Wave Energy Resources Assessment for the China Sea Based on AVISO Altimeter and ERA Reanalysis Data (ID:10412)

Transcription

1 Wave Energy Resources Assessment for the China Sea Based on AVISO Altimeter and ERA Reanalysis Data (ID:4) Junmin Meng, Jie Zhang First Institute of Oceanography, State Oceanic Administration, Qingdao, China Yong Wan College of Information and Control Engineering, China University of Petroleum, Qingdao, China Collard Fabrice CLS.FRANCE.

2 Research Progress. Mean wave period inversion based on AVISO altimeter data. Wave energy assessment for the China Sea based on AVISO altimeter data. Wave power density calculation for shallow water 4. Wave energy assessment for nearshore areas of the China Sea. Planning research

3 . Mean wave period inversion based on AVISO altimeter data Data material and verification Data name Data sources Data Coverage AVISO multi-satellite merged altimeter data CNES & CLS ºN-4N, ºE-9ºE Data Type gridded data Date-Time Time resolution 4h Spatial resolution * Parameters significant wave height (H s ) wind speed (U )

4 4. AVISO buoy_6. Hs/m number of collocated data 4. AVISO buoy_py-. Hs/m number of collocated data RMSE of H s is.6m and.m.

5 Mean wave period inversion Wave height and mean wave period are key parameters for wave energy assessment. Mean wave period can not provided directly by AVISO data. So we established models to calculate mean wave period by wave height and wind speed. Polynomial model (QP_AVISO_model) gt gh = + z n s i.44 a = i( ) i πu U C Input dataset:aviso significant wave height and wind speed Output dataset:era-interim mean wave period(true value)

6 Polynomial subsection model (PQP_AVISO_model) According to significant wave height subsection, many polynomial models were established for each subsection. BP Neural network model (MWP_NN_model) A BP neural network model was established with input nondimensional wave height and output wave age.

7 Comparison for different models Model RMSE (s) QP_AVISO_model.7 PQP AVISO model.86 MWP_NN_model. H98_model.74 Miao_QP_model.6 Miao_PQP_model.9

8 . Wave energy assessment for the China Sea based on AVISO altimeter data Annual mean P w Annual mean usable level frequency

9 Annual mean rich level frequency Coefficient of variation

10 Total wave energy(mwh/a) x Longitude( ) Distribution of total wave energy according to longitude. x 8 Interannual variation for total wave energy Total wave energy(mwh/a) Latitude( ) Distribution of total wave energy according to latitude

11 Distribution of total wave energy according to wave condition Safety index

12 Theoretical annual mean P w Exploitable annual mean P w Wave energy exploitable ratio

13 x Theorectial total wave energy Exploitable total wave energy Total wave energy/j Theoretical wave energy COV Exploitable wave energy COV annual winter spring summer autumn Period Comparison of total wave energy

14 . Wave power density calculation for shallow water In shallow water, when calculating P w we must consider water depth and some coastal influence to improve the accuracy of P w. A novel high order parametric model was established to calculate P w in shallow water based on MASNUM wave model. PP ww = HH ss TT ee HH ss TT ee +. 4 HH ss TT ee +.6 HH ss TT ee +.7 Model Bias/kw/m RMSE/kw/m CC Empirical Model High Order Parametric Model

15 4. Wave energy assessment for nearshore areas of the China Sea 4 N N N 4 6 N 7 9 N4 7 N N 6 N N6 N7 N8 7 8 N N9 N 9 Key areas and stations 8 N N 4 N N N 7 N 9 N N4 8 N N6 4 N7 N8 6 N 6 E 9 E E E 8 E E 4 E 7 E

16 Data material Data name Data sources Data Coverage ERA-Interim data ECMWF ºN-4N, ºE-9ºE Data Type gridded data Date-Time Time resolution Spatial resolution. *. Parameters 6h significant wave height (H s ) mean wave period (T e )

17 Wave power rose NNW 方向 N NNE NNW 方向 N NNE NNW 方向 N NNE NNW 方向 N NNE NW NE NW NE NW NE NW NE WNW ENE WNW ENE WNW WNW ENE ENE W % % % % E W % % % % E W W E % % % % % % E % % % % % % WSW ESE WSW ESE WSW WSW ESE ESE SW SE SW SE SW SE SW SE SSW NNW SSW SSW SSE SSW SSE SSE S S S S N N4 N7 N8 方向 N NNE NNW 方向 N NNE NNW 方向 N NNE NNW 方向 N SSE NNE NW NE NW NE NW NE NW NE WNW ENE WNW ENE WNW ENE WNW ENE W E W % % % % % % % % E W E % % 4% 6% W E % % 4% 6% WSW ESE WSW ESE WSW ESE WSW ESE SW SE SW SE SW SE SW SE SSW S SSE SSW S SSE N N N7 N8 SSW S SSE SSW S SSE

18 Distribution of total wave energy according to wave condition Hs/m Hs/m Te/s Te/s (%) (%) Hs/m Hs/m Te/s Te/s (%) (%) Hs/m Hs/m Te/s N N4 N Te/s N8 N N (%) (%) Hs/m Te/s N7 (%) Hs/m Te/s N8 (%)

19 Maximum P w Station Maximum P w /kw/m N.4 N.48 N. N4 8. N 9.6 N N N8 7. N9 6.7 N 6.6 N 6. N 6.88 N 9.97 N4 4.9 N 6.89 N N N Energy harvesting rate Station Energy harvesting rate/% N 6.6 N 6.9 N 7.7 N4 69. N 76. N N N8 74. N N 8.9 N N N 89. N N 86.4 N6 8.6 N N8 86.6

20 . Planning research To establish atlas of wave energy in the China Sea based on AVISO altimeter data. To study regional division method for wave energy in the China Sea. To study wave energy assessment by SAR data.

21 Thanks for your attention!