Swell (wind) fields obtained from Satellite Observation CLS Christian Ortega - cortega@cls.fr Mission blue Journées Annuelles des Hydrocarbures 2013
Purpose Develop new methodologies to obtain swell (wind) statistics and forecasts from radar SAR satellites «deep» (>200m) to coastal waters Olivier Clasperis
Content Context & Objectives Swell from SAR images: principle & Fireworks Coastal swell field emulation: pilot in Iroise sea Improving «deep» sea swell product Conclusion & Perspectives
Collected Localisation Satellite (CLS) overview Created: 21 April 1986 Core activities: Commercial operation of satellite systems for positioning, data collection, ocean observation and surveillance Developing added-value applications and services based on satellite remote-sensing data; Sectors of activity: environmental surveillance, sustainable management of marine resources, maritime security, oil and gas; Shareholders: 2012 income: 79 M Page 4
A CITEPH project Reference: CITEPH-04-2009 «Emulateur d état de mer (vagues, vents et courants) à haute résolution en zone côtière à partir d image satellite» Leader: CLS Partner: IFREMER Sponsors: Total, Technip CITEPH budget : 300k 13-oct-2013 Journées Annuelles des Hydrocarbures 23-24 octobre 2013
Why use satellite data? (versus existing methods) Long swell can severly impact operations at sea, but Some swell are not well represented by wave models Their propagation is poorly known in coastal areas Radar SAR satellite observations provide: High resolution (few km) swell (and wind) data Independant from the models In deep ocean and coastal area Satellite approach is likely to improve information on swell, safety & minimize costs Oct-2013 Référence CITEPH: CITEPH-04-2009
O&G & RME target applications Design (O&G RME) Refined swell characteristics for structure design (statistical studies) In particular storms & cross swells Weather sensitive Marine operation (O&G RME) Sea state statistics to prepare operations at sea Swell forecast in support of the marine operation Renewable Marine Energy (RME) Identify best sites and assess energy potential 03-avr-2013 Référence CITEPH: CITEPH-04-2009
Plan Context & Objectives Swell from SAR images: principle & Fireworks Coastal swell field emulation: pilot in Iroise sea Improving «deep» sea swell product Conclusion & Perspectives
Extracting swell (& wind) from sea roughness Short centi/decimetric waves: Roughness source, distributed along the long wave profiles. extraction of wind (V/D) Long waves (swell Wavelenght>150 m) : Modify the distribution of the short waves and the local incidence angle. Periodical signal detected on the SAR image can be related to the swell. extraction of swell spectra: H s, λ p, T p, D p Medium waves: Produce an average «noise» on the SAR image wind sea (waves) cannot be observed
Radar SAR satellite & waves Satellite SAR is the only instrument able to provide global swell spectra measurements SAR provides swell directional spectra (H s,t p,λ p,d p ) via 2 modes: - Wave mode (automatic aquisitions) - default mode in medium to deep waters - images of 10x10 km collected every 100 km - Image mode - acquisition on request OR automatic for «super-sites» (Iroise sea, Aghulas currents, Gulf Stream, Northern Europe )
SAR image & wave acquisition modes (ENVISAT) Application «deep» sea: swell tracking Application coastal wave emulation
Radar SAR space missions SAR satellite mission Period Band Owner ERS-2 03/1995-07/2011 C ESA ASAR (ENVISAT) 03/2002-05/2012 C ESA Sentinel 1 (2) 2014 2015 C ESA CFOSAT 2014-2015 C CNSA-SOA-CNES
CLS involvement in Waves and radar SAR data Responsible for the calibration/validation of SAR wave mode data for ENVISAT mission (2002-2012) + production for Sentinel-1 satellites (wave, wind ) The (CLS) Vigisat acquisition station, in Brest, used since 2011 (special negociated agreement with ESA), for the reception and processing of ESA SAR satellites SAR data received in near real time at CLS Dedicated processing systems developed for Vigisat Consequent data catalog available IFREMER is responsible for the mission wave mode archiving re-processing (ERS- 1/2 and ENVISAT missions).
