areas than women (p < 0.001), consistent with their larger body size. Bone area increased equally over life in both sexes by ∼15% (p < 0.001) at central sites and by ∼16% and slightly more in men at peripheral sites. Decreases in trabecular vBMD began before midlife and continued throughout life (p < 0.001), whereas cortical vBMD decreases began in midlife. Average decreases in trabecular vBMD were greater in women (−55%) than in men (−46%, p < 0.001) at central sites, but were similar (−24% and −26%, respectively) at peripheral sites. With aging, cortical area decreased slightly, and the cortex was displaced outwardly by periosteal and endocortical bone remodeling. Cortical vBMD decreased over life more in women (∼25%) than in men (∼18%, p < 0.001), consistent with menopausal‐induced increases in bone turnover and bone porosity.
Conclusions: Age‐related changes in bone are complex. Some are beneficial to bone strength, such as periosteal apposition with outward cortical displacement. Others are deleterious, such as increased subendocortical resorption, increased cortical porosity, and, especially, large decreases in trabecular vBMD that may be the most important cause of increased skeletal fragility in the elderly. Our findings further suggest that the greater age‐related decreases in trabecular and cortical vBMD and perhaps also their smaller bone size may explain, in large part, why fragility fractures are more common in elderly women than in elderly men.
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