LANL* is a tool developed for quickly obtaining L* values, six orders of magnitude (~ one million times) faster than convectional approaches that require global numerical field lines tracing and integration. This model is based on a modern machine learning technique (feed-forward artificial neural network) by supervising a large data pool obtained from the IRBEM library, which is the traditional source for numerically calculating the L* values. The pool consists of about 100,000 samples randomly distributed within the magnetosphere (r: [1.03, 11.5] Re) and within a whole solar cycle from 1/1/1994 to 1/1/2005.
There are seven LANL* models, each corresponding to its underlying magnetic field configuration that is used to create the data sample pool. They are Olson and Pfitzer quiet model (OPQuiet), Pfitzer and Olson dynamic model (OPDyn), Tsyganenko 1989 model (T89), Tsyganenko 1996 model (T96), Tsyganenko 2001 quiet model (T01Quiet), Tsyganenko 2003 storm model (T01Storm), and Tsyganenko and Sitnov 2005 model (T05).
The LANL* model uses solar wind conditions (and (G1, G2, G3), (W1, W2, W3, W4, W5, W6) indices for T01 and T05 models respectively), local pitch angle, position, Mcllwain L shell, and magnetic field at the mirror point. The solar wind conditions (and G, W indices for T01 and T05 respectively) used for each LANL* model are consistent with the input for its underlying magnetic field configuration.
When using the CCMC web interface for instantaneous calculation of L* values, the user chooses the underlying magnetic field configuration, position (XYZ-GSM [Re]), pitch angle, start date/time and duration, and output frequency. The solar wind parameters are internally determined from the Qin-Denton omni2 database downloaded from the ViRBO website. The Mcllwain L and magnetic field at the mirror point are also internally determined from the IRBEM library.
For each underlying magnetic field model, below are the valid ranges for the inputs: OPDyn: density ∈ [5, 50], velocity ∈ [300, 500], Dst ∈ [100, 20], rGEO < 60 RE OPQuiet: rGEO < 15.0 RE T89: Kp ∈ [0, 9], rGEO < 70 RE T96: Dst ∈ [-100, 20], Pdyn ∈ [0.5, 10], |IMF By|< 10. |IMF Bz| < 10, rGEO < 40 RE T01Quiet: Dst ∈ [-50, 20], Pdyn ∈ [0.5, 5], |IMF By| < 5, |IMF Bz| < 5, G1 ∈ [0, 10], G2 ∈ [0, 10], xGSM > -15 RE T01Storm: xGSM > -15 RE T05: xGSM > -15 RE
In addition, the LANLstar model presents its constraint based on its learning/training process: Dst [nT]: (-422, 62) Solar wind velocity [km/s]: (237.1, 1188.5) Solar wind density [cm3]: (0.1, 100) IMF By [nT]: (-47.9, 40.26) IMF Bz [nT]: (-62.6, 47.2) Dynamic pressure [nPa]: (0.03, 69.4)
The output from the LANL* model are L* value at the selected time, position, and pitch angle as well as McllWain L shell, the second adiabatic invariant I, magnetic field at the mirror point, and solar wind conditions (and G, W indices for T01 and T05 respectively).
Model is time-dependant.
- Magnetosphere / Inner Magnetosphere / RingCurrent
- Magnetosphere / Inner Magnetosphere / RadiationBelt
Space Weather Impacts
- Near-earth radiation and plasma environment (aerospace assets functionality)
- Yu, Y., J. Koller, V. K. Jordanova, S. G. Zaharia, R.W. Friedel, S. K.Morley, Y. Chen, D. Baker, G. D. Reeves, and H. E. Spence (2014), Application and testing of the L* neural network with the self-consistent magnetic field model of RAM-SCB, J. Geophys. Res. Space Physics, 119, doi:10.1002/2013JA019350
- Yu, Y., J. Koller, S. Zaharia, and V. Jordanova (2012), L* neural networks from different magnetic field models and their applicability, Space Weather, 10, S02014, doi:10.1029/2011SW000743.
- Koller, J., and S. Zaharia (2011), LANL* V2.0: Global modeling and validation, Geosci. Model Dev., 4(3), 669-675, doi:10.5194/gmd-4-669-2011.
- Koller, J., G. D. Reeves, and R. H. W. Friedel (2009), LANL* V1.0: A radiation belt drift shell model suitable for real-time and reanalysis applications, Geosci. Model Dev., 2(2), 113-122.
- Olson, W. P., and K. A. Pfitzer (1977), Magnetospheric Magnetic Field Modeling, Ann. Sci. Rep. F44620-75-C-0033, Air Force Off. of Sci. Res., Arlington, Va.
- Pfitzer, K. A., W. P. Olson, and T. Mogstad (1988), A time-dependent, source-driven magnetospheric magnetic field model [abstract], Eos Trans. AGU, 69(16), 426.
- Tsyganenko, N. A. (1989), A magnetospheric magnetic field model with a warped tail current sheet, Planet. Space Sci., 37, 5-20, doi:10.1016/0032-0633(89)90066-4.
- Tsyganenko, N. A. (1995), Modeling the Earth's magnetospheric magnetic field confined within a realistic magnetopause, J. Geophys. Res., 100(A4), 5599-5612, doi:10.1029/94JA03193.
- Tsyganenko, N. A. (1996), Effects of the solar wind conditions on the global magnetospheric configuration as deduced from data-based field models, in European Space Agency Publication ESA SP-389, p.181.
- Tsyganenko N. A. (2002), A model of the near magnetosphere with a dawn-dusk asymmetry - 1. Mathematical Structure, J. Geophys.Res., 107, A8 10.1029/2001JA000219.
- Tsyganenko N. A. (2002), A model of the near magnetosphere with a dawn-dusk asymmetry - 2. Parameterization and fitting to observations, J. Geophys.Res., 107, A7 10.1029/2001JA000220.
- Tsyganenko, N. A., H. J. Singer, and J. C. Kasper (2003), Storm-time distortion of the inner magnetosphere: How severe can it get?, J. Geophys. Res., 108(A5), 1209, doi:10.1029/2002JA009808.
- Tsyganenko, N. A., and M. I. Sitnov (2005), Modeling the dynamics of the inner magnetosphere during strong geomagnetic storms, J. Geophys. Res., 110, A03208, doi:10.1029/2004JA010798.
- Roederer, J. G. (1970), Dynamics of Geomagnetically Trapped Radiation,Springer, Berlin.
Code Languages: Python 2
- Yiqun Yu, Los Alamos National Laboratory (Model Developer)
- Josef Koller, Los Alamos National Laboratory (Model Developer)
- Lutz Rastaetter, NASA GSFC CCMC (CCMC Model Host)
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