OceanPredict'24

Session 5

Impact of observations on forecasting systems
Session 5.4

Session 5.1 Machine learning

G. Martinez Balbontin - Mercator Ocean International Optimizing Global-Scale Seasonal Marine Biogeochemical Forecasting with Compact Neural Networks

P. Heimbach - UT Austin Differentiable Programming for hybrid ocean data assimilation and machine learning

A. Pesnec & H. Bull - Amphitrite Integrating SWOT data into a deep learning model for real-time high-resolution prediction of ocean surface currents

C. Amadio - OGSIntegrating BGC-Argo predicted profiles via Convolutional Neural Networks into the Data Assimilation of the Copernicus Mediterranean biogeochemical model

A. Garcia - ICM-CSICModel-Based Feasibility of Using Data-Driven Techniques to Reconstruct Ocean Interiors from Surface and In-Situ Data

D. Horemans - Virginia Institute of Marine Science Evaluating the prediction skill of correlative estuarine species distribution models trained with mechanistic model output

Session 5.2 Data assimilation

C. Barron - U.S. Naval Research Laboratory Validation and assimilation of satellite sea surface temperature to characterize sub-mesoscale features in assimilative ocean and coupled earth system prediction models

F. Counillon - NERSCHybrid covariance super-resolution data assimilation

T. Singh - NERSCSupermodelling towards improved climate prediction

J. D'Addezio - U.S. Naval Research LaboratoryOcean Data Assimilation Towards Submesoscales

S. Ohishi - RIKENDeterministic and Ensemble forecasts of Kuroshio south of Japan

A. Moore - University of California Santa CruzWeak Constraint 4D-Var Data Assimilation in the Regional Ocean Modeling System (ROMS) using a Saddle-Point Algorithm

Session 5.3 Observational systems

S. Kido - JAMSTECPreliminary results of SynObs Flagship OSEs–Assessments on impact of satellite altimetry versus Argo profiles– 

G. Smith - Environment and Climate Change CanadaImpact of Observations on ECCC's Global Ocean Analysis, GIOPS

M. Gharbi Dit Kacem - OGSRetrieval of Biogeochemical Properties in Marine Waters Using a Newly Introduced Inversion of the Three-stream Irradiance Model 

E. Remy - Mercator Ocean InternationalObserving and assimilating total surface velocities: Challenges and perspectives with the ODYSEA mission 

F. Gasparin - IRD-LEGOSIdentifying spatial and temporal oceanic scales constrained by existing and future observations 

C. Gourcuff - Euro-Argo ERICEuropean contribution to the OneArgo array 

Session 5.4 Impact of observations on forecasting systems

B. King - National Oceanography CentreOneArgo – Evolving and extending Argo’s missions and data delivery. Achievements, status and outlook 

M. Benkiran - Mercator Ocean InternationalImpact of SWOT Data in a global high-resolution analysis and forecasting system

J. Waters - Met OfficeInvestigating the potential impact of assimilating total surface current velocity data in the Met Office’s global ocean forecasting system 

H. Ngodock - U.S. Naval Research LabComparison of two ways of assimilating SWOT observations using NCODA-4DVAR 

C. Tanajura - UFBA and REMOOSSEs with SWOT and Gliders in the Southwest South Atlantic with HYCOM+RODAS 

L. Cucurull - NOAADevelopment of Observing Quantitative Assessment Capabilities for Ocean Applications at NOAA 

Session 5.5 Biogeochemistry

T. Wakamatsu - The Nansen CenterMitigating Phytoplankton Phenology Mismatches in the Arctic Ocean Biogeochemical Reanalysis 

H. Morrison - BSHIncorporating the Framework for Aquatic Biogeochemical Models (FABM) into the ocean modelling framework NEMO v4.2.1 

R. Sauzede - CNRSEnhancing BGC-Argo Chlorophyll-a Data Quality and Uniformity Using Machine Learning 

L. Macé - University of LiègeContribution of radiative transfer modelling to a stochastic biogeochemical forecasting system in the Black Sea L. Macé - University of Liège

E. Douglass - U.S. Naval Research LabImproving Forecasts and Nowcasts at High Latitudes 

Q. Hyvernat - CNRS/Mercator Ocean InternationalOptimisation of biogeochemical model parameters using BGC-ARGO profiling floats 

Session 5.6 Digital twins

T. Finn - CEREA, École des Ponts ParisTechData-driven sea-ice modelling with generative deep learning 

I. Larroche - AmphitriteHigh-resolution operational forecasts of ocean surface currents for optimal ship routing 

J. Skakala - PMLTracking harmful algae blooms in the western English Channel using digital twins

C. Durand - ENPCFour-dimensional variational data assimilation with a sea-ice thickness emulator

Y. Drillet - Mercator Ocean InternationalEDITO-Model Lab: towards the next generation of ocean numerical models

G. Forget - MITDigital Twins for Ocean Robots

Session 5.7 Improvements of operational systems

K. Mogensen - ECMWF Effects of atmosphere and ocean horizontal model resolution on upper ocean response forecasts in four major hurricanesEffects of atmosphere and ocean horizontal model resolution on upper ocean response forecasts in four major hurricanes

F. YU - NMEFC Towards a lightweight global ocean forecasting system: Development of “Mazu” Ocean Models in China

M. Sprengel - Deutscher Wetterdienst Ocean Data Assimilation in the Earth System Model of the DWD

J. Kim - KIASPDevelopment and initial performance evaluation of the KIAPS weakly-coupled atmosphere-ocean-sea ice data assimilation system

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