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Scale-Separated Dynamic Mode Decomposition and Ionospheric Forecasting
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  • Daniel Jay Alford-Lago,
  • Christopher W Curtis,
  • Alexander Ihler,
  • Katherine Anne Zawdie
Daniel Jay Alford-Lago
Naval Information Warfare Center Pacific

Corresponding Author:[email protected]

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Christopher W Curtis
San Diego State University
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Alexander Ihler
UC Irvine
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Katherine Anne Zawdie
Naval Research Laboratory
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Abstract

We present a method for forecasting the foF2 and hmF2 parameters using modal decompositions from measured ionospheric electron density profiles. Our method is based on Dynamic Mode Decomposition (DMD), which provides a means of determining spatiotemporal modes from measurements alone. Our proposed extensions to DMD use wavelet decompositions that provide separation of a wide range of high-intensity, transient temporal scales in the measured data. This scale separation allows for DMD models to be fit on each scale individually, and we show that together they generate a more accurate forecast of the time-evolution of the F-layer peak. We call this method the Scale-Separated Dynamic Mode Decomposition (SSDMD). The approach is shown to produce stable modes that can be used as a time-stepping model to predict the state of foF2 and hmF2 at a high time resolution. We demonstrate the SSDMD method on data sets covering periods of high and low solar activity as well as low, mid, and high latitude locations.