Hindcasts of Sea Surface Wind around the Korean Peninsula Using the WRF Model: Added Value Evaluation and Estimation of Extreme Wind Speeds

Kim, Ho-Jin; Heo, Ki-Young; Kim, Nam Hoon; Kwon, Jungkee

doi:10.3390/atmos12070895

Cited by 3 publications

(5 citation statements)

References 54 publications

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“…In an effort to enhance the accuracy of the estimation, it is proposed to further optimize the ML algorithms by employing the mesoscale simulations results obtained using the Weather Research and Forecasting Model [ 28 ]. This numerical model has been widely used by industry and academia for weather forecasts, wind energy, and pollutant propagation applications [ 29 , 30 , 31 , 32 ], to name a few examples. The model executions are carried out in three nested domains with resolutions of 6 km (outermost domain), 3 km (middle domain), and 1.5 km (innermost domain), always fed by a global weather forecast such as the high-resolution Global Ensemble Forecast System (GEFS) [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks, Mesoscale Models, and LiDAR Measurements

García-Gutiérrez

López

Domínguez

et al. 2023

Sensors

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This paper introduces a novel methodology that estimates the wind profile within the ABL by using a neural network along with predictions from a mesoscale model in conjunction with a single near-surface measurement. A major advantage of this solution compared to other solutions available in the literature is that it requires only near-surface measurements for prediction once the neural network has been trained. An additional advantage is the fact that it can be potentially used to explore the time evolution of the wind profile. Data collected by a LiDAR sensor located at the University of León (Spain) is used in the present research. The information obtained from the wind profile is valuable for multiple applications, such as preliminary calculations of the wind asset or CFD modeling.

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Section: Methodsmentioning

confidence: 99%

Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks, Mesoscale Models, and LiDAR Measurements

García-Gutiérrez

López

Domínguez

et al. 2023

Sensors

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“…The FLOPs were measured with an input with fixed dimensions at (3,40,40) for a fair comparison. The proposed method has higher FLOPs compared with the reference methods, and the FLOPs of the primal model account for a high proportion of the entire model.…”

Section: Computational Efficiencymentioning

confidence: 99%

“…As one of the major sources of momentum for the Ocean, sea surface wind (SSW), also called ocean surface wind, is a key parameter for a variety of studies such as ocean waves, ocean circulation, and air-sea interaction [1]. Accurate and timely observation of SSW is valuable for a wide range of applications such as numerical weather prediction (NWP), marine environmental monitoring and transportation, search and rescue missions for natural and manmade maritime hazards, and wind energy assessment [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the advantages of large scale, high frequency, dynamic, remote sensing (RS) observations for SSW from scatterometer, radiometer, altimeter, and synthetic aperture radar (SAR) onboard satellites, serve in many meteorology and oceanographic applications [4]. In addition, there are also studies that turned to reanalysis data that are generated globally by numerical models as an alternative [3,5]. Nevertheless, most existing SSW data products from either RS or numerical models involve a coarse spatial resolution, which is insufficient for regional or local studies.…”

Section: Introductionmentioning

confidence: 99%

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A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme

et al. 2022

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Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. the highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD.

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“…The mean magnitude of the complex correlation was 0.83 and the phase angles were <10 • except DH. A detailed comparison between the hindcast, observation, and satellite data for 10 m winds can be found in Kim et al [41]. Table 4 shows the statistical measures between the hindcast and in situ data for significant wave height and peak period in the 2014-2016 period.…”

Section: Model Assessment Of 10 M Wind and Wavementioning

confidence: 99%

Characteristics of Swell-like Waves in the East Coast of Korea Using Atmospheric and Wave Hindcast Data

et al. 2022

Self Cite

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The long-term trend of swell-like waves invading the east coast of Korea was identified by using observations and hindcast data from 1979 to 2016. We defined a swell-like wave as a wave with a height of 2 m and a peak period of 10 s on the basis of a literature review of human casualties and property damage in the region. In total, 179 swell-like wave cases were detected from 1979 to 2016, with 132 cases caused by extratropical cyclones (ETCs). The track density analysis indicated that the ETCs were mainly generated on the east coast of China, over the East/Japan Sea, and over the Kuroshio-Oyashio extension region and then moved northeast. This reflects the prevailing wind direction, which was the most significant factor in generating the swell-like waves. The number of swell-like waves has been significantly increasing since the 2000s. This increasing trend of swell-like waves is linked with the synoptic eddy activity with a correlation of 0.53. They were associated with the reversed meridional gradient of surface air temperature and the consequent negative vertical wind shear anomaly near 40° N.

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Hindcasts of Sea Surface Wind around the Korean Peninsula Using the WRF Model: Added Value Evaluation and Estimation of Extreme Wind Speeds

Cited by 3 publications

References 54 publications

Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks, Mesoscale Models, and LiDAR Measurements

Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks, Mesoscale Models, and LiDAR Measurements

A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme

Characteristics of Swell-like Waves in the East Coast of Korea Using Atmospheric and Wave Hindcast Data

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