Figure 2 - uploaded by Geng Xia
Content may be subject to copyright.
Flow diagram showing the filters involved in the sea-breeze identification method for the 1-year (Sept. 2019-Aug. 2020) simulation period.
Source publication
With the planned construction of vast offshore wind farms along the US East Coast, identifying and understanding key coastal processes, such as sea breezes, has become a critical need for the sustainability and development of US offshore wind energy. In this study, a new two-step identification method is proposed to detect and characterize three ty...
Context in source publication
Context 1
... search for sea breeze events is tem- Kain-Fritsch ( Kain and Fritsch, 1990;Kain, 2004) porally constrained to the hours between 08:00 to 20:00 LT, as land breezes are more likely to occur at night. Figure 2 shows the flow diagram of the two-step approach to identify sea breeze events from the model simulation. The first step is applied at a regional scale to identify days with flow regimes that have potential for pure, backdoor and corkscrew sea breeze development. ...
Similar publications
This study analyzes wind and wave data from the coastal region of central Kerala during the years 2015 and 2016 to investigate the impact of wind patterns and wave characteristics on the coastal environment before the onset of the monsoon season. It is essential to comprehend these factors when evaluating coastal conditions, sea state, and regional...
Citations
... Version 4.2.1 allows for modifying the amount of TKE produced by wind turbines and ensures turbulence advection (Archer et al., 2020). Two nested domains comprise 6 and 2 km horizontal resolutions (Pronk et al., 2022;Xia et al., 2022;Bodini et al., 2023;Redfern et al., 2023), respectively, and the inner nest begins 20 grid cells into the parent domain (Fig. 1). This same domain and period of study have been used to explore interactions between power production and sea breezes (Xia et al., 2022). ...
... Two nested domains comprise 6 and 2 km horizontal resolutions (Pronk et al., 2022;Xia et al., 2022;Bodini et al., 2023;Redfern et al., 2023), respectively, and the inner nest begins 20 grid cells into the parent domain (Fig. 1). This same domain and period of study have been used to explore interactions between power production and sea breezes (Xia et al., 2022). Fine vertical resolution (10 m) near the surface stretches aloft, with 17 levels within the lowest 200 m as recommended by Tomaszewski and Lundquist (2020). ...
The mid-Atlantic will experience rapid wind plant development due to its promising wind resource located near large population centers. Wind turbines and wind plants create wakes, or regions of reduced wind speed, that may negatively affect downwind turbines and plants. We evaluate wake variability and annual energy production with the first yearlong modeling assessment using the Weather Research and Forecasting model, deploying 12 MW turbines across the domain at a density of 3.14 MW km−2, matching the planned density of 3 MW km−2. Using a series of simulations with no wind plants, one wind plant, and complete build-out of lease areas, we calculate wake effects and distinguish the effect of wakes generated internally within one plant from those generated externally between plants. We also provide a first step towards uncertainty quantification by testing the amount of added turbulence kinetic energy (TKE) by 0 % and 100 %. We provide a sensitivity analysis by additionally comparing 25 % and 50 % for a short case study period. The strongest wakes, propagating 55 km, occur in summertime stable stratification, just when New England's grid demand peaks in summer. The seasonal variability of wakes in this offshore region is much stronger than the diurnal variability of wakes. Overall, yearlong simulated wake impacts reduce power output by a range between 38.2 % and 34.1 % (for 0 %–100 % added TKE). Internal wakes cause greater yearlong power losses, from 29.2 % to 25.7 %, compared to external wakes, from 14.7 % to 13.4 %. The overall impact is different from the linear sum of internal wakes and external wakes due to non-linear processes. Additional simulations quantify wake uncertainty by modifying the added amount of turbulent kinetic energy from wind turbines, introducing power output variability of 3.8 %. Finally, we compare annual energy production to New England grid demand and find that the lease areas can supply 58.8 % to 61.2 % of annual load. We note that the results of this assessment are not intended to make nor are they suitable to make commercial judgments about specific wind projects.
