23 research outputs found

    Predicting the time derivative of local magnetic perturbations

    Full text link
    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106153/1/jgra50798.pd

    The STONE Curve: A ROC‐Derived Model Performance Assessment Tool

    Get PDF
    A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations, that is, the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve—plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero‐intercept unity‐slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data‐model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth’s inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.Plain Language SummaryScientists often try to reproduce observations with a model, helping them explain the observations by adjusting known and controllable features within the model. They then use a large variety of metrics for assessing the ability of a model to reproduce the observations. One such metric is called the relative operating characteristic (ROC) curve, a tool that assesses a model’s ability to predict events within the data. The ROC curve is made by sliding the event‐definition threshold in the model output, calculating certain metrics and making a graph of the results. Here, a new model assessment tool is introduced, called the sliding threshold of observation for numeric evaluation (STONE) curve. The STONE curve is created by sliding the event definition threshold not only for the model output but also simultaneously for the data values. This is applicable when the model output is trying to reproduce the exact values of a particular data set. While the ROC curve is still a highly valuable tool for optimizing the prediction of known and preclassified events, it is argued here that the STONE curve is better for assessing model prediction of a continuous‐valued data set.Key PointsA new event‐detection‐based metric for model performance appraisal is given with sliding thresholds in both observational and model valuesThe new metric is like the relative operating characteristic curve but uses continuous observational values, not just categorical statusThe new metric is used on real‐time model predictions of common geomagnetic activity parameters, demonstrating its features and strengthsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156486/2/ess2610.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156486/1/ess2610_am.pd

    CalcDeltaB: An efficient postprocessing tool to calculate ground‐level magnetic perturbations from global magnetosphere simulations

    Full text link
    Ground magnetic field variations can induce electric currents on long conductor systems such as high‐voltage power transmission systems. The extra electric currents can interfere with normal operation of these conductor systems; and thus, there is a great need for better specification and prediction of the field perturbations. In this publication we present CalcDeltaB, an efficient postprocessing tool to calculate magnetic perturbations Δ B at any position on the ground from snapshots of the current systems that are being produced by first‐principle models of the global magnetosphere‐ionosphere system. This tool was developed during the recent “d B /d t ” modeling challenge at the Community Coordinated Modeling Center that compared magnetic perturbations and their derivative with observational results. The calculation tool is separate from each of the magnetosphere models and ensures that the Δ B computation method is uniformly applied, and that validation studies using Δ B compare the performance of the models rather than the combination of each model and a built‐in Δ B computation tool that may exist. Using the tool, magnetic perturbations on the ground are calculated from currents in the magnetosphere, from field‐aligned currents between magnetosphere and ionosphere, and the Hall and Pedersen currents in the ionosphere. The results of the new postprocessing tool are compared with Δ B calculations within the Space Weather Modeling Framework model and are in excellent agreement. We find that a radial resolution of 1/30 R E is fine enough to represent the contribution to Δ B from the region of field‐aligned currents. Key Points Developed tool to compute magnetic perturbations on the ground Too validated using existing SWMF implementation Model validation independent from Delta‐B calculation within each modelPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/1/Contributions_E4_highlat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/2/AuxiliaryMaterial_README_v2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/3/Contributions_E1_highlat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/4/Contributions_E2_highlat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/5/Contributions_E3_midlat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/6/Contributions_E2_midlat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/7/Contributions_E1_midlat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/8/Contributions_E3_highlat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/9/Contributions_E5_midlat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/10/Contributions_E4_midlat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/11/Contributions_E6_midlat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/12/swe20180.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/13/Contributions_E6_highlat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109314/14/Contributions_E5_highlat.pd

    Unifying the validation of ambient solar wind models

    Get PDF
    Progress in space weather research and awareness needs community-wide strategies and procedures to evaluate our modeling assets. Here we present the activities of the Ambient Solar Wind Validation Team embedded in the COSPAR ISWAT initiative. We aim to bridge the gap between model developers and end-users to provide the community with an assessment of the state-of-the-art in solar wind forecasting. To this end, we develop an open online platform for validating solar wind models by comparing their solutions with in situ spacecraft measurements. The online platform will allow the space weather community to test the quality of state-of-the-art solar wind models with unified metrics providing an unbiased assessment of progress over time. In this study, we propose a metadata architecture and recommend community-wide forecasting goals and validation metrics. We conclude with a status update of the online platform and outline future perspectives
    corecore