Acta Optica Sinica, Volume. 44, Issue 24, 2401004(2024)
Application of Bidirectional Long Short‐Term Memory Network in Doppler Lidar Wind Profile Prediction
Fig. 1. Doppler lidar. (a) Appearance of the lidar; (b) schematic diagram of the DBS 5 beams
Fig. 4. Performance evaluation metrics for predicting 24 wind profiles using the Bi-LSTM model are R2, RMSE, and MAE. (a)(b)(c) u component; (d)(e)(f) v component
Fig. 5. Box plots of evaluation metrics for predicting the u and v components of 24 wind profiles using multiple look backs for two models. (a) R2; (b) RMSE; (c) MAE
Fig. 6. Missing wind profiles prediction based on Bi-LSTM. (a) u component; (b) v component; (c) data acquisition rate
Fig. 7. Wind profile prediction based on Bi-LSTM model. (a) 15:30 on 21 April; (b) 12:00 on 22 April
Fig. 8. Box plots of evaluation metrics for predicting the u and v components of 24 wind profiles at low and high altitudes. (a) R2; (b) RMSE; (c) MAE
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Wenchao Lian, Xiaoquan Song, Zhaoyang Hao, Ping Jiang. Application of Bidirectional Long Short‐Term Memory Network in Doppler Lidar Wind Profile Prediction[J]. Acta Optica Sinica, 2024, 44(24): 2401004
Category: Atmospheric Optics and Oceanic Optics
Received: Apr. 22, 2024
Accepted: May. 13, 2024
Published Online: Dec. 16, 2024
The Author Email: Song Xiaoquan (songxq@ouc.edu.cn)
CSTR:32393.14.AOS240891