Chinese student
work on storms done
at HZG
von Storch Hans
Institute of Coastal Research
Helmholtz-Zentrum Geesthacht
Germany
Chen Fei
陈飞
Dissertation completed in 2013
Multi-decadal climatology of Polar Lows over the North Pacific by regional climate model
Publications:
Chen F., Geyer B., Zahn M., von Storch H., 2012: Toward a Multi-Decadal Climatology of North
Pacific Polar Lows Employing Dynamical Downscaling. Terr. Atmos. Ocean. Sci. 23: 291–301.
Chen F.) and H. von Storch, 2013 : Trends and variability of North Pacific Polar Lows, Advances in
Meteorology 2013, ID 170387, 11 pages, http://dx.doi.org/10.1155/2013/170387
Chen, F., H. von Storch, Y. Du, and L. Wu, 2014: Polar Low genesis over North Pacific under
different scenarios of Global Warming, Clim. Dyn. 10.1007/s00382-014-2117-5
Xia Lan 夏兰
Dissertation completed in 2013:
Long-term variability of storm track characteristics


Publications
L. Xia, H. von Storch, and F. Feser, 2012: Quasistationarity of centennial Northern Hemisphere
midlatitude winter storm tracks. Climate Dynamics, in
revision
L. Xia, M. Zahn, K. I. Hodges, F. Feser and H. von
Storch, 2012: A comparison of two identification and
tracking methods for polar lows. Tellus A, 64, 17196.
3
Since 2012
A multi-decadal highresolution hindcast of
wind and waves for Bohai
and Yellow Sea
李德磊
Supervision: Hans
von Storch and
Beate Geyer
4
Motivations & Objectives
Motivations
 Despite numerous studiesvdevoted to climate change of areas like North Sea, Baltic Sea and
Mediterranean Sea, only few attention has paid on the Chinese marginal sea Bohai and Yellow Sea;
 A monsoon climate regime is dominant over Bohai and Yellow Sea, which is characterized by
complex physiography and scarce observation network;
 A climatology of wind and wave conditions are needed for many applications such as wind farming
and risk assessment.
Objectives:
 Describe (hindcast) atmospheric conditions over Bohai and Yellow Sea using the regional
atmopsheric model CCLM with 7km resolution;
 Describe the wave conditions of Bohai and Yellow Sea with the omogeneous wind forcing provided
by the atmospheric hindcast;
 Verify the reliability of wind and wave field sby comparing with observation, extract the statistical
characteristics of wind and wave fields - with special attention paid on the extreme events
5
First step: A comparison of high-resolution modeled wind speed
driven by different forcing datasets over Bohai and Yellow Sea
Methodology
COSMO-CLM V4.14 (CCLM)
•
Three simulations with 7-km resolution over
Bohai and Yellow Sea (Fig. 1.) for the year
2006.
•
Different forcing datasets : CCLM 55km (hourly
output, 0.5°, downscaled from NCEP1 1.8°);
NCEP-CFSR (~ 55 km); ERAinterim (~ 80 km).
Comparison with observational data
9 land stations, including 8 airport stations; 8
offshore stations.
BIAS
Ration of short term
Standard Deviation (RSD)
Fig. 1. Orography and station locations
𝑥𝑚 -𝑥𝑜
𝜎𝑓𝑚
𝜎𝑓𝑜
Normalized Mean Square
Error (NMSE)
𝑀𝑆𝐸(𝑥𝑚 , 𝑥𝑜 )/(𝜎𝑚 𝜎𝑜 )
Correlation Coefficient (R)
𝐶𝑂𝑉(𝑥𝑚 , 𝑥𝑜 ) /(𝜎𝑚 𝜎𝑜 )
Brier Skill Score (BSS)
1 − 𝑀𝑆𝐸(𝑥𝑚 , 𝑥𝑜 )
/𝑀𝑆𝐸(𝑥𝑟 , 𝑥𝑜 )
Standard Deviation Error
(STDE)
𝑀𝑆𝐸(𝑥𝑚 , 𝑥𝑜 ) − 𝐵𝐼𝐴𝑆 2
Table 1. Statistical metrics and their definition
6
Results
Evaluation of the wind speed time series

