11-Oct. 2017

Young Ho Kim*, Hyun Keun Jin, Gyun-Do Pak

yhkim@kiost.ac.kr

Korea Institute of Ocean Science & Technology

Introduction1

2 KIOST Northwest Pacific Prediction System

3 Assessment & Observation Sensitivity test

2

Contents

4 Summary & Discussion

3

1. Introduction

4

1. Introduction

Throughout our society, the impact of climate change has increased. Though the number of tropical cyclones

crossing Korean Peninsula decreased during last decade, extreme events like heavy rainfall, heat wave in summer

and heavy snow in winter have increased recently. In addition, extreme low salinity water or warm water has

directly damaged fishery industry and aquaculture in the coastal seas of Korea.

Heavy snow in early 2017Cultured fish mortality in 2017

5

Not only climate events but also maritime accidents have also increased. In addition, scale of each event has

enlarged. For example, Korean society has experienced a terrible tragedy in 2014. A cruise vessel sank and more

than 300 people drowned to be killed in 2014.

To improve the predictability on extreme climate evens and to respond maritime accidents, the social and national

demands for accurate information of the ocean have steadily increased.

KIOST (Korea Institute of Ocean Science and Technology) has developed the ocean and climate prediction

systems by applying ocean data assimilation based on the Ensemble Optimal Interpolation.

In my talk, I would like to introduce the KIOST Regional Ocean Prediction System.

1. Introduction

Korean Navy vessel sunk in 2010

Cruise vessel sunk in 2014

Oil Spill in 2007

6

Atmos. Forcing data ECMWF data & KMA UM data

Open Boundary Condition MYOCEAN Daily data

Runoff data RiVIDS climatology data

Tide Mixing parameter TPX7.0 (TOPEX/POSEIDON Inverse Model)

DA input data

SST NOAA OI SST daily data (once a day)

Profile GTSPP Real-time data, ARGO data and KODC data (once a week)

Model : GFDL-MOM5

Study area : 5 - 63ºN, 99 - 170ºE

Resolution : 1/24 º & 51 layers

DA method : Ensemble Optimal Interpolation ( EnOI )

Prediction cycle : Every Wednesday (from 2017-03-01 to present)

Duration : Total 24days (DA for 14 days, Prediction for 10 days) Bottom topography of the study area

Table 1. Input data for KIOST-OPEM system

2. Prediction System - Model

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Initial 1

Model Run

EnKF or EnOI or OI

Get Background

Get ObservationObservation

Calculate Kalman Gain

Calculate Analysis

Initial 2 Initial 3 …

Forecast 1 Forecast 2 Forecast 3 …

Analysis 1 Analysis 2 Analysis 3 …

{Ensemble Run, Nens}

Computing domain (N proc. for each ensemble)

{transfer to filter domain}

{return to computing domain}

Filter domain (N x Nens proc. for analysis)

Ocean Data Assimilation (ODA)

1000

1500

2000

2500

3000

100

200

300

400

500

600

700

800

APEX

CTD-911

해면고도(T/P, ERS)

해면수온(NOAA)

해양자동관측(APEX)

해양관측자료(수온,염분) (CTD, XBT, APEX,TAO/TRITON)

Data

Input

Theoretical background : Ensemble Kalman Filter, Ensemble OI, OI

Technical features : Parallel processing, data decomposition (numerically high efficient)

System features : Module inside the GFDL MOM4p1(Ocean component model)

ODA system

Data management

GFDL CM2.1 coupled GCM

Resolution

ATM : 2.5°×2°, Ocean : 1° (1/3° in latitude around equator)

Do slow time steps (ocean) {

call flux_ocean_to_ice

call ICE_SLOW_UP

Do fast time steps (atmos) {

call flux_calculation

call ATMOS_DOWN

call flux_down_from_atmos

call LAND_FAST

call ICE_FAST

call flux_up_to_atmos

call ATMOS_UP

} END DO

call ICE_SLOW_DN

call flux_ice_to_ocean

call OCEAN

} END DO

Main Program (GFDL CM2.1)

call LAND_SLOW

Ocean

Update

Numerical Model

2. Prediction System – Ocean data assimilation

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timet = i -1

ax

fx

t = i

Md

t = i + 1

P f

fx d

P f

Mfx d

P f

axax

Data Assimilation System based on Ensemble OI

Control variables : T, S

2. Prediction System – Ocean data assimilation

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2. Prediction System – applying ODA

KIOST Climate ReanalysisENSO Prediction

Climate Prediction

Regional Ocean Prediction

MYOCEAN

ECMWF &

KMA data

NOAA OI SST

GTSPP profile

ARGO profile

DA

Input

data

Permanent DA-Run

(7-day)

Tentative DA-run

(7-day)

Surface Boundary

Condition

Open Boundary

Condition

Prediction Run

(10-day)

Every W

edn

esda

y

About Ocean Prediction System

Schematic diagram for KIOST OPEM

• OPEM (Ocean Predictability Experiment for Marine

environment)

GFDL-MOM5 & DASK

Tw

o-step

DA

system

:

Min

imize in

put d

ata loss

for D

A

2. Prediction System – schematic diagram

11Time Schedule of the KIOST-OPEM system

About Ocean Prediction System

• OPEM (Ocean Predictability Experiment for Marine

environment)

2. Prediction System – Time table

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About Ocean Prediction System

SBS News, 19-Jul-2017

Korea Times, 19-Jul-2017 KUKMINILBO, 19-Jul-2017

• Prediction for the coastal upwelling in the eastern coast of Korea

• An unusual coastal upwelling accompanied by unprecedented cold surface waters colder than 15°C (Park and Kim, 2010).

