Browsing by Author "Tobiska, W. Kent"

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    Comparison of a Neutral Density Model With the SET HASDM Density Database
    Weimer, Daniel R.; Tobiska, W. Kent; Mehta, Piyush M.; Licata, R. J.; Drob, Douglas P.; Yoshii, Jean (American Geophysical Union, 2021-12)
    The EXospheric TEMperatures on a PoLyhedrAl gRid (EXTEMPLAR) method predicts the neutral densities in the thermosphere. The performance of this model has been evaluated through a comparison with the Air Force High Accuracy Satellite Drag Model (HASDM). The Space Environment Technologies (SET) HASDM database that was used for this test spans the 20 years 2000 through 2019, containing densities at 3 hr time intervals at 25 km altitude steps, and a spatial resolution of 10 degrees latitude by 15 degrees longitude. The upgraded EXTEMPLAR that was tested uses the newer Naval Research Laboratory MSIS 2.0 model to convert global exospheric temperature values to neutral density as a function of altitude. The revision also incorporated time delays that varied as a function of location, between the total Poynting flux in the polar regions and the exospheric temperature response. The density values from both models were integrated on spherical shells at altitudes ranging from 200 to 800 km. These sums were compared as a function of time. The results show an excellent agreement at temporal scales ranging from hours to years. The EXTEMPLAR model performs best at altitudes of 400 km and above, where geomagnetic storms produce the largest relative changes in neutral density. In addition to providing an effective method to compare models that have very different spatial resolutions, the use of density totals at various altitudes presents a useful illustration of how the thermosphere behaves at different altitudes, on time scales ranging from hours to complete solar cycles.
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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.