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Role of the diurnal temperature in determining daily dew point.

机译:昼夜温度在确定每日露点中的作用。

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Atmospheric moisture is an important factor in moderating air temperature, at all scales, and is critical to human comfort, vegetation growth, climate classification, and climate change. Atmospheric moisture records are relatively scarce across both space and time, while maximum and minimum temperature are the two most commonly measured climate variables, with records going back over 100 years at thousands of locations around the world. In humid regions, moisture can be estimated from temperature with good results, but those models do not work well in arid regions, as the minimum temperature rarely approaches the dew point. Since arid regions exist at the margins of sustainability and are undergoing rapid development, it is important to extend the moisture record back in time as far as possible. This research created a linear regression model to estimate mean daily dew point temperature from daily minimum temperature and diurnal temperature range in an arid region (Arizona). The model performed significantly better than previous models for arid locations, with an average mean absolute error of 4.76°C for agricultural sites, 4.46°C for urban sites, 4.96°C for remote high elevation sites, and 4.13°C for Phoenix airport.; Eight years of daily data at 83 weather stations in Arizona were used to develop the regression model. The generalized version of this model has an average of 3°C less error, both systematic and unsystematic, than two previous models, for the arid region. The model was generalized by interpolating the regression coefficients from the development stations, using the ordinary kriging, and extracting regression coefficients from the raster grid at other test locations. The model is simple, objective, retains most of the natural variability of the data, requires no special calibration, and can be applied to any location. Although this study found a high spatial correlation for temperature and humidity between sites, the diurnal temperature-dew point relationship varied widely, due to the microclimate characteristics, and more importantly, as a result of the extreme variability of humidity in arid regions.
机译:大气湿度是在所有规模上降低气温的重要因素,对于人类舒适度,植被生长,气候分类和气候变化至关重要。时空上的大气湿度记录相对稀少,而最高和最低温度是两个最常测量的气候变量,全球数千个地点的记录可追溯到100年前。在潮湿地区,可以从温度估算出湿度,效果很好,但是这些模型在干旱地区效果不佳,因为最低温度很少会接近露点。由于干旱地区处于可持续发展的边缘,并且正在快速发展,因此,尽可能早地恢复水分记录非常重要。这项研究创建了一个线性回归模型,可以从干旱地区(亚利桑那州)的每日最低温度和昼夜温度范围估算平均每日露点温度。该模型在干旱地区的性能明显优于以前的模型,农业场所的平均平均绝对误差为4.76°C,城市场所为4.46°C,偏远高海拔场所为4.96°C,凤凰城机场为4.13°C。 ;使用亚利桑那州83个气象站的八年每日数据来开发回归模型。与干旱地区的两个先前模型相比,该模型的广义版本的系统和非系统错误平均少3°C。通过使用常规克里金法对来自开发站的回归系数进行插值,然后从其他测试位置的栅格网格中提取回归系数,可以对模型进行概括。该模型简单,客观,保留了数据的大多数自然可变性,不需要特殊的校准,并且可以应用于任何位置。尽管这项研究发现了站点之间温度和湿度的高度空间相关性,但由于微气候特征,更重要的是干旱地区湿度的极端变化,昼夜温度-露点关系变化很大。

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