| 宁波桃树花期预报方法 |
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| 引用本文： | 姚日升,涂小萍,丁烨毅,黄鹤楼,胡波.宁波桃树花期预报方法[J].气象科技,2014,42(1):180-186. |
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| 作者姓名： | 姚日升 涂小萍 丁烨毅 黄鹤楼 胡波 |
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| 作者单位： | 浙江省宁波市气象局，宁波 315012;浙江省宁波市气象局，宁波 315012;浙江省宁波市气象局，宁波 315012;浙江省宁波市气象局，宁波 315012;浙江省宁波市气象局，宁波 315012 |
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| 基金项目： | 国家重大科技支撑计划项目(2011BAK07B02 05)、宁波市科技计划项目（2011C50078）、浙江省气象局科技计划项目（2010YB05）资助 |
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| 摘 要： | 以宁波奉化桃花为例,应用区域中尺度自动气象站的逐时资料,分析前期光、温、湿条件的变异系数及这些要素与花期的相关系数得出:时积温(度·时)相对日积温(度·日)更能体现其与花期的内在关系,根据变异系数和相关系数的极值来确定预报因子。在此基础上利用欧洲中期天气预报中心(ECMWF)细网格资料的中期预报产品,采用BP神经网络,建立花期精细化预报模型,应用于实际预报。结果表明,利用中期数值预报产品和适当的预报模型进行花期中期预报是可行的,取得了较好的预报效果,提高了气象为农业服务水平。
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| 关 键 词： | 花期预报 积温 ECMWF 细网格预报 BP神经网络 |
| 收稿时间： | 2012/11/6 0:00:00 |
| 修稿时间： | 2013/3/22 0:00:00 |
A Method for Forecasting Peach Flowering in Ningbo |
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| Yao Risheng,Tu Xiaoping,Ding Yeyi,Huang Helou and Hu Bo.A Method for Forecasting Peach Flowering in Ningbo[J].Meteorological Science and Technology,2014,42(1):180-186. |
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| Authors: | Yao Risheng Tu Xiaoping Ding Yeyi Huang Helou and Hu Bo |
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| Institution: | Ningbo Meteorological Bureau, Zhejiang, Ningbo 315012;Ningbo Meteorological Bureau, Zhejiang, Ningbo 315012;Ningbo Meteorological Bureau, Zhejiang, Ningbo 315012;Ningbo Meteorological Bureau, Zhejiang, Ningbo 315012;Ningbo Meteorological Bureau, Zhejiang, Ningbo 315012 |
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| Abstract: | Based on the hourly data of the regional automatic weather stations in Nmgbo, an anaiyms is made of the variability coefficients of sunshine duration, temperature, and humidity, as well as the relation between these meteorological elements and flowering periods. The results show that hour-by-hour accumulated temperatures （ ℃.h） indicate better relation compared with day-by-day accumulated temperature, and the predictors are obtained based on the extreme variability and correlation coefficients. The BP neural network method is applied to set up mid-term flowering forecast models, and ECMWF fine- grid model products are used as well. Trial forecasts perform quite well in forecasting blossom time and duration. The method can help improve meteorological service for agricultural activities. |
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| Keywords: | flowering forecast accumulated temperature ECMWF fine grid product BP neural network |
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