Mr Timothy Kodikara

• BSc in Aviation Engineering, Riga Technical University, Latvia
• MSc in Space Sciences, Department of Physics, University of Helsinki, Finland

Interests

• Numerical modelling of the thermosphere-ionosphere-magnetosphere system
• Space weather and space tracking
• Thermospheric variability and electrodynamics
• Total energy budget of the magnetosphere-ionosphere-thermosphere system

Project

Research title: Effects of Energetic and Dynamic Coupling of the Magnetosphere-Ionosphere-Thermosphere System on the Estimation of Upper Atmospheric Density

The unpredictability attached to the safety of operational satellites and manned spacecraft has never been greater in the full current of human existence. Space administrators around the world are developing SSA systems to cope with the challenges of growing threats in space operations.

Subsequently, the tracking of space debris is a topic that has gained significant traction over the recent years. Space debris is essentially any object orbiting the Earth of which motion cannot be controlled from ground (e.g., defunct satellites, debris leftover from satellite collisions and asteroids).

Space debris poses a significant threat to the safe operation of satellites and human space missions.

In this enterprise of orbit tracking and prediction, the accuracy of gravitational and upper-atmospheric models significantly influences the ability to assure the safety of those satellites that are of concern to us.

The orbit of an object around the Earth is governed by a number of forces, including the non-uniform gravity of the Earth, gravitational attraction of the Sun and the Moon, pressure exerted by solar radiation, albedo forces, anisotropic radiation from the surface of the spacecraft and atmospheric drag.

Although central body gravity forces are the strongest on an orbiting body, there are no large ambiguities in the gravitational models that are used for orbit determination (OD) and orbit prediction (OP).

The largest error in predicting the orbit of a space object in low Earth orbit (LEO) is atmospheric drag due to modeling errors in the interaction between the object and its environment and the atmospheric mass density.

Modelling of the upper atmosphere is highly challenging due to the complex dynamic and physical processes involved, in particular space weather variability and the unpredictable nature of the Sun that drives it.

Space weather is inimical to humanity's technologies in many magnitudes more than terrestrial weather. Therefore, a deeper understanding of space weather events in near-Earth environment is imperative to improve our preparedness to face the associated risks and hazards that crosses all aspects of modern society.

While space weather's strongest manifestation on astrodynamics in LEO region is through atmospheric drag via the uncertainty in mass density, at higher altitudes beyond LEO, space weather's influence is through solar radiation pressure.

There are various techniques for measuring thermospheric mass density.

Different types of models oriented for this purpose have been appraised in many studies.

Empirical models of thermospheric density are based on climatologies derived from observations.

They are particularly limited in their capacity to respond to the highly variable space weather conditions whereas physical models numerically solve the fluid equations to derive evolving thermospheric parameters such as density, temperature, winds and composition. Capturing the evolving thermospheric responses are critical for a forecast model (e.g., satellite tracking errors at 400 km during even moderate magnetic storms are about 65% greater than quiet times).

Looking at the remarkable growth and resilience in terms of nowcast and forecast capabilities in physical models of terrestrial weather, atmospheric researchers are sanguine about similar prospects for physics-based upper atmospheric models.

The primary aims of the research are as follows:

• To characterise and quantify the uncertainties caused by space weather on OD and OP
• To enhance the forecasting capabilities of physical models for the purposes of OD and OP
• To develop new mathematical models and algorithms to reduce the level of relative errors in state-of-the-art density estimation techniques

Robust and reliable OD and OP are becoming ever so crucial with the unremitting growth of space-borne object population.

An accurate density model is expected to significantly change the current level of OP and help ameliorate the predicament of near-Earth space asset protection.