A Sensitivity Analysis of the Wind Forcing Effect on the Accuracy of Large-Wave Hindcasting

Wang, Taiping; Yang, Zhaoqing; Wu, Wei‐Cheng; Grear, Molly

doi:10.3390/jmse6040139

Cited by 22 publications

(10 citation statements)

References 18 publications

(34 reference statements)

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“…Though these differences are minor, the total energy is proportional to the square of the wave height. Thus, these minor differences are essential to capture as they can lead to dramatically different forecasts, affecting not only wave energy estimates, but also forecasts of hurricane-induced waves [35][36][37] and storm surges [38,39]. Consequently, the joint EMD-LSTM model displays a dramatically lower RMSE for wave forecasts compared to LSTM alone and thus, wherever possible, should be used.…”

Section: Resultsmentioning

confidence: 99%

Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network

Zhou

Bethel

Sun

et al. 2021

JMSE

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Wave forecasts, though integral to ocean engineering activities, are often conducted using computationally expensive and time-consuming numerical models with accuracies that are blunted by numerical-model-inherent limitations. Additionally, artificial neural networks, though significantly computationally cheaper, faster, and effective, also experience difficulties with nonlinearities in the wave generation and evolution processes. To solve both problems, this study employs and couples empirical mode decomposition (EMD) and a long short-term memory (LSTM) network in a joint model for significant wave height forecasting, a method widely used in wind speed forecasting, but not yet for wave heights. Following a comparative analysis, the results demonstrate that EMD-LSTM significantly outperforms LSTM at every forecast horizon (3, 6, 12, 24, 48, and 72 h), considerably improving forecasting accuracy, especially for forecasts exceeding 24 h. Additionally, EMD-LSTM responds faster than LSTM to large waves. An error analysis comparing LSTM and EMD-LSTM demonstrates that LSTM errors are more systematic. This study also identifies that LSTM is not able to adequately predict high-frequency significant wave height intrinsic mode functions, which leaves room for further improvements.

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Section: Resultsmentioning

confidence: 99%

Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network

Zhou

Bethel

Sun

et al. 2021

JMSE

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“…Accurate wind forcing is extremely important for the wave hindcast. Wang et al [11] evaluated several wind products, and concluded that the Climate Forecast System Reanalysis (CFSR) provided the best overall performance in predicting wave heights using either WWIII or UnSWAN. The CFSR wind data are available from to 2010 at hourly temporal and 0.5-degree spatial resolution.…”

Section: B Model Configurationsmentioning

confidence: 99%

High-resolution hindcasts for U.S. wave energy resource characterization

Zhou¹,

Neary²

2020

Int. J. Mar. Ene.

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The marine and hydrokinetic (MHK) industry is at an early stage of development and has the potential to play a significant role in diversifying the U.S. energy portfolio and reducing the U.S. carbon footprint. Wave energy is the largest among all the U.S. MHK energy resources, which include wave energy, ocean current, tidal-instream, ocean thermal energy conversion, and river-instream. Wave resource characterization is an essential step for regional wave energy assessments, Wave Energy Converter (WEC) project development, site selection and WEC design. The present paper provides an overview of a joint modelling effort by the Pacific Northwest National Laboratory and Sandia National Laboratories on high-resolution wave hindcasts to support the U.S. Department of Energy’s Water Power Technologies Office’s program of wave resource characterization, assessment and classifications in all US coastal regions. Topics covered include the modelling approach, model input requirements, model validation strategies, high performance computing resource requirements, model outputs and data management strategies. Examples of model setup and validation for different regions are provided along with application to development of classification systems, and analysis of regional wave climates. Lessons learned and technical challenges of the long-term, high-resolution regional wave hindcast are discussed.

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“…These models, however, underpredict large waves and extreme n-year significant wave heights. The main source of this underbias is due to limitations of most wind reanalysis datasets, namely their inability to resolve fine-scale high-energy wind gusts, e.g., [22,23]. Model performance studies commonly emphasize predictions of common sea state statistics (bulk parameters) on average, e.g., H s and peak period, T p .…”

Section: Introductionmentioning

confidence: 99%

Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment

Neary

Ahn

Seng

et al. 2020

JMSE

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Best practices and international standards for determining n-year return period extreme wave (sea states) conditions allow wave energy converter designers and project developers the option to apply simple univariate or more complex bivariate extreme value analysis methods. The present study compares extreme sea state estimates derived from univariate and bivariate methods and investigates the performance of spectral wave models for predicting extreme sea states at buoy locations within several regional wave climates along the US East and West Coasts. Two common third-generation spectral wave models are evaluated, a WAVEWATCH III® model with a grid resolution of 4 arc-minutes (6–7 km), and a Simulating WAves Nearshore model, with a coastal resolution of 200–300 m. Both models are used to generate multi-year hindcasts, from which extreme sea state statistics used for wave conditions characterization can be derived and compared to those based on in-situ observations at National Data Buoy Center stations. Comparison of results using different univariate and bivariate methods from the same data source indicates reasonable agreement on average. Discrepancies are predominantly random. Large discrepancies are common and increase with return period. There is a systematic underbias for extreme significant wave heights derived from model hindcasts compared to those derived from buoy measurements. This underbias is dependent on model spatial resolution. However, simple linear corrections can effectively compensate for this bias. A similar approach is not possible for correcting model-derived environmental contours, but other methods, e.g., machine learning, should be explored.

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A Sensitivity Analysis of the Wind Forcing Effect on the Accuracy of Large-Wave Hindcasting

Cited by 22 publications

References 18 publications

Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network

Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network

High-resolution hindcasts for U.S. wave energy resource characterization

Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment

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