| 草面温度在霜预报中的应用 |
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| 引用本文： | 郝玲,史逸民,史达伟,顾春雷.草面温度在霜预报中的应用[J].中国农学通报,2020,36(15):94-99. |
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| 作者姓名： | 郝玲 史逸民 史达伟 顾春雷 |
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| 作者单位： | 连云港市气象局,江苏连云港 222006 |
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| 基金项目： | 江苏省气象局青年科研基金“连云港地区基于草温的霜的预报模型”(Q201708);江苏省预报员专项“地面辐合线在江苏省强对流预报预警中的应用”(JSYBY201810) |
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| 摘 要： | 为了避免农作物遇霜后遭受冻害,本研究采用草面温度对霜进行预测。利用连云港气象观测站2014—2016年逐时气象要素,包括气温、0 cm地温、露点温度、水汽压、气压以及2 min平均风速等气象要素作为影响连云港地区草面温度的关键因子,并以这6个要素作为属性特征,以草温作为标志量构建训练样本集,结合KNN数据挖掘算法构建草温预测模型,并根据草温判别是否有霜出现。结果表明：基于该算法构建的草温预测模型效果较好,预报平均误差1.2℃;根据草温预测霜的准确率高达90.2%,尤其对初终霜的预报具有很好的指示意义。因此,引入草温作为霜的预报指标,对于避免农作物遭受霜害具有十分重要的意义。
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| 关 键 词： | 农作物 霜 草面温度 关键因子 KNN算法 预测模型 |
| 收稿时间： | 2019-02-25 |
Grassland Temperature: Application in Frost Forecasting |
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| Hao Ling,Shi Yimin,Shi Dawei,Gu Chunlei.Grassland Temperature: Application in Frost Forecasting[J].Chinese Agricultural Science Bulletin,2020,36(15):94-99. |
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| Authors: | Hao Ling Shi Yimin Shi Dawei Gu Chunlei |
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| Institution: | Lianyungang Meteorological Bureau, Lianyungang Jiangsu 222006 |
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| Abstract: | To avoid freezing damage to crops after frost, we used grassland temperature to predict frost. Based on hourly meteorological data from Lianyungang Meteorological Observatory during 2014-2016, including temperature, 0 cm ground temperature, dew point temperature, water vapor pressure, air pressure and 2 min average wind speed, which were the key factors affecting the grassland temperature in Lianyungang, we took these 6 elements as attribute features, and constructed a training sample set with grass temperature as a marker. The KNN data mining algorithm is combined to construct a grass temperature prediction model, and the frost occurrence is judged according to the grass temperature. The results showed that: the grass temperature prediction model based on the algorithm could achieve better prediction effect, the forecast average error was 1.2℃; according to the grass temperature, the accuracy of frost forecast reached 90.2%, which had a good indication especially for the prediction of the initial and the last frost. Therefore, the introduction of grass temperature as a prediction index of frost is of significance for avoiding crop damage. |
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| Keywords: | crop frost grassland temperature key factor K-Nearest Neighbor (KNN) prediction model |
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