paulinte

• Nipen, Thomas Nils; Haugen, Håvard Homleid; Ingstad, Magnus Sikora; Nordhagen, Even Marius; Salihi, Aram Farhad & Tedesco, Paulina Souza [Show all 14 contributors for this article] (2025). Regional data-driven weather modeling with a global stretched-grid. Artificial Intelligence for the Earth Systems (AIES). ISSN 2769-7525. p. 1–29. doi: 10.1175/aies-d-25-0001.1. Full text in Research Archive Show summary

  Abstract We present Bris, a data-driven weather model suitable for regional forecasting applications. The model extends the Artificial Intelligence Forecasting System by introducing a stretched-grid architecture that dedicates higher resolution over a regional area of interest and maintains a lower resolution elsewhere on the globe. The model is based on graph neural networks, which naturally affords arbitrary multi-resolution grid configurations. The model is applied to short-range weather prediction for the Nordics, producing forecasts at 2.5 km spatial and 6 h temporal resolution. Bris is pre-trained on 43 years of global ERA5 data at 31 km resolution and is further refined using 3.3 years of 2.5 km resolution operational analyses from the MetCoOp Ensemble Prediction System (MEPS). The performance of the model is evaluated against surface observations from measurement stations across Norway and is compared to short-range weather forecasts from MEPS. In terms of root mean squared error, Bris outperforms both the control run and the ensemble mean of MEPS for 2 m temperature. Bris also produces competitive precipitation and wind speed forecasts, but is shown to underestimate extreme events.

• 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. Full text in Research Archive Show summary

  Storm surges can give rise to extreme floods in coastal areas. The Norwegian Meteorological Institute (MET Norway) produces 120 h regional operational storm surge forecasts along the coast of Norway based on the Regional Ocean Modeling System (ROMS), using a model setup called Nordic4-SS. Despite advances in the development of models and computational capabilities, forecast errors remain large enough to impact response measures and issued alerts, in particular, during the strongest storm events. Reducing these errors will positively impact the efficiency of the warning systems while minimizing efforts and resources. Here, we investigate how forecasts can be improved with residual learning, i.e., training data-driven models to predict the residuals in forecasts from Nordic4-SS. A simple error mapping technique and a more sophisticated Neural Network (NN) method are tested. The simple error mapping technique provides a reduction in the Root Mean Square Error (RMSE) of less than 4%. Using the NN residual correction method, the RMSE in the Oslo Fjord is reduced by 36% for lead times of one hour, 9% for 24 h, and 5% for 60 h. Therefore, the residual NN method is a promising direction for correcting storm surge forecasts, especially on short timescales. Moreover, it is well adapted to being deployed operationally, as (i) the correction is applied on top of the existing model and requires no changes to it, (ii) all predictors used for NN inference are already available operationally, (iii) prediction by the NNs is very fast, typically a few seconds per station, and (iv) the NN correction can be provided to a human expert who may inspect it, compare it with the model output, and see how much correction is brought by the NN, allowing to capitalize on human expertise as a quality validation of the NN output. While no changes to the hydrodynamic model are necessary to calibrate the neural networks, they are specific to a given model and must be recalibrated when the numerical models are updated.

• Nipen, Thomas Nils; Haugen, Håvard Homleid; Ingstad, Magnus Sikora; Nordhagen, Even Marius; Salihi, Aram Farhad Shafiq & Tedesco, Paulina Souza [Show all 14 contributors for this article] (2024). Regional data-driven weather modeling with a global stretched-grid. arXiv.org. ISSN 2331-8422. p. 1–11. doi: 10.48550/arXiv.2409.02891. Full text in Research Archive
• Kristensen, Nils Melsom; Tedesco, Paulina Souza; Rabault, Jean; Aarnes, Ole Johan; Sætra, Øyvind & Breivik, Øyvind (2024). NORA-Surge: A storm surge hindcast for the Norwegian Sea, the North Sea and the Barents Sea. Ocean Modelling. ISSN 1463-5003. 191. doi: 10.1016/j.ocemod.2024.102406. Full text in Research Archive Show summary

  Knowledge about statistics for water level variations along the coast due to storm surge is important for the utilization of the coastal zone. An open and freely available storm surge hindcast archive covering the coast of Norway and adjacent sea areas spanning the time period 1979–2022 is presented. The storm surge model is forced by wind stress and mean sea level pressure taken from the non-hydrostatic NORA3 atmospheric hindcast. A dataset consisting of observations of water level from more than 90 water level gauges along the coasts of the North Sea and the Norwegian Sea is compiled and quality controlled, and used to assess the performance of the hindcast. The observational dataset is distributed in both time and space, and when considering all the available quality controlled data, the comparison with modeled water levels yield a mean absolute error (MAE) of 9.7 cm and a root mean square error (RMSE) of 12.4 cm. Values for MAE and RMSE scaled by the standard deviation of the observed storm surge for each station are 0.42 and 0.54 standard deviations, respectively. When considering the geographical differences in characteristics of storm surge for different countries/regions, the values of MAE and RMSE are in the range 5.7–13.9 cm and 7.6–17.8 cm respectively, and 0.33–0.46 and 0.42–0.59 standard deviations for the scaled values. The minimum and maximum values for water level in the hindcast are −2.60 m and 3.92 m. In addition, 100-year return level estimates are calculated from the hindcast, with minimum and maximum values of, respectively, −2.75 m and 3.98 m. All minimum and maximum values are found in the southern North Sea area.

• Tedesco, Paulina Souza; Lenkoski, Alex; 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. 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

• 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. 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.

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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. Full text in Research Archive

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