High-resolution wave climate analysis in the Helgoland area - GKSS

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14 2 Dynamical wave modeling Figure 2.4: Quantile-quantile plots of SWH between satellites (x-axis) and WAM model results (y-axis) for common dates during the period 1998-2000. Quantiles from 0.01 to 0.99 are shown with 0.01 interval. To evaluate the correspondence of the HF modeled wave heights with different altimeter datasets, the quantile-quantile plots were used and correlation, bias and standard deviation were calculated for each pair of datasets (Fig. 2.4). Modeled SWH data shows similar behaviour with respect to ERS Pot and TOPEX datasets characterized by overestimation of waves higher than about 2-3 meters. For TOPEX this overestimation is more pronounced and appears for the wave heights starting from 2 meters which amounts to 20% of the highest waves. In case of the ERS Pot dataset the modeled SWH values are too large for upper 15% of the waves. The bias is larger with respect to TOPEX for the modeled data (0.23 m for TOPEX vs. 0.12 m for ERS Pot). With respect to ERS Pot, model results appear to be slightly more scattered with standard deviation 0.66 for ERS Pot and 0.58 for TOPEX. These moderate deviations together with rather high correlation coefficients suggest a generally good agreement between modeled and observed SWH up to 85 percentiles. Coming to ERS Met data, it can be seen that the shape of quantile-quantile dependency is similar to that of ERS Pot with obvious bias detected from the comparison of these two satellite altimeter sources. Nevertheless, the correlation between ERS Met and HF wave heights is slightly higher than for other satellites. Summarizing, modeled HIPOCAS fine grid wave heights are overestimated for upper 10-15% of the waves for the German Bight, which corresponds to earlier validation experiments carried out within HIPOCAS project for the southern North Sea. 2.2.2 Comparison of the K-model results with in situ measurements For the period of interest there were no long-term measurements close to coastal facilities but within the model area two wave observed datasets are available. The first one is
2.2 Model validation 15 provided by the BSH (Bundesamt für Seeschiffahrt und Hydrographie) waverider buoy located approximately one kilometer south-west from the island. The water depth there is about 20 m. The measured quantities comprise 9 parameters from which significant wave height, peak period and mean direction for the period from March 1998 to October 2001 are used for the comparison with the K-model results. Another data source is the WaMoS II (Wave and Surface Current Monitoring System) radar (Hessner et al. [2001]) permanently mounted on a telecommunication tower on the main island since March 1998 and providing wave parameters averaged over the rectangular surface area in a distance of about 500 m south-west from the islands (Fig. 2.1). Figure 2.5: Hindcast (KMH) and observed wave parameters at DWP and the central point from the area covered by radar measurements for October 1998. From top to bottom: significant wave height in meters, peak period in seconds and mean direction (coming from) in degrees. Buoy measurements are shown as crosses, radar measurements are shown as circles. The K-model hindcast at the buoy (DWP) location is given by a blue line; hindcast at the central point of radar rectangular is given by a thin brown line. At first, to assess the quality of modeled instantaneous values with respect to observations, significant wave heights, peak periods and mean directions from all three data sources (K-model, buoy and radar) were compared for October 1998 (Fig. 2.5) and in general a good agreement between all datasets can be inferred. A closer look at differences provides a reasonable explanation for the major part of them. At the first decade of examined month the
Page 1: High-resolution wave climate analys
Page 4 and 5: Die Berichte der GKSS werden kosten
Page 6 and 7: Hochaufgelöste Analyse des Seegang
Page 8 and 9: iv CONTENTS 4.2 Future wave climate
Page 10 and 11: vi LIST OF FIGURES 2.9 Quantile-qua
Page 12 and 13: viii LIST OF FIGURES
Page 14 and 15: x LIST OF TABLES
Page 16 and 17: 2 1 Introduction modeling and multi
Page 18 and 19: 4 1 Introduction statistics by the
Page 20 and 21: 6 2 Dynamical wave modeling et al.
Page 22 and 23: 8 2 Dynamical wave modeling through
Page 24 and 25: 10 2 Dynamical wave modeling includ
Page 26 and 27: 12 2 Dynamical wave modeling of the
Page 30 and 31: 16 2 Dynamical wave modeling modele
Page 32 and 33: 18 2 Dynamical wave modeling 4.5 4
Page 34 and 35: 20 2 Dynamical wave modeling Near t
Page 36 and 37: 22 2 Dynamical wave modeling
Page 38 and 39: 24 3 Statistical-dynamical approach
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64 4 Wave climate assessments for p
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74 5 Conclusions and Discussion an
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76 5 Conclusions and Discussion Con
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78 5 Conclusions and Discussion
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80 BIBLIOGRAPHY S Coles. An introdu
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82 BIBLIOGRAPHY S Moghimi, G Gayer,
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84 BIBLIOGRAPHY K Woth, R Weisse, a
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86 A Techniques for variability ana
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88 A Techniques for variability ana
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90 B List of acronyms STOWASUS-2100
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High-resolution wave climate analysis in the Helgoland area - GKSS

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