Swell from SAR wave mode: Fireworks
Swell from SAR wave mode: how it works Wind field rebuilding Detection of storm sources and refocalisation of observations made along satellite track. Synthesis of propagated swell field along great circles in all directions and until reaching coastline.
«Deep» ocean swell products cross swell maps Statistical maps processed over a year Risk area for navigation Cross-swells: 2 energetic (Hss>1m) swell systems crossing at a significant angle (>45 )
«Deep» ocean swell products virtual wave buoy Extract swell field on a given spot Time evolution of H s, λ p, T p, D p for each swell system Total swell wave height for all systems Provide wave spectra & statistics Provide 48 to 72 hours forecasts Interest Early warning for energetic waves Swell climatology for RME resources, Offshore structure design
Plan Context & Objectives Swell from SAR images: principle & Fireworks Coastal swell field emulation: pilot in Iroise sea Improving «deep» sea swell product Conclusion & Perspectives
Methodology - Principle For a given swell (direction, peak period, Hs), the propagation of swell fields is mainly determined by: bathymetry ocean currents Phenomena can be reproduced There is a relation between the measure of a swell field in a given spot and its high resolution spatial distribution
Methodology Coastal swell emulation, 3 steps 1. Build databases: low resolution (BR, in-situ observations or model data) and high resolution (HR 5 km, SAR data) 2. Learning: associate low (in-situ) and high resolution swell fields 3. Produce (emulate) HR swell fields (Hs, Dir, wavelength) using low resolution data (in-situ waverider or wave model) & weighted averages Y HR( p ) = F Y W p ( ) { X BR, k, X HR, k} BR
SAR HR database over Europe & swell map example (1) 0 600 1200 1600 WSM image catalog
Pilote site & in-situ data Iroise sea Directionnal wave buoy : 02911 - Les Pierres Noires Location : 048 17,42'N - 004 58,1'W Depth : 60 m One directionnal spectra every 30 min (1488 for 31 days) In service since 2008-2009
Impossible d afficher l image. Swell spectral analysis & partition (1) Extracting swell systems from SAR image database (181 images)
A single swell system A single swell system
Two swell systems cross seas Two swell systems cross seas
Linking together the same (SAR) swell systems (1) Extraction of different swell systems and their reference wavenumber and direction For each grid point from fixed geographic grid Retro propagation of wavevector to evaluate the incident swell wavenumber and direction. 3 3 2 This retro propagation is based on local wavenumber and direction from SAR grid partitions 1
Classifying and gridding swell systems (1) Characterisation of the swell situation from spectral partitions: two swell systems here Fixed geographic grid SAR image grid Isle Swell field partitions restituted on a SAR grid. Vectors indicate direction and wavelength. Two swell systems are indicated by different colors (red & blue).
