Robert Scott Weigel

Geospace Environment Modeling 2008–2009 Challenge: Ground magnetic field perturbations

helps the users of the modeling products to better understand the capabilities of the models and to choose the approach that best suits their specific needs. Further, metrics!based analyses are important for addressing the differences between various modeling approaches and for measuring and guiding the progress in the field. In this paper, the metrics!based results of the ground magnetic field perturbation part of the Geospace Environment Modeling 2008‐2009 Challenge are reported. Predictions made by 14 different models, including an ensemble model, are compared to geomagnetic observatory recordings from 12 different northern hemispheric locations. Five different metrics are used to quantify the model performances for four storm events. It is shown that the ranking of the models is strongly dependent on the type of metric used to evaluate the model performance. None of the models rank near or at the top systematically for all used metrics. Consequently, one cannot pick the absolute“winner”: the choice for the best model depends on the characteristics of the signal one is interested in. Model performances vary also from event to event. This is particularly clear for root!mean!square difference and utility metric!based analyses. Further, analyses indicate that for some of the models, increasing the global magnetohydrodynamic model spatial resolution and the inclusion of the ring current dynamics improve the models’capability to generate more realistic ground magnetic field fluctuations.

Geomagnetically induced currents: science, engineering, and applications readiness

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.

Probability distribution invariance of 1-minute auroral-zone geomagnetic field fluctuations

[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.

Community-wide validation of geospace model local K-index predictions to support model transition to operations

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/SWPCs 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.

Predictability of large geomagnetic disturbances based on solar wind conditions

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.

The New Computational and Data Sciences Undergraduate Program at George Mason University

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/.

TSDS: high-performance merge, subset, and filter software for time series-like data

Time Series Data Server (TSDS) is a software package for implementing a server that provides fast super-setting, sub-setting, filtering, and uniform gridding of time series-like data. TSDS was developed to respond quickly to requests for long time spans of data. Data may be served from a fast database, typically created by aggregating granules (e.g., data files) from a remote data source and storing them in a local cache that is optimized for serving time series. The system was designed specifically for time series data, and is optimized for requests where the longest dimension of the requested data structure is time. Scalar, vector, and spectrogram time series types are supported. The user can interact with the server by requesting a time series, a date range, and an optional filter to apply to the data. Available filters include strides, block average/minimum/maximum, exclude, and inequality. Constraint expressions are supported, which allow such operations as a request for data from one time series when a different time series satisfied a specified relationship. TSDS builds upon DAP (Data Access Protocol), NcML (netCDF Mark-up language) and related software libraries. In this work, we describe the current design of this server, as well as planned features and potential implementation strategies.

Autoplot: a browser for scientific data on the web

Autoplot is software developed for the Virtual Observatories in Heliophysics to provide intelligent and automated plotting capabilities for many typical data products that are stored in a variety of file formats or databases. Autoplot has proven to be a flexible tool for exploring, accessing, and viewing data resources as typically found on the web, usually in the form of a directory containing data files with multiple parameters contained in each file. Data from a data source is abstracted into a common internal data model called QDataSet. Autoplot is built from individually useful components, and can be extended and reused to create specialized data handling and analysis applications and is being used in a variety of science visualization and analysis applications. Although originally developed for viewing heliophysics-related time series and spectrograms, its flexible and generic data representation model makes it potentially useful for the Earth sciences.

Using Virtual Observatories for Heliophysics Research

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.

VxOware: software for managing virtual observatory metadata

The recent Heliophysics Virtual Observatory (VxO) effort involves the development of separate observatories with a low overlap in physical domain or area of scientific specialization and a high degree of overlap in metadata management needs. VxOware is a content and metadata management system. While it is intended for use by a VxO specifically, it can also be used by any entity that manages structured metadata. VxOware has many features of a content management system and extensively uses the W3C recommendations for XML (Extensible Markup Language), XQuery (XML Query), and XSLT (Extensible Style Sheet Language Transformations). VxOware has features such as system and user administration, search, user-editable content, version tracking, and a wiki. Besides virtual observatories, the intended user-base of VxOware includes a group or an instrument team that has developed a directory structure of data files and would like to make this data, and its associated metadata, available in the virtual observatory network. One of the most powerful features of VxOware is the ability to link any type of object in the observatory to other objects and the ability for every object to be tagged.