Projects

Radoslav Košta

International projects

ANTICIPATE – ANTICIPATE: extended-range multi-hazard predictions and early warnings
ANTICIPATE: dlhodobé (rozšírené) predpovede viacerých typov rizík a včasné varovania
Program:	COST
Duration:	29.10.2025 – 28.10.2029
Project leader:	Ing. Košta Radoslav
Annotation:	Operational extreme weather forecasts and early warnings are generally limited to timescales of up to around 10 days and to predicting single events, such as flooding or a heatwave. However, a new generation of experimental ‘extended-range’ weather predictions that extend up to 46 days have been developed over the last decade by the world’s leading meteorological centres. A key motivation of exploring this prediction timescale is to bridge the gap between timescales, incorporate the latest ‘multi-hazard’ approaches, and improve early warnings and anticipatory actions. Currently, however, the extended-range prediction and the multi-hazard communities are largely disconnected. To date, there has been no coordinated effort to build a network that connects these disciplines and communities towards the development of operational systems. However, it is essential that these communities come together to explore windows of opportunity and instigate a step-change in the way forecasts are designed, produced and used. To address this challenge, ANTICIPATE will create the first pan-European network focused on extended-range multi-hazard predictions and warnings. ANTICIPATE will bring together existing but largely disconnected disciplines, operational practitioners and stakeholders (including extreme weather forecasting, extended-range prediction and climate dynamics, disaster risk reduction, multi-hazards, and communications) to drive forward advancements in the science, training, communication and application that will support next generation of effective early warnings that enable preparedness and action across hazards and forecasting lead times. ANTICIPATE will provide vital leadership in multi-hazard predictions and warnings, address gaps and challenges, and educate the next generation of forecasters and communicators for societal benefit.
Project website:	https://www.cost.eu/actions/CA24144/

National projects

CAUSMET – Methods and algorithms for causal analysis and quantification of measurement uncertainty
Názov projektu Metódy a algoritmy kauzálnej analýzy a kvantifikácie neistôt meraní
Program:	SRDA
Duration:	1.9.2026 – 31.12.2029
Project leader:	Doc. RNDr. Witkovský Viktor, CSc.
Annotation:	The project develops advanced methods and algorithms for causal analysis of stochastic and deterministic processes and for quantifying measurement uncertainties. It addresses methodological challenges in the analysis of time series and dynamical data, where correlation alone is insufficient to reveal the mechanisms governing system behavior. Manyapplications, therefore, require identifying causal relations between variables while reliably characterizing uncertainties arising from measurement processes, noise, and incomplete observations.The project will develop classical and modern approaches to causal analysis of time series based on probabilistic and statistical modeling, and integrate them with algorithms enabling statistical inference and prediction in the presence of randomness, measurement errors, and uncertainty.Modern applications in physical, biomedical, economic, environmental, and linguistic measurements, as well as in the social sciences (education, psychology), generate large and complex datasets with intricate dependence structures and temporal dynamics. A significant project component is hence the study of stochastic dynamical models, including diffusion processes, as a natural framework for modeling random dynamics observed via measurement time series. When modeling complex temporal or spatio-temporal data using kriging, causal structure will serve as a key starting point.The project also advances uncertainty methods for quantifying measurement uncertainties in line with modern metrology and aims to establish a unified methodological framework combining causal analysis, dynamical modeling, and statistical inference and forecasting. Interdisciplinary collaboration among the Institute of Measurement Science of the SAS, theMathematical Institute of the SAS, and the Faculty of Science of P. J. Šafárik University creates favorable conditions for the development of new theoretical results, efficient algorithms, and their applications.