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Forecast of frost days based on monthly temperatures

机译：根据每月温度预测霜冻天数

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Although frost can cause considerable crop damage, and practices have been developed to mitigate forecasted frost, frost forecasting technologies have not changed for years. This paper reports on a new method based on successive application of two models to forecast the number of monthly frost days for several Community of Madrid (Spain) meteorological stations. The first is an autoregressive integrated moving average (ARIMA) stochastic model that forecasts minimum monthly absolute temperature (t(min)) and average monthly minimum temperature (mu(t)) following Box and Jenkins methodology The second model relates monthly temperatures (t(min), mu(t)) to the minimum daily temperature distribution during one month. Three ARIMA models were identified. They present the same seasonal behaviour (integrated moving average model) and different non-seasonal part: autoregressive model (Model 1), integrated moving average model (Model 2) and autoregressive and moving average model (Model 3). The results indicate that minimum daily temperature (t(dmin)) for the meteorological stations studied followed a normal distribution each month with a very similar standard deviation through out the years. This standard deviation obtained for each station and each month could be used as a risk index for cold months. The application of Model 1 to predict minimum monthly temperatures produced the best frost days forecast. This procedure provides a tool for crop managers and crop insurance companies to assess the risk of frost frequency and intensity, so that they can take steps to mitigate frost damage and estimate the damage that frost would cause.
机译：尽管霜冻会严重损害农作物，并且已经开发出减轻霜冻预报的方法，但是霜冻预报技术多年没有改变。本文报告了一种新方法，该方法基于两个模型的连续应用来预测马德里（西班牙）几个气象站的每月霜冻天数。第一个是采用Box和Jenkins方法预测的最低每月绝对温度（t（min））和平均每月最低温度（mu（t））的自回归综合移动平均值（ARIMA）随机模型，第二个模型涉及每月温度（t（ min），mu（t））到一个月内的最低每日温度分布。确定了三个ARIMA模型。他们呈现出相同的季节性行为（集成的移动平均模型）和不同的非季节部分：自回归模型（模型1），集成移动平均模型（模型2）以及自回归和移动平均模型（模型3）。结果表明，所研究的气象站的最低每日温度（t（dmin））遵循每月正态分布，并且多年来的标准偏差非常相似。每个站点和每个月获得的标准差可用作寒冷月份的风险指数。应用模型1预测最低每月温度可产生最佳的霜冻天数预测。该程序为农作物管理者和农作物保险公司提供了一种评估霜冻频率和强度的风险的工具，以便他们可以采取措施减轻霜冻的危害并估算霜冻可能造成的危害。

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