Chinese student work on storms done at HZG

von Storch HansInstitute of Coastal Research

Helmholtz-Zentrum GeesthachtGermany

Chen Fei 陈飞

Dissertation completed in 2013Multi-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/10.1155/2013/170387Chen, 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

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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.

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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

Since 2012

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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

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First step: A comparison of high-resolution modeled wind speed driven by different forcing datasets over Bohai and Yellow Sea

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

Methodology

BIAS -

Ration of short term Standard Deviation (RSD)

Normalized Mean Square Error (NMSE)

/( )

Correlation Coefficient (R) /( )

Brier Skill Score (BSS) /

Standard Deviation Error (STDE)

Table 1. Statistical metrics and their definition

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Results

CCLM 7km

CCLM-CFSR

CCLM-ERAint

CCLM 55km CFSR ERAint NCEP1

Land stations

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 0.45 0

  Offshore stations

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

Table 2. Statistical measures of modeled and observed wind speeds.

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)

Evaluation of the wind speed time series

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Representation of wind speed distribution

Fig. 2. Frequency distribution of wind speeds

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.

Land stations offshore stations

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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

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Which one rank the best among forcing datasets and among downscaled results?

Fig. 4. Inter-comparisons of different forcing datasets and their downscaled wind speeds: (a, b) for land stations and (c, d) for offshore stations.

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-ERAint is slightly better than CCLM-CFSR.

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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.

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A multi-decadal high-resolution hindcast of

storm surges for Bohai sea

冯建龙Supervision: Jiang Wensheng and Ralf

Weisse

Since 2013

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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.

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A comparison of typhoon information of different data sets

The study domain and locations of Bohai Sea, Yellow Sea

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

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Results

Typhoon

Best-tr OUC-gao

NECP-1

ERA-int

199415 25 23.1 13.0 15.625 22.1 13.5 17.025 21 13.6 22.125 22 14.0 22.6

198509 25 21.8 14.5 14.225 20.3 15.3 14.630 27.3 15 16.525 24.2 14 16.9

198506 25 15.1 13.4 13.725 16.7 13.5 15.325 17.3 14 2120 17.2 14 18.1

Table1. The comparison of Vmax(m/s) Typhoon

Best-tr OUC-gao

NECP-1

ERA-int

198211 25 18.7 14.4 18.6

25 17.9 13.5 16.425 16.3 13.0 1520 17.4 13.2 14.1

197416 30 24.1 13.6

30 26.7 15.630 23.5 15.425 17.4 14

196705 25 20 1125 18.3 12.125 17.6 11.320 16.9 13.1

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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

221 fs2f

2

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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

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Storm surge case simulation

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

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 HZG-7 km performs better

.

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Conclusions and outlooks

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 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

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Thanks for your attention!