Positive biases at offshore
stations;

Ratio oi short term variability
RSD < 1 except for CCLM
7km at land stations;

Downscaled results add short
term variability
(RSDdyndown>RSDglobal);

Generally lower correlation R
values for downscaled results;

Lower Brier skill score BSS
values after downscaling in
most cases (comparing with
NCEP1)
Table 2. Statistical measures of modeled and observed wind speeds.
CCLM
7km
CCLM- CCLM- CCLM
CFSR ERAint 55km
CFSR
Land stations
ERAint NCEP1
BIAS
1.06
0.31
0.41
-0.13
-0.05
0.30
0.54
RSD
1.06
0.83
0.87
0.85
0.77
0.76
0.82
NMSE
1.19
0.87
0.77
0.84
0.79
0.81
1.51
R
0.58
0.61
0.67
0.64
0.68
0.67
0.50
BSS
0.01
0.44
0.49
0.42
0.49
Offshore stations
0.45
0
BIAS
1.56
0.44
0.56
0.63
0.39
0.21
0.46
RSD
0.95
0.79
0.80
0.92
0.77
0.73
0.73
NMSE
1.13
0.67
0.68
0.87
0.65
0.66
0.95
R
0.57
0.72
0.72
0.61
0.76
0.74
0.60
BSS
-0.45
0.30
0.29
-0.11
0.33
0.32
0
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Representation of wind speed distribution
Land stations
offshore stations
Modeled wind speeds
underestimate the
records of observed
wind in the range of
0.0 m s-1 -1.0 m s-1,
and fit to the observation
at high wind speeds.
Fig. 2. Frequency distribution of wind speeds
8
Is there added value to forcing datasets?
 The bias for high wind
speeds at land stations is
strongly reduced by all
downscaled results;
 For high wind speeds of
offshore stations, large
bias reduction is
generated for NCEP1
downscaled simulations
CCLM 55km and CCLM
7km, and is reduced
slightly by downscaling
ERAint; but CCLM-CFSR
generated larger bias;
Fig. 3. Comparison of wind speed bias (blue) and STDE (red) between forcing datasets
and downscaled results: (a, b, c) for land stations and (d, e, f) for offshore stations
 Error variance is not
reduced by all downscaled
results.
9
Which one rank the best among forcing
datasets and among downscaled results?
 CCLM 55km exhibits a
somewhat less precision
(standard deviation) than
the other driving analyses
at offshore stations;
 CCLM-CSFR and CCLMERAint products are rather
similar, and have much
higher precision than
CCLM 7km;
 CCLM-ERAint is slightly
better than CCLM-CFSR.
Fig. 4. Inter-comparisons of different forcing datasets and their downscaled wind speeds:
(a, b) for land stations and (c, d) for offshore stations.
10
Conclusions and outlooks
Conclusions
 The downscaling results are realistic with more details than their driving data sets.
 The downscaling simulations driven by ERAint and CFSR are consistent with each other in
reproduction of local wind speeds, with the one driven by ERAint slightly better. They generate local
wind estimates superior to the one driven by CCLM 55km.
 Due to dynamical downscaling short term variability of wind speed is added, and the biases are much
reduced at land stations for high wind speeds while at offshore stations the reduction of biases are
lesst so.
 The error variance (bias2 + stddev 2) is not reduced by downscaling.
Outlooks
 The long-term simulation driven by ERAinterim has been finished; the verification of wind will be
done by comparing with satellite data;
 Estimate the wave conditions of Bohai and Yellow Sea with wind forcing provided by the atmospheric
hindcast;
 Extract the statistical characteristics of wind and wave hindcast, especially the extreme events.
 Publication almost done.
 More wind data, in particular from Shandong province needed.
11
Since 2013
A multi-decadal highresolution hindcast of
storm surges for Bohai sea
冯建龙
Supervision: Jiang
Wensheng and Ralf
Weisse
12
Motivations & Objectives
Motivations
 Climate change will plays a critical role in increasing the intensity of a storm surges, however, little
attention has paid on the Chinese marginal sea Bohai Sea;
 Reliable and long enough time series of wind are available for establishing a long time storm surge
simulating in the Bohai Sea.
Objectives:
 Select atmospheric conditions over Bohai and Yellow Sea from different data sets;
 Reconstruct the storm surge conditions of Bohai Sea with such homogeneous wind forcing data;
 Verify the reliability of storm surge simulations by comparing with observation, extract the statistical
characteristics of storm surge s, derive scenarios of possible future conditions.
13
A comparison of typhoon information of
different data sets
Typhoon comparison
data sets:
Best Track Data (CMA)
NCEP1
ERA- int
Hindcasting wind data (OUC-Gao)
Six typhoons are selected from 1960
which impacted the Bohai Sea
199415
198509
198506
198211
197416
196705
The study domain and locations
of Bohai Sea, Yellow Sea
14
Results
Table1. The comparison of Vmax(m/s)
Typhoo Best-tr
n
OUCgao
NECP1
ERAint
Typhoo Best-tr
n
OUCgao
NECP1
ERAint
199415
25
23.1
13.0
15.6
198211
25
18.7
14.4
18.6
25
22.1
13.5
17.0
25
17.9
13.5
16.4
25
21
13.6
22.1
25
16.3
13.0
15
25
22
14.0
22.6
20
17.4
13.2
14.1
25
21.8
14.5
14.2
30
24.1
13.6
25
20.3
15.3
14.6
30
26.7
15.6
30
27.3
15
16.5
30
23.5
15.4
25
24.2
14
16.9
25
17.4
14
25
15.1
13.4
13.7
25
20
11
25
16.7
13.5
15.3
25
18.3
12.1
25
17.3
14
21
25
17.6
11.3
20
17.2
14
18.1
20
16.9
13.1
198509
198506
197416
196705
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Comparison with the satellite data
Method
 The Brier skill score S defined by
s  1   f2 2
 f2
where
denotes error variance between regional model data and verifying satellite observations,
2
and  is the corresponding error variance between a reference data and satellite observations. When
S >0, then the winds downscaled by the regional model fit the observations better than the reference
data;

