Development of an index for frost prediction: Technique and validation
Development of an index for frost prediction: Technique and validation
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霜冻预测指数的开发：技术和验证
DOI：
10.1002/met.1807
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发表时间：
2019
影响因子：
2.7
通讯作者：
V. M. Silva
中科院分区：
文献类型：
--
作者：
J. R. Rozante;E. R. Gutierrez;P. S. Silva Dias;Alex Almeida Fernandes;D. Alvim;V. M. Silva
提出了一个霜冻预报指标，并根据观测结果进行了校准。它考虑到：（1）有利于或不利于霜冻的主要气象变量;（2）赋予这些变量的权重;（3）这些变量的平均值和标准差，仅适用于霜冻发生的情况，如观测到的温度≤ 6°C。用于霜冻指数IG（来自葡萄牙语，Agndice de Geada）的气象变量由区域天气预报模型进行数值预测。校准过程结果的结果是温度具有最大的贡献，其次是压力和风，而其他变量被调整以服从权重之和等于1的限制。在指数校准和阈值确定后，该方法被应用于2017年冬季，并考虑了2018年5月的案例研究。为了验证新的指数是否能令人满意地有助于天气预报，使用IG的结果与数值区域模式的温度输出进行了比较。结果发现，对于三个选定的地区，并为所有的预测小时，IG产生更好的结果比模型的直接温度预测。因此，得出的结论是，在操作环境中使用的IG可能会提供相当大的改善霜冻事件的预测技能。
An index for frost prediction is proposed and calibrated against observations. It takes into account: (1) the main meteorological variables that favour or oppose to frost; (2) weights attributed to these variables; and (3) means and standard deviations of these variables, only for cases in which frost occurs, as defined by observation of temperatures that are ≤ 6°C. The meteorological variables used for the frost index IG (from the Portuguese, Índice de Geada) are numerically predicted by a regional weather forecast model. An outcome of the calibration processes results is that temperature has the largest contribution, followed by pressure and winds, while the other variables were adjusted to obey the restriction that the sum of weights are equal to 1. After index calibration and threshold determination, the method was applied for the 2017 winter season, and a case study for May 2018 was also considered. In order to verify whether the new index can satisfactorily contribute to the weather forecasting, the results using the IG were compared with the temperature outputs of the numerical regional model. It was found that for three selected areas, and for all the forecasted hours, the IG produces better results than the model's direct temperature forecasts. Thus, it was concluded that the use of the IG in an operational environment potentially provides considerable improvement in the predictive skill of frost events.