Anton Eliassen, Lars Petter Røed and Øyvind Sætra Numerical Ocean Weather Prediction at the Norwegian Meteorological Institute: Safeguarding life, property and the environment at sea Anton Eliassen, Lars Petter Røed and Øyvind Sætra Presented at the opening of the Mohn-Sverdrup Center, Bergen Oct. 20, 2004

The met.no mandate Provide: Broadcast: Services for military purposes: Financed by the Norwegian Government to safeguard life, property and the environment at land and sea. Provide: general weather forecasts, climate information, warnings of severe weather to the general public and forecasts for search and rescue operations Broadcast: Using Norwegian Broadcasting Company (public radio/TV), Using Internet and other generally available distribution channels Services for military purposes: In case of a war, the met.no services are transferred to military command

But safety at sea is more than meteorology …. stand-by emergency services ”Prestige” Oil drift and combatment Search and rescue

... more than meteorology …. monitoring and forecasting of the transport and dispersion of nutrients, algae, fish larvae, contaminants 20.10.2004 18UTC Diatome concentrations Chlorophyll concentrations

... more than meteorology …. safeguarding life and property regarding: Ship traffic Offshore operations Military operations Fisheries Recreation, leisure yachting

This requires timely predictions Surface conditions: Waves Water level Ice drift Ice concentration Ice thickness Profiles with depth of Temperature Salinity Currents Current and salinity at 10m depth Wave spectrum

met.no’s NOWP system Atmosphere Ice Ocean circulation Waves Drift Oil OSI-SAF conc. Atmosphere OSI-SAF SST Ice ECMWF MI-IM Concentration Thickness Drift Ocean circulation HIRLAM Wind Temperature Pressure Clouds Precipitation Humidity Wind stress Heat flux MICOM HYCOM MI-POM Water level Temperature Salinity Current Turbulence incl tides Typical suite of operational forecast models, with emergency drift models embedded. Note the variety of models giving the same type of information – typical weather forecasting procedure. Drift forecast service must give access to all and default to the presumed best. NWP models are the backbone of the system. HIRLAM run in-house, main model for forcing ocean models . ECMWF data used for forcing HIRLAM and for direct forcing of ocean models Ocean circ.: MI-POM is the current operational code, HYCOM being implemented as alternative / potential replacement. Waves: WAM Ice model is 2-way coupled to MI-POM, run on basin-scale model of Arctic. Ecosystem: module developed by IMR in Bergen All the greenish boxes are models run routinely, 1-2 times daily, with forecasts out to as much as 10 days. There are also two stand-by models shown in reddish: Drift: actually 2 models, one for ships and one for small floating objects. Oil: 3D with novel bottom blowout module. All 3 application models are cooperative efforts: DnV, USCG, IMR The various arrows show how the forcing fields are delivered between the models. In addition, there is river forcing, which has become increasingly important with the advent of ecosystem modeling. This is a limited amount of data assimilation in the current system. Waves Drift Oil Ecosys. MI-WAM Dir. spectrum Sign. Wave height Mean period & dir. Peak period & dir. Ship/Leeway Trajectory Probability Drag charact. OD3D Dispersion Drift (3D) Weathering DeepBlow IMR / MI Nutrients Oxygen Sedimentation Flag. & diatom Rivers Volume flux Nutrient loads Altimeter Hs

Forecast samples Hs 20 Oct. 2004 18UTC SSH 20 Oct. 2004 18UTC

Forecast samples SST 20.10.2004 18UTC SSC and SSS 20.10.2004 18UTC

How good are the predictions? Validation of Arctic20 against SST from satellite imagery (OSI-SAF) OSI-SAF: SST 168 hr composite 15.10.2004 00UTC Arctic20: SST 18.10.2004 00UTC

How good are the predictions? Validation of SST in terms of the mean RMS error (oC) Mean score Jul-Sep 2004

SAR Search and Rescue: Leeway model Split search area due to bimodal leeway (split is exaggerated for illustration) Individual trajectory Initial cloud shows uncertainty in Last Known Position (LKP)

Leeway bimodality Experiments produce two stable drift directions, left and right of downwind, for same type of object. NB! Jibing not important. In practice, we cannot know which side will be “selected” by the object.  leeway model must produce both trajectories

POMS: Pilot Ocean Monitoring System http://moncoze.met.no

OSI-SAF SST Lista Sep. 28, 2000 01:15 UTC NORWAY DENMARK Note eddies along southern tip of Norway Note also filament north of Denmark DENMARK

Snapshot of surface currents Sep. 28, 2000 00 UTC Kristiansand Lista ● ● Norwegian Meteorological Institute has today a forecasting system for the Skagerrak area employing MI-POM. MI_POM is met.no’s version of the Princeton Ocean Model, which I am sure most of you know. Anyway, this is a 3-D numerical ocean model with sigma coordinates in the vertical and it is based on primitive equations. ●This is a part of the model domain, in our model with 4km mesh size. This model is nested within a much larger model domain with 20 km mesh size. We use Hirlam data as atmospheric forcing and produce 60 hours prognosis. Here, we have picked a day with high mesoscale variability, more specific the 28’th of Setpthember 2001. The arrows show here the surface current. As you can see, this is a day with a lot of mesoscale activity. Note especially the eddies around the Lista area. ● Reference point

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