Research on application of LSTM deep neural network on historical observation data and reanalysis data for sea surface wind speed forecasting

Wang Guosong; Wang Xidong; Hou Min; Qi Yiquan; Song Jun; Liu Kexiu; Wu Xinrong; Bai Zhipeng

doi:10.3969/j.issn.0253-4193.2020.01.008

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Wang Guosong，Wang Xidong，Hou Min, et al. Research on application of LSTM deep neural network on historical observation data and reanalysis data for sea surface wind speed forecasting[J]. Haiyang Xuebao，2020, 42(1)：67–77，doi:10.3969/j.issn.0253−4193.2020.01.008

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Research on application of LSTM deep neural network on historical observation data and reanalysis data for sea surface wind speed forecasting

doi: 10.3969/j.issn.0253-4193.2020.01.008

Wang Guosong^{1, 2
,},
Wang Xidong¹,
Hou Min^{3
,
,},
Qi Yiquan¹,
Song Jun⁴,
Liu Kexiu²,
Wu Xinrong²,
Bai Zhipeng⁵

1.
College of Oceanography, Hohai University, Nanjing 210098, China
2.
National Marine Data and Information Service, Tianjin 300171, China
3.
Tianjin Binhai New Area Meteorology Administration, Tianjin 300457, China
4.
Operational Oceanographic Institution, School of Marine Science and Environment Engineering, Dalian Ocean University, Dalian 116023, China
5.
61741 Troops, Beijing 100094, China

Received Date: 2019-01-15
Rev Recd Date: 2019-06-22

Available Online: 2021-04-21

Publish Date: 2020-01-25

Abstract

Abstract

Based on historical meteorological observation data and reanalysis data, the application of LSTM (longs short-term memory) deep neural network in short-term forecasting of sea surface wind speed under supervised learning was studied. Five representative meteorological stations in the offshore were taken as research areas. Twenty-one variables, which has been quality control and preprocessing, were selected as the prediction factors, and two LSTM deep neural network frameworks (OBS_LSTM and ALL_LSTM) were constructed. The 6 h wind speed forecast of WRF model over two-nesting domains in 2017 was included to validate the real performance of the proposed model. The result indicate that, the LSTM wind speed forecast models could significantly reduce the forecast error, RMSE was reduced by 41.3% and 38.8%, and MAE was reduced by 43.0% and 40.0%, respectively; wind speed error statistics and strong wind events comparison shows that, LSTM model can grasp sensitive information such as topography and typhoon, and superior to WRF model. The ALL_LSTM model has the smallest prediction error, good stability and robustness, and OBS_LSTM model has a wider range of application.
- deep neural network,
- LSTM,
- sea surface wind speed,
- short-term forecasting,
- WRF model

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References(61)

References

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