activity
20182021
most citedThe current state and future directions of modeling thermosphere density enhancements during extreme magnetic storms

30 citations · 33 across the 2 of their papers we have counts for

collaborators

5 papers

physics.space-ph202130 cited

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…

cs.LG20213 cited

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…

physics.space-ph2020

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…

physics.ao-ph2018

A new transformative framework for data assimilation and calibration of physical ionosphere-thermosphere models

Piyush M. Mehta, Richard Linares

Accurate specification and prediction of the ionosphere-thermosphere (IT) environment, driven by external forcing, is crucial to the space community. In this work, we present a new…

physics.space-ph2018

A quasi-physical dynamic reduced order model for thermospheric mass density via Hermitian Space Dynamic Mode Decomposition

Piyush M. Mehta, Richard Linares, Eric K. Sutton

Thermospheric mass density is a major driver of satellite drag, the largest source of uncertainty in accurately predicting the orbit of satellites in low Earth orbit (LEO) pertinen…