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International audience; Heliophysics data analysis often involves combining diverse science measurements, many of them captured as time series. Although there are now only a few commonly used data file formats, the diversity in mechanisms for automated access to and aggregation of such data holdings can make analysis that requires intercomparison of data from multiple data providers difficult. The Heliophysics Application Programmer's Interface (HAPI) is a recently developed standard for accessing distributed time series data to increase interoperability. The HAPI specification is based on the common elements of existing data services, and it standardizes the two main parts of a data service: the request interface and the response data structures. The interface is based on the REpresentational State Transfer (REST) or RESTful architecture style, and the HAPI specification defines five required REST endpoints. Data are returned via a streaming format that hides file boundaries; the metadata is detailed enough for the content to be scientifically useful, e.g., plotted with appropriate axes layout, units, and labels. Multiple mature HAPI-related open-source projects offer server-side implementation tools and client-side libraries for reading HAPI data in multiple languages (IDL, Java, MATLAB, and Python). Multiple data providers in the US and Europe have added HAPI access alongside their existing interfaces. Based on this experience, data can be served via HAPI with little or no information loss compared to similar existing web interfaces. Finally, HAPI has been recommended as a COSPAR standard for time series data delivery.

Interoperability between datasets in Heliophysics and Planetary archives is increasingly important to address complex science questions about space weather and planetary plasma environments. Yet fo...

This paper is the primary deliverable of the very first NASA Living With a Star Institute Working Group, Geomagnetically Induced Currents (GIC) Working Group. The paper provides a broad overview of the current status and future challenges pertaining to the science, engineering, and applications of the GIC problem. Science is understood here as the basic space and Earth sciences research that allows improved understanding and physics-based modeling of the physical processes behind GIC. Engineering, in turn, is understood here as the “impact” aspect of GIC. Applications are understood as the models, tools, and activities that can provide actionable information to entities such as power systems operators for mitigating the effects of GIC and government agencies for managing any potential consequences from GIC impact to critical infrastructure. Applications can be considered the ultimate goal of our GIC work. In assessing the status of the field, we quantify the readiness of various applications in the mitigation context. We use the Applications Readiness Level (ARL) concept to carry out the quantification.

We present the construction of a new white-light coronal brightness index (CBI) from the entire archive of observations recorded by the Large Angle Spectrometric Coronagraph (LASCO) C2 camera between 1996 and 2017, comprising two full solar cycles. We reduce all fully calibrated daily C2 observations of the white light corona into a single daily coronal brightness observation for every day of observation recorded by the instrument, with mean daily brightness values binned into 0.1 Rsun radial x 1 degree angular regions from 2.4 -- 6.2 Rsun for a full 360-degrees. As a demonstration of the utility of the CBI, we construct a new solar irradiance proxy that correlates well with a variety of direct solar irradiance observations, with correlations shown to be in the range of 0.77-0.89. We also present a correlation mapping technique to show how irradiance correlations depend on, and relate to, coronal structure/locations, and to demonstrate how the LASCO CBI can be used to perform long-term "spatial correlation" studies to investigate relationships between the solar corona and any arbitrary concurrent geophysical index. Using this technique we find possible relationships between coronal brightness and plasma temperature, interplanetary magnetic field magnitude and (very weakly) proton density., Comment: 35 pages, 9 Figures. Accepted to Solar Physics

The Heliophysics Application Programmer’s Interface (HAPI) represents a grass-roots effort to develop a common interface that data providers can use to offer time series data for computer-to-computer data access. Multiple data centers with Heliophysics and Planetary data are adopting this emerging standard. Several scientific analysis packages (Autoplot, SPEDAS) seamlessly pull from HAPI servers, and we have created and are enhancing Python libraries that can be used to read HAPI data into useful data structures within personalized analysis code. The HAPI specification and supporting software are available on GitHub. We will discuss the latest developments of the HAPI specification, current server and client implementations, all in the context of the push for increased interoperability within Heliophysics and Planetary data analysis. https://hapi-server.github.io/

