日光温室小气候要素预报模型研究

了解日光温室小气候要素变化规律,建立小气候要素预报模型,可以对日光温室资源合理开发,为日光温室小气候调控提供依据。应用辽宁省沈阳市东陵区日光温室暖棚内农田小气候观测仪采集的逐小时气温、相对湿度数据,将冬季、春季、秋季和晴天、多云、阴天几种情况进行组合,计算棚内日最高气温、日最低气温、日平均相对湿度,分析其变化规律,并利用自动气象观测站数据,建立基于逐步回归方法的预报模型。分析结果表明,日光温室内日最高气温和日最低气温呈现春季、秋季温度值接近,冬季明显低于春秋两季的特征；而冬季日平均相对湿度高于春秋两季。日最高气温具有显著的从晴天至多云至阴天减少的变化特征,日最低气温特征不如日最高气温明显；日平均相对湿度为从晴天至多云至阴天增加的变化特征。所建立的日光温室小气候要素逐步回归预报模型,均通过了显著性检验,相关系数为0.608~0.933,相对误差范围为0.1%~19.0%,相对误差平均值为2.7%~9.9%。

Microclimate Forecasting Model in Sunlight Greenhouse

To study the change regulation of microclimate in sunlight greenhouse and establish the forecasting model of microclimate can provide a basis for rational development of greenhouse resources and microclimate control in sunlight greenhouse. We used hourly temperature and relative humidity to form microclimate observation instrument in sunlight greenhouse in Dongling of Shenyang, and calculated daily maximum temperature, daily minimum temperature, daily average relative humidity by the combination of winter, spring, autumn and sunny, cloudy, overcast data, to analyze the microclimate change regulation. And a series of forecasting models based on stepwise regression method were established by using data from automatic meteorological observation station. The results showed that the daily maximum temperature and daily minimumtemperature in sunlight greenhouse of spring and autumn were close, and that of winter was significantly lower than that of spring and autumn, but the daily average relative humidity in winter was higher than that in spring and autumn. The daily maximum temperature reduced from sunny to cloudy to overcast days, characteristics ofthe daily maximum temperature were not obvious, but the daily average relative humidity increased from sunny to cloudy to overcast days. The stepwise regression forecasting models of microclimate in sunlight greenhouse all passed the significance test, the correlation coefficient was from 0.608 to 0.933, the relative error range wasfrom 0.1% to 19.0%, and the average value of relative error was from 2.7% to 9.9%.