Impact of Ice Data Quality and Treatment on Wave Hindcast Statistics in Seasonally Ice-Covered Seas

Tuomi, Laura; Kanarik, Hedi; Björkqvist, Jan-Victor; Marjamaa, Riikka; Vainio, Jouni; Hordoir, Robinson; Höglund, Anders; Kahma, Kimmo K.

doi:10.3389/feart.2019.00166

Cited by 19 publications

(28 citation statements)

References 24 publications

(34 reference statements)

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“…These nodes were for convenience provided the median result across 5 km latitude bands before combined statistics, maps, and cross-correlation of different aspects was generated/performed. The work in [40] showed for a limited time period of four ice-seasons that wave simulations using ice chart data, as was done in [6], had slightly better accuracy than using modeled ice concentrations, but overall, the differences were small for the mean statistics of significant wave height for the different approaches.…”

Section: Wind and Sea-ice Datamentioning

confidence: 98%

“…Other studies have focused on areas that are generally ice-free [36][37][38]. The seasonal ice-cover of the Baltic Sea [39,40] is, however, known to influence the wave climate, and Tuomi et al [41] suggested ways to calculate different types of statistics taking the ice-time into account. In practice, experience from both the WESA (Wave Energy for a Sustainable Archipelago) project [42][43][44] and several field tested deployment strategies [45,46] of wave energy equipment have increased the awareness of wave energy as an available potential renewable energy source in the region.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Extreme and Metocean Conditions in the Swedish Exclusive Economic Zone for Wave Energy

et al. 2020

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Here, accessibility to near-shore and offshore marine sites is evaluated based on wave and ice conditions. High-resolution third-generation wave model results are used to examine the operation and maintenance conditions for renewable energy sources with a focus on wave energy. Special focus is given to the wave field and ice characteristics for areas within the Swedish Exclusive Economic Zone including analysis of return levels for extreme values for significant wave height, which provides guidance for dimensioning wave energy converters. It is shown that the number of weather windows and accessibility are influenced by distance from the coast and sea-ice conditions. The longest waiting periods for the closest weather window that is available for Operation and Maintenance (O&M) is in ice-free conditions shown to be strongly correlated with the fetch conditions. The sheltered Baltic Sea is shown to have very high accessibility if marine infrastructure and vessels are designed for access limits of significant wave height up to 3 m. In the northern basins, the waiting periods increase significantly, if and when the ice-conditions are found to be critical for the O&M activity considered. The ice-conditions are examined based on compiled operational sea-ice data over a climatic time period of 34 years. The results are location specific for the Swedish Exclusive Economic Zone, but the analysis methods are transferable and applicable to many other parts of the world, to facilitate assessment of the most promising areas in different regions.

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Section: Wind and Sea-ice Datamentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Assessment of Extreme and Metocean Conditions in the Swedish Exclusive Economic Zone for Wave Energy

et al. 2020

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“…The hindcast of Björkqvist et al (2018Björkqvist et al ( ) covered 1965Björkqvist et al ( -2005; the data originate from the wave model SWAN (Simulating WAves Nearshore, Booij et al, 1999) that was forced with the 6 h resolution BaltAn65+ wind product (Luhamaa et al, 2011). The hindcast of Tuomi et al (2019Tuomi et al ( ) covered 1978Tuomi et al ( -2013 and originated from the wave model WAM (WAve Model, Komen et al, 1994) that was forced with 5 winds from a downscaled ERA-Interim product having a 3 h resolution (Dee et al, 2011;Berg et al, 2013). Both hindcasts accounted for the seasonal ice cover and their data were available every hour, which we interpolated to 30 minute values.…”

Section: Wave Model Datamentioning

confidence: 99%

“…Although the WAM hindcast had an overall negative bias, the authors found that WAM slightly overestimated the highest wave heights (over 4 m). We used 2011-2013 Bothnian Sea wave measurements and found that the WAM hindcast of Tuomi et al (2019) had a 0.03 m bias and a 0.36 m RMSE. Our comparison showed no overestimation of the highest wave heights.…”

Section: Wave Model Datamentioning

confidence: 99%

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Wave height return periods from combined measurement–model data: A Baltic Sea case study

Björkqvist¹,

Rikka²,

Alari³

et al. 2020

Preprint

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Abstract. This paper presents how to account for the lack of sampling variability in model data when they are combined with wave measurements. We addressed the dissimilarities between the types of data by either: i) low-pass filtering the observations or ii) adding synthetic sampling variability to the model. Measurement–model times series combined with these methods served as the basis for return period estimates of a high wave event in January 2019. During this storm northerly wind speeds in the Baltic Sea rose to 32.5 m s−1 and an unprecedented significant wave height of 8.1 m was recorded in the Bothnian Sea sub-basin. Both methods successfully consolidated the combined time series but produced slightly different results: using low-pass filtered observations gave lower estimates for the return period than using model data with added sampling variability. Extremes in both types of data followed the same type of theoretical distributions, and our best estimate for the return period was 104 years (95 % confidence 39–323 years). A similar wave event can potentially be more likely in the future climate, and this aspect was discussed qualitatively.

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Wave-Ice Interaction for Regional Applications: SWAN Developments and Validations

Kumar¹,

Rogers²,

Thomson³

et al. 2020

Preprint

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Impact of Ice Data Quality and Treatment on Wave Hindcast Statistics in Seasonally Ice-Covered Seas

Cited by 19 publications

References 24 publications

Assessment of Extreme and Metocean Conditions in the Swedish Exclusive Economic Zone for Wave Energy

Assessment of Extreme and Metocean Conditions in the Swedish Exclusive Economic Zone for Wave Energy

Wave height return periods from combined measurement–model data: A Baltic Sea case study

Wave-Ice Interaction for Regional Applications: SWAN Developments and Validations

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