We consider two types of events, identified by decreases in Dst below a threshold value and increases in the equatorial mass density at geosynchronous altitudes, ρeq, above a threshold value using the Takahashi et al. [2010] dataset. From the Dst events and 1-day averages, we find that there is a statistically weak and small-amplitude difference between ρeq on the day of the event and the days before and after. When hourly averages are considered, a significant peak is found to occur six hours after event onset, and the primary factor that determines the post-onset peak amplitude in ρeq is elevated F10.7. In addition, for hourly averages, ρeq following the onset of a Dst event depends on the north-south component of the interplanetary magnetic field, Bz after the time of onset, with higher average Bz four hours after the event onset corresponding to larger ρeq value 7-11 hours after onset. From the ρeq events, we find a weak dependence on Bz after the onset of an event, with higher average Bz four hours after the event onset corresponding to larger ρeq values 24-36 hours after onset.

We present the latest result of a community-wide space weather model validation effort coordinated among the Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), model developers, and the broader science community. Validation of geospace models is a critical activity for both building confidence in the science results produced by the models and in assessing the suitability of the models for transition to operations. Indeed, a primary motivation of this work is supporting NOAA/SWPC's effort to select a model or models to be transitioned into operations. Our validation efforts focus on the ability of the models to reproduce a regional index of geomagnetic disturbance, the local K-index. Our analysis includes six events representing a range of geomagnetic activity conditions and six geomagnetic observatories representing midlatitude and high-latitude locations. Contingency tables, skill scores, and distribution metrics are used for the quantitative analysis of model performance. We consider model performance on an event-by-event basis, aggregated over events, at specific station locations, and separated into high-latitude and midlatitude domains. A summary of results is presented in this report, and an online tool for detailed analysis is available at the CCMC.

We present results of a global survey of single-pixel intensity power spectra from a 12-hour time period on 26 June 2013 in a 1600x1600 pixel region from four channels of the Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA) instrument. We extract single-pixel time series from a derotated image sequence, fit models to the power spectra of these time series, and study the spatial dependence of the model parameters. Two power spectra models are considered: i) a three-parameter power-law + tail model and ii) a power-law + tail model + three-parameter localized Lorentzian, the latter to model periodicity. Spectra are well-described by at least one of these models for all pixel locations, with the spatial distribution of best-fit model parameters shown to provide new and unique insights into turbulent, quiescent and periodic features in the EUV corona and upper photosphere. Findings include: individual model parameters correspond clearly and directly to visible solar features; detection of numerous quasi-periodic three- and five-minute oscillations; observational identification of concentrated magnetic flux as regions of largest power-law indices [n]; identification of unique spectral features of coronal holes and filaments; identification of sporadic and pervasive five-minute oscillations throughout the corona; detection of the known global ~4.0-minute chromospheric oscillation; "coronal bullseyes" appearing as radially decaying periodicities over sunspots and sporadic foot-point regions; and "penumbral periodic voids" appearing as broad rings around sunspots in 1600 and 1700 {\AA} in which spectra contain no statistically significant periodic component., Comment: 45 pages, 12 figures. Accepted for publication in Solar Physics

The results of a genetic algorithm optimization of the WINDMI model using the Blanchard-McPherron substorm data set is presented. A key result from the large-scale computations used to search for convergence in the predictions over the database is the finding that there are three distinct types of vx Bs -AL waveforms characterizing substorms. Type I and III substorms are given by the internally-triggered WINDMI model. The analysis reveals an additional type of event, called a type II substorm, that requires an external trigger as in the northward turning of the IMF model of Lyons (1995). We show that incorporating an external trigger, initiated by a fast northward turning of the IMF, into WINDMI, a low-dimensional model of substorms, yields improved predictions of substorm evolution in terms of the AL index. Intrinsic database uncertainties in the timing between the ground-based AL electrojet signal and the arrival time at the magnetopause of the IMF data measured by spacecraft in the solar wind prevent a sharp division between type I and II events. However, within these timing limitations we find that the fraction of events is roughly 40% type I, 40% type II, and 20% type III.

