Using IS4DVAR Data Assimilation in the near coastal modeling system

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Using IS4DVAR Data Assimilation Using IS4DVAR Data Assimilation in the near coastal modeling systemin the near coastal modeling system

Ivica Janeković & Brian PowellIvica Janeković & Brian Powell

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#ifdef LAYOUT

• Region of interest, domain, bathymetry• Forward model setup• IS4DVAR setup• TLM validation• Problems on the way• Results & discussion• Preliminary results – still running, suggestions are

welcome

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Domain

• Hawaiian Islands -> challenge for model skill• Complex bathymetry, forcing, boundary…• Hawaiian Ridge – major barotropic to baroclinic tidal

conversion site -> Kaena Ridge, Niihau-Maui • Coastal dynamics in shallow/slope region on high

resolution model for transport study of ordnance• We used nested approach from 2 model grids above

– 4 km Hawaiian Islands Grid– -> 1.2 km Oahu-Maui Grid– -> 200 - 900 m Western Oahu Grid

• Our focus is Western Oahu region & DA• 5 ADCP observations at “nice position” (07/09-07/10)• SST data from MODIS T/A

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Kaena Ridge

Niihau

Kauai

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ADCP

West Oahu Grid

12 km

33 km

(59,35)

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Bathymetry transect

ADCP

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Forward model setup

• We used hi-res bathymetry + LP smoothing– variable rx0 smoothing, iterative way, many grids…

• 20 vertical levels (on the edge with rx1(!))– increased number of levels in surface zone -> Θ_s= 7, Θ _b= 0.1– vtransform, vstretching = 2– variable resolution, in region where we have ADCPs (~200m)

while at boundary ~900m (outer grid 1.2km)• Spin-up from 2004 - 2009 using:

– forcing with local MM5 (1.5km), WRF (1.5km)– boundary from HIOG (1.2km outer model), tides inside

• Model setup using:– U3 for advection, GLS - gen, IS4DVAR, small diffusion and

viscosity, clamped BC,…• Forward model is doing well with tides

– dominantly tidally driven (barotropic/baroclinic)– we want to improve circulation & phase

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IS4DVAR setup• in the beginning 40, 30, 20 inner loops, 1 outer loop• time window of 3 days (based on init and MTLM)• clamped boundary conditions• adjust initial filed + atmo. forcing, NO BRY ADJUSTMENT• OBS:

– ADCPs (1 2 3 4)• discarded at the bottom • model/obs depths doesn’t match, bottom layer?!• ADCP 2 out of phase

– SSH (1 2 3 4)• de-mean, added model mean value, nr. 2 faulty

– MODIS T/A SST• not many data, proximity to coast, small domain

• small grid (tiles), short time step of 60s (ncpu?)• small de-correlation scale (patchy fields)• run experiments with:

– using only ADCPs– using ADCPs + SSH– using ADCPs + SSH + MODIS SST

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NLM-TLM approximation

• In order to check validity of TLM approx:– we made inside domain perturbation (“wrong” SST)– run MTLM (10 inner loops)– used 40 (Modified Gram-Schmidt ortho-normalized)

perturbations in init field for all state variables– horizontal de-correlation scale set to 3 km– run TLM and NLM (40 members)– correlation (NLM,TLM) for all state variables– system is OK for 5 days, high correlation (>0.6)– mixing in the 1st time step for salt, unstable perturbs– mostly driven by atmo and boundary forcing

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Effect of init field inside NLM

• we run NLM from day_0 (1st of Nov 2009) using: – real BC, atmo. forcing and initial field – -> state_0

• we run NLM as before but with:– initial field randomly picked (Jun 2009) and then only “changed”

ocean_time = day_0 – -> state_1

• compute correlation btw state_0 & state_1 for all state variables in time

• After 2-3 days information in init field is swept away by BC and atmo forcing, correlation is round 1.

