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Browsing by Author "Licata, Richard J."

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    Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model
    Licata, Richard J.; Mehta, Piyush M.; Weimer, Daniel R.; Tobiska, W. Kent (American Geophysical Union, 2021-12)
    The community has leveraged satellite accelerometer data sets in previous years to estimate neutral mass density and exospheric temperatures. We utilize derived temperature data and optimize a nonlinear machine-learned (ML) regression model to improve upon the performance of the linear EXospheric TEMPeratures on a PoLyhedrAl gRid (EXTEMPLAR) model. The newly developed EXTEMPLAR-ML model allows for exospheric temperature predictions at any location with one model and provides performance improvements over its predecessor. We achieve reductions in mean absolute error of 2 K on an independent test set while providing similar error standard deviation values. Comparing the performance of both EXTEMPLAR models and the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Extended model (NRLMSISE-00) across different solar and geomagnetic activity levels shows that EXTEMPLAR-ML has the lowest mean absolute error across 80% of conditions tested. A study for spatial errors demonstrated that at all grid locations, EXTEMPLAR-ML has the lowest mean absolute error for over 60% of the polyhedral grid cells on the test set. Like EXTEMPLAR, our model's outputs can be utilized by NRLMSISE-00 (exclusively) to more closely match satellite accelerometer-derived densities. We conducted 10 case studies where we compare the accelerometer-derived temperature and density estimates from four satellites to NRLMSISE-00, EXTEMPLAR, and EXTEMPALR-ML during major storm periods. These comparisons show that EXTEMPLAR-ML generally has the best performance of the three models during storms. We use principal component analysis on EXTEMPLAR-ML outputs to verify the physical response of the model to its drivers.
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    MSIS-UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification
    Licata, Richard J.; Mehta, Piyush M.; Weimer, Daniel R.; Tobiska, W. Kent; Yoshii, Jean (American Geophysical Union, 2022-11)
    The Mass Spectrometer and Incoherent Scatter radar (MSIS) model family has been developed and improved since the early 1970's. The most recent version of MSIS is the Naval Research Laboratory (NRL) MSIS 2.0 empirical atmospheric model. NRLMSIS 2.0 provides species density, mass density, and temperature estimates as function of location and space weather conditions. MSIS models have long been a popular choice of thermosphere model in the research and operations community alike, but-like many models-does not provide uncertainty estimates. In this work, we develop an exospheric temperature model based in machine learning that can be used with NRLMSIS 2.0 to calibrate it relative to high-fidelity satellite density estimates directly through the exospheric temperature parameter. Instead of providing point estimates, our model (called MSIS-UQ) outputs a distribution which is assessed using a metric called the calibration error score. We show that MSIS-UQ debiases NRLMSIS 2.0 resulting in reduced differences between model and satellite density of 25% and is 11% closer to satellite density than the Space Force's High Accuracy Satellite Drag Model. We also show the model's uncertainty estimation capabilities by generating altitude profiles for species density, mass density, and temperature. This explicitly demonstrates how exospheric temperature probabilities affect density and temperature profiles within NRLMSIS 2.0. Another study displays improved post-storm overcooling capabilities relative to NRLMSIS 2.0 alone, enhancing the phenomena that it can capture.