The upper atmosphere changes significantly in temperature, density and composition as a result of solar cycle variations, which causes severe storms and flares, and increases in the amount of absorbed solar radiation from solar energetic events. Satellite orbits are consequently affected by this process, especially those in low Earth orbit (LEO). In this paper, we present a model of atmospheric drag effects on the trajectory of two hypothetical LEO satellites of different ballistic coefficients, initially injected at h = 450 km. We investigate long-term trends of atmospheric drag on LEO satellites due to solar forcing induced atmospheric perturbations and heating at different phases of the solar cycle, and during short intervals of strong geomagnetic disturbances or magnetic storms. We show dependence of orbital decay on the severity of both solar cycle and phase and the extent of geomagnetic perturbations. The result of the model compares well with observed decay profile of some existing LEO satellites and provide a justification of the theoretical considerations used here.

In this paper we continue the community-wide rigorous modern space weather model validation efforts carried out within GEM, CEDAR and SHINE programs. In this particular effort, in coordination among the Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), modelers, and science community, we focus on studying the models' capability to reproduce observed ground magnetic field fluctuations, which are closely related to geomagnetically induced current phenomenon. One of the primary motivations of the work is to support NOAA SWPC in their selection of the next numerical model that will be transitioned into operations. Six geomagnetic events and 12 geomagnetic observatories were selected for validation.While modeled and observed magnetic field time series are available for all 12 stations, the primary metrics analysis is based on six stations that were selected to represent the high-latitude and mid-latitude locations. Events-based analysis and the corresponding contingency tables were built for each event and each station. The elements in the contingency table were then used to calculate Probability of Detection (POD), Probability of False Detection (POFD) and Heidke Skill Score (HSS) for rigorous quantification of the models' performance. In this paper the summary results of the metrics analyses are reported in terms of POD, POFD and HSS. More detailed analyses can be carried out using the event by event contingency tables provided as an online appendix. An online interface built at CCMC and described in the supporting information is also available for more detailed time series analyses.

[1] This paper reports the metrics-based results of the Dst index part of the 2008–2009 GEM Metrics Challenge. The 2008–2009 GEM Metrics Challenge asked modelers to submit results for four geomagnetic storm events and five different types of observations that can be modeled by statistical, climatological or physics-based models of the magnetosphere-ionosphere system. We present the results of 30 model settings that were run at the Community Coordinated Modeling Center and at the institutions of various modelers for these events. To measure the performance of each of the models against the observations, we use comparisons of 1 hour averaged model data with the Dst index issued by the World Data Center for Geomagnetism, Kyoto, Japan, and direct comparison of 1 minute model data with the 1 minute Dst index calculated by the United States Geological Survey. The latter index can be used to calculate spectral variability of model outputs in comparison to the index. We find that model rankings vary widely by skill score used. None of the models consistently perform best for all events. We find that empirical models perform well in general. Magnetohydrodynamics-based models of the global magnetosphere with inner magnetosphere physics (ring current model) included and stand-alone ring current models with properly defined boundary conditions perform well and are able to match or surpass results from empirical models. Unlike in similar studies, the statistical models used in this study found their challenge in the weakest events rather than the strongest events.

Several research groups are presently modeling key portions of the solar–terrestrial environment. Large-scale models of the solar corona, of the interplanetary medium, and of the coupled magnetosphere–ionosphere system are providing important insights into the dynamics and temporal evolution of these regions during normal, as well as disturbed, conditions. Some collaborative groups are now working on even more grand syntheses of models and are striving for a true end-to-end modeling capability. The challenges of such integrated modeling are immense: vastly different spatial and temporal scales must be addressed and codes must be synchronized to a high degree. Because much of the underlying physics at micro- and meso-scales remains unknown (or only poorly known), many key interface regions must be treated using empirical or semi-empirical methods. Validating the results from such global-scale models is, in itself, a major undertaking. And last, but by no means least, the question of how available (sparse) data are assimilated into end-to-end models remains a major challenge to the community. This paper provides an overview of the present status and future challenges in end-to-end modeling of the Sun–Earth system.

