基于实时天气预报尺度风场的精细化台风三维风场重构方法研究

刘皓月, 任贺贺, 柯世堂, 邱嘉琦

太阳能学报 ›› 2026, Vol. 47 ›› Issue (2) : 462-469.

PDF(2155 KB)
欢迎访问《太阳能学报》官方网站,今天是
PDF(2155 KB)
太阳能学报 ›› 2026, Vol. 47 ›› Issue (2) : 462-469. DOI: 10.19912/j.0254-0096.tynxb.2024-1743

基于实时天气预报尺度风场的精细化台风三维风场重构方法研究

  • 刘皓月1,2, 任贺贺1,2, 柯世堂1,2, 邱嘉琦1,2
作者信息 +

REFINED 3D RECONSTRUCTION OF TYPHOON WIND FIELD BASED ON REAL-TIME WEATHER FORECAST SCALE WIND FIELD

  • Liu Haoyue1,2, Ren Hehe1,2, Ke Shitang1,2, Qiu Jiaqi1,2
Author information +
文章历史 +

摘要

基于机器学习中的超分辨率重构技术,提出基于混合下采样跳跃连接(DSC)/多尺度(MS)模型的台风三维风场超分辨率重构方法,开展不同网格尺度之间的三维风场超分辨率重构研究。通过对比分析台风整体流场、空间不同区域平均风剖面、径向风速分布等误差,结果表明,千米及以下尺度风场可较好地重构精细化台风三维风场,而千米尺度以上风场重构误差较大,初步实现了台风风场重构从千米量级到百米量级跨越。

Abstract

Based on the super-resolution reconstruction technique in machine learning, this paper proposes a hybrid downsampling skip connection (DSC)/multi-scale (MS) model for super-resolution reconstruction of three-dimensional typhoon wind fields, and conducts research on super-resolution reconstruction of three-dimensional wind fields across different grid scales. Through comparative analysis of errors in the overall typhoon flow field, average wind profiles in different spatial regions, and radial wind speed distributions, the results indicate that wind fields at kilometer-scale and below can effectively reconstruct refined three-dimensional typhoon wind fields, while reconstruction errors are relatively larger for wind fields above kilometer-scale. This study preliminarily achieves a transition in typhoon wind field reconstruction from kilometer-scale to hectometer-scale.

关键词

台风 / 超分辨率重构 / 深度学习 / 多尺度 / 精细化风场 / 千米尺度风场

Key words

typhoon / super-resolution reconstruction / multi-scale / deep learning / refined wind field / kilometer-scale wind field

引用本文

导出引用
刘皓月, 任贺贺, 柯世堂, 邱嘉琦. 基于实时天气预报尺度风场的精细化台风三维风场重构方法研究[J]. 太阳能学报. 2026, 47(2): 462-469 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1743
Liu Haoyue, Ren Hehe, Ke Shitang, Qiu Jiaqi. REFINED 3D RECONSTRUCTION OF TYPHOON WIND FIELD BASED ON REAL-TIME WEATHER FORECAST SCALE WIND FIELD[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 462-469 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1743
中图分类号: TU312+.1    TK81   