Linking the (SAR) swell systems to the swell at the buoy (2) Learning step (2) Waverider buoy reference wavevector (SAR) swell reference vector Isle Associating the (SAR) swell system on a fixed geographic grid and associated reference incident wavevector at the waverider buoy
Emulation process (3) Once swell spectra partitionning and association between wave buoy data and HR (SAR) swell systems (done in steps 1 & 2): Search in the learning database the sets of SAR swell systems with a wavenumber and direction at the buoy location that are close to swell wavenumber and direction measured by the buoy. For each swell system measured by the buoy, an (HR) swell field is produced at each grid point when more than one SAR observation matches the givenwave buoy partition. The wave field is produced by averaging all the associated SAR swell fields contained in the database & propagated over the coastal area (time of occurrence)
Swell propagation : calculating time of occurrence (3) Propagating the swell emulated at the reference point to the coastal area Low resolution incident wavevector at the buoy Estimation of the propagation time between HR observations ( ) to LR information ( ). D =Cg.t Cg is the the group velocity of the reference wavevector. Isle
Results Bathy. (left) & density of SAR images (right) All swell systems
Results Bathy. (left) & density of SAR images (right) Example of a frequent swell : W direction, 250 m wavelength
Results Bathy. (left) & dominant wavelength (right) Example of a frequent swell houle: direction W, 250 m wavelength
Results Bathy. (left) & significant swell (right) Example of a frequent swell houle: direction W, 250 m wavelength
Results Bathymetry (left) & wave direction (right) Example of a frequent swell houle: direction W, 250 m wavelength
Hs comparison at les Pierres Noires buoy RMS diff : 40cm RMS diff : 51cm Physical tidal current taken into account Offshore (W005,6 N48,4) Statistical Tide level within 2m RMS diff : 45cm Coastal (Les Pierres Noires)
Results: emulability tradeoff Direction within 20, wavelength within 25m, all tides : 84.6% emulability Direction within 20, wavelength within 25m, tide level within 2m : 62.4% emulability RMS difference between emulated and buoy Hs from 0.49m to 0.45m when tide level within 2m RMS difference of 0.40m between WW3 and buoy Limitation: short wavelength (100-200m), dataset size Tradeoff between accuracy of emulated field and percentage of conditions than can be emulated.
Plan Context & Objectives Swell from SAR images: principle & Fireworks Coastal swell field emulation: pilot in Iroise sea Improving «deep» sea swell product Conclusion & Perspectives
Swell at deeper waters (>200m) - Fireworks Satellite Observations L2 Swell field rebuilding Publications: R. Husson, F. Ardhuin, F. Collard, B. Chapron, et A. Balanche, «Revealing forerunners on Envisat s wave mode ASAR using the Global Seismic Network», Geophys. Res. Lett., vol. 39, no. 15, p. L15609, août 2012. R. Husson, Thesis manuscript, Development and validation of a global observation-based swell model using Synthetic Aperture Radar operating in wave mode, 2012 Swell fields Propagated observations Synthetic fields Filtering and gridding Virtual buoys Fireworks
Enhancing fireworks using «coastal» SAR image dataset Without using data from image mode Using data from image mode Graphical data, global view
Swell «deep» waters Fireworks - Wavelenght Synthetic fields Wavelengths Propagated data Filtered and gridded data Graphical or scientific output formats(netcdf) Global view, available for each swell field
Swell at «deep» waters Fireworks (Hs) Synthetic fields - Hss Propagated data Filtered and gridded data Graphical or scientific output formats(netcdf) Global view, available for each swell field
Swell at «deeper» depth Fireworks wave & wind
Results «Deep» ocean Standard errors SAR/in-situ/model data inter-comparison Observations - Level 2 Champs Synthetic fields - Level3
Results «Deep» ocean Standard errors SAR/in-situ/model data inter-comparison Significant height[cm] Periode [s] Direction [deg] SAR data - level 3 < 30* 0.4 11 Wavewatch III model < 30** 0.8 12 *Performance dependent on SAR sampling quality criteria ** Performance dependent on storm localization localisation (Energy dissipation problem in models)
Plan Context & Objectives Swell from SAR images: principle & Fireworks Coastal swell field emulation: pilot in Iroise sea Improving «deep» sea swell product Conclusion & Perspectives
Conclusion & Perspectives Coastal swell Coastal Swell: from R&D to operational Concept is validated Work with operators (Total, Shell, Statoil ) to implement emulators in severe areas such as NW Ireland, Shetlands, Offshore Norway. Possibly through a JIP. Historical / Statistical studies then operational forecasts «Deep» sea Swell: operational Clear advantage on model in storm areas Historical / Statistical studies now Swell forecasts available after Sentinel-1 satellite launch (2014)
Analog dev. for wind: Image mode product/statistics Exemple de rose des vents pour Horns Rev calculée à partir du catalogue d images ASAR disponibles à CLS: Comparaison avec 6 ans de sortie ECMWF SAR 1x1 km ECMWF (0.5 ) 10-minute meteorological observations
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