... Owing to their importance for an understanding of weather conditions in coastal environments, sea-breezes have been widely studied in recent years, particularly to determine their effects on air quality [11][12][13], as well as the launch sites for aerospace vehicles [14,15], at Alcântara Launch Center and Kennedy Space Center, respectively. Despite the increasing growth of offshore wind energy worldwide, there are surprisingly few studies that have investigated the relationship between sea-breezes and wind energy systems [16,17]. Miller et al. [9] suggest that the coastal influence of sea breezes varies and depends on the type of sea breeze, which is aligned to the shoreline relative to the prevailing alongshore wind component. ...
... The corkscrew (backdoor) sea-breeze is a type that occurs when the prevailing wind has an alongshore component with the land to the left (right). Xia et al. [17] calculated that the power production of a 10 MW offshore wind turbine would be roughly 3 to 4 times greater than the other types during a corkscrew sea-breeze. This underscores the importance of these studies for the wind energy industry in coastal regions and offshore environments. ...
his research focuses on understanding a meteorological phenomenon that is related to operational disruptions in a wind power plant on the North-East coast of Brazil. The study employed enhanced weather observations based on SODAR and LIDAR wind profilers, micrometeorological masts, ERA-5 reanalysis data, wind power production data from Brazilian power grid operators, and GOES-16 thermal and visible imagery. On August 15, 2023, Brazil experienced a widespread blackout, which was largely caused by down-ramp events that affected several wind farms at the same time. The factors leading to the phenomenon were categorized into three driven-dependent categories: i) a sharp fall in dew-point temperature, ii) the presence of a cumulus cloud line just above the coastline in the early morning, and/or iii) rainfall over the adjoining sea, providing the feasibility to nowcasting this severe wind ramp. The results derived from the conceptual model suggest that sea-breezes play a significant role in the net wind at the turbine hub-height, and often exceed the influence of large-scale flow. The net wind is considered to be caused by the interplay between trade winds and consistent breezes. It is hoped this research can provide a valuable insight into the complex interactions between local meteorological phenomena and wind energy production on the North-East coast of Brazil, and that its application has the potential to improve the operational forecasting of wind power plants.
... Moreover, local winds are very relevant to considerations of wind energy due to the strong dependence of the wind power potential on the wind speed, the possible deflection of wind by the terrain, or mountaininduced turbulence. Increased attention has been given to local winds lately, mostly to the sea breeze as it affects the wind profile and wind power potential in offshore and coastal areas (e.g., Mazon et al. 2015;Steele et al. 2015;Xia et al. 2022), but also to the local winds in Arctic areas such as Greenland (e.g., Radu et al. 2019). ...
Thermally-driven circulations are a frequent meteorological phenomenon in complex Arctic terrain, but the Arctic fjord breeze, a combined sea-breeze and up-valley wind, has received little attention. A field campaign was conducted in the valley Adventdalen in Svalbard in summer 2022 using a Scanning Doppler Lidar and automatic weather stations. It is shown that a local up-valley circulation occurred frequently in this valley, and that it was driven by the temperature and pressure gradient between valley and fjord, i.e., a fjord breeze. The fjord breeze existed in both large-scale up-valley and down-valley winds. Its strength, extent and depth varied due to the diurnal cycle of solar irradiation as well as the interaction with large-scale winds. In contrast to typical lower-latitude breezes, the Arctic fjord breeze could persist over several days. The breeze was found to be relatively strong even under small horizontal temperature contrasts and opposing large-scale winds, possibly due to an increase in the thermal pressure gradient by the surrounding topography.
... On the Amapá coast, the land breeze begins to have an influence starting at 19 h UTC (4 p.m. LT), when the land cools more quickly than the ocean, reversing the temperature gradient. Previous studies [72] and recent ones [73] show the influence of breezes at great heights, such as 100 m. At all hours, the wind direction remains predominantly from the northeast, due to the strong influence of the trade winds. ...
This article examines the potential for wind and solar energy generation in the state of Amapá, Brazil, using ERA5 data from between 1991 and 2020. Key metrics considered include wind power density, capacity factor, photovoltaic potential, and concentrated solar power output. Analyses revealed pronounced wind speeds offshore during summer and in continental regions during spring. Solar irradiance was notably higher in the spring. Differences in wind potential were observed between northern and southern offshore areas. Concentrated solar power efficiency and photovoltaic potential were influenced by location and cloud cover, respectively. Overall, summer presents the best offshore wind energy potential, while spring is optimal for onshore solar energy in Amapá. This study underscores the importance of understanding local climatic patterns when planning energy installations in the region.
