Predicting extreme weather and sea level for nuclear power plant safety – PREDICT

The safety management over the life cycle of a nuclear power plant requires probability estimates of exceptional weather and sea level conditions in the current and future climate. The main objective of PREDICT is to develop and maintain research expertise and methods needed for assessing probabilities of occurrence of safety-relevant single and compound extreme weather and marine events, both in the range of 0–15 days ahead and in decadal time scales of recent past and future climatic changes. The expected results include improved probability estimates for intense coastal snowfall, coastal flooding risks and compounding extreme events, such as heavy rain and high sea level.

Project information

Project duration: 1.2.2019–31.1.2023

Funding: The Finnish State Nuclear Waste Management Fund through SAFIR2022, the Finnish Nuclear Power Plant Safety Research Programme 2019–2022 (http://safir2022.vtt.fi/)

Project structure: Extreme weather in changing climate (WP1); Extreme sea level (WP2); Improving forecasts of extreme weather and sea level events (WP3).

Project description (pdf)

Figure: Simultaneously occurring two or more external events, such as high wind speed and high sea level, may cause more severe consequences than single incidents alone. Image: Shutterstock.

Abstracts, posters and presentations

Laine M., Räty O., Särkkä J., Leijala U., & Johansson M. M., 2020: NH038-0002 – Bayesian Hierarchical Modeling of Sea Level Extremes. AGU Fall Meeting 2020, Online Everywhere (1. –17.12.2020).

Särkkä, J., Räihä, J., Kämäräinen, M. & K. Jylhä, 2020: Simulating extreme sea levels at the Baltic Sea coast from synthetic cyclones. EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10271.

Olsson, T. & Jylhä, K., 2019: Case studies of sea-effect snowfall on the Finnish coast with ERA5 data. EMS Annual Meeting Abstracts, 16, EMS2019-850.

Olsson, T., Luomaranta, A., Jylhä, K., Wu, J., Jeworrek, J., Dieterich, C. & Rutgersson, A. 2019: Detecting sea-effect snowfalls on Finnish coastlines. EMS Annual Meeting Abstracts, 16, EMS2019-483.

Leijala, U., Johansson, M. M., Särkkä, J. & Pellikka, H. 2019. GC11M-1181: Studying the Extrapolation Techniques and Uncertainty Related to the Coastal Flood Risk Estimates in Finland. AGU Fall Meeting 2019, San Francisco (9.–13.12.2019)

Overview of PREDICT in the Annual Meeting of the European Meteorological Society (9.–13.9.2019):

Publications

Björkqvist, J.-V., Rikka, S., Alari, V., Männik, A., Tuomi, L., and Pettersson, H., 2020. Wave height return periods from combined measurement–model data: a Baltic Sea case study, Nat. Hazards Earth Syst. Sci., 20, 3593–3609. Olsson, T., Luomaranta, A., Jylhä, K., Jeworrek, J., Perttula, T., Dieterich, C., Wu, L., Rutgersson, A., and Mäkelä, A., 2020: Statistics of sea-effect snowfall along the Finnish coastline based on regional climate model data, Adv. Sci. Res., 17, 87–104. Pellikka, H., Laurila, T., Boman, H., Karjalainen, A., Björkqvist, J., Kahma, K., 2020. Meteotsunami occurrence in the Gulf of Finland over the past century. Nat. Hazards Earth Syst. Sci., 20, 2535–2546.

Luomaranta, A., 2020: Characteristics of winter climate in Finland in a warming world. Finnish Meteorological Institute Contributions, 169, Doctor of Philosophy thesis.

Pellikka, H., 2020: Dark-blue horizon: Sea level rise and meteotsunamis on the Finnish coast. Finnish Meteorological Institute Contributions, 167, Doctor of Philosophy thesis.

Björkqvist, J.-V., 2020: Waves in Archipelagos. Finnish Meteorological Institute Contributions, 159, Doctor of Philosophy thesis.

Björkqvist, J.-V., Vähä-Piikkiö, O., Alari, V., Kuznetsova, A, and Tuomi, L., 2020. WAM, SWAN and WAVEWATCH III in the Finnish archipelago – the effect of spectral performance on bulk wave parameters, Journal of Operational Oceanography, 13, 55–70.

Gregow H., Rantanen, M., Laurila T. K., & Mäkelä, A. 2020: Review on winds, extratropical cyclones and their impacts in Northern Europe and Finland. Finnish Meteorological Institute, Reports 2020:3, 36 p.

Laurila T. K., Sinclair, V. A, & Gregow, H., 2019: The Extratropical Transition of Hurricane Debby (1982) and the Subsequent Development of an Intense Windstorm over Finland. Monthly Weather Review, 148, 377–401.

Ukkonen, P. & Mäkelä, A., 2019: Evaluation of machine learning classifiers for predicting deep convection. Journal of Advances in Modeling Earth Systems, 11, 1784–1802.

Preceding project EXWE

Links to EXWE presentations and publications (pdf)