arXiv subjects

Nils Melsom Kristensen

Publications and source records attributed to Nils Melsom Kristensen.

5 recordsLinked to original sources

arXiv2026

Modeling the Hydrodynamics in the Oslofjord using ADCIRC

This study introduces a new unstructured computational mesh for hydrodynamic simulations of the Oslofjord. The mesh was created with global bathymetry and shoreline data, using OceanMesh2D. It contains 70,410 nodes, with a resolution at the coastline of 50 meters. We use the new mesh to create an ADCIRC model of the fjord. The model is run for four time periods with different characteristics, and validated against the current state of the art and elevation gauges in the fjord. Results show that the model achieves similar results to the model currently used for forecasting in Norway, while requiring much less computation time. Three different combinations of tidal constituents are used to force the model, and analyze the cost and benefits of using additional constituents, finding that they slightly improve results. However, the skill of the tidal forcing boundary condition is limited, because of the small domain of the Oslofjord. In order to further reconcile the results' deviation from the gauge data, especially during extreme weather events, the water surface elevation output from a global ADCIRC model was used to force the model instead of tides.

cs.CE↗

arXiv2026

Flo: A data-driven limited-area storm surge model

We present Flo, a data-driven storm surge model, covering the North Sea, Norwegian Sea and Barents Sea. The model is built using the Anemoi framework for creating machine learning weather forecasting systems, developed by the European Centre for Medium-Range Weather Forecasts and partners. The model is based on a graph neural network, and is capable of simulating water level due to atmospheric effects (wind stress and inverse barometer effect, i.e. the non-tidally induced part of the total water level; the residual water level) at a horizontal resolution of 4 km and a temporal resolution of 1 hour with a quality comparable to the numerical model on which it was trained. The model was trained using a dataset consisting of 43 years of atmospheric data from the 3-km Norwegian Reanalysis hindcast for mean sea level pressure and winds, and the NORA-Surge hindcast for water level. Evaluation was done by comparing results from hindcast runs of the Flo model against independent observations of more than 90 water level gauges along the European coast, and against the NORA-Surge hindcast. The evaluation shows that Flo produces hindcasts with accuracy similar to the NORA-Surge hindcast, and it is shown that the model can resolve key physical processes. As the NORA-Surge hindcast used for training does not include data assimilation, Flo is not expected to systematically outperform the numerical model when evaluated against observations. Nevertheless, the present work represents an important step towards complementing traditional physics-based storm surge modelling with machine learning approaches and the framework establishes a strong foundation for future developments, particularly for training storm surge models that offer more flexibility for incorporating observations and other additional data sources.

physics.ao-ph↗

arXiv2026

Diagnostic vs dynamic representation of the inverse barometer effect in a global ocean model and its potential for probabilistic storm surge forecasting

The global ocean model NEMO is run in a series of stand-alone configurations (2015-2022) to investigate the potential for improving global medium-range storm surge forecasts by including the inverse barometer effect. The analysis focus on the residual water level, i.e. the water level variations not due to tides. Here, we compare a control experiment, where the inverse barometer effect was not included, against a run dynamically forced with mean sea level pressure. In the control experiment, the inverse barometer effect was then calculated diagnostically and added to the ocean model sea surface elevation, resulting in a total of three experiments to investigate. We compare against the global GESLA3 water level data set and find that the inclusion of the inverse barometer effect reduces the root-mean-square error by $\sim 1~cm$ on average. When we mask out all data where the observed residual water level is less than $\pm1$ or $\pm2$ standard deviations, including the inverse barometer effect reduces the RMS error by $4-5$ cm. While both methods reduce water level errors, there are regional differences in their performance. The run with dynamical pressure forcing is seen to perform slightly better than diagnostically adding the inverse barometer effect in enclosed basins such as the Baltic Sea. Finally, an ensemble forecast experiment with the Integrated Forecast System of the European Centre for Medium-range Weather Forecasts demonstrates that when the diagnostic inverse barometer effect is included for a severe storm surge event in the North Sea (Storm Xaver, December 2013), the ensemble spread of water level provides a stronger and earlier indication of the observed maximum surge level than the when the effect is excluded.

physics.ao-ph↗

arXiv2024

NORA-Surge: A storm surge hindcast for the Norwegian Sea, the North Sea and the Barents Sea

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 modelled 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, repsectively. 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.

physics.ao-ph↗

arXiv2023

Bias Correction of Operational Storm Surge Forecasts Using Neural Networks

Storm surges can give rise to extreme floods in coastal areas. The Norwegian Meteorological Institute produces 120-hour 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 events. Reducing these errors will positively impact the efficiency of the warning systems while minimizing efforts and resources spent on mitigation. 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. Using the NN residual correction method, the Root Mean Square Error in the Oslo Fjord is reduced by 36% for lead times of one hour and 9% for 24 hours. 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.

physics.ao-ph↗