Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea

Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the accurate reconstructed offshore winds which can be used for offshore wind resource assessment. First, wind speeds retrieved from Synthetic Aperture Radar (SAR) and Scatterometer ASCAT images were validated against in situ measurements from seven coastal meteorological stations in South China Sea (SCS). The wind roses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and ASCAT agree well with these observations from the corresponding in situ measurements. The statistical results comparing in situ wind speed and SAR-based (ASCAT-based) wind speed for the whole co-located samples show a standard deviation (SD) of 2.09 m/s (1.83 m/s) and correlation coefficient of R 0.75 (0.80). When the offshore winds (i.e., winds directed from land to sea) are excluded, the comparison results for wind speeds show an improvement of SD and R, indicating that the satellite data are more credible over the open ocean. Meanwhile, the validation of satellite winds against the same co-located mast observations shows a satisfactory level of accuracy which was similar for SAR and ASCAT winds. These satellite winds are then assimilated into the Weather Research and Forecasting (WRF) Model by WRF Data Assimilation (WRFDA) system. Finally, the wind resource statistics at 100 m height based on the reconstructed winds have been achieved over the study area, which fully combines the offshore wind information from multiple satellite data and numerical model. The findings presented here may be useful in future wind resource assessment based on satellite data

Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

We study the wind climate and its long-term variability in the North Sea and South China Sea, areas relevant for offshore wind energy development, using satellite-based wind data, because very few reliable long-term in-situ sea surface wind observations are available. The Special Sensor Microwave Imager (SSM/I) ocean winds extrapolated from 10 m to 100 m using the Charnock relationship and the logarithmic profile method are compared to Weather Research and Forecasting (WRF) model results in both seas and to in-situ observations in the North Sea. The mean wind speed from SSM/I and WRF differ only by 0.1 m/s at Fino1 in the North Sea, while west of Hainan in the South China Sea the difference is 1.0 m/s. Linear regression between SSM/I and WRF winds at 100 m show correlation coefficients squared of 0.75 and 0.67, standard deviation of 1.67 m/s and 1.41 m/s, and mean difference of −0.12 m/s and 0.83 m/s for Fino1 and Hainan, respectively. The WRF-derived winds overestimate the values in the South China Sea. The inter-annual wind speed variability is estimated as 4.6% and 4.4% based on SSM/I at Fino1 and Hainan, respectively. We find significant changes in the seasonal wind pattern at Fino1 with springtime winds arriving one month earlier from 1988 to 2013 and higher winds in June; no yearly trend in wind speed is observed in the two seas

Response of the Coastal Ocean to Tropical Cyclones

The Northwest Pacific and the South China Sea region are the birthplaces of most monsoon disturbances and tropical cyclones and are an important channel for the generation and transmission of water vapor. The Northwest Pacific plays a major role in regulating interdecadal and long-term changes in climate. China experiences the largest number of typhoon landfalls and the most destructive power affected by typhoons in the world. The hidden dangers of typhoon disasters are accelerating with the acceleration of urbanization, the rapid development of economic construction and global warming. The coastal cities are the most dynamic and affluent areas of China’s economic development. They are the strong magnetic field that attracts international capital in China, and are also the most densely populated areas and important port groups in China. Although these regions are highly developed, they are vulnerable to disasters. When typhoons hit, the economic losses and casualties caused by gale, heavy rain and storm surges were particularly serious. This chapter reviews the response of coastal ocean to tropical cyclones, included sea surface temperature, sea surface salinity, storm surge simulation and extreme rainfall under the influence of tropical cyclones

Online Wind-Atlas Databases and GIS Tool Integration for Wind Resource Assessment: A Spanish Case Study

Renewable energy sources are becoming increasingly integrated into the electricity-generation sector, being eco-friendly solutions, decreasing global warming, and improving the energy transition process. Among the different renewables, wind energy is considered a mature, clean, renewable, and inexhaustible technology as well, becoming one of the main resources in a sustainable framework. Aiming to evaluate the wind resource, scientific contributions have mostly presented a common basis: historical data campaigns of the wind resource mainly considering wind speed—including the module, direction, standard deviation, etc. However, online wind-atlas databases are becoming tools widely used for both wind-resource assessment and optimal wind-power locations. Under this framework, this study analyzed and compared such online wind data sources and their integration with GIS tools for optimal wind-resource-assessment purposes. The proposed methodology identified the corresponding wind-atlas databases directly on their websites and indirectly through the wind data used in relevant contributions about the optimal location of wind sites. Our contribution to the scientific community is thus the review and comparison of these atlas databases for reducing the barrier to access wind data—including GIS-tool-integration analysis. The limitations raised by civil societies, particularly regarding environmental and bird concerns, were not included in this study. Nevertheless, the authors are aware of these concerns and limitations. A Spanish case study was also included in this work, comparing both estimated and collected wind-atlas databases in terms of wind-resource assessment.2021-2

Quantifying the Contribution of Mean Flow and Eddy Advection to the Warm SST Bias in the Southeast Tropical Atlantic Region

In current-generation climate models, the warm sea surface temperature (SST) bias problem is most commonly seen in the eastern boundary upwelling systems (EBUSs), and is most pronounced and most prevalent in the Southeast Tropical Atlantic (SETA) region. Previous studies have shown that the coastal wind pattern in this region, namely the Benguela low-level coastal jet (BLLCJ), is of great importance for the generation of such SST bias, because the coastal ocean circulation is highly sensitive to the off-shore structure of the wind forcing. Using an eddy-resolving regional ocean model, we first show that the SST bias in the region is drastically reduced when forced with simulated winds from a high-resolution regional atmospheric model. We subsequently demonstrate that the SST bias is highly sensitive to the spatial structure of the wind stress curl (WSC). We also find that when the ocean model is forced by a realistic high-resolution wind, the ocean model resolution is of second order importance in reducing the SST bias. Furthermore, we use a double-time average (DTA) method to quantify the contribution of heat budget terms, and show that the horizontal advection contributes significantly to the SST bias. We then examined the question: To what extent do ocean eddies play a role in balancing the coastal ocean heat budget and affecting the SST bias? By experimenting with a submesoscale eddy-permitting regional ocean model, we show that ocean eddies in the Southeast Tropical Atlantic region are most energetic near the Angola-Benguela Front (ABF), the Lüderitz Upwelling Cell region and the Agulhas Leakage region. In these three regions, comparisons between the two model simulations forced with the low- vs high-resolution winds suggest that the SST bias is mainly generated by mean flow advection with ocean eddies playing the role of counteracting the warming induced by the mean flow advection in this region