A comparison between Advanced Scatterometer and Weather Research and Forecasting wind speeds for the Japanese offshore wind resource map

Takeyama, Yuko; Ohsawa, Teruo; Tanemoto, Jun; Shimada, Shinichi; Kozai, Katsutoshi; Kogaki, Tetsuya

doi:10.1002/we.2503

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Thanks to their extensive spatial coverage, wind products retrieved from SAR can be utilized for mapping offshore wind resources at the basin scale. This has been demonstrated for the seas of Europe [29], Japan [30,31], China [32,33], the Great Lakes [34], and the east coast of the US [35]. The mapping of wind resources from satellite observations typically follows the same approach as wind resource assessment based on time series observations from in situ or model data [36].…”

Section: Introductionmentioning

confidence: 99%

Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment

Badger

Fujita²,

Orzel³

et al. 2023

Energies

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The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s−1 and negative biases of −0.4 m s−1 and −1.0 m s−1, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms.

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Section: Introductionmentioning

confidence: 99%

Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment

Badger

Fujita²,

Orzel³

et al. 2023

Energies

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“…There is considerable interest in installing offshore wind farms in Japan to join other countries in moving toward a carbon‐neutral society. To assess wind resources, in 2017, Japan published the Offshore Wind Information System, which includes offshore wind atlases within the exclusive economic zone based on 500‐m‐grid Weather Research and Forecasting model 1 simulations and satellite observations 2,3 . Moreover, a new maritime renewable energy policy, which enable wind farm developers to occupy some areas for offshore wind farm development for up to 30 years, has been also announced by the Japanese government to stimulate the market.…”

Section: Introductionmentioning

confidence: 99%

Validation of near‐shore wind measurements using a dual scanning light detection and ranging system

et al. 2022

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This paper reports results from a 2-month validation campaign of near-shore wind measurements taken by a dual scanning light detection and ranging (LiDAR) system at a coastal site in Japan. A meteorological mast and a vertical profiling LiDAR device were deployed at an offshore research station approximately 1.5 km from the coast.Offshore winds at heights of 66, 120, and 180 m above sea level were observed by the scanning LiDAR system. Comparisons with a sonic anemometer found that the radial velocities had a coefficient of determination of more than 0.99 without unreasonable bias. The accuracy of 10-min mean wind speeds and directions was then evaluated. The 10-min values from the dual scanning LiDAR system were accurate enough to satisfy the acceptance criteria used for floating LiDAR systems. In addition, the vertical velocity and direction profiles from the dual scanning LiDAR system were compared with those from a vertical profiling LiDAR device. The performance of turbulence intensity (TI) measurements was also evaluated. Although the TIs from the dual scanning LiDAR system were slightly lower than those from the sonic anemometer, they were equivalent to those from the cup anemometers. This investigation concluded that the near-shore wind measurements using the dual scanning LiDAR system are beneficial for reducing near-shore wind measurement costs, because the system can measure not only 10-min wind speed and direction, but also parameters associated with assessing site-specific conditions without installing offshore meteorological masts.

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Short‐Term Offshore Wind Power Prediction Based on Significant Weather Process Classification and Multitask Learning Considering Neighboring Powers

Yang,

Peng,

Zhang

et al. 2024

Wind Energy

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Offshore wind power is an important technology for low‐carbon power grids. To improve the accuracy, a short‐term offshore wind power prediction method based on significant weather process classification and multitask learning considering neighboring powers is presented in this paper. First, a novel weather process classification method, in which the samples are divided into pieces of waves based on extreme points and are quantified with labels of energy level and fluctuation level, is proposed to classify samples into multiple types of significant weather processes for independent modeling. Second, a multitask learning method, in which the power sequences in neighboring offshore wind farms are innovatively introduced as a new input feature, is proposed for modeling wind power prediction for each wind farm inside a neighboring region under each weather process class. Case studies are presented to verify the effectiveness and superiority of the proposed method. Based on this new method, the 4‐h ultra‐short‐term root mean squared error (RMSE), 24‐h day‐ahead RMSE, 4‐h ultra‐short‐term mean absolute error (MAE), and 24‐h day‐ahead MAE can be reduced by 1.45%, 2.1%, 1.15%, and 1.85%, respectively, compared with benchmark methods, which verify the effectiveness of the proposed method.

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A comparison between Advanced Scatterometer and Weather Research and Forecasting wind speeds for the Japanese offshore wind resource map

Cited by 4 publications

References 12 publications

Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment

Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment

Validation of near‐shore wind measurements using a dual scanning light detection and ranging system

Short‐Term Offshore Wind Power Prediction Based on Significant Weather Process Classification and Multitask Learning Considering Neighboring Powers

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