 Two regional model data : OUC-gao, HZG-7km (Li Delei)
 Two reference data: ERA-int, HZG-55km
 Satellite data: Blended wind data from NOAA
16
Results
Both regional model data
sets are better than the
reference data ERA-int.
(white and red)
The HZG-7 km (delei) is
better when considering
all the wind than the
OUC-gao, but when
considering only the wind
speed above 15m/s the
OUC-gao is better than
the HZG-7km.
The results for HZG-55km
are similar
Skill scores of the OUC and HZG data sets: red and white – better than ERA-int
17
Storm surge case simulation
 The OUC-gao and HZG-7
km data yield better
results than then ERA-int
data in all stations;
 In the case 2003-10
OUC-gao performs better ,
while in 2007-03 the HZG7 km performs better
.
Two storm surge cases in Bohai Sea are selected to test the performance of
the wind data from different data sets
18
Conclusions and outlooks
Conclusions
 Both the OUC-gao and HZG-7km data sets are more reliably than the NCEP ERA-int and HZG55km data sets;
 The results from the two storm surge cases results indicate that the data are suitable for long time
simulation of storms surge statistics in the Bohai Sea
Outlooks
 Hindcast the long-time storm surge conditions of Bohai Sea with wind forcing provided by Gao
Shanhong (OUC) and Li Deilei (HZG)
 Extract the statistical characteristics of the storm surge hindcast.
 Derive scenarios for the future with the help of downscaled climate change scenarios
19
Thanks for your attention!
20