The Impact of a Reduced High‐Wind Charnock Parameter on Wave Growth With Application to the North Sea, the Norwegian Sea, and the Arctic Ocean

Breivik, Øyvind; Carrasco, Ana; Haakenstad, Hilde; Aarnes, Ole Johan; Behrens, Arno; Bidlot, Jean‐Raymond; Björkqvist, Jan-Victor; Bohlinger, Patrik; Furevik, Birgitte Rugaard; Staneva, Joanna; Reistad, Magnar

doi:10.1029/2021jc018196

Cited by 15 publications

(2 citation statements)

References 51 publications

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“…33,34 A validation of the wave hindcast against a control run without a high-wind reduction of the Charnock coefficient, covering the period 2011-2012, shows that the wave-height bias is dramatically reduced in high-wind events (particulary above 30 m/s). For more details, see Breivik et al 35…”

Section: Ocean Wave Dataset (Wam)mentioning

confidence: 99%

Norwegian offshore wind power—Spatial planning using multi‐criteria decision analysis

Solbrekke,

Sorteberg

2023

Wind Energy

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The Norwegian government recently agreed on the goal 30by40, which involves opening Norwegian offshore areas to host 30 GW of installed wind power by 2040. We address this goal by presenting a first mapping of wind power suitability scores (WPSS) for the entire Norwegian economic zone (NEZ) using a multi‐criteria decision analysis framework (MCDA), namely, the analytical hierarchical process (AHP) approach. We obtain WPSS considering relevant criteria like wind resources, techno‐economic aspects, social acceptance, environmental considerations, and met‐ocean constraints such as wind and wave conditions. The results starts with a baseline scenario, where the criterion importance is pairwise compared in the context of balancing economic incentives and conflicting interests. Additionally, to reveal regions that are robust to changes in criterion importance, we carry out a sensitivity analysis by introducing three additional scenarios. These scenarios represent stereotypical actors with distinct preferences for siting of wind farms: the investor, the environmentalist, and the fisherman. The results show that the southern part of the NEZ is the most suitable and robust region for offshore wind power deployment. This region receives the highest suitability category (“very high” suitability for wind power application) throughout all the scenarios. Areas in the Norwegian part of the Barents Sea and the near‐coastal areas outside mid‐Norway are also well suited regions, but these are more sensitive to the choice of criterion importance. The use of AHP within the framework of MCDA is shown to be a promising tool for pinpointing the best Norwegian offshore areas for wind power application.

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Section: Ocean Wave Dataset (Wam)mentioning

confidence: 99%

Norwegian offshore wind power—Spatial planning using multi‐criteria decision analysis

Solbrekke,

Sorteberg

2023

Wind Energy

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“…With a resolution of 3 km, NORA3 downscales the ERA5 reanalysis providing an improved wind field, especially in mountainous areas and along the coastline [38,39], and performs much better than ERA5 with regards to the observed maximum wind. The downscaling is based on the HARMONIE-AROME model [40,41,34] (Cycle 40h1.2), a nonhydrostatic numerical weather prediction model that explicitly resolves deep convection [38,39,42]. While the operational storm surge model is forced with the deterministic model from ECMWF, the hindcast, NORA-SS, is forced with data from NORA3.…”

Section: Nora-ssmentioning

confidence: 99%

Bias Correction of Operational Storm Surge Forecasts Using Neural Networks

Tedesco¹,

Rabault²,

Sætra³

et al. 2023

Preprint

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Storm surges can give rise to extreme floods in coastal areas. The Norwegian Meteorological Institute (MET Norway) 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 storm 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 (RMSE) 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.

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The ERA5 global reanalysis from 1940 to 2022

Soci,

Hersbach,

Simmons

et al. 2024

Quart J Royal Meteoro Soc

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We provide a description and concise evaluation of the European Centre of Medium‐range Weather Forecasts Reanalysis v.5 (ERA5) global reanalysis from an additional extension back to 1940 that was released in March 2023, including its timely updates to the end of 2022. The ERA5 product from 1979 to end 2020 and a preliminary back extension from 1950 to 1978 have already been described elsewhere. The new back extension that spans 1940 to 1978 represents the official release and supersedes the preliminary product. Currently, the ERA5 data record extends over more than 83 years of hourly global three‐dimensional fields for many quantities that describe the global atmosphere, land surface, and ocean waves at a horizontal resolution of about 31 km. ERA5 relies on the ingestion of sub‐daily in‐situ and satellite observations, and the number of these increases from 17,000 per day in 1940 to 25 million per day by 2022. Accordingly, the quality of the reanalysis improves throughout the period. Over the Northern Hemisphere ERA5 generally provides a reliable representation of the synoptic situation from the early 1940s and provides long‐term variability that is in line with other datasets. Over the Southern Hemisphere, however, for the early period the description of ERA5 seems mainly statistical. Furthermore, there is a small deviation in surface temperature compared with reconstructions based on monthly aggregations of observations over land before 1946. For this period, the absence of upper air temperature observations reveals a model cold bias in the lower stratosphere. For the period from 1950 to 1978, the final release described here improves on the suboptimal treatment of International Best Track Archive for Climate Stewardship observations in the preliminary release, with, as a result, a much more homogeneous representation of tropical cyclones over the entire ERA5 record. Longer spin‐up periods also have a beneficial impact on soil moisture.

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The Impact of a Reduced High‐Wind Charnock Parameter on Wave Growth With Application to the North Sea, the Norwegian Sea, and the Arctic Ocean

Cited by 15 publications

References 51 publications

Norwegian offshore wind power—Spatial planning using multi‐criteria decision analysis

Norwegian offshore wind power—Spatial planning using multi‐criteria decision analysis

Bias Correction of Operational Storm Surge Forecasts Using Neural Networks

The ERA5 global reanalysis from 1940 to 2022

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