摘要
利用2010/2011年度冬季日光温室内小气候观测资料对冬季日光温室内温度变化特征进行分析,并结合温室外温度以及用云遮系数法和风级风速转换方法得到的室外总云量和最大风速,采用主成分回归分析法建模,以对日光温室内日最低气温进行预报。结果表明,(1)通过云遮系数法和风速风级转化标准模拟的室外总云量和最大风速误差较小。(2)日光温室内最低气温与温室内前一天的各小气候要素有较好的相关性,此外,温室内外各气象要素之间也存在显著的相关性。(3)主成分回归提取了温室内小气候要素主成分、温室外天气状况与温度主成分、风速主成分3个主要因子。建立的日光温室内最低气温预报模型,其复相关系数为0.857,并通过显著性检验。(4)利用2011/2012年冬季温室资料对低温预报模型进行检验,预测值与实际值之间的平均绝对误差小于1℃,平均相对误差在13%以内,整个冬季的均方根误差为1.1℃。说明所建日光温室内低温预报模型有较高的精度,能够满足温室内最低温度的预测需求。
To forecast minimum temperature inside greenhouse,the minimum temperature forecast model was established based on meteorological observation data inside the solar greenhouse in winter of 2010 and 2011,by using of principal component regression.The characteristic of temperature,total cloud cover and maximum wind velocity was discussed through cloud covered coefficient method and standard of wind velocity conversion.The results showed that the simulation errors of cloud cover and wind velocity were reasonable.There was a good correlation between minimum temperature inside the solar greenhouse and microclimate elements in greenhouse last day.Moreover,similar correlation also existed between meteorological elements inside and outside greenhouse.The multiple correlation coefficient of the model was 0.857,and approved by significant testing.The average absolute errors of forecast minimum temperature inside greenhouse in different weather conditions were less than 1℃,the average relative errors were within 13%,and the RMSE was 1.1℃ during the whole winter by using principal component regression method.The results indicated that minimum temperature forecast model had quite precisely for predicting minimum temperature inside greenhouse,which could meet the forecast requirements for greenhouse microclimate.
出处
《中国农业气象》
CSCD
北大核心
2013年第3期306-311,共6页
Chinese Journal of Agrometeorology
基金
公益性行业(气象)科研专项"设施农业气象灾害预警及防御关键技术"(GYHY201006028)
关键词
日光温室
温度特征
主成分回归
低温预报
Solar greenhouse
Temperature characteristic
Principal component regression
Low temperature forecast
