Wind Characteristics in the Taiwan Strait: A Case Study of the First Offshore Wind Farm in Taiwan

Near-surface wind variability over spatiotemporal scales relevant to plume tracking insects

Odor plume tracking is important for many organisms, and flying insects have served as popular model systems for studying this behavior both in field and laboratory settings. The shape and statistics of the airborne odor plumes that insects follow are largely governed by the wind that advects them. Prior atmospheric studies have investigated aspects of microscale wind patterns with an emphasis on characterizing pollution dispersion, enhancing weather prediction models, and for assessing wind energy potential. Here, we aim to characterize microscale wind dynamics through the lens of short-term ecological functions by focusing on spatial and temporal scales most relevant to insects actively searching for odor sources. We collected and compared near-surface wind data across three distinct environments (sage steppe, forest, and urban) in Northern Nevada. Our findings show that near-surface wind direction variability decreases with increasing wind speeds and increases in environments with greater surface complexity. Across environments, there is a strong correlation between the variability in the wind speed (i.e., turbulence intensity) and wind direction (i.e., standard deviation in wind direction). In some environments, the standard deviation in the wind direction varied as much as 15°-75° on time scales of 1-10 min. We draw insight between our findings and previous plume tracking experiments to provide a general intuition for future field research and guidance for wind tunnel design. Our analysis suggests a hypothesis that there may be an ideal range of wind speeds and environment complexity in which insects will be most successful when tracking odor plumes over long distances.

Quantity and type of coastal debris pollution in Taiwan: A rapid assessment with trained citizen scientists using a visual estimation method

Ongoing monitoring of the distribution and composition of coastal debris is a prerequisite for efficient management and cleanups. Therefore, we conducted a rapid assessment of coastal debris along the 1210 km coastline of Taiwan using a visual estimation method. Forty-nine citizen scientists were intensively trained to correctly identify the volume and types of debris. At 121 sampling locations randomly placed along Taiwan's coastline, the citizen scientists recorded the pollution level and the three most abundant debris types within a 100-m transect during four surveys in 2018-2019. Averaging over the four surveys, the mean amount of coastal debris was estimated to be 406.6 kg/km, and the three most abundant debris types were plastic bottles, foamed plastics, and fishing nets and ropes. Using a statistical test which avoids spatial pseudoreplication, we showed that north-facing coastlines had significantly higher pollution levels than the other coastlines, which we suggest is deposited there during strong winter winds. We also showed that fishery-related debris was a much more important part of coastal debris when the volume of it was determined instead of just the number of items. Mean pollution levels were further associated with wind speed, coastline type, and the distance to presumed pollution sources. Our results compare well with similar surveys conducted in Japan and South Korea. In each country, the debris was highly aggregated, which means it was concentrated in a few highly polluted localities. Therefore, the visual estimation method can effectively guide cleanup efforts to the most polluted areas and also reliably generate long-term monitoring data.

Wind and Sea Breeze Characteristics for the Offshore Wind Farms in the Central Coastal Area of Taiwan

Renewable energy is crucial for achieving net zero emissions. Taiwan has abundant wind resources and most major wind farms are offshore over the Taiwan Strait due to a lack of space on land. A thorough study that includes time series modeling of wind speed and sea breeze identification and evaluation for Taiwan’s offshore wind farms was conducted. The time series modeling identified two periodic (annual and diurnal) components and an autoregressive model for multiple-year wind speed time series. A new method for sea breeze type identification and magnitude evaluation is proposed. The method (named as EACH) utilizes a vector and an ellipse to represent the wind condition of a day. Verification of the type identification determined by the new method in two cases of different seasons has been conducted by using surface weather charts and wind data measured by lidar. It is a concise, effective, and programmable way to filter a number of dates for type identification and speed change precursor of sea breeze. We found that the typical daily wind power production of corkscrew sea breeze in Central Taiwan is more than 33 times that of pure sea breeze and more than 9 times that of backdoor sea breeze, which highlights the impact of sea breeze types on wind power.

Assessment of the Offshore Wind Energy Potential in the Romanian Exclusive Economic Zone

The European offshore wind market is continuously expanding. This means that, together with significant technological developments, new coastal environments should be considered for the implementation of the wind farms, as is the case of the Black Sea, which is targeted in the present work. From this perspective, an overview of the wind energy potential in the Romanian exclusive economic zone (EEZ) in the Black Sea is presented in this work. This is made by analyzing a total of 20 years of wind data (corresponding to the time interval 2000–2019) coming from different sources, which include ERA5 reanalysis data and satellite measurements. Furthermore, a direct comparison between these datasets was also carried out. Finally, the results of the present work indicate that the Romanian offshore areas can replicate the success reported by the onshore wind projects, of which we can mention the Fantanele-Cogealac wind farm with an operating capacity of 600 MW.