Wind assessment in a coastal environment using synthetic aperture radar satellite imagery and a numerical weather prediction model

Beaucage, Philippe; Glazer, Anna; Choisnard, Julien; Yu, Wei; Bernier, Monique; Benoit, Robert; Lafrance, Gaétan

doi:10.5589/m07-043

Cited by 24 publications

(10 citation statements)

References 35 publications

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“…It involves particularly complex sea wind conditions due to the presence of many mountains, islands and fjords [24]. The induced variability of LR-HR relationships [25] makes it an interesting study area for the targeted downscaling of HR wind fields. In this respect, we report a statistical analysis of the LR-HR relationships in the study area.…”

Section: Data and Study Areamentioning

confidence: 99%

“…Increasing differences are observed when getting closer to the coast. Such discrepancies in coastal areas are due to the coarser resolution of ECMWF data that prevents the model from accounting for the local changes in topography and surface land-sea roughness, as well as to the fact that small-scale features of non-homogeneous winds, such as sharp gradients due to atmospheric fronts, coastal jets and wind shadows, cannot be represented with coarse numerical simulations [26], [25]. Figure 1a further stresses that coarsescale-to-fine-scale relationships vary from one point to another.…”

Section: Data and Study Areamentioning

confidence: 99%

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Learning-Based Emulation of Sea Surface Wind Fields From Numerical Model Outputs and SAR Data

Fablet²,

Chapron

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The availability of sea surface wind conditions with a high-resolution space-time sampling is a critical issue for a wide range of applications. Currently, no observation systems nor model forecasts provide relevant information with a high sampling rate both in space and time. Synthetic Aperture Radar (SAR) satellite systems deliver high-resolution sea surface fields, with a spatial resolution below 0.01 • , but they are also characterized by a large revisit time up 7-to-10 days for temperate zones. Meanwhile, operational model predictions typically involve a high temporal resolution (e.g. every 6 h), but also a low spatial resolution (0.5 •). With a view to leveraging both data sources, we investigate statistical downscaling schemes. In this study, a new model based on a machine learning method, namely Support Vector Regression (SVR), is built to reconstruct high-resolution sea surface wind fields from low-resolution operational model forecasts. The considered case study off Norway demonstrates the relevance of the proposed SVR model. It outperforms stateof-the-art approaches (namely, linear, analog and Empirical Orthogonal Function (EOF) downscaling models) in terms of mean square error. It also realistically reproduces complex spacetime variabilities of the observed SAR wind fields. We further discuss the SVR model as a generalization of the popular linear and analog models.

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Section: Data and Study Areamentioning

confidence: 99%

Section: Data and Study Areamentioning

confidence: 99%

Learning-Based Emulation of Sea Surface Wind Fields From Numerical Model Outputs and SAR Data

Fablet²,

Chapron

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Horstmann and Koch [], Koch and Feser [], and Beaucage et al . [] compared wind assessment in coastal waters from SAR and NWP models and demonstrated that SAR satellite imagery and NWP models provided reliable estimates of fine‐scale surface wind fields.…”

Section: Introductionmentioning

confidence: 99%

“…By statistically combining SAR data and data from numerical weather predictions (NWP), Portabella et al [2002] contributed to establishing the consistency between these different data sources. Horstmann and Koch [2005], Koch and Feser [2006], and Beaucage et al [2007] compared wind assessment in coastal waters from SAR and NWP models and demonstrated that SAR satellite imagery and NWP models provided reliable estimates of fine-scale surface wind fields.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric boundary layer rolls: Quantification of their effect on the hydrodynamics in the German Bight

