New methods to improve the vertical extrapolation  of near-surface offshore wind speeds

Optis, Mike; Bodini, Nicola; Debnath, Mithu; Doubrawa, Paula

doi:10.5194/wes-6-935-2021

Cited by 28 publications

(37 citation statements)

References 33 publications

(46 reference statements)

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“…A future outlook is the use of machine learning to extrapolate the satellite data to lidar heights for a direct comparison. This is foreseen through training models on lowlevel atmospheric data and predicting wind speeds at multiple heights similar to that of [45]. Having more lidar systems mounted on existing ferry routes would help bridge the gap of data scarcity offshore and provide more datasets to train extrapolation models.…”

Section: Discussionmentioning

confidence: 99%

Comparing Offshore Ferry Lidar Measurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF

2022

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This article highlights the inter-comparisons of the wind measurement techniques available in deep water areas working towards combining them to obtain optimal estimates of the wind power potential. More specifically, this article presents comparisons of the Ferry Lidar Experiment wind data with those of the Advanced Scatterometer (ASCAT), the FINO2 meteorological mast, and the New European Wind Atlas (NEWA) simulations performed using the Weather Research, and Forecasting (WRF) mesoscale model. To be comparable to ASCAT surface winds, which are referenced at 10 m, the ferry lidar and FINO2 wind profile measurements were extrapolated down to 10 m using atmospheric stability information derived from the bulk Richardson number formulation. ASCAT had the lowest associated error compared with that of the ferry lidar in near-neutral atmospheric stratifications, whereas FINO2, despite a distance range of 30 km and a moving ferry lidar target, had the highest correlation and lowest RMSE in all atmospheric conditions. Due to the high frequency of low-level jets caused by the proximity to land from all directions as well as typically stable atmospheric conditions, the extrapolated ferry lidar measurements underpredicted the ASCAT 10 m wind speeds. WRF consistently underperformed compared to the other measurement methods, even with the ability to directly compare results with all other sources at all heights.

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Section: Discussionmentioning

confidence: 99%

Comparing Offshore Ferry Lidar Measurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF

2022

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“…Selection of this case day was based on the jet-identification criteria of [18] and motivated by the excellent agreement between WRF mesoscale model predictions and the observations. Mesoscale data were provided by the same model setup as used by other researchers that recently studied the same offshore region [19]. Figure 1 illustrates the differences between LES predicted mean fields for the four different MMC approaches, with 10-minute, planar averages plotted.…”

Section: Validation Against Observation Datamentioning

confidence: 99%

Structure of Offshore Low-Level Jet Turbulence and Implications to Mesoscale-to-Microscale Coupling

Jayaraman

Quon

et al. 2022

J. Phys.: Conf. Ser.

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This paper explores realistic nonstationary atmospheric boundary layer (ABL) turbulence arising from nonstationarity at the mesoscale, particularly within offshore low-level jets with implications to offshore wind farms, using high-fidelity multiscale large-eddy simulations (LES). To this end, we analyzed the single-point turbulence statistical structure of a North-Atlantic offshore LLJ event simulated using high-resolution LES (AMR-Wind). The nonstationary LLJ is simulated using a mesoscale-to-microscale coupled (MMC) simulation procedure involving data assimilation of mesoscale velocity and temperature data from the Weather Research and Forecasting (WRF) model. Unlike the assimilation of mesoscale velocity data into the LES, the direct assimilation of temperature profiles had a strong impact on turbulence stratification, thereby causing erroneous predictions of turbulence both above and within the jet layer. Various approaches to mitigate this effect have resulted in multiple (four) variants of this MMC strategy. Outcomes from this work clearly show that the turbulence within the low-level jet is a strong function of the MMC approach as the turbulence structure within the low-level jet is dependent on the flux of residual turbulence from outside the jet, which in turn depends on the temperature forcing history. Additionally, the turbulence predicted by all these different methods (as well as the observation data) show similar deviations from equilibrium as evidenced by comparisons with idealized atmospheric turbulence structure obtained using the same numerical method. In general, we observe that the predicted LLJ turbulence tends to differ from canonical ABL turbulence with comparable shear. Particularly, the combination of shear and turbulence observed in such nonstationary low-level turbulence cannot be matched using equilibrium settings and therefore, represents a critical use-case for both testing and leveraging meso–micro coupling strategies.

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“…where x is a sample in the testing set and y is the final value. The RandomForestRegressor module in Python's scikit-learn package (Pedregosa et al, 2011), previously used for wind extrapolation in Bodini and Optis (2020); Optis et al (2021), was implemented for this study.…”

Section: Random Forest Modelmentioning

confidence: 99%

“…The present study is based on training the RFM using discrete, instantaneous retrievals of wind speed and direction from ASCAT rather than the typical 10 min measured time-series used in other studies (Vassallo et al, 2020;Bodini and Optis, 2020;Optis et al, 2021). In this section, the effect of discrete sampling on the RFM training is explored utilising the 12-year long ASCAT observation period.…”

Section: Data Sampling Characteristicsmentioning

confidence: 99%

Vertical extrapolation of ASCAT ocean surface winds using machine learning techniques

Hatfield

Hasager²,

Karagali³

2022

Preprint

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Abstract. The increasing demand for wind energy offshore requires more hub-height relevant wind information while larger wind turbine sizes require measurements at greater heights. In situ measurements are harder to acquire at higher atmospheric levels; meanwhile the emergence of machine-learning applications has led to several studies demonstrating the improvement in accuracy for vertical wind extrapolation over conventional power-law and logarithmic profile methods. Satellite wind retrievals supply multiple daily wind observations offshore, however only at 10 m height. The goal of this study is to develop and validate novel machine-learning methods using satellite wind observations and near-surface atmospheric measurements to extrapolate wind speeds to higher heights. A machine-learning model is trained on 12 years of collocated offshore wind measurements from a meteorological mast (FINO3) and space-bourne wind observations from the Advanced Scatterometer (ASCAT). The model is extended vertically to predict the FINO3 vertical wind profile. Horizontally, it is validated against the NORA3 meso-scale model reanalysis data. In both cases the model slightly over-predicts the wind speed with differences of 0.25 and 0.40 m s-1 respectively. An important feature in the model training process is the air-sea temperature difference, thus satellite sea surface temperature observations were included in the horizontal extension of the model, resulting in 0.20 m s-1 differences with NORA3. A limiting factor when training machine-learning models with satellite observations is the small finite number of daily samples at discrete times; this can skew the training process to higher/lower wind speed predictions depending on the average wind speed at the satellite observational times. Nonetheless, results shown in this study demonstrate the applicability of using machine learning techniques to extrapolate long-term satellite wind observations when enough samples are available.

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New methods to improve the vertical extrapolation of near-surface offshore wind speeds

Cited by 28 publications

References 33 publications

Comparing Offshore Ferry Lidar Measurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF

Comparing Offshore Ferry Lidar Measurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF

Structure of Offshore Low-Level Jet Turbulence and Implications to Mesoscale-to-Microscale Coupling

Vertical extrapolation of ASCAT ocean surface winds using machine learning techniques

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