Abstract:We simulated the entire month of January 2005 using the Space Weather Modeling Framework (SWMF) with observed solar wind data as input. We conducted this simulation with and without an inner magnetosphere model and tested two different grid resolutions. We evaluated the model's accuracy in predicting Kp, SYM‐H, AL, and cross‐polar cap potential (CPCP). We find that the model does an excellent job of predicting the SYM‐H index, with a root‐mean‐square error (RMSE) of 17–18 nT. Kp is predicted well during storm … Show more
“…These metrics are summarized in Table . We note that the reported accuracy of this SYM‐H prediction is comparable to the operational configuration of the SWMF reported for this same month by Haiducek et al () and that the RAM predictions are less biased.…”
Section: Application Of the Standardized Assessment Setsupporting
confidence: 83%
“…The 8‐differential‐equation model of Horton and Doxas () produces an output that can be considered a synthetic AL value. Gleisner and Lundstedt () adopted their neural network model for AE prediction, Bala et al () used their neural net for AE forecasts, Amariutei and Ganushkina () used the ARMAX model for predicting AL, Zhang and Moldwin () included AE in their probabilistic forecast of geomagnetic activity, and Haiducek et al () computed AL from the SWMF model results.…”
Section: Prior Assessment Of Index Prediction Modelsmentioning
confidence: 99%
“…Specifically, along with Kp, Devos et al () includes a prediction algorithm for F10.7. Using the SWMF model suite, Haiducek et al () simulated the northern and southern hemisphere cross polar cap potential and compared with an observation‐based estimate of this value.…”
Section: Prior Assessment Of Index Prediction Modelsmentioning
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.
“…These metrics are summarized in Table . We note that the reported accuracy of this SYM‐H prediction is comparable to the operational configuration of the SWMF reported for this same month by Haiducek et al () and that the RAM predictions are less biased.…”
Section: Application Of the Standardized Assessment Setsupporting
confidence: 83%
“…The 8‐differential‐equation model of Horton and Doxas () produces an output that can be considered a synthetic AL value. Gleisner and Lundstedt () adopted their neural network model for AE prediction, Bala et al () used their neural net for AE forecasts, Amariutei and Ganushkina () used the ARMAX model for predicting AL, Zhang and Moldwin () included AE in their probabilistic forecast of geomagnetic activity, and Haiducek et al () computed AL from the SWMF model results.…”
Section: Prior Assessment Of Index Prediction Modelsmentioning
confidence: 99%
“…Specifically, along with Kp, Devos et al () includes a prediction algorithm for F10.7. Using the SWMF model suite, Haiducek et al () simulated the northern and southern hemisphere cross polar cap potential and compared with an observation‐based estimate of this value.…”
Section: Prior Assessment Of Index Prediction Modelsmentioning
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.
“…By comparing the results obtained with the three simulations, we are able to assess qualitatively how sensitive the model‐derived K values are to the model settings. The three simulations are the same as those in Haiducek et al (), and details on the settings can be found there and in Haiducek et al (), which shares some of the settings in common. We describe them briefly here: SWMFa : Same settings as Ilie et al (), but with modifications to improve stability (details in Haiducek et al, ).…”
Section: Methodsologymentioning
confidence: 99%
“…A 0.25 R E cell size in the expected region of IB formation. Settings described in detail in Haiducek et al () where this model configuration is referred to as the “Hi‐res” configuration. SWMFc : A 1 million cell grid with settings based on those used operationally by the NOAA Space Weather Prediction Center. A 0.5 R E cell size in the expected region of IB formation.…”
There is considerable evidence that current sheet scattering (CSS) plays an important role in isotropic boundary (IB) formation during quiet time. However, IB formation can also result from scattering by electromagnetic ion cyclotron waves, which are much more prevalent during storm time. The effectiveness of CSS can be estimated by the parameter
K=Rcrg, the ratio of the field line radius of curvature to the particle gyroradius. Using magnetohydrodynamic and empirical models, we estimated the parameter K associated with storm time IB observations on the nightside. We used magnetic field observations from spacecraft in the magnetotail to estimate and correct for errors in the K values computed by the models. We find that the magnetohydrodynamic and empirical models produce fairly similar results without correction and that correction increases this similarity. Accounting for uncertainty in both the latitude of the IB and the threshold value of K required for CSS, we found that 29–54% of the IB observations satisfied the criteria for CSS. We found no correlation between the corrected K and magnetic local time, which further supports the hypothesis that CSS played a significant role in forming the observed IBs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.