We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities., 15 pages, 10 figures; added some notes to address various reviewer comments

Metrics and model validation represent two key elements upon which the success of the Center for Integrated Space Weather Modeling (CISM) hinges. The routine calculation of important operationally- and scientifically-motivated metrics permits us to objectively measure and track the ability of coupled CISM models to predict essential space weather quantities. The rationale for CISM metrics selection is developed and the list of 29 metrics, along with the baseline models, first-generation physics models, and the data sets needed to compute skill scores, are outlined. While metrics provide a means for the objective assessment of long-term model improvement, model validation—the comprehensive, systematic quantitative comparison of model output with observations—is required for identifying and documenting model strengths and weaknesses. Two representative examples of initial validation efforts are summarized. The first uses case study analysis techniques and comparison with in situ observations during real events to explore the range of validity of the Lyon–Fedder–Mobarry (LFM) MHD simulation during magnetic storms. The second uses a statistical approach to compare the climatology of plasma sheet bulk properties (density, temperature, magnetic field, flows) deduced from both spacecraft observation and modeled by the LFM code. r 2004 Elsevier Ltd. All rights reserved.

The Wang–Sheeley–Arge (WSA) method is used to predict the solar wind speed (and certain other parameters) near the Earth’s orbit based upon solar surface measurements. This approach gives a predicted solar wind time series with a lead time of three to four days. Such forecasted solar wind conditions can then be convolved with linear and nonlinear filters in order to provide a predicted set of geomagnetic indices or various particle flux estimates. In order to illustrate the method in a concrete way, we present here a demonstration of an end-to-end empirical forecast of relativistic electrons in the outer Van Allen radiation belt. Past work has shown that radiation belt electron fluxes are highly dependent on the speed of the solar wind striking the magnetosphere. We develop filters that predict electron fluxes using the WSA estimates of solar wind speed at L1, which allows for 3–4 days lead times. We compare the prediction efficiency (PE) provided by these filters with filters developed to use 3–4 day old values of the solar wind velocity measured at L1 and 3–4 day old values of the measured electron fluxes themselves. It is found that the WSA method provides PEs of the electron flux that are slightly lower than that provided by using old L1 or the autocorrelated electron flux data. r 2004 Elsevier Ltd. All rights reserved.

Within the CISM project empirical models serve as baselines against which to monitor the increase in predictive skill of physics-based numerical codes as they advance. Empirical models also establish a suite of forecast models from which to evolve toward greater forecast accuracy by incorporating numerical codes as they advance enough to increase forecast skill. Establishing a suite of forecast models allows the CISM project to contribute results of use to operational space weather forecasting earlier than might otherwise be possible. Developing a suite of empirical models allows CISM to address issues of data ingestion early in the project under relatively simple conditions. Out of the data-ingestion effort has come a technique of probabilistic forecasts, which allows one to ingest information on IMF B z from solar data instead of from measurements taken only at the Lagrangian L1 point. To implement the evolution from empirical to numerical forecast models, CISM has adopted a coupled two-line system of model development, a science-model line interacting with a forecast-model line.

