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Estimating extreme daily pollen loads for Szeged, Hungary using previous-day meteorological variables

机译：使用前一天的气象变量估算匈牙利塞格德的每日极端花粉负荷

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The aim of this paper is to analyse how meteorological elements relate to extreme Ambrosia pollen load on the one hand and to extreme total pollen load excluding Ambrosia pollen on the other for Szeged, Southern Hungary. The data set comes from a 9-year period (1999-2007) and includes previous-day means of five meteorological variables and actual-day values of the two pollen variables. Factor analysis with special transformation was performed on the meteorological and pollen load data in order to find out the strength and direction of the association of the meteorological and pollen variables. Then, using selected low and high quantiles corresponding to probability distributions of Ambrosia pollen and the remaining pollen loads, the quantile and beyond-quantile averages of pollen loads were compared and evaluated. Finally, a nearest neighbour (NN) technique was applied to discriminate between extreme and non-extreme pollen events using meteorological elements as explaining variables. The observed below or above quantile events are compared with events obtained from NN decisions. The number of events exceeding the quantile of 90% and not exceeding that of 10% is strongly underestimated. However, the procedure works well for quantiles of 20 and 80%, and even better for those of 30 and 70%. Using a nearest neighbour technique, explaining variables in decreasing order of their influence on Ambrosia pollen load are temperature, global solar flux, relative humidity, air pressure and wind speed, while on the load of the remaining pollen are temperature, relative humidity, global solar flux, air pressure and wind speed.
机译：本文的目的是一方面分析匈牙利南部塞格德（Szeged）的气象要素如何一方面与极端的Ambrosia花粉负荷有关，另一方面与极端的总花粉负荷（不包括Ambrosia花粉）有关。数据集来自一个为期9年（1999-2007年）的数据，其中包括五个气象变量的前一天平均值以及两个花粉变量的实际一天值。为了找出气象和花粉变量关联的强度和方向，对气象和花粉负荷数据进行了特殊转换的因子分析。然后，使用选择的低分位数和高分位数对应于Ambrosia花粉的概率分布和剩余的花粉负荷，对花粉负荷的分位数和超出分位数的平均值进行比较和评估。最后，应用最近邻（NN）技术，以气象要素为解释变量来区分极端和非极端花粉事件。将观察到的低于或高于分位数的事件与从NN决策获得的事件进行比较。强烈低估了超过90％的分位数但不超过10％的事件数。但是，该程序对于20％和80％的分位数效果很好，对于30％和70％的分位数甚至更好。使用最近邻技术，按变量，温度，总体太阳通量，相对湿度，空气压力和风速对变量的影响程度从高到低依次解释，而其余花粉的变量为温度，相对湿度，全局太阳光。通量，气压和风速。

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