Within the scope of urban climate modeling, weather analogs are used to downscale large-scale reanalysis-based information to station time series. Two novel approaches of weather analogs are introduced which allow a day-by-day comparison with observations within the validation period and which are easily adaptable to future periods for projections. Both methods affect the first level of analogy which is usually based on selection of circulation patterns. First, the time series were bias corrected and detrended before subsamples were determined for each specific day of interest. Subsequently, the normal vector of the standardized regression planes (NVEC) or the center of gravity (COG) of the normalized absolute circulation patterns was used to determine a point within an artificial coordinate system for each day. The day(s) which exhibit(s) the least absolute distance(s) between the artificial points of the day of interest and the days of the subsample is/are used as analog or subsample for the second level of analogy, respectively. Here, the second level of analogy is a second selection process based on the comparison of gridded temperature data between the analog subsample and the day of interest. After the analog selection process, the trends of the observation were added to the analog time series. With respect to air temperature and the exceedance of the 90th temperature quantile, the present study compares the performance of both analog methods with an already existing analog method and a multiple linear regression. Results show that both novel analog approaches can keep up with existing methods. One shortcoming of the methods presented here is that they are limited to local or small regional applications. In contrast, less pre-processing and the small domain size of the circulation patterns lead to low computational costs.

在城市气候模拟的范围内，天气类似物用于将基于大规模再分析的信息缩减为站点时间序列。引入了两种新的天气模拟方法，它们可以与验证期内的观测值进行日常比较，并且很容易适应未来的预测时段。两种方法都会影响第一类比，这通常是基于对流通模式的选择。首先，在确定每个感兴趣的特定日期的子样本之前，对时间序列进行偏差校正和去趋势处理。随后，使用标准化回归平面（NVEC）的法线向量或标准化绝对循环模式的重心（COG）来确定每天人工坐标系内的一个点。在感兴趣的天的人造点与子样本的天之间的最小绝对距离最远的那一天分别用作第二类比的模拟物或子样本。在此，第二类比是基于模拟子样本与感兴趣日期之间的栅格化温度数据的比较的第二选择过程。在模拟选择过程之后，观察趋势将添加到模拟时间序列中。关于空气温度和超过第90个温度分位数，本研究比较了两种模拟方法与已经存在的模拟方法和多元线性回归的性能。结果表明，两种新颖的模拟方法都可以跟上现有方法。这里介绍的方法的一个缺点是它们仅限于本地或较小的区域应用程序。相反，较少的预处理和循环模式的较小域大小导致较低的计算成本。