A data assimilation system by using DMI ocean model BSHcmod
description
Transcript of A data assimilation system by using DMI ocean model BSHcmod
A data assimilation system by using DMI
ocean model BSHcmod
Jiang Zhu, Ye Liu, Shiyu Zhuang, Jun She, Per
Institute of Atmospheric Physics
Chinese Academy of Sciences
YEOS Annual meeting and Workshop on Yellow Sea Operational Oceanography24-25 April 2008, Copenhagen, Denmark
Outlines
• Motivations
• Bathymetry-following covariance : A recursive filter approach
• Some test results
• Conclusions and next steps
Motivations
• The North-Baltic Sea and the Yellow Sea are both shallow seas;
• The North-Baltic Sea is more observed than the Yellow Sea and provides a test bed for a data assimilation system of shallow coastal/shelf seas;
• BSHcmod for North-Baltic Sea has a SST data assimilation system and does not have a profile data assimilation system yet;
• We first develop a profile data assimilation system for BSHcmod in North-Baltic Sea.
0 5 1 0 1 5 2 0 2 5 3 0
L o n g i t u d e
5 0
5 5
6 0
6 5
D M U o b s e r v a t i o n
B S H o b s e r v a t i o n
I C E S o b s e r v a t i o n
0 5 1 0 1 5 2 0 2 5 3 0
L o n g i t u d e
5 0
5 5
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D M U o b s e r v a t i o n
B S H o b s e r v a t i o n
I C E S o b s e r v a t i o n
The spatial distribution of T/S profiles observation used in the experiments in 2005
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The
Numb
er o
f TS
Pro
file
s
The daily total number of profiles in 2005.
Bathymetry-following covariance : A recursive filter approach
Basic formulism: solving a minimization of the following cost function
Though theoretically equivalent, the variational approach is used instead of Optimal Interpolation (OI) scheme for easy handling of
• additional penalty terms added to the cost function;
• imposing inequality constraints to avoid density reversal.
Considering the narrow channels and complex coastal lines, the inhomogeneous, anisotropic background error covariance is necessary to propagate information.
A Bathymetry-following covariance is used in the infinitesimal differential form:
22
2 2
1 ( )exp
2
T
br f
dy dy df
L L
B
Analysis incremental from a single observation
IsotropicAnisotropic
An recursive filter using the aspect tensor A defined by
12 2
( ) ( )T
r f
f f
L L
I
A
can be constructed after determination of the two length scales.
6 6.5 7 7.5 8 8.5 9 9.5 105
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14
Error field correlation length (m)
Ho
rizo
nta
l co
rre
latio
n le
ng
th
0.28
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0.32
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0.36
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0.4
0.42
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RMSE of Temperature is shown as function of the two length scales Lf and Lr using all observed profiles in 26 Apr. 2005.
(9 grid points, 9.5m)
Some test results
The assimilation system is setup at the two model grid area : coarse grid area and fine grid area.
However, here only implementting the coarse grid area assimilation and only presentatting some coarse assimilation results.
We assimilated the T/S profiles into the cmod every day at 12:00 for a 20-day period (Jan 16 2005 to 3 Feb 2005. )
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1-13
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1-31 2-2
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The
Numb
er o
f T/
S Pr
ofile
s
The daily total number of profiles in experiment period.
The impact of the assimilation scheme to forcasting effect can be vertified by
• all the observation data before assimilation and
• the withheld BSH profiles (the yellow points)
Obs
Ana
For
Locations of profiles in the experiment.
0 5 1 0 1 5 2 0 2 5 3 0
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1-15 1-17 1-19 1-21 1-23 1-25 1-27 1-29 1-31 2-2 2-4 0.3
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1.1
Time (day)
RM
SE
( o
C )
with assimilationwithout assimilation
The overall RMSEs for T verified daily just before the assimilation.
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Time (day)
RM
SE
(p
su)
with assimilation without assimilation
The overall RMSEs for S verified daily just before the assimilation.
Little impact could be due to the large S observation error setup (0.5psu).
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Time (day)
RM
SE
(
o C )
Without assimilationWith assimilation
The RMSEs for T at 9m verified daily just before the assimilation.
The RMSEs for S at 9m verified daily just before the assimilation.
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Time (day)
RM
SE
(p
su)
with assimilationwithout assimilation
4.083W 0 3E 6E 9E 12E 15E 18E 21E 24E 27E 30E48.55N
50N
52N
54N
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58N
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62N
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66N
Longitude (o)
La
titu
de
(o )
-0.6
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1The temperature analysis increment at 4m depth, on Feb. 3 2005.
4.083W 0 3E 6E 9E 12E 15E 18E 21E 24E 27E 30E48.55N
50N
52N
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66N
Longitude ( o )
La
titu
de
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The salinity analysis increment at 4m depth, on Feb. 3 2005.
4.083W 0 3E 6E 9E 12E 15E 18E 21E 24E 27E 30E48.55N
50N
52N
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56N
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60N
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66N
Longitude ( o )
La
titu
de
( o )
-1
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0.2The temperature analysis increment at 15m depth, on Feb. 3 2005.
4.083W 0 3E 6E 9E 12E 15E 18E 21E 24E 27E 30E48.55N
50N
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58N
60N
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64N
66N
Longitude ( o )
La
titu
de
( o )
-0.5
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0.5The salinity analysis increment at 15m depth, on Feb. 3 2005.
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Time (day)
Dep
th (m
)
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Time (day)
Dep
th (m
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Time (day)
De
pth
(m
)
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Obs
Simu Assi
Verified using independent profiles: Temperature
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Time (day)
Dep
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Time (day)
Dep
th(m
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Time (day)
detp
h (m
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Obs
Simu Assi
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S at 6m
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Time (day)
Sa
linity
(p
su)
observationwith assi.(Lf=9,Lr=10without assi.with assi (Lf=9,Lr=13)
S at 6m
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L o n g i t u d e
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Latit
ude
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Time (day)S
alin
ity (
psu
)
Observationwithout assimilationwith assimilation
S at 6m
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33
33.5
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Time (day)
Sa
linity
(p
su)
Observationwithout assimilationwith assimilation
S at 30m
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Time (day)
Te
mp
era
ture
(o C )
observation without assimilationwith assimilation
T at 30m
T at 13m T at 29m
Assimilation minus Simulation on Feb. 3, 2005
Conclusions and next steps
• Assimilation of profiles can improve the temperature and salinity forecasts in the North-Baltic Sea, especially the cold, fresh water mass in the Danish strait is more realistic;
• More parameter-tuning in the assimilation system;
• Perform one-year long experiment and verification;
• Installation in DMI;
• SST assimilation or water level assimilation.
END
Thanks