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

Hatfield, Daniel; Hasager, Charlotte Bay; Karagali, Ioanna

doi:10.3390/rs14061427

Cited by 2 publications

(3 citation statements)

References 41 publications

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“…In general, the two wind directions are close, with ASCAT observing a slightly smaller wind direction, consistent with the modest veering of the wind direction expected under typical atmospheric conditions and significantly smaller than the severe veer observed on 16 April 2018. The results align with [30] comparing ASCAT to ferry lidar wind direction near FINO2 in the Baltic Sea. Based on the comparison results from Horns Rev, the wind direction from ASCAT is considered reliable.…”

Section: Wind Measurementssupporting

confidence: 82%

Wind Farm Blockage Revealed by Fog: The 2018 Horns Rev Photo Case

Hasager,

Nygaard,

Poulos

2023

Energies

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Fog conditions at the offshore wind farm Horns Rev 2 were photographed on 16 April 2018. In this study, we present the results of an analysis of the meteorological conditions on the day of the photographs. The aim of the study was to examine satellite images, meteorological observations, wind turbine data, lidar data, reanalysis data, and wake and blockage model results to assess whether wind farm blockage was a likely cause for the formation of fog upstream of the wind farm. The analysis indicated the advection of warm and moist air mass from the southwest over a cool ocean, causing cold sea fog. Wind speeds at hub height were slightly above cut-in, and there was a strong veer in the shallow stable boundary layer. The most important finding is that the wake and blockage model indicated stagnant air mass arcs to the south and west of the wind farm. In the photographs, sea fog is visible in approximately the same area. Therefore, it is likely that the reduced wind triggered the sea fog condensation due to blockage in this area. A discrepancy between the blockage model and sea fog in the photographs appears in the southwest direction. Slightly higher winds might have occurred locally in a southwesterly direction, which may have dissolved sea fog. The wake model predicted long and narrow wind turbine wakes similar to those observed in the photographs. The novelty of the study is new evidence of wind farm blockage. It fills the gap in knowledge about flow in wind farms. Implications for future research include advanced modeling of flow phenomena near large offshore wind farms relevant to wind farm operators.

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Section: Wind Measurementssupporting

confidence: 82%

Wind Farm Blockage Revealed by Fog: The 2018 Horns Rev Photo Case

Hasager,

Nygaard,

Poulos

2023

Energies

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“…Ferry-mounted lidar systems (Gottschall et al, 2018) have been compared with ASCAT winds (Hatfield et al, 2022b); they can also provide spatial sampling not achieved when measurement systems are moored at specific locations. Although the dataset used in Gottschall et al (2018) covered a period of only 5 months, the concept involves mounting lidar systems on established ferry routes, thus providing the opportunity for longer time series measurements over established paths.…”

Section: Discussionmentioning

confidence: 99%

“…Synthetic-aperture radar (SAR) and scatterometer wind measurements have been used to characterize offshore wind resources (Karagali et al, 2018a;Remmers et al, 2019;Hasager et al, 2020;Ahsbahs et al, 2020). Advanced Scatterometer (ASCAT) winds were compared to numerical model simulations (Karagali et al, 2018b) and ferry lidar measurements, showing better agreement than the mesoscale model simulations (Hatfield et al, 2022b). AS-CAT winds are optimized for consistent wind measurement accuracy (Verhoef et al, 2017), stability (Rivas et al, 2017) and bias (Belmonte Rivas and Stoffelen, 2019).…”

Section: Introductionmentioning

confidence: 99%

Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques

Hatfield¹,

Hasager²,

Karagali³

2023

Wind Energ. Sci.

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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-borne 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 NORwegian hindcast Archive (NORA3) mesoscale 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 proof-of-concept study demonstrate the limited applicability of using machine-learning techniques to extrapolate long-term satellite wind observations when enough samples are available.

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

Cited by 2 publications

References 41 publications

Wind Farm Blockage Revealed by Fog: The 2018 Horns Rev Photo Case

Wind Farm Blockage Revealed by Fog: The 2018 Horns Rev Photo Case

Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques

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