Müller

Stanev

Schulz‐Stellenfleth

et al. 2013

J. Geophys. Res. Oceans

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[1] Atmospheric boundary layer rolls and their impact on upper ocean circulation were investigated using a combination of two high-resolution data sources: (1) data from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT with a spatial sampling of approximately 500 m Â 500 m and (2) continuous observations taken at the research platform FINO 1 with 2 min temporal sampling at eight heights between 33 and 100 m. The parallel analysis of instantaneous image data in combination with the FINO 1 time series enabled us to quantify both the spatial and temporal dynamics of mesoscale and submesoscale wind variations. The influence of these variations with different temporal and spatial scales on the hydrodynamics of the German Bight was addressed using outputs from a three-dimensional circulation model. It was demonstrated that while the coupling between wind and tidal forcing triggered substantial responses at mesoscales, the response of surface currents and sea surface temperature to the atmospheric boundary layer rolls appeared relatively weak. However, these ocean surface responses closely follow the surface footprint of the atmospheric boundary layer rolls, the signatures of which become more pronounced in the absence of strong tidal flows.

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“…Satellite-borne remote sensing techniques, which measure surface winds over the water surface, provide a unique opportunity for observing wind fields in coastal areas and open oceans. Such datasets have been widely used in the application of wind resource estimation and wind power mapping in different offshore regions such as the northern European coasts (Remmers et al, 2019;Hasager et al, 2011;Badger et al, 2010;Christiansen et al, 2006;Hasager et al, 2002), St. Lawrence River in Canada (Beaucage et al, 2007;Beaucage et al, 2011), southeastern Asia (Zaman, et al, 2019), the southeastern Brazilian coasts (Pimenta et al, 2008) and the south China Sea (Chang et al, 2015). This research focuses mainly in the resource perspective of offshore wind power aiming to provide a high spatial and temporal offshore wind assessment using remote sensing wind-products and characterizing the wind variability offshore the northern coasts of British Columbia (BC).…”

Section: The Research Focus 1introductionmentioning

confidence: 99%

Assessing offshore wind power resources in British Columbia

Bakri¹

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Wind resources are investigated and estimated offshore of the northern and central coasts of British Columbia, Canada. Remote sensing-based wind speed observations from a Synthetic Aperture Radar (SAR) mounted on the Canadian RADARSAT-2 satellite are used for mapping offshore winds. In addition, in-situ wind speed observations extracted from several buoys distributed in the study region are used to analyze the temporal and spatial wind speed variations in relation to wind power generation. Sustained winds above several wind turbine thresholds are analyzed and values of 50-yr and 100-yr return extreme wind speed levels are calculated. The wind variability analysis suggests few interruptions to power generation by either very low wind speeds or extreme wind speed events with high spatial variability between offshore areas and sites located within the coastal mountains. The SAR wind speed fields are characterized by a high spatial resolution but cover a period of less than 2.5 years with a random temporal availability. The SAR fields are extrapolated to reanalysis long-term wind fields that are available over a climatological time period with a sub-daily temporal resolution but a coarse spatial resolution. The extrapolation procedure is developed by applying a statistical downscaling model and a bias-based correction method. Wind fields from both methods are validated against the in-situ observations from buoys. The extrapolated wind fields are used for mapping offshore winds by creating a robust wind climatology that represents the mesoscale wind variance as well as the diurnal wind variability. This wind climatology is used to calculate the wind statistics and power density, in addition to estimate offshore wind resources. Viable areas for wind power development are defined by using high resolution bathymetric data and considering the general environmental and ecological constraints in the region. The estimated offshore wind resource energy using only theiv determined viable areas is found to resemble a large portion of the current total power generation in British Columbia. Most suitable areas for offshore wind farms are determined by developing criteria based on a combination of the turbine tower technology, water depth zoning and power density values.

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Wind assessment in a coastal environment using synthetic aperture radar satellite imagery and a numerical weather prediction model

Cited by 24 publications

References 35 publications

Learning-Based Emulation of Sea Surface Wind Fields From Numerical Model Outputs and SAR Data

Learning-Based Emulation of Sea Surface Wind Fields From Numerical Model Outputs and SAR Data

Atmospheric boundary layer rolls: Quantification of their effect on the hydrodynamics in the German Bight

Assessing offshore wind power resources in British Columbia

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