arXiv subjects

Johannes Röhrs

Publications and source records attributed to Johannes Röhrs.

3 recordsLinked to original sources

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↗

arXiv2024

An OpenMetBuoy dataset of Marginal Ice Zone dynamics collected around Svalbard in 2022 and 2023

Sea ice is a key element of the global Earth system, with a major impact on global climate and regional weather. Unfortunately, accurate sea ice modeling is challenging due to the diversity and complexity of underlying physics happening there, and a relative lack of ground truth observations. This is especially true for the Marginal Ice Zone (MIZ), which is the area where sea ice is affected by incoming ocean waves. Waves contribute to making the area dynamic, and due to the low survival time of the buoys deployed there, the MIZ is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs are affordable enough to be deployed in large number, and gather information about drift (GPS position) and waves (1-dimensional elevation spectrum). This provides data focusing on the area around Svalbard with unprecedented spatial and temporal resolution. We expect that this will allow to perform validation and calibration of ice models and remote sensing algorithms.

physics.ao-ph↗

arXiv2020

Evaluating the leeway coefficient for different ocean drifters using operational models

The water following characteristics of six different drifter types are investigated using two different operational marine environmental prediction systems: one produced by Environment and Climate Change Canada (ECCC) and the other produced by the Norwegian Meteorological Institute (METNO). These marine prediction systems include ocean circulation models, atmospheric models, and surface wave models. Two leeway models are tested for use in drift object prediction: an implicit leeway model where the Stokes drift is implicit in the leeway coefficient, and an explicit leeway model where the Stokes drift is provided by the wave model. Both leeway coefficients are allowed to vary in direction and time in order to perfectly reproduce the observed drifter trajectory. This creates a time series of the leeway coefficients which exactly reproduce the observed drifter trajectories. Mean values for the leeway coefficients are consistent with previous studies which utilized direct observations of the leeway. For all drifters and models, the largest source of variance in the leeway coefficient occurs at the inertial frequency and the evidence suggests it is related to uncertainties in the ocean inertial currents.

physics.ao-ph↗