... Version 4.2.1 allows for modification of the amount of TKE produced by turbines and ensures turbulence advection (Archer et al., 2020). Two nested domains comprise 6-km and 2-km horizontal resolutions (Pronk et al., 2022;Xia et al., 2022), respectively (Fig. 1). This same domain and period of study have been used to explore interactions between power production and sea breezes (Xia et al., 2022). ...
... Two nested domains comprise 6-km and 2-km horizontal resolutions (Pronk et al., 2022;Xia et al., 2022), respectively (Fig. 1). This same domain and period of study have been used to explore interactions between power production and sea breezes (Xia et al., 2022). Fine vertical resolution (10 m) near the surface stretches aloft, with 17 levels within the lowest 200 m as recommended by Tomaszewski and Lundquist, 90 (2020). ...
The mid-Atlantic will experience rapid wind plant development due to its promising wind resource located near large population centers. Wind turbines and wind plants create wakes, or regions of reduced wind speed, that may negatively affect downwind turbines and plants. Long mid-Atlantic wakes are causing growing concern. We evaluate wake variability and annual energy production with the first year-long modeling assessment using the Weather Research and Forecasting Model, deploying 12-MW turbines across the domain at a density of 3.14 MW km−2, matching the planned density of 3 MW km−2. Using a series of simulations with no wind plants, one wind plant, and complete build-out of lease areas, we calculate wake effects and distinguish the effect of wakes generated internally within one plant from those generated externally between plants. The strongest wakes, propagating 58 km, occur in summertime stable stratification, just when New England’s grid demand peaks in summer. The seasonal variability of wakes in this offshore region is much stronger than diurnal variability of wakes. Overall, the mean year-long wake impacts reduce power output by 35.9 %. Internal wakes cause greater year-long power losses (27.4 %) compared to external wakes (14.1 %). Additional simulations quantify wake uncertainty by modifying the added amount of turbulent kinetic energy (TKE) from turbines, introducing power output variability of 3.8 %. Finally, we compare annual energy production (AEP) to New England grid demand and find that the lease areas can supply roughly 60 % of annual load.
... Numerous sea breeze detection methods have come up in past studies. Among these, automatic detection is much more efficient than manual, subjective identification (Azorin-Molina et al., 2011b;Gustavsson et al., 1995;Wei, 2012;Xia et al., 2022). The commonly used automatic identification methods can be divided into three categories: sea breeze index, remote-sensing and filter methods. ...
... Thus, some sea breeze days may be missed in practice. Finally, the filter method is a generic technique adopted for various spatial scales; it primarily uses a set of tests covering a wide range of possible scenarios to determine the likelihood of a sea breeze (Furberg et al., 2002;Xia et al., 2022). Since each step of the filter method can be refined to different levels of strictness, the performance of each filter can be easily identified. ...
The sea breeze characteristics for 30 years of summer (1981–2010) in the coastal region of Jiangsu Province were analysed in this study, using the four‐step filter method based on ECMWF Fifth‐Generation Reanalysis (ERA5) data. The results showed that the filtering method reasonably identified three types of sea breeze. Additionally, approximately 23% of the total summer days remained; the corkscrew type dominates the three types of sea breeze; and the backdoor sea breeze was the least common. Except for the type of backdoor sea breeze, the other two types had relatively fixed dominant weather types on three coastlines. The synoptic weather patterns in the 500‐hPa level corresponding to each sea breeze type were different. Further, the details of the diurnal cycle of lower tropospheric circulation indicated the strength of the onshore wind and the location of the large potential temperature gradient at noon being influenced by sea breeze type and coastal location. The wind strength for the three types of sea breeze on the middle coastline was stronger than that on the other two coastlines. On the three coastlines, strong onshore winds were concentrated in areas near the coastline, where they reached their maximum strengths around noon. On the southern coastline, strong onshore winds moved further inland.