We test the ability of a data-derived model of geomagnetic activity, originally optimized to have a high prediction efficiency (PE), for its ability to predict only large geomagnetic disturbances. Correlation-based metrics, such as prediction efficiency, are often used as a measure of model performance. This metric puts equal weight on prediction of both large and small measurements. However, for space weather purposes, one is often interested in knowing only if a large disturbance event will occur so less emphasis should be placed on small measurements. If only large events are of interest, then a correlation metric is not the best measure of model performance. In this work, we determine how well a data-derived model, originally optimized to have a high prediction efficiency, predicts large geomagnetic events. The ratio of the number of correct to false alarm forecasts, R/sub F/, is used as an event-predictor metric. It is shown that in the electrojet regions the data-derived model that predicts the north-south component of the ground magnetic field B/sub x/ has a spatial R/sub F/ profile similar to that of the prediction efficiency. Maximal values of R/sub F/=4 are found at 0300 MLT when an event is defined as an excursion in the hourly-averaged north-south component of the ground magnetic field below -400 nT. Whereas the local time profile of PE(B/sub x/) is similar to R/sub F/(B/sub x/), the profile of PE(|dB/sub x//dt|) differs substantially from R/sub F/(|dB/sub x//dt|) in the noon sector. Epoch analysis shows that the poor performance in the noon sector is a result of pre-event levels of |dB/sub x//dt| not being clearly separated from post-event levels.

Convergent Cross-Mapping (CCM) is a technique for computing specific kinds of correlations between sets of times series. It was introduced by Sugihara et al. and is reported to be "a necessary condition for causation" capable of distinguishing causality from standard correlation. We show that the relationships between CCM correlations proposed in \cite{Sugihara2012} do not, in general, agree with intuitive concepts of "driving", and as such, should not be considered indicative of causality. It is shown that CCM causality analysis implies causality is a function of system parameters for simple linear and nonlinear systems. For example, in a RL circuit, both voltage and current can be identified as the driver depending on the frequency of the source voltage. It is shown that CCM causality analysis can, however, be modified to identify asymmetric relationships between pairs of time series that are consistent with intuition for the considered example systems for which CCM causality analysis provided non-intuitive driver identifications. This modification of the CCM causality analysis is introduced as "pairwise asymmetric inference" (PAI) and examples of its use are presented., 8 pages, 9 figures

[1] We describe an algorithm for intracalibration of measurements from plasma or energetic particle detectors on a three-axis stabilized platform. Modeling and forecasting of Earth's radiation belt environment requires data from particle instruments, and these data depend on measurements which have an inherent calibration uncertainty. Pre-launch calibration is typically performed, but on-orbit changes in the instrument often necessitate adjustment of calibration parameters to mitigate the effect of these changes on the measurements. On-orbit calibration practices for particle detectors aboard spin-stabilized spacecraft are well established. Three-axis stabilized platforms, however, pose unique challenges even when comparisons are being performed between multiple telescopes measuring the same energy ranges aboard the same satellite. This algorithm identifies time intervals when different telescopes are measuring particles with the same pitch angles. These measurements are used to compute scale factors which can be multiplied by the pre-launch geometric factor to correct any changes. The approach is first tested using measurements from GOES-13 MAGED particle detectors over a 5-month time period in 2010. We find statistically significant variations which are generally on the order of 5% or less. These results do not appear to be dependent on Poisson statistics nor upon whether a dead time correction was performed. When applied to data from a 5-month interval in 2011, one telescope shows a 10% shift from the 2010 scale factors. This technique has potential for operational use to help maintain relative calibration between multiple telescopes aboard a single satellite. It should also be extensible to inter-calibration between multiple satellites.

[1] Solar wind density has been argued to have a strong effect on geomagnetic storms. Elevated solar wind density tends to occur in time intervals when the solar wind electric field is large. This complicates the analysis required to identify a solar wind density influence because the solar wind electric field is the dominant driver of geomagnetic storms. Statistical studies have consistently shown that the independent correlation between solar wind density and geomagnetic storm intensity (via a proxy, such as the Dst index) is small. Modeling considerations predict a significant geomagnetic storm dependence on the plasma sheet density, which is indirectly connected to solar wind density. In this work, the solar wind density influence is quantified using two statistical measures: (1) data-derived impulse response functions and (2) the relationship between the integrated value of Dst to the integrated value of the solar wind electric field during geomagnetic storm intervals. Results from both approaches indicate that the solar wind density modifies the geoefficiency or the ability of a given value of the solar wind electric field to create a Dst disturbance. The impulse response method also predicts that solar wind density explains the difference in geoefficiency, as opposed to the solar wind dynamic pressure. Although the geoefficiency effect is large, its influence is shown to be small when only large storms are considered because large storms typically have large density.

