The SpacePy space science package at 12 years

For over a decade, the SpacePy project has contributed open-source solutions for the production and analysis of heliophysics data and simulation results. Here we introduce SpacePy's functionality for the scientific user and present relevant design principles. We examine recent advances and the future of SpacePy in the broader scientific Python ecosystem, concluding with some of the work that has used SpacePy.Comment: 14 pages, 3 figures, accepted for publication in Frontiers in Astronomy and Space Science

Conductance Model for Extreme Events : Impact of Auroral Conductance on Space Weather Forecasts

Ionospheric conductance is a crucial factor in regulating the closure of magnetospheric field-aligned currents through the ionosphere as Hall and Pedersen currents. Despite its importance in predictive investigations of the magnetosphere - ionosphere coupling, the estimation of ionospheric conductance in the auroral region is precarious in most global first-principles based models. This impreciseness in estimating the auroral conductance impedes both our understanding and predictive capabilities of the magnetosphere-ionosphere system during extreme space weather events. In this article, we address this concern, with the development of an advanced Conductance Model for Extreme Events (CMEE) that estimates the auroral conductance from field aligned current values. CMEE has been developed using nonlinear regression over a year's worth of one-minute resolution output from assimilative maps, specifically including times of extreme driving of the solar wind-magnetosphere-ionosphere system. The model also includes provisions to enhance the conductance in the aurora using additional adjustments to refine the auroral oval. CMEE has been incorporated within the Ridley Ionosphere Model (RIM) of the Space Weather Modeling Framework (SWMF) for usage in space weather simulations. This paper compares performance of CMEE against the existing conductance model in RIM, through a validation process for six space weather events. The performance analysis indicates overall improvement in the ionospheric feedback to ground-based space weather forecasts. Specifically, the model is able to improve the prediction of ionospheric currents which impact the simulated dB/dt and {\Delta}B, resulting in substantial improvements in dB/dt predictive skill

Thank You to Space Weather Peer Reviewers

Space Weather Editors recognize contribution from peer reviewers.Key PointThank you to Space Weather 2017 reviewersPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144655/1/swe20690.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144655/2/swe20690_am.pd

Long‐lived plasmaspheric drainage plumes: Where does the plasma come from?

Long‐lived (weeks) plasmaspheric drainage plumes are explored. The long‐lived plumes occur during long‐lived high‐speed‐stream‐driven storms. Spacecraft in geosynchronous orbit see the plumes as dense plasmaspheric plasma advecting sunward toward the dayside magnetopause. The older plumes have the same densities and local time widths as younger plumes, and like younger plumes they are lumpy in density and they reside in a spatial gap in the electron plasma sheet (in sort of a drainage corridor). Magnetospheric‐convection simulations indicate that drainage from a filled outer plasmasphere can only supply a plume for 1.5–2 days. The question arises for long‐lived plumes (and for any plume older than about 2 days): Where is the plasma coming from? Three candidate sources appear promising: (1) substorm disruption of the nightside plasmasphere which may transport plasmaspheric plasma outward onto open drift orbits, (2) radial transport of plasmaspheric plasma in velocity‐shear‐driven instabilities near the duskside plasmapause, and (3) an anomalously high upflux of cold ionospheric protons from the tongue of ionization in the dayside ionosphere, which may directly supply ionospheric plasma into the plume. In the first two cases the plume is drainage of plasma from the magnetosphere; in the third case it is not. Where the plasma in long‐lived plumes is coming from is a quandary: to fix this dilemma, further work and probably full‐scale simulations are needed. Key Points Plasmaspheric drainage plumes can persist for weeks The source of the plasma supplying the long‐lived plumes is unknown Candidate sources include outflow from the tongue of ionizationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108632/1/jgra51234.pd

Real‐Time SWMF at CCMC: Assessing the Dst Output From Continuous Operational Simulations

