Space Weather Modeling Framework Ensemble Simulations
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Bibliographic record
Abstract
<strong>Space Weather Modeling Framework ensemble simulations</strong> This archive contains folders for 41 simulations with the operational geospace configuration of the University of Michigan's Space Weather Modeling Framework[1]. The operational configuration uses the University of Michigan's BATS-R-US magnetohydrodynamics code[2], the Ridley ionospheric electrodynamics solver[3], and the Rice Convection Model[4] inner magnetosphere model. More details of the operational geospace configuration are given by [5]. These simulations were performed for, and used in, the paper > "Perturbed Input Ensemble Modeling with the Space Weather Modeling Framework",<br> > S.K. Morley, D.T. Welling and J.R. Woodroffe,<br> > Space Weather, 2018. doi: 10.1029/2018SW002000 <em>Directory Structure</em><br> All numbered directories are members of a perturbed input ensemble. The directory labeled "orig" is the reference (unperturbed) simulation. Each directory is structured identically. Each run directory contains `PARAM.in`, `LAYOUT.in` and `magin_GEM.dat` files. This set of files consistutes the required inputs for each run that are invariant. That is, these files are identical between runs and control the setup of the model and the types of outputs generated. Each run directory also contains an `IMF.dat` file that sets the upstream boundary condition. This file differs between each simulation. The values in each ensemble member have been perturbed from the values given in the reference simulation using a block resampling of measurement errors between an L1 solar wind monitor and a near-Earth monitor. Each run directory also contains `GM` and `GM\IO2` subdirectories. The `GM\IO2` subdirectory contains simulation output from the global magnetosphere module. Three files are present for each simulation: `geoindex_e20100404-190000.log`, `magnetometers_e20100404-190000.mag`, and `log_e20100404-190000.log`. These are standard SWMF log files that can be parsed and analyzed using, for example, the `pybats` module in the SpacePy[6] software package[7]. The simulation ouput includes ground magnetic perturbations at a set of magnetic observatory locations, local K indices, an estimated Kp index, a 1 minute resolution Sym-H/Dst index equivalent and simulated auroral electrojet indices. <br> <em>Basic Analysis</em><br> To derive the time derivative of the horizontal ground magnetic perturbation (dB/dt) the magnetometer log file can be loaded using SpacePy<br> <pre><code class="language-python">>>> import spacepy.pybats.bats >>> magdata = spacepy.pybats.bats.MagFile('run_001/GM/IO2/magnetometers_e20100404-190000.mag') >>> magdata.calc_h() #calculates horizontal from North and East components >>> magdata.calc_dbdt() #calculates time derivatives</code></pre> To then calculate binned maxima in the dB/dt time series, e.g., for the Yellowknife (YKC) station<br> <pre><code class="language-python">>>> import datetime as dt >>> import numpy as np >>> import spacepy.toolbox as tb >>> dBdt_max20, bintimes = tb.windowMean(magdata['YKC'], time=subset['time'], winsize=dt.timedelta(minutes=20), overlap=dt.timedelta(0), st_time=dt.datetime(2010,4,5), op=np.max)</code></pre> <br> and to turn this into a binary event series indicating a threshold crossing<br> <pre><code class="language-python">>>> threshold = 1.1 #nT/s >>> predicted_event = np.asarray(dBdt_max20) >= threshold</code></pre> <br> Assuming that the observational data are obtained from NASA's CCMC and similarly processed, the event validation statistics can be calculated and displayed using the PyForecastTools package[8].<br> <pre><code class="language-python">>>> import verify >>> c_table = verify.Contingency2x2.fromBoolean(predicted_event, observed_event) >>> ctable.summary(ci='bootstrap', verbose=True)</code></pre> <em>Footnotes</em><br> [1] Tóth, G., I. V. Sokolov, T. I. Gombosi, D. R. Chesney, C. R. Clauer, D. L. D. Zeeuw, K. C. Hansen, K. J. Kane, W. B. Manchester, R. C. Oehmke, K. G. Powell, A. J. Ridley, I. I. Roussev, Q. F. Stout, O. Volberg, R. A. Wolf, S. Sazykin, A. Chan, B. Yu, and J. KÃşta (2005), Space weather modeling framework: A new tool for the space science community, Journal of Geophysical Research: Space Physics, 110(A12), doi:10.1029/2005JA011126. [2] de Zeeuw, D. L., T. I. Gombosi, C. P. T. Groth, K. G. Powell, and Q. F. Stout (2000), An adaptive MHD method for global space weather simulations, IEEE Transactions on Plasma Science, 28(6), 1956–1965, doi:10.1109/27.902224. [3] Ridley, A. J., T. I. Gombosi, and D. L. DeZeeuw (2004), Ionospheric control of the magnetosphere: conductance, Annales Geophysicae, 22(2), 567–584, doi:10.5194/angeo-22-567-2004. [4] Toffoletto, F., S. Sazykin, R. Spiro, and R. Wolf (2003), Inner magnetospheric modeling with the Rice convection model, Space Science Reviews, 107(1), 175–196, doi: 10.1023/A:1025532008047. [5] Haiducek, J. D., D. T. Welling, N. Y. Ganushkina, S. K. Morley, and D. S. Ozturk (2017), SWMF global magnetosphere simulations of January 2005: Geomagnetic indices and cross-polar cap potential, Space Weather, 15(12), 1567–1587, doi: 10.1002/2017SW001695. [6] Morley, S. K., J. Koller, D. T. Welling, B. A. Larsen, M. G. Henderson, and J. T. Niehof (2011), Spacepy - a Python-based library of tools for the space sciences, in Proceedings of the 9th Python in science conference (SciPy 2010), Austin, TX. [7] SpacePy is packaged on PyPI, with the official git repository on SourceForge and an unofficial mirror on github. [8] PyForecastTools is packaged on PyPI and the repository is on github. The latest release is archived on Zenodo with doi: 10.5281/zenodo.1256921. The citation for v1.0.1 is Steve Morley. (2018, June 28). drsteve/PyForecastTools: PyForecastTools: Version 1.0.1 (Version v1.0.1). Zenodo. http://doi.org/10.5281/zenodo.1299389
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it