2. Prediction System – Broadcast article

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3. Results : 10-day prediction

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• Observed data : ARGO profile (Temp)

• Model data : KIOST-OPEM, HYCOM

3. Results - Assessment

Fig. Scatter plot for Salinity between OPEM

prediction data and ARGO profile data from 0 to

500m (07-Jun, 2017 ~ 26-Sep, 2017).

Fig. This figure is same as fig.#, but for

HYCOM data.

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Fig. Temperature profiles & RMS Error

between ARGO and KIOST OPEM-DA data.

(Left) Temp profiles, (Right) RMS Error

Fig. This figure is same as fig.#, but for

HYCOM data.

3. Results - Assessment

• Observed data : ARGO profile (Temp)

• Model data : KIOST-OPEM, HYCOM

16Fig. Scatter plot for salinity between ARGO and

KIOST OPEM-DA data.

• Observed data : ARGO profile (Salt)

• Model data : KIOST-OPEM, HYCOM

Fig. This figure is same as fig.8, but for

HYCOM data.

3. Results - Assessment

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Fig. Salinity profiles & RMS Error between

ARGO and KIOST OPEM-DA data.

(Left) Salt profiles, (Right) RMS Error

Fig. This figure is same as fig.#, but for

HYCOM data.

3. Results - Assessment

• Observed data : ARGO profile (Salt)

• Model data : KIOST-OPEM, HYCOM

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Fig. Scatter plot for temperature between OPEM

prediction data and ARGO profile data from 0 to

500m (07-Jun, 2017 ~ 26-Sep, 2017).

Fig. This figure is same as fig.#, but for

HYCOM data.

3. Results - Assessment

• Observed data : ARGO profile (Temp)

• Model data : KIOST-OPEM, HYCOM

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Fig. Temperature profiles & RMS Error between

ARGO and KIOST OPEM prediction data.

(Left) Temp profiles, (Right) RMS Error

Fig. This figure is same as fig.8, but for

HYCOM data.

3. Results - Assessment

• Observed data : ARGO profile (Temp)

• Model data : KIOST-OPEM, HYCOM

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Fig. Scatter plot for Salinity between OPEM

prediction data and ARGO profile data from 0 to

500m (07-Jun, 2017 ~ 26-Sep, 2017).

Fig. This figure is same as fig.#, but for

HYCOM data.

3. Results - Assessment

• Observed data : ARGO profile (Salt)

• Model data : KIOST-OPEM, HYCOM

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Fig. Salinity profiles & RMS Error between

ARGO and KIOST OPEM prediction data.

(Left) Salt profiles, (Right) RMS Error

Fig. This figure is same as fig.#, but for

HYCOM data.

3. Results - Assessment

• Observed data : ARGO profile (Salt)

• Model data : KIOST-OPEM, HYCOM

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3. Results - Assessment

OPEM Obs. HYCOM

Temperature section

Salinity section

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• Duration : Jan. ~ Dec., 2015

• Observed data : ARGO profiles, KODC profiles (bi-monthly station data)

• Model data : All-DA, KODC-Free and No DA

ARGO profiles KODC profiles

All-DA.

(same to OPEM-DA)O O

KODC-Free O X

No-DA

(Free-run)X X

Experiment description of the sensitivity test.

3. Results

Korean Hydrographic Dataset

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3. Results

Fig. Mean SST distributions and RMS Error maps of the sensitivity test.

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3. Results

Temperature profiles & RMSE for temperature test

• Observed data : ARGO profile

• Model data : ALL DA case, KODC-Free case and No-DA case

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4. Summary & Discussion

By applying the DASK (Ocean data assimilation system of KIOST), we have developed the

Northwestern Pacific real-time prediction system (OPEM).

By comparing with observations and other forecasts (US-NRL HYCOM), the OPEM has been

evaluated : Comparable with HYCOM, better detail structure in regional case.

Observation sensitivity test suggests that the Korean hydrographic data may take an effect to the

predictability in the Northwestern Pacific.

But, more detail analysis is required about the dynamical process how the regional ocean influences

the open ocean through shallow straits.

KIOST are developing the advanced prediction system coupling the Bio-Geo-Chemical model to

the KIOST-OPEM system.

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Reference

• Evensen, G., 2003, The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynamics, 53: 343-

367

• Oke, P. R. , Sakov, P. , and Corney, S. R. , 2007, Impacts of localisation in the EnKF and EnOI: Experiments with a small model,

Ocean Dyn., 57, 32–45.

• Kim, Y.H., Hwang C., Choi B.-J. , 2015. An assessment of ocean climate reanalysis by the data assimilation system of KIOST from 19

47 to 2012. Ocean Modell. 91, 1-22.

• K. Fukudome et al, 2010, Seasonal volume transport variation in the Tsushima Warm Current through the Tsushima Strait from 10

years of ADCP observations, Journal of Oceanography, 66, 539-551

• Park K. A and Kim K. R., 2010, Unprecedented coastal upwelling in the East/Japan Sea and linkage to long‐term large‐scale

variations, GEOPHYSICAL RESEARCH LETTERS, VOL. 37,