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CHAC: a weather pattern classification system for regional climate downscaling of daily precipitation. (Special Issue: The 6th European Framework Programme CLARIS Project: a Europe-South America Network for climate change assessment and impact studies.)

机译:CHAC:一种天气模式分类系统,用于逐日降水的区域气候缩减。 (特刊:第六个欧洲框架计划CLARIS项目:一个用于气候变化评估和影响研究的欧洲-南美网络。)

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A weather pattern clustering method is applied and calibrated to Argentinean daily weather stations in order to predict daily precipitation data. The clustering technique is based on k-means and is applied to a set of 17 atmospheric variables from the ERA-40 reanalysis covering the period 1979-1999. The set of atmospheric variables represent the different components of the atmosphere (dynamical, thermal and moisture). Different sensitivity tests are applied to optimize (1) the number of observations (weather patterns) per cluster, (2) the spatial domain size of the weather pattern around the station and (3) the number of members of the ensembles. All the sensitivity tests are compared using the ROC (Relative Operating Characteristic) Skill Score (RSS) derived from the ROC curve used to assess the performance of a predictive system. First, we found the number of observations per cluster to be optimum for values larger than 39. Second, the spatial domain size (~4 degrees x 4 degrees ) was found to be closer to a local scale than to a synoptic scale, certainly due to a dominant role of the moisture components in the optimization of the transfer function. Indeed, when reducing the set of variables to the subset of dynamical variables, the predictive skill of the method is significantly reduced, but at the same time the domain size must be increased. A potential improvement of the method may therefore be to consider different domains for dynamical and non-dynamical variables. Third, the number of members per ensembles of simulations was estimated to be always two to three times larger than the mean number of observations per cluster (meaning that at least all the observed weather patterns are selected by one member). The skill of the statistical method to predict daily precipitation is found to be relatively homogeneous all over the country for different thresholds of precipitation.
机译:应用天气模式聚类方法并将其校准到阿根廷的每日气象站,以预测每日的降水数据。聚类技术基于k均值,并应用于ERA-40再分析中涵盖1979-1999年的一组17个大气变量。大气变量集代表大气的不同组成部分(动态,热和湿气)。应用了不同的敏感性测试来优化(1)每个群集的观测值(天气模式)的数量,(2)站周围天气模式的空间域大小,以及(3)集合成员的数量。使用从ROC曲线得出的ROC(相对工作特征)技能得分(RSS)对所有灵敏度测试进行比较,该得分用于评估预测系统的性能。首先,我们发现对于大于39的值,每个聚类的观测数量是最佳的。其次,发现空间域大小(〜4度x 4度)更接近于局部尺度而不是天气尺度,这肯定是由于水分成分在优化传递函数中起主导作用。实际上,当将变量集减少为动态变量的子集时,该方法的预测技巧将显着降低,但同时必须增加域大小。因此,该方法的潜在改进可能是为动态和非动态变量考虑不同的域。第三,估计每个模拟集合的成员数总是比每个聚类的平均观测数大2到3倍(这意味着至少所有观测到的天气模式是由一个成员选择的)。在全国各地,由于降水的不同阈值,用于预测每日降水的统计方法的技能被认为是相对同质的。

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