The upper ocean dynamics at high-resolution: applications and challenges

Transcription

1 The upper ocean dynamics at high-resolution: applications and challenges R. Fablet Telecom Bretagne, département SC UMR LabSTICC/TOMS

2 Satellite ocean sensing: context A variety of sensors and modalities narrow-swath sensors, eg altimeters SAR sensors, eg wind field wide-swath sensors, eg temperature IMT, November

3 Satellite ocean sensing: context From low-resolution. to high-resolution SSH SAR Chl-A SST VIGISAT Low-resolution: ~25km (0.25 ) eg, Altimetry (SSH), Radiometer (SST) Key issue High-resolution: 1-4km (0.01 ) or < 1km eg, SAR (currents), Infrared (SST, Ocean Colour) How to deliver daily HR geophysical field anywhere and anywhen from Québec, 3 October 2013 the irregular space-time sampling of satellite sensors? SST

4 High-resolution ocean sensing: what for? IMT, November

5 High-resolution ocean sensing: what for? Eg, high-resolution monitoring of deepwater horizon spill Collard et al., 2010 IMT, November

6 High-resolution ocean sensing: what for? Eg, understanding physics-to-ecology interactions/transfer Top marine predators track Lagrangian coherent structures, Tew Kai et al., PNAS GPS seabird tracks Need for «coherent» spatial observation scales Short trips Long trips Europa island Ocean dynamics IMT, November

7 High-resolution ocean sensing: what for? Eg, understanding physics-to-ecology interactions/transfer Reconstructing and understanding seabass migration patterns from DSTs, Pontual et al., ICES Symp Fish data DST data (T & D) data Temp. and bathymetry Satellite data MARS3D data IMT, November 2014 Signal processing (tracking model, HMM) 7

8 High-resolution ocean sensing: what for? Eg, impacts of fine-scale physical processes onto ecosystem dynamics (Bertrand et al., Nature Com. 2014) Ocean turbulence creates «small» pelagic oasis for marine life (plankton, pelagic fish, seabirds,.). IMT, November

9 The upper ocean dynamics at highresolution: from an irregular space-time sampling to anywhere and anywhen? A signal processing perspective: challenges and expected contributions 9 IMT, November 2014

10 The upper ocean dynamics at high-resolution anywhere and anywhen? The challenge: an irregular multimodal space-time sampling Space-time satellite sampling Missing data issues with multisensor/multiscale sources 10 IMT, November 2014

11 The upper ocean dynamics at highresolution anywhere and anywhen? The state-of-the-art: model-driven data assimilation Objective: combine a prior ocean model and available observations Operational models: eg, NEMOVAR (ECMWF), Wavewatch III (NOAA) Limitations: Complexity of the numerical resolution Model complexity vs. Model «genericity» 11 IMT, November 2014

12 The upper ocean dynamics at highresolution anywhere and anywhen? Towards data-driven assimilation models: why? HR observations are irregularly sampled in space and time. Archived database of observation and simulation data But. 1) low resolution observations are generally available eg, low-resolution observations 2) we may learn a lot from past observations. eg, high-resolution observations Key objective: learning new multi-scale/multi-modal representations 12 Québec, October 2013 of ocean dynamics from multi-sensor remote sensing archives

13 The upper ocean dynamics at highresolution anywhere and anywhen? Towards data-driven assimilation models: our strategy Database of past multimodal observations and/or simulations New observations Reconstruction of HR dynamics Key objective: learning new multi-scale/multi-modal representations 13 Québec, October 2013 of ocean dynamics from multi-sensor remote sensing archives

14 The upper ocean dynamics at highresolution anywhere and anywhen? Preliminary results (1): Model-free stochastic filters (e.g. EnKF, particle filter) Proof-of-concept validated on chaotic dynamics (Lorentz model) Model-free/data-driven dynamics for the assimilation 14 of geophysical systems Tandeo et al, 2014 Québec, October 2013

15 The upper ocean dynamics at highresolution anywhere and anywhen? Preliminary results (2): deterministic transfer function 15 Learning ECMWF-to-SAR transfer functions Québec, October 2013 for HR wind field emulation He-Guelton et al, 2014

16 The upper ocean dynamics at highresolution anywhere and anywhen? Preliminary results (3): stochastic transfer function LR (AMSR) SST HR (METOP) SST Simulated SST I HR I LR Spectral density HR detail marginals Multifractal Spectrum 16 Learning stochastic LR-to-HR transfer functions Québec, October 2013 for HR SST emulation Fablet et al, 2013

17 The upper ocean dynamics at highresolution anywhere and anywhen? Preliminary results (4): multimodal transfer function Application to a one-year AMSR/SST-AVISO/SSH series SQG-like assumption: local relationships between local SST patches and sea surface currents Learning from joint SST-SSH observations Québec, October 2013 Towards high-resolution sea surface currents 17 from a joint SST-SSH analysis Tandeo et al, 2013

18 The upper ocean dynamics at highresolution anywhere and anywhen? Preliminary results (4): big data challenge Nephelae demonstrator: dedicated architecture to process terabytes-to-petabytes of data Ex.: High-resolution Sea Surface Temperature 200Go daily to be processed to fill cloud gaps Processing time : Months with traditional system 8 hours with Nephelae architecture Towards high-performance architecture for ocean-big-data Québec, 18 October 2013 management and processing Piolle et al, 2013

19 The upper ocean dynamics at highresolution anywhere and anywhen? EMOCEAN project ( ANR grant (French NSF)) Partners: Telecom Bretagne (R. Fablet), Ifremer (B. Chapron), Ocean Data Lab (spin-off, F. Collard), OUC (G. Chen) Main tasks Case-studies Learning multi-modal data-driven representations of ocean surface dynamics Stochastic reconstruction of HR ocean surface geophysical fields from partial observation series High-performance architecture for "realtime" implementation HR composite fields Short-term dispersal scenarii 19 IMT, November 2014

20 Acknowledgements Joint work with B. Boussidi, P. Tandeo, R. Garello (Telecom Bretagne), E. Autret, B. Chapron, L. He-Guelton, H. de Pontual (Ifremer), F. Collard (Ocean Data Lab), A. Bertrand (IRD) Merci pour votre attention