45 citations · 142 across the 10 of their papers we have counts for
10 papers
Reduced Order Probabilistic Emulation for Physics-Based Thermosphere Models
Richard J. Licata, Piyush M. Mehta
The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particul…
Advanced ensemble modeling method for space object state prediction accounting for uncertainty in atmospheric density
Smriti Nandan Paul, Richard J. Licata, Piyush M. Mehta
For objects in the low Earth orbit region, uncertainty in atmospheric density estimation is an important source of orbit prediction error, which is critical for space situational a…
Stochastic modeling of physical drag coefficient -- its impact on orbit prediction and space traffic management
Smriti Nandan Paul, Phillip Logan Sheridan, Richard J. Licata +1
Ambitious satellite constellation projects by commercial entities and the ease of access to space in recent times have led to a dramatic proliferation of low-Earth space traffic. I…
Understanding variability in HASDM to support space traffic management
W. Kent Tobiska, Marcin D. Pilinski, Shaylah Mutschler +5
With more commercial constellations planned, the number of Low Earth Orbit (LEO) objects is set to TRIPLE in two years. The growth in LEO objects directly increases the probability…
Calibrated and Enhanced NRLMSIS 2.0 Model with Uncertainty Quantification
Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer +2
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…
Science through Machine Learning: Quantification of Poststorm Thermospheric Cooling
Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer +3
Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators…
Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application
Richard J. Licata, Piyush M. Mehta
Machine learning (ML) has often been applied to space weather (SW) problems in recent years. SW originates from solar perturbations and is comprised of the resulting complex variat…
The current state and future directions of modeling thermosphere density enhancements during extreme magnetic storms
Denny M. Oliveira, Eftyhia Zesta, Piyush M. Mehta +4
Satellites, crewed spacecraft and stations in low-Earth orbit (LEO) are very sensitive to atmospheric drag. A satellite's lifetime and orbital tracking become increasingly inaccura…
Machine-Learned HASDM Model with Uncertainty Quantification
Richard J. Licata, Piyush M. Mehta, W. Kent Tobiska +1
The first thermospheric neutral mass density model with robust and reliable uncertainty estimates is developed based on the SET HASDM density database. This database, created by Sp…
Benchmarking Forecasting Models for Space Weather Drivers
Richard J. Licata, W. Kent Tobiska, Piyush M. Mehta
Space weather indices are commonly used to drive operational forecasts of various geospace systems, including the thermosphere for mass density and satellite drag. The drivers serv…