11-Oct. 2017
Young Ho Kim*, Hyun Keun Jin, Gyun-Do Pak
Korea Institute of Ocean Science & Technology
Introduction1
2 KIOST Northwest Pacific Prediction System
3 Assessment & Observation Sensitivity test
2
Contents
4 Summary & Discussion
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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
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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
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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,