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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:

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chinese student work on storms done at hzg

Chinese student work on storms done at HZG

von Storch Hans

Institute ofCoastal Research

Helmholtz-Zentrum Geesthacht

Germany

chen fei
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
slide3

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: Quasi-stationarity 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.
slide4

Since 2012

  • A multi-decadal high-resolution hindcast of wind and waves for Bohai and Yellow Sea

李德磊

Supervision: Hans von Storch and Beate Geyer

motivations objectives
Motivations & Objectives
  • Motivations
  • Despitenumerousstudiesvdevotedtoclimatechangeof 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 omogeneouswind 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
slide6
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.

Fig. 1. Orography and station locations

Table 1. Statistical metrics and their definition

results
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 measuresofmodeledandobserved wind speeds.

slide8

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

slide9

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;
  • Error variance is not reduced by all downscaled results.

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

slide10

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 CCLM-ERAint products are rather similar, and have much higher precision than CCLM 7km;
  • CCLM-ERAintis 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.

conclusions and outlooks
Conclusionsandoutlooks
  • 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 + stddev2) 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.
  • Publicationalmostdone.
  • More wind data, in particularfromShandongprovinceneeded.
slide12

Since 2013

  • A multi-decadal high-resolution hindcast of storm surges for Bohai sea

冯建龙

Supervision: Jiang Wenshengand Ralf Weisse

motivations objectives1
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 BohaiSea 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.
a comparison of typhoon information of different data sets
Acomparison 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

results1
Results

Table1. The comparison of Vmax(m/s)

slide16

Comparison with the satellite data

  • Method
  • The Brier skill score S defined by
  • where denotes error variance between regional model data and verifying satellite observations, 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
slide17

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:redandwhite – betterthan ERA-int

slide18

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-gaoperforms better , while in 2007-03 the HZG-7 km performs better
  • .

Two storm surge cases in Bohai Sea are selected to test the performance of the wind data from different data sets

conclusions and outlooks1
Conclusionsandoutlooks
  • Conclusions
  • Both the OUC-gao and HZG-7km data sets are more reliably than the NCEP ERA-int and HZG-55km 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 BohaiSea with wind forcing provided by GaoShanhong (OUC) and Li Deilei(HZG)
  • Extract the statistical characteristics of the storm surge hindcast.
  • Derive scenarios for the future with the help of downscaledclimatechangescenarios