Prediction of daily reference evapotranspiration by a multiple regression method based on weather forecast data
Prediction of daily reference evapotranspiration by a multiple regression method based on weather forecast data
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基于天气预报数据的多元回归法预测日参考蒸散量
DOI：
10.1080/03650340.2012.727400
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发表时间：
2013-10
期刊：
影响因子：
--
通讯作者：
Luo, Yufeng
中科院分区：
文献类型：
--
作者：
Wang, Weiguang;Yang, Shihong;Wei, Qi;Luo, Yufeng
每日参考蒸散量（ET 0）的预测是实时灌溉调度的基础。提出了一种基于ET 0 季节变化规律和公共天气预报数据的多元回归方法，用于华东地区的ET 0 预测。采用预测最高气温（T max）、最低气温（T min）和天气状况指数（WCI），通过多元线性回归计算5个时区（10天、月度、季节、半年和年）下的修正系数。使用预报天气数据作为输入，测试多元回归方法对 ET 0 预测的可行性，并选择最合适的月度模式。平均绝对误差 (AAE) 和均方根误差 (RMSE) 分别为 0.395 和 0.522 mm d−1。 ET 0 预测误差随着温度预测误差的增加而线性增加。 3 K 以内的温度误差可能会导致可接受的 ET 0 预测，AAE 和平均绝对相对误差 (AARE) 分别 <0.142 mm d−1 和 5.8%。然而，由于校正系数对WCI的敏感性较高，且1个秩偏差引起的WCI相对误差较大，因此WCI中的1个秩误差会导致ET 0 预测出现较大误差。提高天气预报的准确性，特别是WCI预测的准确性，有助于根据公共天气数据更好地估计ET 0。
Prediction of daily reference evapotranspiration (ET 0) is the basis of real-time irrigation scheduling. A multiple regression method for ET 0 prediction based on its seasonal variation pattern and public weather forecast data was presented for application in East China. The forecasted maximum temperature (T max), minimum temperature (T min) and weather condition index (WCI) were adopted to calculate the correction coefficient by multilinear regression under five time-division regimes (10 days, monthly, seasonal, semi-annual and annual). The multiple regression method was tested for its feasibility for ET 0 prediction using forecasted weather data as the input, and the monthly regime was selected as the most suitable. Average absolute error (AAE) and root mean square error (RMSE) were 0.395 and 0.522 mm d−1, respectively. ET 0 prediction errors increased linearly with the increase in temperature prediction error. A temperature error within 3 K is likely to result in acceptable ET 0 predictions, with AAE and average absolute relative error (AARE) <0.142 mm d−1 and 5.8%, respectively. However, one rank error in WCI results in a much larger error in ET 0 prediction due to the high sensitivity of the correction coefficient to WCI and the large relative error in WCI caused by one rank deviation. Improving the accuracy of weather forecasts, especially for WCI prediction, is helpful in obtaining better estimations of ET 0 based on public weather data.
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影响因子：
6.2
作者：
Antonio R. Pereira
通讯作者：
Antonio R. Pereira
影响因子：
2.4
作者：
Li Dao-xi
通讯作者：
Li Dao-xi
DOI：
--
发表时间：
2005
期刊：
Journal of Wuhan University of Hydraulic and Electric Engineering
影响因子：
--
作者：
Cai Xue-liang
通讯作者：
Cai Xue-liang
影响因子：
2.7
作者：
Er-Raki, S.;Chehbouni, A.;Allen, R.
通讯作者：
Allen, R.
DOI：
10.17660/actahortic.2000.537.13
发表时间：
2000-10
期刊：
--
影响因子：
--
作者：
P. Duce;R. Snyder;S. Soong;D. Spanò;M. I. Ferreira;H. Jones
通讯作者：
P. Duce;R. Snyder;S. Soong;D. Spanò;M. I. Ferreira;H. Jones