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Caspian sea border and bathymetry 

Caspian sea border and bathymetry 

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Conference Paper
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The procedure for a long-term wave hindcast in the Caspian sea has been reviewed through this paper. The third generation wave model of SW prepared by DHI water and Environment was employed in order to long term simulation of wind waves. Model was forced by ECMWF model wind data girded over the whole area. Model was calibrated using wave measuremen...

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Citations

... Depth variation of the study area, Caspian Sea(Allahdadi et al., 2004) ...
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Climate change impacts and adaptations is subject to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The results of this study revealed that climate change does not affect the wind climate over the study area, remarkably. However, a small decrease was projected for future simulation revealing a slightly decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution when winter and summer have the highest values of power. The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.
... Depth variation in the study area, Caspian Sea(Allahdadi et al., 2004). ...
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Climate change impacts and adaptations are ongoing issues that are attracting the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken into consideration. An adaptive neuro-fuzzy inference system (ANFIS)-based post-processing technique was used to match the power outputs of the regional climate model (RCM) with those obtained from reference data. The near-surface wind data obtained from an RCM were used to investigate climate change impacts on the wind power resources in the Caspian Sea. After converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results were investigated to reveal mean annual power, seasonal and monthly variability for 20 year historical (1981–2000) and future (2081–2100) periods. The results revealed that climate change does not notably affect the wind climate over the study area. However, a small decrease was projected in the future simulation, revealing a slight decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution, with winter and summer having the highest values. The results indicate that the middle and northern parts of the Caspian Sea have the highest values of wind power. However, the results of the post-processing technique using the ANFIS model showed that the real potential of wind power in the area is lower than that projected in the RCM.
... The 3-hourly snapshots of CFSR wind fields around two reference times (t1 and t2) are shown in boundaries, the model was forced using the wave parameters obtained from a global WWWIII model with a spatial resolution of 0.5 • and temporal resolution of 3 h. Following Whalen and Ochi (1978), Ochi (1998), and Allahdadi et al. (2004b), a Joint North Sea Wave Project (JONSWAP) frequency spectrum with the average enhanced parameter of γ = 3.3 was chosen for converting parametric wave data to 2-D spectra along the boundary. Due to the dominant westto-east wind over the modeling area during the simulation period, it is less likely for boundary waves to propagate toward the modeling area. ...
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... The 3-hourly snapshots of CFSR wind fields around two reference times (t1 and t2) are shown in boundaries, the model was forced using the wave parameters obtained from a global WWWIII model with a spatial resolution of 0.5 • and temporal resolution of 3 h. Following Whalen and Ochi (1978), Ochi (1998), and Allahdadi et al. (2004b), a Joint North Sea Wave Project (JONSWAP) frequency spectrum with the average enhanced parameter of γ = 3.3 was chosen for converting parametric wave data to 2-D spectra along the boundary. Due to the dominant westto-east wind over the modeling area during the simulation period, it is less likely for boundary waves to propagate toward the modeling area. ...
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The performance of two methods for quantifying whitecapping dissipation incorporated in the SWAN wave model is evaluated for waves generated along and off the U.S. East Coast under energetic winter storms with a predominantly westerly wind. Parameterizing the whitecapping effect can be done using the Komen-type schemes, which are based on mean spectral parameters, or the saturation-based (SB) approach of van der Westhuysen (2007), which is based on local wave parameters and the saturation level concept of the wave spectrum (we use Komen and Westhuysen to denote these two approaches). Observations of wave parameters and frequency spectra at four NDBC buoys are used to evaluate simulation results. Model-data comparisons show that when using the default parameters in SWAN, both Komen and Westhuysen methods underestimate wave height. Simulations of mean wave period using the Komen method agree with observations, but those using the Westhuysen method are substantially lower. Examination of source terms shows that the Westhuysen method underestimates the total energy transferred into the wave action equations, especially in the lower frequency bands that contain higher spectral energy. Several causes for this underestimation are identified. The primary reason is the difference between the wave growth conditions along the East Coast during winter storms and the conditions used for the original whitecapping formula calibration. In addition, some deficiencies in simulation results are caused along the coast by the slanting fetch effect that adds low-frequency components to the 2-D wave spectra. These components cannot be simulated partly or entirely by available wind input formulations. Further, the effect of boundary layer instability that is not considered in the Komen and Westhuysen whitecapping wind input formulas may cause additional underestimation.
... In this section, more detailed characteristics of winds over these regions are presented by examining timeseries of wind speed and direction and long-term wind roses at four locations over the Gulf of Oman and the northern Arabian Sea. Timeseries of wind speed and direction were obtained from ECMWF-ERA40 (Allahdadi et al., 2004) database (see Figure 1 for the location of points). Timeseries of wind speed extracted from these locations for the year of 2002 shows a significant difference in seasonal variations of wind regime in the Gulf of Oman compared to the northern Arabian Sea ( Figure 5). ...
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... The process was completed by examining different values of white capping parameters for wave height and wave period as mentioned in section 4.1. Model spectral parameters including number of spectral directions, number of spectral frequencies, and the minimum frequency in model calculations were obtained from literature (Komen et al. [34]; Moon et al. [14]; Kumar and Stone [15]; Allahdadi et al. [35]). The final parameters used for the model are summarized in Table 2. ...
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