Jozef Skakala works at PML in the area of ecosystem modelling and biogeochemical data assimilation.
Jozef’s background (at all levels, from an undergraduate to PhD) is in theoretical physics, with PhD in the area of General Theory of Relativity (Victoria University in Wellington, New Zealand, 2011). This was followed by postdoctoral work (2011-2014) in theoretical astrophysics and fundamental physics in Brazil and India, where Jozef combined his interest in fundamental science with his desire to gain experience with life within diverse cultures in the developing world. In 2014 Jozef followed his long-term fascination with complex systems and his concern about environmental issues, and started to work in marine science at PML, from 2016 as a part of MEMP group.
Jozef’s research typically combines his mathematical background with a desire to understand complex ecosystem functioning. Most of Jozef’s more recent work was focused on biogeochemical data assimilation on the North-West European Shelf, with implications for operational forecasting and ``smart’’ observing systems. Jozef has also worked in applying (multi-)fractals to improve understanding of scale-relationships in oceanographic fields and to assess the skill of oceanographic models. A large part of Jozef’s experience is in development of low-complexity models to emulate the real world highly complex dynamics. Jozef focuses on developing techniques to understand and reduce the complexity of our current ecosystem models (e.g. using complex networks) and emulate those models with both AI/machine learning techniques and deterministic emulators. The purpose of this work is, among others, to improve the computational efficiency of our models, improve model parametrization, provide us with model uncertainty estimates and improve our understanding of ecosystem emergent phenomena.
- SEAMLESS: Services based on Ecosystem data AssiMiLation: Essential Science and Solutions
Recent publications
- Higgs, I; Skákala, J; Bannister, R; Carrassi, A; Ciavatta, Stefano; 2024. Investigating ecosystem connections in the shelf sea environment using complex networks. Biogeosciences.
- Skákala, J; Awty-Carroll, K; Menon, PP; Wang, K; Lessin, G; 2023. Future digital twins: emulating a highly complex marine biogeochemical model with machine learning to predict hypoxia. Frontiers in Marine Science.
- Ford, DA; Grossberg, S; Rinaldi, G; Menon, PP; Palmer, MR; Skákala, J; Smyth, TJ; Williams, CAJ; Lorenzo Lopez, A; Ciavatta, S; 2022. A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts. Frontiers in Marine Science.
- Fowler, AM; Skákala, J; Ford, D; 2022. Validating and improving the uncertainty assumptions for the assimilation of ocean‐colour‐derived chlorophyll a$$ a $$ into a marine biogeochemistry model of the Northwest European Shelf Seas. Quarterly Journal of the Royal Meteorological Society.
- Skákala, J; Bruggeman, J; Ford, D; Wakelin, S; Akpınar, A; Hull, T; Kaiser, J; Loveday, BR; O’Dea, E; Williams, CAJ; Ciavatta, S; 2022. The impact of ocean biogeochemistry on physics and its consequences for modelling shelf seas. Ocean Modelling.