I did my undergraduate in mathematics and computer science at the Babeș-Bolyai University in Cluj-Napoca in my native Romania. Then I moved to Finland, where I attended the International Master Programme in Space Sciences at the University of Helsinki. During the time spent there, I also learned to speak Finnish. After graduating, I continued living in Helsinki for a while, working and volunteering, until I decided it was finally time to do a postgraduate degree. Besides my main research topic, I have a strong interest in astronomy, cosmology and linguistics (I speak five languages, with varying degrees of fluency).
My research interests are fairly broad. Mostly I am interested in methods for analyzing, manipulating and visualizing real-life physical data using computers.
My PhD’s working title is ‘Applying ensemble Kalman filtering to ionospheric assimilation’. (subject to change)
Comprehensive, global and timely specifications of the earth’s ionosphere are required to ensure the effective operation, planning and management of many radio frequency systems. Many techniques have been developed to measure ionospheric refractivity; these include ground and space-based ionosondes and the use of Global Positioning System (GPS) measurements made with both ground and space-based receivers.
Ionospheric data assimilation systems are currently under development that will combine disparate ionospheric measurements with an ionospheric model. The problem is mathematically under-determined since the amount of information that can be extracted from most ionospheric measurements is low compared to the required resolution of the electron density field under investigation. Therefore it is necessary to utilise a priori information about the state of the ionosphere in order to solve the inverse problem. Many inverse techniques have been proposed; however, this project will investigate the application of the local ensemble transform Kalman filter (LETKF) to physical models of the ionosphere/thermosphere system. The LETKF is a method whereby the data assimilation is performed in local “regions” around each model grid point. Each region is processed independently, naturally leading to parallelization, and the grids are later assembled into the global analysis. The LETKF has been well-tested and shown to be both computationally efficient and flexible. However, it has not yet been applied to the ionosphere. The intention of this project is to achieve a significant improvement in current ionospheric forecasting.
This implies considering a number of important factors. Probably the key and the most time- and work-intensive is which model components should be perturbed and how? Although a lot of theoretical work has been done on ensemble Kalman filters this project aims to go behind on the theoretical and look at the consequences of applying an EnKF to a pre-exisiting data assimilation scheme.