参考文献

[1] 韩然, 王珑, 王同光, 等. 台风不同区域中的风力机动力响应特性研究[J]. 太阳能学报, 2020, 41(10): 251-258.
HAN R, WANG L, WANG T G, et al.Danamic response characteristics of wind turbine in different regions of typhoon[J]. Acta energiae solaris sinica, 2020, 41(10): 251-258.
[2] 史军, 徐家良, 穆海振. 上海近海海上最大风速的估算及数值模拟[J]. 太阳能学报, 2017, 38(4): 991-998.
SHI J, XU J L, MU H Z.Estimation and numerical simulation of maximum wind speed in Shanghai offshore[J]. Acta energiae solaris sinica, 2017, 38(4): 991-998.
[3] 李强, 张秀芝, 王乔乔, 等. 15°~35°N、105°~130°E海域台风极值风速分析[J]. 太阳能学报, 2015, 36(1): 85-89.
LI Q, ZHANG X Z, WANG Q Q, et al.The analysis of typhoon extreme wind in 15°35°N, 105°130°E waters[J]. Acta energiae solaris sinica, 2015, 36(1): 85-89.
[4] SPIRIDONOV V, BAEZ J, TELENTA B, et al.Prediction of extreme convective rainfall intensities using a free-running 3-D sub-km-scale cloud model initialized from WRF km-scale NWP forecasts[J]. Journal of atmospheric and solar-terrestrial physics, 2020, 209: 105401.
[5] 余文林, 柯世堂. 基于WRF与CFD嵌套的台风下大型风力机流场作用与气动力分布[J]. 太阳能学报, 2020, 41(12): 260-269.
YU W L, KE S T.Flow field action and aerodynamic loads distribution for large-scale wind turbine under typhoon based on nesting of WRF and CFD[J]. Acta energiae solaris sinica, 2020, 41(12): 260-269.
[6] BRUNTON S L, NOACK B R.Closed-loop turbulence control: progress and challenges[J]. Applied mechanics reviews, 2015, 67(5): 050801.
[7] SRINIVASAN P A, GUASTONI L, AZIZPOUR H, et al.Predictions of turbulent shear flows using deep neural networks[J]. Physical review fluids, 2019, 4(5): 054603.
[8] AVANZO M, GAGLIARDI V, STANCANELLO J, et al.Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy[J]. Medical physics, 2021, 48(10): 6257-6269.
[9] SELVI S, AGGARWAL K, PANDURANGAN R, et al.Retraction Note: enhancing the accuracy of target detection in remote video surveillance analytics through federated learning[J]. Optical and quantum electronics, 2024, 56(10): 1769.
[10] KIM J, LEE J K, LEE K M.Accurate image super-resolution using very deep convolutional networks[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 2016: 1646-1654.
[11] LAW H, DENG J.CornerNet: detecting objects as paired keypoints[C]//Computer Vision-ECCV 2018. Cham: Springer, 2018: 765-781.
[12] WU K, LI X M.Deep learning for retrieving omni-directional ocean wave spectra from spaceborne synthetic aperture radar[J]. Remote sensing of environment, 2024, 314: 114386.
[13] FUKAMI K, FUKAGATA K, TAIRA K.Super-resolution reconstruction of turbulent flows with machine learning[J]. Journal of fluid mechanics, 2019, 870: 106-120.
[14] FUKAMI K, FUKAGATA K, TAIRA K.Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows[J]. Journal of fluid mechanics, 2021, 909: A9.
[15] KIM H, KIM J, WON S, et al.Unsupervised deep learning for super-resolution reconstruction of turbulence[J]. Journal of fluid mechanics, 2021, 910: A29.
[16] ZHANG J C, ZHAO X W.Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning[J]. Applied energy, 2021, 300: 117390.
[17] FARANDA D, MESSORI G, BOURDIN S, et al.Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics[J]. Climate dynamics, 2023, 61(9): 4393-4409.
[18] 战庆亮, 刘鑫, 张冠华, 等. 桥面风场时程重构的机器学习方法[J]. 中国公路学报, 2023, 36(8): 22-31.
ZHAN Q L, LIU X, ZHANG G H, et al.Wind time history reconstruction around bridge deck based on machine learning[J]. China journal of highway and transport, 2023, 36(8): 22-31.
[19] 陈蕻峰, 王贺, 李岩, 等. 组合两步分解和ARIMA-LSTM的短期风速预测研究[J]. 太阳能学报, 2024, 45(2): 164-171.
CHEN H F, WANG H, LI Y, et al.Short-term wind speed prediction by combining two-step decomposition and ARIMA-LSTM[J]. Acta energiae solaris sinica, 2024, 45(2): 164-171.
[20] 李聪健, 高航, 刘奕. 基于数值模拟和机器学习的风场快速重构方法[J]. 清华大学学报(自然科学版), 2023, 63(6): 882-887.
LI C J, GAO H, LIU Y.Fast reconstruction of a wind field based on numerical simulation and machine learning[J]. Journal of Tsinghua University (science and technology), 2023, 63(6): 882-887.
[21] NGIAM J, CHEN Z, CHIA D, et al.Tiled convolutional neural networks[J]. Advances in neural information processing systems, 2010, 23(1): 1279-1287.
[22] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 2016: 770-778.
[23] DU X F, QU X B, HE Y F, et al.Single image super-resolution based on multi-scale competitive convolutional neural network[J]. Sensors, 2018, 18(3): 789.

基金

国家自然科学基金(52478530; 52321165649); 博士后自然科学基金(2022M711618); 中央高校基本科研业务费专项资金(NC2024006)

PDF(2155 KB)

Accesses

Citation

Detail

段落导航
相关文章

/