[1] A variety of observations have shown strong seasonal variations in a vast array of magnetosphere-ionosphere parameters, including field-aligned currents, cross polar cap potential, and precipitating electron energy flux. In this paper we examine how these variations are modeled in the Lyon-Fedder-Mobarry (LFM) global-scale magnetohydrodynamic simulation of the coupled solar wind-magnetosphere-ionosphere system. In order to account for changes in the solar wind conditions caused by the seasonal variation of the Earth's dipole tilt we carefully select the solar wind parameters so that the effective driving conditions are the same across the March, June, and December intervals examined. The seasonal variation of the field-aligned current strengths is in good agreement with observations, with the sunlit hemisphere having more current than the dark hemisphere in the June and December intervals. However, in order to bring the modeled precipitating electron energy flux into better agreement with the observations we need to utilize a modified current-voltage relationship which includes a proxy for illumination effects. We provide a detailed description of the LFM's magnetosphere-ionosphere coupling interface including how illumination effects are incorporated into the model. This methodology for including these effects does not allow for determining if changes in conductance or ionospheric density are responsible for the changes. In addition to improving the agreement with observations the new version of the current-voltage relationship results in enhanced geomagnetic activity in the March interval examined and suppression of activity during the June interval.

We describe the new undergraduate science degree program in Computational and Data Sciences (CDS) at George Mason University (Mason), which began offering courses for both major (B.S.) and minor degrees in Spring 2008. The overarching theme and goal of the program are to train the next-generation scientists in the tools and techniques of cyber-enabled science (e-Science) to prepare them to confront the emerging petascale challenges of data-intensive science. The Mason CDS program has a significantly stronger focus on data-oriented approaches to science than do most computational science and engineering programs. The program has been designed specifically to focus both on simulation (Computational Science) and on data-intensive applications (Data Science). New courses include Introduction to Computational & Data Sciences, Scientific Data and Databases, Scientific Data & Information Visualization, Scientific Data Mining, and Scientific Modeling & Simulation. This is an interdisciplinary science program, drawing examples, classroom materials, and student activities from a broad range of physical and biological sciences. We will describe some of the motivations and early results from the program. More information is available at http://cds.gmu.edu/.

[1] Although space weather is modeled after tropospheric weather, both in its conception as a weather system and in our efforts to forecast it, no capability exists today for assimilating magnetospheric data into space weather simulations. In this paper a scheme is proposed for assimilating magnetospheric data into a global MHD code. The scheme is similar to ensemble Kalman filters, but it is less reliant on dense data coverage and allows numerical models easier adherence to conservation laws. Three different estimates of the computational cost of the proposed scheme indicate that it is easily achievable with current computational resources.

[1] Several basic results from decision theory as applied to rare event forecasts are reviewed, and an alternative method for comparing rare event forecasts is presented. A fundamental result is that for a large class of users only interested in economic utility, the relevant performance quantity is the number of correct and false alarm forecasts. This is contrasted with the reality that most forecast models are optimized to have a high data-model correlation, which does not always correspond to maximum economic utility. The value score (VS) developed by Wilks (2001) partially resolves this disconnect between modeler- and user-relevant metrics. Although the value score is closer to what is most likely of interest to a user, maximal VS does not necessarily correspond to maximal utility for the realistic case where the cost and benefit are dependent on the amplitude of the forecasted event. An alternative comparison and presentation method is proposed which may resolve this problem. For the class of users considered, full specification of model performance requires computation of the probability of correct, false alarm, and missed forecasts at several amplitude levels and warning time spans. Examples of the computations involved for the modeler and user are given for predictions of large-amplitude energetic electron fluence and geomagnetic storms parameterized by the Dst index.

