Paulina Souza Tedesco

• Tedesco, Paulina Souza; Rabault, Jean; Sætra, Martin Lilleeng; Kristensen, Nils Melsom; Aarnes, Ole Johan & Breivik, Øyvind [Show all 8 contributors for this article] (2024). Bias correction of operational storm surge forecasts using Neural Networks. Ocean Modelling. ISSN 1463-5003. 188, p. 1–21. doi: 10.1016/j.ocemod.2024.102334.
• Rabault, Jean; Müller, Malte; Voermans, Joey; Brazhnikov, Dmitry; Turnbull, Ian & Marchenko, Aleksey [Show all 28 contributors for this article] (2023). A dataset of direct observations of sea ice drift and waves in ice. Scientific Data. ISSN 2052-4463. 10. doi: 10.1038/s41597-023-02160-9. Full text in Research Archive Show summary

  Variability in sea ice conditions, combined with strong couplings to the atmosphere and the ocean, lead to a broad range of complex sea ice dynamics. More in-situ measurements are needed to better identify the phenomena and mechanisms that govern sea ice growth, drift, and breakup. To this end, we have gathered a dataset of in-situ observations of sea ice drift and waves in ice. A total of 15 deployments were performed over a period of 5 years in both the Arctic and Antarctic, involving 72 instruments. These provide both GPS drift tracks, and measurements of waves in ice. The data can, in turn, be used for tuning sea ice drift models, investigating waves damping by sea ice, and helping calibrate other sea ice measurement techniques, such as satellite based observations.

• Tedesco, Paulina Souza; Lenkoski, Frank Alexander; Bloomfield, Hannah C. & Sillmann, Jana (2023). Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes. Environmental Research Letters. ISSN 1748-9326. 18(3). doi: 10.1088/1748-9326/acb6aa. Full text in Research Archive Show summary

  A transition to renewable energy is needed to mitigate climate change. In Europe, this transition has been led by wind energy, which is one of the fastest growing energy sources. However, energy demand and production are sensitive to meteorological conditions and atmospheric variability at multiple time scales. To accomplish the required balance between these two variables, critical conditions of high demand and low wind energy supply must be considered in the design of energy systems. We describe a methodology for modeling joint distributions of meteorological variables without making any assumptions about their marginal distributions. In this context, Gaussian copulas are used to model the correlated nature of cold and weak-wind events. The marginal distributions are modeled with logistic regressions defining two sets of binary variables as predictors: four large-scale weather regimes (WRs) and the months of the extended winter season. By applying this framework to ERA5 data, we can compute the joint probabilities of co-occurrence of cold and weak-wind events on a high-resolution grid (0.2◦).Our results show that (a) WRs must be considered when modeling cold and weak-wind events, (b) it is essential to account for the correlations between these events when modeling their joint distribution, (c) we need to analyze each month separately, and (d) the highest estimated number of days with compound events are associated with the negative phase of the North Atlantic Oscillation (3 days on average over Finland, Ireland, and Lithuania in January, and France and Luxembourg in February) and the Scandinavian blocking pattern (3 days on average over Ireland in January and Denmark in February). This information could be relevant for application in sub-seasonal to seasonal forecasts of such events

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• Tedesco, Paulina Souza; Rabault, Jean; Sætra, Martin Lilleeng; Kristensen, Nils Melsom; Aarnes, Ole Johan & Breivik, Øyvind [Show all 8 contributors for this article] (2022). Bias Correction of Operational Storm Surge Forecasts Using Neural Networks.

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