基于公众天气预报预测塑料大棚逐日极端气温  

Prediction of daily extreme temperatures in plastic greenhouse based on public weather forecast

作　　者：邹学智[1,2] 申双和[1,2] 曹雯[1,2] 段春锋[1,2] 李倩[1,2] 

机构地区：[1]南京信息工程大学江苏省农业气象重点实验室,南京210044 [2]南京信息工程大学应用气象学院,南京210044

出　　处：《气象科学》2014年第2期187-192,共6页Journal of the Meteorological Sciences

基　　金：科技部公益性行业(气象)科研专项(GYHY20090623;GYHY201106043);江苏高校优势学科建设工程(PAPD)项目

摘　　要：利用浙江省慈溪市的公众天气预报和草莓大棚内极端气温的观测数据,构建一个以室外日最高气温、最低气温、相对湿度、最大风级、白天和夜间天空状况作为输入变量,棚内日最高气温和日最低气温作为输出变量的BP神经网络预测模型。用以预测草莓大棚室内日最高气温和日最低气温。结果表明,该模型对大棚内日最高气温、日最低气温的训练值和实测值之间的均方根误差分别为4.0℃和1.3℃,绝对误差则分别为3.2℃和1.0℃;日最高气温和日最低气温的预测值和实测值之间的均方根误差分别为3.6℃和1.2℃,绝对误差为3.0℃和1.0℃。该模型数据获取方便,实用性强,模拟精度较高,可以较准确的预测未来温室内的极端气温,为温室管理和调控提供依据。The public weather forecast and observed meteorological data in the plastic greenhouse in Cixi city, Zhejiang province have been used to set up a BP neural network model, in order to predict daily extreme temperatures in the plastic greenhouse, whose input variables were daily maximum and minimum temperatures, relative humidity, maximum wind force scale, day and night weather condition, and whose output variables were the maximum and mininum temperatures in the plastic greenhouse. The results show that the root mean squared error（RMSE） and absolute error（AE） between trained and measured values of daily maximum temperatures in plastic greenhouse were 4. 0 ℃ and 3.2 ℃, while the daily minimum tem- perature were 1.3 ℃ and 1.0 ℃. Furthermore, RMSE between predicted and measured values of the daily maximum and minimum temperatures were 3.6 ℃ and 1.2 ℃, while the AE were 3.0 ℃ and 1.0 ℃, re- spectively. With easy access to data and wide practicability, this model could accurately predict the coming extreme temperatures in the plastic greenhouse and provide scientific basis for greenhouse management and environment regulation.

关 键 词：极端气温预测 公众天气预报 BP神经网络 塑料大棚 

分 类 号：P457.3[天文地球—大气科学及气象学]