Since the early 1990s a series of spacecraft missions have completely transformed our view of the electron radiation belts. This paper summarizes a number of new results on the structure and dynamics of the belts obtained from those measurements. First, the structure of the outer electron belt is discussed with emphasis on the regions (P0-P2) distinguished on the basis of the time variations of the electron flux. Each region is characterized by distinct set of acceleration and loss processes. While these processes are traditionally represented by diffusion models, new empirical models have emerged in the last decade. These models are developed from the observed dynamics of the flux as a function of L shell and energy. We have developed such models in each Pi region, and introduce methods of writing them as empirical diffusion-convection models. Since any realistic space weather model must be driven by interplanetary activity parameters, we discuss the development of input-output models (filters) focusing on those driven by the solar wind velocity. In addition to the plasma velocity, other solar wind and IMF parameters are important for each outer-belt region. Taken together, these parameters describe geoeffective solar wind structures. This precursor information can be used to advance the forecast lead time. The expected impact of these modeling approaches to radiation belt forecasting is discussed.

[1] Among the many challenges facing space physics today is the need for an analysis and visualization package which can examine the results from numerical and empirical models as well as observational data. In this paper we introduce the CISM-DX package as a possible answer to this problem. We have created a tool which is capable of completing highly sophisticated visualizations of the results from a diverse set of models throughout the connected Sun-Earth system. We begin with an overview of the open source software programs, OpenDX and Octave, which form the basis of our package. Next we discuss the extensions to these programs that we have added to enhance their utility to the space physics community. We also discuss the extensive database of observations that is included with CISM-DX. Results from heliospheric simulations are used to highlight the ease at which a novice user can begin to utilize these tools. An energy budget calculation during a magnetospheric substorm simulation illustrates the more advanced analysis possible with the CISM-DX package. Next we highlight the extensive database of observational measurements included with the distribution, along with tools for displaying them. This package is being released under an open source licensing agreement to facilitate and encourage community use and contribution.

[1] Prior studies have examined the time-stationary (and quasi-stationary) dynamic response of relativistic electrons in the Earth's outer radiation belt to changes in solar wind bulk speed using linear prediction filters [Baker et al., 1990; Vassiliadis et al., 2002]. For this study, we have implemented an adaptive system identification scheme, based on the Kalman filter with process noise, to determine optimal time-dependent electron response functions. The nonlinear dynamic response of the radiation belts can then be tracked in time by recursively updating the optimal linear filter coefficients as new observations become available. We demonstrate a significant improvement in zero-time-lag electron log-flux “predictions” relative to models that are based on time-stationary linear prediction filters, while incurring only a slight increase in computational complexity. We conclude by discussing modifications necessary for an operational specification and forecast model, including the assimilation of real-time data, more sophisticated model structures, and a more practical gridded description of the radiation belt state.

[1] An analysis of large-amplitude relativistic electron fluxes, Je, is given using models that predict their appearance based on solar wind precursors. It has been noted that relativistic electron flux bursts in the magnetosphere are associated with high-speed solar wind streams. This observation is used to motivate several models that quantify the association in terms of 2 × 2 contingency tables. For each model, the minimal cost structure for which the model would be useful is computed as a function of large threshold values of Je. The first model is based on the observation that a threshold crossing in the daily averaged solar wind velocity, VSW, tends to precede large relativistic electron fluxes. The optimal ratio of correct to false alarms forecasts found using this algorithm is 18:4 for a threshold corresponding to amplitudes of Je at L = 4.4 above Jc = 103 particles/str·cm2·s (corresponding to 110 total events). The second model allows for jumps in the solar wind to be an event indicator and yields slight improvements in the forecast ratio for larger values of Jc. The dependence of the optimal forecast ratio on L shell is also considered. It is shown that there are L values for which a threshold crossing of the daily average of VSW from below to above 600 km/sec is a sufficient condition for the appearance of large-amplitude relativistic electron fluxes on one of the following three days. It is also shown that the condition of a threshold crossing of VSW above 600 km/s is not a necessary condition, because ≃80% of events were not preceded by this condition.

