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<!-- Geospace Page -->
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  <title>Geospace - Romeo O. M.</title>
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  <meta name="author" content="Orlando M. Romeo" />
  <meta name="description" content="Geospace Research conducted by Orlando M. Romeo" />
  <meta name="keywords" content="geospace, space weather, space physics, radiation belts, RBSP, Dst, machine learning, AI, Orlando Romeo, Orlando M. Romeo" />
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    <h1>Geospace Research</h1>
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      <div>
        <h2>Space Weather</h2>
        <img src="../images/research/sw2018.gif" alt="Space Weather">
        <p> Detrending Fluctuation Analysis (DFA) was applied on solar wind velocity measurements (1999-2000) from the Advanced Composition Explorer (ACE) spacecraft at L1
          to detect long-range correlations from hours to over several days. Based on the results of the DFA, we implemented time delay embedding to each component
          of the solar wind velocity to determine any temporal correlations for forecasting space weather conditions, with delays spanning from 1 to 24 hours.
          In addition, we relied on singular value decomposition and nearest neighbor tree search to help guide our analysis for space weather prediction.
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        <h2>Radiation Belt Modeling</h2>
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          <source src="../images/research/EarthRB.mp4" type="video/mp4">
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        <p>
        This project focused on data-derived modeling techniques for the application to Earth's radiation belts. We tested our methods first with chaotic systems,
        such as the Lorentz attractor, by implementing black box dynamical system techniques, such as delay embedding, singular value decomposition, and nearest
        neighbor averaging. With these techniques, we could then apply them to Van Allen Probe data of the Earth’s radiation belts, specifically electron energy
        flux as a function of L-shell. We first created synthetic radiation belt intensity (RBI) data based on concepts of diffusion and random walk using Dst
        index values. Once this was completed, the code and parameters were altered to model radiation belt intensity in 2015, and was verified by applying other
        years from 2010 to 2017. The Python code for forecasting future outputs of the Lorenz attractor was adapted to the RBI data, implementing an input of Dst
        into the system. The synthetic data went through delay embedding, singular value decomposition, and the nearest neighbor methods to predict the RBI data for
        2016 at one L-shell location. Parameters, such as amount of delays, principle components, and nearest neighbors, were altered in over two thousand combinations
        for each varying prediction step in time. As well, other inputs were implemented into the reconstruction and forecast of the system, such as the gradient of the
        RBI at a certain L-shell location and RBI values at upper L-shell locations. Finally, using two inputs of Dst index and RBI vaues at upper L-shell location, the
        entire spectrum of 2016 RBI from L-shells 2.5 to 7 were forecasted for the entire year.
        </p>
        <img src="../images/research/RBSP.png" alt="Martian crustal magnetism">
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        <h2>South Atlantic Anamoly</h2>
        <img src="../images/research/SAA.png" alt="SAA">
        <p>The purpose of this study was to discover a correlation between fluctuations in the total power intensity of the South Atlantic Anomaly (SAA)
          and changes in solar wind and interplanetary magnetic field (IMF) activity. The SAA is a region of the Earth’s magnetic field where high concentrations of
          energetic particles reside due to the offset of the inner radiation belt from the Earth’s rotational axis. Due to the high radiation of photons, many satellites
          that cross this region are forced to shut their instruments down in fear of permanent damage. The photon count from the SAA region was extracted, filtered, and fit
          to a spherical harmonic model in order to calculate the SAA daily averaged intensity. The SAA data was then correlated with the solar wind speed and z-component of
          the interplanetary magnetic field strength to understand the causes of the South Atlantic Anomaly. At the end of this investigation, a yearly variation in SAA intensity
          was discovered, but no significant correlation was found between
          all three datasets spanning across an entire year. This would suggest that solar wind and IMF activity do not affect the overall trend of the SAA on a yearly basis,
          signifying that there are other factors that affect this phenomenon in the magnetic field. However, future work could expand this study with other data methods beyond
          correlation coefficient tests, as well as investigate other solar wind properties. Once these other possible factors are discovered, they can be utilized
          to predict the intensity of the SAA to warn satellites when to collect data in this area.
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