<p>The wave field in the Baltic Sea has been modelled for a two-year period with the spectral wave model HYPAS. There is a large seasonal variation in the field and a minor annual one, both reflect the wind variation in the area. Since the Baltic Sea is fetch limited, the dominant wind direction is important for the maximum wave heights.</p><p>By studying the modelled wave energy density in combination with bottom type maps, the effect of the wave field on the sediment surface is examined. Up to half the bottoms in the Baltic Sea are affected ~25% of the time. A statistical relation between wave energy density and bottom types is found for the Gulf of Riga, but in the rest of the area the sediment maps were to coarse. It is, due to this, not possible to say if the result is valid for the whole area or if it is site specific.</p><p>During resuspension events the remineralisation is increased since deposited organic material is reintroduced into the watermass and there exposed to higher levels of oxygen. This process could act as an increased regional source of nitrogen in nutrient budgets and thus influence the conditions for nitrogen fixation and perhaps explain some of the geographical differences in the nitrogen fixation rates.</p>

The wave field in the Baltic Sea has been modelled for a two-year period with the spectral wave model HYPAS. There is a large seasonal variation in the field and a minor annual one, both reflect the wind variation in the area. Since the Baltic Sea is fetch limited, the dominant wind direction is important for the maximum wave heights. By studying the modelled wave energy density in combination with bottom type maps, the effect of the wave field on the sediment surface is examined. Up to half the bottoms in the Baltic Sea are affected ~25% of the time. A statistical relation between wave energy density and bottom types is found for the Gulf of Riga, but in the rest of the area the sediment maps were to coarse. It is, due to this, not possible to say if the result is valid for the whole area or if it is site specific. During resuspension events the remineralisation is increased since deposited organic material is reintroduced into the watermass and there exposed to higher levels of oxygen. This process could act as an increased regional source of nitrogen in nutrient budgets and thus influence the conditions for nitrogen fixation and perhaps explain some of the geographical differences in the nitrogen fixation rates.

In May of 2000 Jason-1, a joint project between NASA and the French space agency CNES, will be launched. Its mission is to continue the highly successful gathering of data which TOPEX/Poseidon has collected since August of 1992. The main goal of Jason-1 is to achieve higher accuracy in measuring the mean sea level (MSL). In order to do so, the electromagnetic (EM) bias must be estimated more accurately because it is the largest contributing error. This thesis presents two different studies which add to the knowledge and improve estimation of the EM bias, and thus assists Jason-1 in achieving its primary goal. Oceanographic data collected from two different experiments are analyzed; on in the Gulf of Mexico (GME) and the other in Bass Strait, Australia (BSE). The first study is a spatial analysis of the backscattered power versus the phase of the wave. Its purpose is to determine why the normalized EM bias stops increasing and levels out at high wind speeds (about 11 m/s) and then decreases at higher wind speeds. Two possible causes are investigated. First, it could be due to a shift in the backscatter power modulation to the forward or rear face of the wave crests. Second, it may be due to the backscatter power becoming more homogeneous throughout the wave profile. This study is novel because it uses the knowledge of the spatial distribution of both the backscatter and wave displacement for the study of the EM bias. Both contribute to the EM bias decrease, but the latter cause seems to be the dominant effect. This study is performed on GME data. The second study uses two different nonparametric regression (NPR) techniques to estimate the EM bias. A recent study of satellite data from the TOPEX/Poseidon altimeter supports that the bias is modeled better using NPR regression. A traditional parametric fit is compared to two NPR techniques with GME data. The parametric fit is a variation of NASA's equation used to estimate EM bias for their Geophysical Data Records (GDRs). The two NPR techniques used are the Nadaraya-Watson Regression (NWR) and Local Linear Regression (LLR) estimators. Two smoothing kernel functions are used with each NPR technique, namely the Gaussian and the Epanechnikov kernels. NPR methods essentially consist of statistically smoothing the measured EM bias estimates are compared in the wind and significant wave height plane. Another recent study has shown that wave slope is strongly correlated to EM bias. With this knowledge, EM bias is estimated over several two-dimensional planes which include wave slope in attempt to reduce the residual bias. This portion of the study is performed on GME and BSE data. It is shown that a combination of slope, significant wave height, and wind speed used in conjunction with these NPR methods produces the best EM bias estimate for tower data.

Offshore renewable energy systems, such as Wave Energy Converters or an Offshore Wind Turbine, must be designed to withstand extremes of the weather environment. For this, it is crucial both to have a good understanding of the wave and wind climate at the intended offshore site, and of the system reaction and possible failures to different weather scenarios. Based on these considerations, the first objective of this thesis was to model and identify the extreme wind speed and significant wave height at an offshore site, based on measured wave and wind data. The extreme wind speeds and wave heights were characterized as return values after 10, 25, 50, and 100 years, using the Generalized Extreme Value method. Based on a literature review, fragility curves for wave and wind energy systems were identified as function of significant wave height and wind speed. For a wave energy system, a varying failure rate as function of the wave height was obtained from the fragility curves, and used to model the resilience of a wave energy farm as a function of the wave climate. The cases of non-constant and constant failure rates were compared, and it was found that the non-constant failure rate had a high impact on the wave energy farm's resilience. When a non-constant failure rate as a function of wave height was applied to the energy wave farm, the number of Wave Energy Converters available in the farm and the absorbed energy from the farm are nearly zero. The cases for non-constant and an averaged constant failure of the instantaneous non-constant failure rate as a function of wave height were also compared, and it was discovered that investigating the resilience of the wave energy farm using the averaged constant failure rate of the non-constant failure rate results in better resilience. So, based on the findings of this thesis, it is recommended that identifying and characterizing offshore extreme weather climates, having a high repair rate, and having a high threshold limit repair vessel to withstand the harsh offshore weather environment.

This thesis aims to solve the mathematical inverse problem of characterizing sea waves based on the responses obtained from a marine vessel sailing under certain sea conditions. By researching this problem the thesis contributes to the marine industry by improving products that are using ocean behavior for controlling ship's dynamics. Knowledge about the current state of the sea, such as the wave frequency and height, is important for navigation, control, and for the safety of a vessel. This information can be retrieved from specialized weather reports. However, such information is not at all time possible to obtain during a voyage, and if so usually comes with a certain delay. Therefore this thesis seeks solutions that can estimate on-line the waves' state using methods in the field of Artificial Intelligence. The specific investigation methods are Transfer Functions augmented with Genetic Algorithm, Artificial Neural Networks and Case-Based Reasoning. These methods have been configured and validated using the n-fold cross validation method. All the methods have been tested with an actual implementation. The algorithms have been trained with data acquired from a marine simulation program developed in Simulink. The methods have also been trained and tested using monitored data acquired from an actual ship sailing on the Baltic Sea as well as wave data obtained from a buoy located nearby the vessel's route. The proposed methods have been compared with state-of-the art reports in order evaluate the novelty of the research and its potential applications in industry. The results in this thesis show that the proposed methods can in fact be used for solving the inverse problem. It was also found that among the investigated methods it is the Transfer Function augmented with Genetic Algorithm which yields best results. This Master Thesis is conducted under the Master of Engineering Program in Robotics at Mälardalens högskola in Västerås, Sweden. The thesis was proposed by Q-TAGG R&amp;D AB in Västerås, Sweden, a company which specializes in marine vessel dynamics research.