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Advanced Modeling & Observations of Dynamic in the Adriatic Sea Dr.sc. Ivica Janeković, IRB, Zagreb [email protected]

Napredne Metode u Istraživanju Dinamike Jadrana

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Page 1: Napredne Metode u Istraživanju Dinamike Jadrana

Advanced Modeling & Observations

of Dynamic in the Adriatic Sea

Dr.sc. Ivica Janeković,

IRB, Zagreb

[email protected]

Page 2: Napredne Metode u Istraživanju Dinamike Jadrana

Outline

Intro

Adriatic Sea

Observing Adria

New Methods

Empirical

Theoretical

Combination

Examples

Page 3: Napredne Metode u Istraživanju Dinamike Jadrana
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Physical oceanography

Remote sensing – Sea Surface

– Active and passive sensors, ASAR, AATSR

– Repeat cycle / spatial resolution

– HF Radar

Fixed stations/platforms – in situ

– ADCP, CTD, XBT, samples

– Profiler, glider, drifter…

Theory

– Modelling – approximation

– Data Assimilation – Combination

Page 5: Napredne Metode u Istraživanju Dinamike Jadrana
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Chavanne, C., I. Janeković, P. Flament, P.-M. Poulain, M. Kuzmić, and K.-W. Gurgel (2007), Tidal currents in the northwestern Adriatic: High-frequency radio observations and numerical model predictions, J. Geophys. Res., 112, C03S21, doi:10.1029/2006JC003523.

HF radars Goro Ravenna Pesaro

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ADCP database

netCDF?

opendap?

Thredds?

ownership?

Page 11: Napredne Metode u Istraživanju Dinamike Jadrana

Center for Marine Research

Ruđer Bošković Institute (since 1891)

Oceanographic Data Base (since 1972)

40 years of data time series

monthly sampling, 5-6 depths

3 mayor profiles: Rovinj - Po delta

North profile

Western Istrian coast - Kvarner

Chlorophyl a Chl a

Temperature, Salinity T, S

Inorganic Nutrients NO2, NO3, NH4, PO4, Si(OH)4

Dissolved Oxygen O2

Cyanobacteria

Heterotrophic bacteria

Nanoflagelates

Picoeukariotes

Primary production

Bacterial production TdR, Leu

Page 12: Napredne Metode u Istraživanju Dinamike Jadrana

Modelling

Approximation of real state, numerics, resolution, …

Forecast of future state, new knowledge

Virtual laboratory

Global -> Local Nesting (MyOcean boundarys)

Data Assimilation:

Combining observation space with model -> right way

Give answers where and how to observe

Sensitivity analysis

Janeković, I., and B. S. Powell (2012), Analysis of imposing tidal dynamics to nested numerical models, Cont. Shelf Res., 34, 30–40, doi:10.1016/j.csr.2011.11.017.

Page 13: Napredne Metode u Istraživanju Dinamike Jadrana

Zagreb, HAZU Zagreb, HAZU

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Zagreb, HAZU Zagreb, HAZU

Nesting 2km -> 500m

Stong bura wind case

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Data Assimilation

In a start they were primitive, OI,...

Direct consequence of met. development

New, advanced 3D, 4D-Var

ROMS 4D-Var the most advanced OM

Additional benefits:

Combination of model and obs, using evolving errors in time

Optimal observation setup

Sensibility of dynamics w/r to observations, obs sens.

Predictability, persistence, non-linearity of the system

Corrections to the initial, boundary, forcing

Powell, B. S., I. Janeković, G. S. Carter, and M. A. Merrifield (2012), Sensitivity of internal tide generation in Hawaii, Geophys. Res. Lett., 39,L10606, doi:10.1029/2012GL051724.

Page 17: Napredne Metode u Istraživanju Dinamike Jadrana

ROMS 4D-Var demystified

Time evolving error cov. matrices (R, D)

Assimilation of data at exact time and space (4D)

Variational methods for minimizing “cost” function

J = Jo + J

b , δz control vector (assembled of IC, BC, F)

Jo = ½ (G*δz -d)T R-1 (G*δz -d)

Jb =½ (δzT D-1 δz)

∂J/∂δz = 0

δza = [ D-1

+ GT R-1 G ]-1 GT R-1 d

100*300*30*7*8 = 1,68 mil points / time step

Janeković, I., B. S. Powell, D. Matthews, M. A. McManus, and J. Sevadjian (2013), 4D-Var data assimilation in a nested, coastal ocean model: A Hawaiian case study, J. Geophys. Res. Oceans, 118, doi:10.1002/jgrc.20389.

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Satellite SST, SLA HF radials, CTD, ADCP, Glider

Satellite SST CTD, ADCP

Janeković, I., B. S. Powell, D. Matthews, M. A. McManus, and J. Sevadjian (2013), 4D-Var data assimilation in a nested, coastal ocean model: A Hawaiian case study, J. Geophys. Res. Oceans, 118, doi:10.1002/jgrc.20389.

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20 NLM

Perturbations

20 NLM realizations

20 TLM

Perturbations

CO

RE

LA

TIO

N

20 TLM realizations

Linearity of the Adriatic Sea?

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Do we need near real time data?

Correlation btw 20 non-linear and linearized model ensembles

ZETA TEMP SALT EAST WEST

Days since start of simulation

Page 22: Napredne Metode u Istraživanju Dinamike Jadrana

30

5

1-1-2012 19-3-2011 25-6-2011 24-9-2011

15-2-2012 15-2-2012 15-2-2012 15-2-2012

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39

30

1-1-2012 19-3-2011 25-6-2011 24-9-2011

15-2-2012 15-2-2012 15-2-2012 15-2-2012

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TEMPERATURE

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SALINITY

FREE SURFACE

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Ocean interior (and Adriatic) is undersampled

Have to be careful where and what and how to measure

New techniques used to point ↑

Combination of obs and theory is win-win

Provide truth, correct model, produce new dyn. response

Extend information, explain dynamics

Point to the unresolved regions where to measure

Observation sensitivity and valuable contribution

Applied studies, more realistic, benefit

Discussion & Conclusion…