[1] A statistical model of short time-scale geomagnetic fluctuations is developed and used to evaluate how geomagnetic dynamics are influenced by different solar wind controlling parameters. The functional form of the probability distribution function (PDF) that describes extreme-value (greater than 4a) minute-to-minute changes in the ground magnetic field (Δx) at magnetometer station Sodankyla (geomagnetic latitude and longitude of [63.87,107.61]) is shown to be nearly independent of the variables solar wind (SW) forcing, local time (LT), and day of year (DOY). Instead of modifying the intrinsic dynamics, as characterized by the functional form of the PDF of Δx, these variables are shown either to amplify or reduce the absolute level of variability of the fluctuations: The primary difference in the PDF tail of Δx during weak and strong solar wind forcing is the standard deviation, a; the functional form of the PDF = f[Δx/σ(DOY,LT,SW)] is nearly invariant. In a statistical interpretation, we conclude that differences in solar-generated conductivity, seasonal effects, strength of solar wind forcing and variability, and position of the magnetometer ground station in local time do not change the structure of the extreme-value dynamics, as characterized by the probability distribution of Ax, but they serve to amplify the intrinsic variability.

[1] We map the spatial structure of the electron belts over their radial range (L = 1–11 RE) using a total of 25 years of observations by NASA, ISAS, and GPS spacecraft. Correlation analysis reveals significant radial structuring of the outer belt and identifies three regions, while earlier single-spacecraft studies are used to interpret the results. The central region P1 (L = 4.1–7.5 RE) has a well-known slow response (2–3 days) most probably due to shear-hydrodynamic effects of the interplanetary medium, which excite ULF waves at low latitudes. In the innermost region P0 (L = 3.0–4.1 RE) the electron response is rapid ( 7.5 RE) has a much weaker brief response to magnetic field orientation and low interplanetary plasma velocities, anticorrelated with the other two regions. The findings and, in addition, the solar-cycle modulation of the region size strongly suggest that the solar wind excites nonlinear response modes in the inner magnetosphere which are prototypically revealed in the three regions. Given this spatial variability, the optimal approach to next-generation modeling and electron storm forecasting may differ considerably for each region.

[1] In his Forum, P. Vermeesch (Eos, 90(47), 443, doi:10.1029/2009EO470004, 2009) argues that “the strong dependence ofp values on sample size makes them uninterpretable” with an example where p values in a hypothesis test using Pearson's chi-square statistic differed by a factor of 1016 when the sample size decreased tenfold. The data were a sequence of magnitude 4 or larger earthquake events (N = 118,415) spanning 3654 days [U.S. Geological Survey, 2010]. There are two problems with the analysis. First, Vermeesch applied the chi-square test to data with statistical properties that are inconsistent with those assumed in the derivation of the chi-square test. Second, he made an assumption that, using a straightforward calculation, can be shown to be inconsistent with the data. I address here only problems related to the application of statistics without reference to any additional physical processes that may also need to be addressed before statistical analysis is performed (e.g., the physics of how aftershocks are related to main earthquakes).

Scientific satellites, balloons, ground-based instruments, and other observational platforms are producing rich streams of data about the Earth and space. Ensuring widespread access to such data has led to the development of a new type of observatory: the virtual observatory. Existing only in cyberspace, virtual observatories are Web-based interfaces that point users to online data repositories. More important, they allow users not only to access and view multiple sources of information at the same time but also to cross-compare data to build new insights.