Presentation is loading. Please wait.

Presentation is loading. Please wait.

DMI forecasting system for Baltic-North Sea (DMI BSHcmod), and also for Greenland, NW Shelf (Hycom) Jens Murawski (DMI)

Similar presentations


Presentation on theme: "DMI forecasting system for Baltic-North Sea (DMI BSHcmod), and also for Greenland, NW Shelf (Hycom) Jens Murawski (DMI)"— Presentation transcript:

1 DMI forecasting system for Baltic-North Sea (DMI BSHcmod), and also for Greenland, NW Shelf (Hycom) Jens Murawski (DMI)

2 Agenda DMI BSHcmod: An operational Baltic and North Sea model –History –Model features –Quality –Ongoing developments An introduction to –DMI drift model –N. Atlantic/Greenland model – HYCOM –Wave model - WAM

3 2000: BSH kindly provided its operational model BSHcmod 2000: BSHcmod run operationally in DMI with DMI forcing 2003: Information system – http://ocean.dmi.dk http://ocean.dmi.dk 2004: SST assimilation (ODON) 2005: MERSEA Baltic V1 2006: MERSEA Baltic V2 History of BSH/DMIcmod 1998: DMI operational oceanography section 2005: DMI Marine Forecasting Centre 2006: DMI Centre of Ocean and Ice 2006: Marine Ecological Modelling Centre (Jointly by DMI-NERI- DIFRES) Organisation of the DMI

4 Current model features

5 Forcing Open boundary: T/S: dynamic boundary from POLCOMS Monthly climatology Water level: Tides, surge, baroclinically corrected water level River runoff: realtime data from (SMHI, BSH) & adjusted climatology for the rest Meteo. Forcing - DMI-Hirlam 15km/5km, 60/54 hours prognoses

6 Current DMI 3D ocean model: BSHcmod BSHcmod, provided by BSH 2000, developed by DMI 3 nested layers: 6-3-0.5nm Coupled ocean-ice two way nesting Flooding-drying 50 layers (8-2-2…-50) Twice daily, 54h forecast Daily river runoff + climatology NEA: 6 nm, NS/BS: 3 nm, IDW: 0.5 nm

7 DMI BSHcmod: new features 1.) Surface heat flux: Windspeed & Airtemp. dependent 2.) Including vertical Penetration of short wave radiation 3.) Vertikal mixing: K-epsilon, k-omega model adapted 4.) Horizontal mixing: a new term to control div/conv. 5.) Surface momentum flux: currents dependent 6.) Simplified & Full ensemble Kalman filter for SST assimilation

8 Surface heat flux (cont.): SST Change heat flux coeffcient, Kara et al. (2000) CL= f (W,Ts–Ta) Bias [Cº] Comparisson with ODON data for 2001 SST Bias improves ~ 0.1°C

9 Vertical mixing Improving diffusivities of momentum, heat and salt k-ε turbulence model e.g. Axell, JGR (2002) stability functions in terms of shear and gradients of S and T Canuto et al., JPO: Part I (2001) + Part II (2002)

10 k-ω based mixing scheme: salinity in Great Belt (preliminary) Ref. k-ω DMI cmod validation Days since 2000/07/15

11 Bottom temperature: FYN6700053 Old k-ω New k-ω spring summer autumn winter

12 Wind friction Wind induced shear stress is current velocity dependend k-ω surface boundary conditions are z 0 dependend z 0 is either const. or sig. wave heigth dep.

13 New: currents dependend wind friction and wave height dependend roughness length Southern Baltic Sea color: windspeed arrows: currents (magnitude and direction) color: currents (magnitude) arrows: currents (direction)

14 Quality

15 Water level forecasts, peak error DK stations, 2002-2004, 4 models Cmod:<10% in 2004 MIKE 21: 2D FD; official storm surge model; ~18% 2002-2004 MOG2D: 2D FE, from 2003 Staumod: 2D version of Cmod, no stations in IDW MOG2D 2004MOG2D 2003 Staumod 2003 Cmod 2003 Mike21 2003 Staumod 2002 Cmod 2002 Mike21 2002 Mike21 2004 Cmod 2004 Staumod 2004

16 Data assimilation, SST: Larsen et al., JMS 2006 DA model runs for 2001 by assimilating SST from different products (NOAA 12, 14, 16) ControlDA Bias0.780.07 RMSE1.200.64

17 psukts BSH/DMIcmod results Surface currents Salinity

18 Comparison of satelite SST with simulated temperature values

19 Model-data comparison: Drogden Buoy station, T/S at 3.6m (inflow signals) Temperature, year 2004 Salinity, year 2004 Observation (red) Model data (blue) Bias(z) = -0.7,..., -0.3 Std(z) = 0.6,..., 1.1 Bias(z) = -0.03,..., 0.8 Std(z) = 2.0,..., 2.6

20 Surface Temperature 1.0m Bottom Temperature 36.6m Bottom Salinity 36.6m Surface Salinity 1.0m NOVANA station VSJ 20925 (southern Kattegat)

21 North Atlantic/Greenland modelling depth maps: Etopo2 0.5deg-10km res. 22 layers data assim.: sst 2x/day, 66h ECMWF 6hly forcing

22 Outline 1) Ocean modelling: HYCOM setup for the Atlantic and Arctic Ocean (~50 km) Nested around Greenland (~10 km) 2) Drift modelling for 2005: Random walk diffusion Normal distributed in time (40 days window), 1000 particles 2a) Drift as eggs: On top of the Irminger Water component (use fixed density), First feeding larvae after 300 degree-days 2b) Drift as pelagic larvae Surface: 20m, 40m, 60m (use fixed depth) Exponential temperature dependent increase in weight: Wi~Wi-1*exp(T) Settling when reaching 210 mg dry weight

23 SST monthly meanSSS monthly mean Profiles at Diskobay position Operational forecast for Greenland Currents Salinity Temperature Mixed layer deepness Ice thickness and concentration

24 Drift model Emergency module, used primarily for oil spill Other apllications, floating object, dissolved substance, fish larvae drift, … Circulation model add-on module (HYCOM or BSHcmod)

25 The Fu Shan Hai collision, May 2003 The vessel sank at 68m depth, and began to leak fuel oil The oil rises as a plume from the sunken ship

26 10 day simulation of Fu Shan Hai accident

27 Wave model WAM Cycle4 depth maps: Etopo5 sea ice: NCEP 0.5deg-10km-2km resolution 4x/day, 60h

28 Ongoing developments

29

30 Wave-current interaction DMI BSHcmod-WAM coupling –Two-way interface ready –WAM with current refraction running pre- oprational –More coupling mechanisms to be added: (Wave induced mixing)

31 Wave-Current interaction, 01.10.2007 12:00 Sign. wave height up to 7mΔ(sign. wave height) = 1m,…,1.5m Windspeed up to 20m/s Differences of about ± 1m around Greenland

32 SPM modelling GKSS SPM model is coupled with DMI BSHcmod Wave influenced vertical exchange of SPM Horizontal advection as passive tracer (Cmod)

33 Ecological modelling DMI BSHcmod-ERGOM coupling –Framework ready, in model calibration –Assimilating satellite chl-a –Operationalisation

34 Sediment dynamik influences the nutrients concentration in the water column. Sediment concentration regulates the penetration depth of light ERGOM

35 HTTP://OCEAN.DMI.DK Thank you!!


Download ppt "DMI forecasting system for Baltic-North Sea (DMI BSHcmod), and also for Greenland, NW Shelf (Hycom) Jens Murawski (DMI)"

Similar presentations


Ads by Google