arXiv subjects

Piyush M. Mehta

Publications and source records attributed to Piyush M. Mehta.

22 records · Page 2Linked to original sources

arXiv2020

Benchmarking Forecasting Models for Space Weather Drivers

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 serve as proxies for various processes that cause energy flow and deposition in the geospace system. Forecasts of neutral mass density is a major uncertainty in operational orbit prediction and collision avoidance for objects in low earth orbit (LEO). For the strongly driven system, accuracy of space weather driver forecasts is crucial for operations. The High Accuracy Satellite Drag Model (HASDM) currently employed by the United States Air Force in an operational environment is driven by four (4) solar and two (2) geomagnetic proxies. Space Environment Technologies (SET) is contracted by the space command to provide forecasts for the drivers. This work performs a comprehensive assessment for the performance of the driver forecast models. The goal is to provide a benchmark for future improvements of the forecast models. Using an archived data set spanning six (6) years and 15,000 forecasts across solar cycle 24, we quantify the temporal statistics of the model performance.

physics.space-ph↗

arXiv2018

Data-driven framework for real-time thermospheric density estimation

In this paper, we demonstrate a new data-driven framework for real-time neutral density estimation via model-data fusion in quasi-physical ionosphere-thermosphere models. The framework has two main components: (i) the development of a quasi-physical dynamic reduced order model (ROM) that uses a linear approximation of the underlying dynamics and effect of the drivers, and (ii) dynamic calibration of the ROM through estimation of the ROM coefficients that represent the model parameters. We have previously demonstrated the development of a quasi-physical ROM using simulation output from a physical model and assimilation of non-operational density estimates derived from accelerometer measurements along a single orbit. In this paper, we demonstrate the potential of the framework for use with operational measurements. We use simulated GPS-derived orbit ephemerides with 5 minute resolution as measurements. The framework is a first of its kind, simple yet robust and accurate method with high potential for providing real-time operational updates to the state of the upper atmosphere using quasi-physical models with inherent forecasting/predictive capabilities.

physics.space-ph↗

arXiv2018

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

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 transformative framework for data assimilation and calibration of the physical IT models. The framework has two main components: (i) the development of a quasi-physical dynamic reduced order model (ROM) that uses a linear approximation of the underlying dynamics and effect of the drivers, and (ii) data assimilation and calibration of the ROM through estimation of the ROM coefficients that represent the model parameters. A reduced order surrogate for thermospheric mass density from the Thermosphere Ionosphere Electrodynamic General Circulation Model (TIE-GCM) was developed in previous work. This work concentrates on the second component of the framework - data assimilation and calibration of the TIE-GCM ROM. The new framework has two major advantages: (i) a dynamic ROM that combines the speed of empirical models for real-time capabilities with the predictive capabilities of physical models which has the potential to facilitate improved uncertainty quantification (UQ) using large ensembles, and (ii) estimation of model parameters rather than the driver(s)/input(s) which allows calibration of the model, thus avoiding degradation of model performance in the absence of continuous data. We validate the framework using accelerometer-derived density estimates from CHAMP and GOCE. The framework is a first of its kind, simple yet robust and accurate method with high potential for providing real-time operational updates to the state of the upper atmosphere in the context of drag modeling for Space Situational Awareness and Space Traffic Management.

physics.ao-ph↗

arXiv2018

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

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) pertinent to space situational awareness. Most existing models for thermosphere are either physics-based or empirical. Physics-based models offer the potential for good predictive/forecast capabilities but require dedicated parallel resources for real-time evaluation and data assimilative capabilities that have yet to be developed. Empirical models are fast to evaluate, but offer very limited forecasting abilities. This paper presents a methodology of developing a reduced-order dynamic model from high-dimensional physics-based models by capturing the underlying dynamical behavior. This work develops a quasi-physical reduced order model (ROM) for thermospheric mass density using simulated output from NCAR's Thermosphere-Ionosphere-Electrodynamics General Circular Model (TIE-GCM). The ROM is derived using a dynamic system formulation from a large dataset of TIE-GCM simulations spanning 12 years and covering a complete solar cycle. Towards this end, a new reduced order modeling approach, based on Dynamic Mode Decomposition with control (DMDc), that uses the Hermitian space of the problem to derive the dynamics and input matrices in a tractable manner is developed. Results show that the ROM performs well in serving as a reduced order surrogate for TIE-GCM while almost always maintaining the forecast error to within 5\% of the simulated densities after 24 hours.

physics.space-ph↗