黄小燕,何立,赵华生,黄颖,吴玉霜. 2021. Shapley-模糊神经网络方法在华南台风卫星云图的长时效滚动预测中的应用[J]. 气象学报, 79(2):309-327, doi:10.11676/qxxb2021.017
Shapley-模糊神经网络方法在华南台风卫星云图的长时效滚动预测中的应用
Application of Shapley-fuzzy neural network method in long-time rolling forecasting of typhoon satellite image in South China
投稿时间:2019-09-26  修订日期:2020-12-03
DOI:10.11676/qxxb2021.017
中文关键词:  卫星云图  华南区域台风  Shapley-模糊神经网络  k-近邻互信息估计  多元线性回归
英文关键词:Satellite image  Typhoon in South China  Shapley-fuzzy neural network  k-nearest neighbor mutual information estimation  Multiple linear regression
基金项目:国家自然科学基金项目(41765002)、广西自然科学基金重点项目(2017GXNSFDA198030)、广西自然科学基金面上项目(2018GXNSFAA281281、2018GXNSFAA294128、2018GXNSFAA281229)、广西台风与海洋预报服务创新团队项目
作者单位
黄小燕 广西壮族自治区气象科学研究所南宁530022 
何立 广西壮族自治区气象科学研究所南宁530022 
赵华生 广西壮族自治区气象科学研究所南宁530022 
黄颖 广西壮族自治区气象科学研究所南宁530022 
吴玉霜 广西壮族自治区气象台南宁530022 
摘要点击次数: 686
全文下载次数: 488
中文摘要:
      为了更好地利用大量的卫星云图观测资料来提高台风暴雨的预报能力,解决并提高对台风强降水云系变化的预报精度,延长对未来云系变化的预报时效,构建基于合作对策Shapley-模糊神经网络的华南区域台风卫星云图非线性智能计算滚动集合预测模型,对增强卫星云图资料在台风暴雨天气预报中的实用性和及时性具有重要意义。依据2013—2016年华南区域台风影响过程的卫星云图,采用类似于数值预报模式的集合预报方法,通过对间隔6 h的卫星云图云顶亮温样本序列做经验正交函数分解,将提取出的时间系数作为云图预报建模的预报分量。考虑台风云系的发展变化主要受云团环境物理量场的影响,利用数值预报模式的物理量预报产品作为各预报分量的预报因子,并采用k-近邻互信息估计的分步式变量选择算法,通过两步过程实现相关变量的选择与弱相关变量的剔除,分别建立相应时间系数的Shapley-模糊神经网络集合预报模型,进一步将预报得到的各时间系数与空间向量合成,重构得到未来时刻的卫星云图预报图,实现了云图6—72 h的长时效客观滚动预测。试验结果表明,新方案所预测的云图与实况云图相关较高,重构云图的基本轮廓、纹理特征分布、清晰度以及云系强弱方面都比较接近原始云图。另外,研究进一步基于相同的云图预报因子,针对同样的建模和预报样本采用多元线性回归方案进行和新方案一致的云图预测。对比结果表明,这种非线性预报模型比线性方案能更好地预报未来较长时效台风云团的发展、移动的主要特征和变化趋势,其预测的云图与实际云图的主要特征更相似。云图预报时效达到了72 h,具有业务实用价值。
英文摘要:
      In order to make better use of the observational data of a large number of satellite cloud images to improve the forecasting ability of typhoon rain, increase the forecast accuracy of cloud system changes related to typhoon heavy precipitation, and increase the timeliness of the forecast for future cloud system changes, a model is constructed based on the cooperative strategy Shapley-fuzzy neural network for non-linear intelligent calculation of satellite cloud images of typhoons in South China. It is of great significance to enhance the practicability and timeliness of satellite cloud image data in the weather forecast of typhoon rain. Based on satellite cloud images of typhoon processes in South China during 2013—2016, an ensemble forecast method similar to the numerical forecast model is used to decompose the sample sequence of the cloud top brightness temperature of the satellite cloud images at 6 h intervals by the empirical orthogonal function, and the time coefficients decomposed are used as the forecast component in the cloud image forecast model. The development and changes of typhoon clouds are mainly affected by the atmospheric environmental flow field. Here, the physical quantity forecast products of the numerical forecast model are used as the forecast factors of individual forecast components of cloud images. And the stepwise variable selection algorithm of k-nearest neighbor mutual information estimation is adopted to realize the selection of related variables and the elimination of weakly related variables through a two-step process. The Shapley-fuzzy neural network ensemble forecast model of the corresponding time coefficient components is established, and the time coefficients and space vectors of each cloud image obtained by the forecast are further synthesized to reconstruct the forecast cloud image at the future time. In this way, the long-term and objective rolling prediction of the satellite cloud image at forecast lead time from 6 h to 72 h is realized. The prediction results of satellite cloud images of typhoons in southern China show that the cloud images predicted by the new scheme are highly correlated with the observed cloud images. The reconstructed predicted cloud image's basic contour, texture feature distribution, clarity, and cloud strength are relatively close to that of the observed cloud image. In addition, based on the forecast factors of the same cloud images, this paper further used the multiple linear regression scheme to predict the cloud images consistent with the new scheme for the same modeling and forecasting samples. Comparison of the results shows that this nonlinear forecasting model can better predict long-term development, movement, and trend of typhoon cloud cluster than the linear scheme. And the predicted cloud images are more consistent with the main features of the observed cloud images. The valid time of the cloud image forecast in this experiment has reached 72 h, which has a practical significance in operational weather forecasting.
HTML   查看全文   查看/发表评论  下载PDF阅读器