| 自由式滑雪空中技巧赛道风速风向超短期预测与分析 |
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| 作者姓名： | 邓紫薇 邵芸 王国军 黄富祥 杨佳琦 |
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| 作者单位： | 1. 中国科学院空天信息创新研究院, 北京 100094;2. 中国科学院大学, 北京 100049;3. 中科卫星应用德清研究院, 浙江 德清 313200;4. 国家卫星气象中心, 北京 100081;5. 北京大学地球与空间科学学院, 北京 100871 |
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| 基金项目： | 国家重点研发计划(2019YFF0301900)资助 |
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| 摘 要： | 旨在实现对赛道上风速风向的超短期预测,为自由式滑雪空中技巧提供实用有效的风速风向预报信息,为运动员稳定性控制与技术训练提供辅助支持。针对赛道风具有非平稳、波动剧烈的特点,采用离散小波变换提取风速风向序列的特征分量,对低频近似分量建立非线性自回归(NAR)神经网络模型,高频细节分量建立差分自回归移动平均(ARIMA)模型,再将各分量结果组合相加得到最终预测结果。误差分析表明组合模型能有效改善单一模型的预测滞后性,预测精度高同时具备预测风速风向突变的能力。对预测结果进一步分析,将其转换为表征赛道风稳定性的指标,来为运动员提供更直观的预报信息。最后对模型计算用时分析表明该方法能够满足实际应用的需求。
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| 关 键 词： | 滑雪赛道 风速风向预测 小波变换 NAR动态神经网络 ARIMA模型 |
| 收稿时间： | 2020-06-28 |
| 修稿时间： | 2020-08-18 |
Ultra-short-term prediction and analysis of wind speed and direction of freestyle skiing aerial skill track |
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| Authors: | DENG Ziwei SHAO Yun WANG Guojun HUANG Fuxiang YANG Jiaqi |
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| Institution: | 1.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;3.Deqing Academy of Satellite Applications, Deqing 313200, Zhejiang, China;4.National Satellite Meteorological Center, Beijing 100081, China;5.School of Earth and Space Sciences, Peking University, Beijing 100871, China |
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| Abstract: | Freestyle skiing aerial skills are the dominant snow sports in China, and the wind has a particularly significant impact on this sport. This article aims to realize ultra-short-term prediction of wind speed and direction on the track, provide practical and effective forecast information for this sport, and provide auxiliary support for athlete stability control and technical training. In view of the non-stationary and violent fluctuations of the track wind, the discrete wavelet transform is used to extract the characteristic components of the wind speed and direction sequence, the NAR neural network model is established for the low-frequency approximate component, and the ARIMA model is established for the high-frequency detail component, and then the results of each component are combined the final prediction result. The error analysis shows that the combined model can effectively improve the prediction lag of the single model, improve the prediction accuracy and have the ability to predict sudden changes in wind speed and direction. The prediction results is further analyzed, and converted into indicators that characterize track wind stability to provide more intuitive forecast information. Finally, the analysis of model calculation time shows that this method can meet the needs of practical applications. |
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| Keywords: | ski track wind speed and direction prediction wavelet transform NAR dynamic neural network ARIMA model |
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