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| 低温雨雪过程的粒子群-神经网络预报模型 | |
| 引用本文： | 陆虹,翟盘茂,覃卫坚,金龙,谢敏,钱晰,赵华生. 低温雨雪过程的粒子群-神经网络预报模型[J]. 应用气象学报, 2015, 26(5): 513-524. DOI: 10.11898/1001-7313.20150501 |
| 作者姓名： | 陆虹 翟盘茂 覃卫坚 金龙 谢敏 钱晰 赵华生 |
| 作者单位： | 1.广西壮族自治区气候中心, 南宁 530022 |
| 基金项目： | 国家重点基础研究发展计划（2012CB417205）, 广西自然科学基金北部湾重大专项项目（2011GXNSFE018006） |
| 摘 要： | 利用逐日气温和降水量数据、NCEP/NCAR再分析资料以及预报场资料，通过分析提取我国南方区域持续性低温雨雪过程及其预报因子，使用粒子群-神经网络方法建立非线性的统计集合预报模型 (PSONN-EPM)，对我国南方区域持续性低温雨雪过程进行预报试验。结果表明:以过程的冷湿程度及影响范围为标准，将低温雨雪过程分为一般过程和严重过程，并建立不同的预报模型效果较好。通过10 d独立样本预报试验看，基于粒子群-神经网络方法建立的集合预报模型比基于逐步回归方法建立的预报模型的预报平均相对误差小，对严重过程预报能力高于对一般过程预报，且这种非线性统计集合建模方法在建模过程中不需要调整神经网络参数，在实际预报业务中值得尝试。 |
| 关 键 词： | 粒子群算法 神经网络 持续性 低温雨雪 集合预报 |
| 收稿时间： | 2014-12-04 |
| 修稿时间： | 2015-06-09 |
A Particle Swarm Optimization-neural Network Ensemble Prediction Model for Persistent Freezing Rain and Snow Storm in Southern China |
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| Lu Hong,Zhai Panmao,Qin Weijian,Jin Long,Xie Min,Qian Xi and Zhao Huasheng. A Particle Swarm Optimization-neural Network Ensemble Prediction Model for Persistent Freezing Rain and Snow Storm in Southern China[J]. Journal of Applied Meteorological Science, 2015, 26(5): 513-524. DOI: 10.11898/1001-7313.20150501 | |
| Authors: | Lu Hong Zhai Panmao Qin Weijian Jin Long Xie Min Qian Xi Zhao Huasheng |
| Affiliation: | 1.Guangxi Climate Center, Nanning 5300222.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 1000813.Wujiang District Meteorological Service of Jiangsu Province, Suzhou 215200 |
| Abstract: | Based on daily minimum temperature, maximum temperature and precipitation data of 756 stations in China, National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data during 1951-2013 and NCEP 24 h forecast data, a nonlinear statistical ensemble prediction model based on the particle swarm optimization-neural network (PSONN-EPM) is developed for predicting and verifying the regional persistent freezing rain and snow storm process in southern China by analyzing and extracting significant predictors. Results show that model performance can be effectively improved when dividing low-temperature processes into the general process and severe process which are constructed based on cold extents, humidity and influence ranges of the freezing rain and snow storm processes. In 10-day independent forecast test, the average relative errors for the general process and the severe process are 2.04 and 0.6 using stepwise regression equation forecast method, while those are 1.33 and 0.30 by using PSONN-EPM technique. It means forecast errors are reduced by 0.71 and 0.3 as compared with the stepwise regression method. In addition, the predication result for the severe freezing rain and snow storm process is better than that for the general process. The PSONN-EPM integrates predictions of multiple ensemble members, thus the prediction accuracy and stability are higher than those of the traditional linear regression method. Furthermore, such method does not contain any tunable parameters, and is applicable for practical operational weather prediction. |
| Keywords: | particle swarm optimization algorithm neural network persistence freezing rain and snow storm ensemble prediction |
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