• This have us interval how frequent we should assimilate

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correlation (state_0, state_1)

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Results/disscusion

• If only looking at the ADCPs locations– all seems perfect, high correlation (~0.7 - 0.8)

• What is really happening?• We do have high dimensionality case with small

number of obs -> constraint• We made experiments gradually adding obs to see

how it affects solutions and constraints • Did we corrected/destroyed baroclinic tides?• What happened to density filed in whole domain?

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Exp 1: ADCPs only

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Exp 1: ADCPs only

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Exp 1: ADCPs only

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Exp 1: ADCPs only

ADCP 1 Correlation/STDdepth U0 UA V0 VA -53.4 0.68/0.17 0.72/0.15 -0.60/0.15 -0.57/0.15-45.4 0.70/0.18 0.75/0.16 -0.49/0.12 -0.44/0.12-37.4 0.70/0.19 0.76/0.16 -0.38/0.11 -0.27/0.11-29.4 0.71/0.19 0.77/0.16 -0.32/0.11 -0.18/0.10-21.4 0.72/0.19 0.78/0.16 -0.31/0.10 -0.17/0.10-13.4 0.71/0.20 0.77/0.17 -0.25/0.11 -0.21/0.11

ADCP 3 Correlation/STDdepth U0 UA V0 VA -9.9 0.70/0.16 0.78/0.13 0.27/0.04 0.26/0.04 -8.4 0.71/0.16 0.78/0.13 0.27/0.04 0.26/0.04 -6.9 0.71/0.16 0.79/0.13 0.25/0.04 0.25/0.04 -5.4 0.70/0.16 0.79/0.13 0.23/0.04 0.23/0.05 -4.2 0.70/0.16 0.78/0.13 0.21/0.04 0.22/0.05

ADCP 4 Correlation/STDdepth U0 UA V0 VA -5.9 0.56/0.21 0.64/0.18 -0.18/0.07 -0.14/0.08 -3.2 0.56/0.21 0.64/0.19 -0.09/0.08 -0.12/0.09

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Exp 2: ADCPs + SSH

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Exp 2: ADCPs + SSH

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Exp 2: ADCPs + SSH

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Exp 2: ADCPs + SSH

ADCP 1 Correlation/STDdepth U0 UA V0 VA-53.4 0.68/0.17 0.72/0.15 -0.60/0.15 -0.58/0.15-45.4 0.69/0.18 0.75/0.16 -0.49/0.12 -0.46/0.13-37.4 0.70/0.19 0.76/0.16 -0.36/0.11 -0.30/0.11-29.4 0.71/0.19 0.77/0.16 -0.30/0.11 -0.20/0.10-21.4 0.71/0.19 0.77/0.16 -0.29/0.10 -0.16/0.10-13.4 0.71/0.20 0.77/0.17 -0.25/0.11 -0.13/0.10



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Exp 3: ADCPs + SSH + SST

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ADCP 1 Correlation/STDdepth U0 UA V0 VA-53.4 0.68/0.17 0.71/0.16 -0.61/0.14 -0.58/0.15-45.4 0.69/0.18 0.74/0.16 -0.50/0.12 -0.45/0.12-37.4 0.69/0.19 0.75/0.16 -0.39/0.11 -0.29/0.11-29.4 0.70/0.20 0.76/0.16 -0.33/0.10 -0.19/0.10-21.4 0.71/0.20 0.77/0.17 -0.32/0.10 -0.18/0.10-13.4 0.70/0.20 0.76/0.17 -0.27/0.11 -0.17/0.10



Corr(ssh_obs,ssh_nlm_0)=0.75Corr(ssh_obs,ssh_nlm_0)=0.75

Corr(SST_obs,SST_nlm_0)=0.69 Corr(SST_obs,SST_nlm_1)=0.70

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Still work in progress

• All indicates that the key is to fix outer model as much as possible (use DA) in order to get right BC

• Right now we are only adjusting atmo+initial

• We do need to perform observation sensitivity– How sensitive is our system to ADCP obs?

– Ek vs Ep inside model

• Impact studies

• Overlapping could help to avoid shocks

• Would W4DVAR help?

Using IS4DVAR Data Assimilation in the near coastal modeling system

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