The ground‐based magnetometer index of Dst is a commonly used measure of near‐Earth current systems, in particular the storm time inner magnetospheric current systems. The ability of a large‐scale, physics‐based model to reproduce, or even predict, this index is therefore a tangible measure of the overall validity of the code for space weather research and space weather operational usage. Experimental real‐time simulations of the Space Weather Modeling Framework (SWMF) are conducted at the Community Coordinated Modeling Center (CCMC). Presently, two configurations of the SWMF are running in real time at CCMC, both focusing on the geospace modules, using the Block Adaptive Tree Solar wind‐type Roe Upwind Solver magnetohydrodynamic model, the Ridley Ionosphere Model, and with and without the Rice Convection Model. While both have been running for several years, nearly continuous results are available since April 2015. A 27‐month interval through July 2017 is used for a quantitative assessment of Dst from the model output compared against the Kyoto real‐time Dst. Quantitative measures are presented to assess the goodness of fit including contingency tables and a receiver operating characteristic curve. It is shown that the SWMF run with the inner magnetosphere model is much better at reproducing storm time values, with a correlation coefficient of 0.69, a prediction efficiency of 0.41, and Heidke skill score of 0.57 (for a −50‐nT threshold). A comparison of real‐time runs with and without the inner magnetospheric drift physics model reveals that nearly all of the storm time Dst signature is from current systems related to kinetic processes on closed magnetic field lines.Plain Language SummaryAs society becomes more dependent on technologies susceptible to adverse space weather, it is becoming increasingly critical to have numerical models capable of running in real time to nowcast/forecast the conditions in the near‐Earth space environment. One such model is available at the Community Coordinated Modeling Center and has been running for several years, allowing for an assessment of the quality of the result. Comparisons are made against globally compiled index of near‐Earth space storm activity, including numerous statistical quantities and tests. The skill of the model is remarkable, especially when a few hours after each of the cold restarts of the model are removed from the comparison. It is also shown that a global model alone is not that good at reproducing this storm index; a regional model for the inner part of geospace is necessary for good data‐model agreement.Key PointsThe SWMF model has been running in experimental real‐time mode at CCMC for several years, and all saved output is availableThe comparison against real‐time Dst is quite good, especially when a few hours after cold restarts are removed from the comparisonIt is necessary to include an inner magnetospheric drift physics model to reproduce Dst; a real‐time run without one does much worsePeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146631/1/swe20766.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146631/2/swe20766_am.pd

Comparison of predictive estimates of high‐latitude electrodynamics with observations of global‐scale Birkeland currents

Two of the geomagnetic storms for the Space Weather Prediction Center Geospace Environment Modeling challenge occurred after data were first acquired by the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). We compare Birkeland currents from AMPERE with predictions from four models for the 4–5 April 2010 and 5–6 August 2011 storms. The four models are the Weimer (2005b) field‐aligned current statistical model, the Lyon‐Fedder‐Mobarry magnetohydrodynamic (MHD) simulation, the Open Global Geospace Circulation Model MHD simulation, and the Space Weather Modeling Framework MHD simulation. The MHD simulations were run as described in Pulkkinen et al. (2013) and the results obtained from the Community Coordinated Modeling Center. The total radial Birkeland current, ITotal, and the distribution of radial current density, Jr, for all models are compared with AMPERE results. While the total currents are well correlated, the quantitative agreement varies considerably. The Jr distributions reveal discrepancies between the models and observations related to the latitude distribution, morphologies, and lack of nightside current systems in the models. The results motivate enhancing the simulations first by increasing the simulation resolution and then by examining the relative merits of implementing more sophisticated ionospheric conductance models, including ionospheric outflows or other omitted physical processes. Some aspects of the system, including substorm timing and location, may remain challenging to simulate, implying a continuing need for real‐time specification.Key PointsPresents the first comparison between observed field‐aligned currents and models previously evaluated for space weather operational useThe model and observed integrated currents are well correlated, but the ratio between them ranges from 1/3 to 3The 2‐D current densities are weakly correlated with observations implying significant areas for improvements in the modelsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136469/1/swe20415_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136469/2/swe20415.pd

Model Evaluation Guidelines for Geomagnetic Index Predictions

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 SummaryOne 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 PointsWe review existing practices for assessing geomagnetic index prediction models and recommend a “standard set” of metricsAlong with fit performance metrics that use all data‐model pairs in their formulas, event detection performance metrics are recommendedOther aspects of metrics assessment best practices, limitations, uncertainties, and geomagnetic index caveats are also discussedPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/1/swe20790_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/2/swe20790.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/3/swe20790-sup-0001-2018SW002067-SI.pd

The Pierre Auger Observatory: Contributions to the 34th International Cosmic Ray Conference (ICRC 2015)

Contributions of the Pierre Auger Collaboration to the 34th International Cosmic Ray Conference, 30 July - 6 August 2015, The Hague, The NetherlandsComment: 24 proceedings, the 34th International Cosmic Ray Conference, 30 July - 6 August 2015, The Hague, The Netherlands; will appear in PoS(ICRC2015

The Earth: Plasma Sources, Losses, and Transport Processes

This paper reviews the state of knowledge concerning the source of magnetospheric plasma at Earth. Source of plasma, its acceleration and transport throughout the system, its consequences on system dynamics, and its loss are all discussed. Both observational and modeling advances since the last time this subject was covered in detail (Hultqvist et al., Magnetospheric Plasma Sources and Losses, 1999) are addressed