Low-level jets (LLJs), in which the wind speed attains a local maximum at low altitudes, have been found to occur in the U.S. mid-Atlantic offshore, a region of active wind energy deployment as of 2023. In contrast to widely studied regions such as the U.S. southern Great Plains and the California coastline, the mechanisms that underlie LLJs in the U.S. mid-Atlantic are poorly understood. This work analyzes floating lidar data from buoys deployed in the New York Bight to understand the characteristics and causes of coastal LLJs in the region. Application of the Hilbert–Huang transform, a frequency analysis technique, to LLJ case studies reveals that mid-Atlantic jets frequently occur during times of adjustment in synoptic-scale motions, such as large-scale temperature and pressure gradients or frontal passages, and that they do not coincide with motions at the native inertial oscillation frequency. Subsequent analysis with theoretical models of inertial oscillation and thermal winds further reveals that these jets can form in the stationary geostrophic wind profile from horizontal temperature gradients alone—in contrast to canonical LLJs, which arise from low-level inertial motions. Here, inertial oscillation can further modulate the intensity and altitude of the wind speed maximum. Statistical evidence indicates that these oscillations arise from stable stratification and the associated frictional decoupling due to warmer air flowing over a cold sea surface during the springtime land–sea breeze. These results improve our conceptual understanding of mid-Atlantic jets and may be used to better predict low-level wind speed maxima.
Significance Statement
The purpose of this work is to identify and characterize the atmospheric mechanisms that result in an occasional low-level maximum in the wind speed off the U.S. mid-Atlantic coastline. Our findings show that these low-level jets form due to horizontal temperature gradients arising from fronts and synoptic systems, as well as from the land–sea breeze that forces warmer air over the cold ocean surface. This work aids predictability of such jets, improves our understanding of this coastal environment, and has implications for future deployment of offshore wind energy in this region.
Strong winds mean a lot of power production by wind turbines! In the search for the windiest locations with the potential to produce lots of power, the coastal zone has popped up as one of the most appealing places. However, there are some peculiar wind patterns in coastal regions that need to be well-understood to accurately predict how much power can be produced. In this article, we will explain the mechanisms behind two of these wind patterns: the sea breeze and low-level jets.
One of the most prominent mesoscale phenomena in the coastal zone is the sea-breeze/land-breeze circulation. The pattern and its implications for the weather in coastal areas are well described, and with mesoscale-resolving operational NWP models the circulation can be captured. In this study, a straightforward method to identify sea and land breezes based on the change in wind direction in the column above a grid point on the coastline is presented. The method was tested for southern Sweden using archived output from the HARMONIE-AROME model with promising results, describing both the seasonal and diurnal cycles well. In areas with a complex coastline, such as narrow straits, the concept of the land–sea breeze becomes less clear, and several ways to address this problem for the suggested method are discussed. With an operational index of the sea and land breezes, the forecaster can better understand and express the weather situation and add value for people in the coastal zone. Further, the indices can be used to study systematic biases in the model and to create climatologies of the sea and land breezes.
Significance Statement
A wind pattern that is frequently occurring in the coastal zone is the sea-breeze/land-breeze circulation, and the purpose of this study is to test a new method to automatically identify sea breezes and land breezes in weather forecasts. Knowing if a sea breeze or a land breeze is occurring is helpful for the operational weather forecaster in understanding the weather situation. It can also be used to study systematic model behavior, for example, errors in the forecast temperature during sea-breeze conditions. The method has been tested for seven coastal sites in Sweden and shows promising results both in case studies and multiyear statistics.
Offshore wind energy has been rapidly gaining traction more recently and the development of offshore wind farms continues to rise on a global scale in search of a sustainable future energy source. In the meantime, the capital-intensive project of installing wind turbines offshore involves a multitude of logistics activities and maritime operations. The high-risk installation process becomes more complex, in particular, under the uncertain weather conditions at sea such as wind speed and wave height. We present in this paper a hybrid modelling framework that minimises the overall installation time required to build an offshore wind farm, and develop a mixed-integer linear program submodule embedded on the discrete event simulation model. In addition, to take stochastic weather states into consideration, a Markov-Chain Monte Carlo method is used to generate weather instances with uncertainty. The weather occurrence serves as an integrated look-ahead viewpoint in making turbine component installation decisions. Further, the experiments of sensitivity analysis are designed to draw insights regarding the effects that the number of turbines, the number of vessels, and the seasonality have on the total project timespan. The computational results demonstrate the efficacy of our proposed hybrid solution approach, tested on the case of Southwest Offshore Wind Farm in South Korea.
