Leah Eisenstein, Benedikt Schulz, J. Pinto, P. Knippertz
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In Part 1 of this study (Eisenstein et al., 2022a), we introduced the objective and flexible identification tool RAMEFI (RAndom-forest-based MEsoscale wind Feature Identification), which distinguishes between the WJ, CFC and CS as well as CJ and SJ combined. RAMEFI is based on a probabilistic random forest trained on station observations of 12 storm cases over Europe. Being independent of spatial distribution, RAMEFI can also be applied to gridded data. Here, we use RAMEFI to compile a climatology over 19 extended winter seasons (October–March 2000–2019) based on high-resolution regional reanalyses of the German Consortium for Small-scale Modelling (COSMO) model over Europe. This allows the first ever long-term objective statistical analysis of the mesoscale wind features, including their occurrence frequency, geographical distribution and characteristics. For western and central Europe, we demonstrate that the CS is prominent in most winter storms, while CFC is the least common cause of high winds, both in terms of frequency and affected area. However, probably due to convective momentum transport, CFC is on average the cause of the highest gusts after the CJ and has the highest gust factor. As expected, CFC high-wind areas show high levels of humidity and overcast conditions. In contrast, the CS is characterised by sunnier conditions interspersed by patchy cumulus clouds, leading to a broader cloud cover distribution than for other features. The WJ produces the weakest winds on average but affects a larger area than CJ. Central Europe is more strongly affected by WJ and CFC winds, while the CJ usually occurs farther north over the North and Baltic seas, northern Germany, Denmark and southern Scandinavia. System-relative composites show that the WJ and CFC tend to occur earlier in the cyclone life cycle than the CJ and CS. Consistently, the CS is the most common cause of high winds over eastern Europe, where cyclones tend to occlude, represented by a narrowing warm sector and weakening cold front. The WJ mostly occurs within the south-eastern quadrant of a cyclone bordered by the narrow CFC in the west. However, the location of CFC varies greatly between cases. The CS occurs in the south-western quadrant, while the CJ appears closer to the cyclone centre, sometimes stretching into the south-eastern quadrant. This objective climatology largely confirms previous, more subjective investigations but puts these into climatological context. 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These high winds are mostly associated with five mesoscale features: the warm (conveyor belt) jet (WJ); the cold (conveyor belt) jet (CJ); cold frontal convection (CFC); strong cold-sector (CS) winds; and, in some cases, the sting jet (SJ). The timing within the cyclone's life cycle, the location relative to the cyclone core and further characteristics differ between these features and, hence, likely also their associated forecast errors. In Part 1 of this study (Eisenstein et al., 2022a), we introduced the objective and flexible identification tool RAMEFI (RAndom-forest-based MEsoscale wind Feature Identification), which distinguishes between the WJ, CFC and CS as well as CJ and SJ combined. RAMEFI is based on a probabilistic random forest trained on station observations of 12 storm cases over Europe. Being independent of spatial distribution, RAMEFI can also be applied to gridded data. Here, we use RAMEFI to compile a climatology over 19 extended winter seasons (October–March 2000–2019) based on high-resolution regional reanalyses of the German Consortium for Small-scale Modelling (COSMO) model over Europe. This allows the first ever long-term objective statistical analysis of the mesoscale wind features, including their occurrence frequency, geographical distribution and characteristics. For western and central Europe, we demonstrate that the CS is prominent in most winter storms, while CFC is the least common cause of high winds, both in terms of frequency and affected area. However, probably due to convective momentum transport, CFC is on average the cause of the highest gusts after the CJ and has the highest gust factor. As expected, CFC high-wind areas show high levels of humidity and overcast conditions. 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引用次数: 0
摘要
摘要与外热带气旋相关的强风是欧洲最危险的自然灾害之一。这些强风主要与五个中尺度特征有关:暖(传送带)喷流(WJ);冷(传送带)喷流(CJ);冷锋对流(CFC);强冷空气(CS);以及在某些情况下的刺喷流(SJ)。这些特征在气旋生命周期中的时间、相对于气旋核心的位置和其他特征各不相同,因此也可能存在相关的预报误差。在本研究的第一部分(Eisenstein 等人,2022a)中,我们介绍了客观灵活的识别工具 RAMEFI(基于森林的中尺度风特征识别),它可以区分 WJ、CFC 和 CS 以及 CJ 和 SJ。RAMEFI 基于对欧洲 12 个风暴观测站观测数据进行训练的概率随机森林。RAMEFI 与空间分布无关,因此也可应用于网格数据。在此,我们利用 RAMEFI,基于德国小尺度模拟联合会(COSMO)模型在欧洲的高分辨率区域再分析,编制了 19 个冬季延长季节(2000 年 10 月至 2019 年 3 月)的气候学数据。这首次对中尺度风特征进行了长期客观的统计分析,包括其出现频率、地理分布和特征。对于欧洲西部和中部,我们证明 CS 在大多数冬季风暴中都很突出,而 CFC 在频率和受影响面积方面都是最不常见的大风原因。然而,可能是由于对流动量传输的原因,CFC 平均是继 CJ 之后导致最高阵风的原因,并且具有最高的阵风系数。不出所料,CFC 高风速地区的湿度和阴雨条件都很高。相比之下,CS 的特点是阳光充足,夹杂着零星积云,导致云层分布比其他特征更广。WJ 产生的平均风力最弱,但影响的区域比 CJ 大。中欧受 WJ 和 CFC 风的影响更大,而 CJ 通常出现在更北边的北海和波罗的海、德国北部、丹麦和斯堪的纳维亚半岛南部。系统相关合成显示,WJ 和 CFC 在气旋生命周期中出现的时间往往早于 CJ 和 CS。一直以来,CS 是造成东欧上空大风的最常见原因,气旋往往会在那里闭塞,表现为暖扇缩小和冷锋减弱。WJ 大多发生在气旋的东南象限内,与西部狭窄的 CFC 相邻。然而,不同情况下 CFC 的位置差异很大。CS 出现在西南象限,而 CJ 出现在离气旋中心更近的地方,有时会延伸到东南象限。这种客观的气候学研究在很大程度上证实了之前更为主观的调查,但也将这些调查纳入了气候学背景中。它允许对特征特性进行更详细的分析,并为未来研究中的模式评估和预报评价奠定了坚实的基础。
Identification of high-wind features within extratropical cyclones using a probabilistic random forest – Part 2: Climatology over Europe
Abstract. Strong winds associated with extratropical cyclones are one of the most dangerous natural hazards in Europe. These high winds are mostly associated with five mesoscale features: the warm (conveyor belt) jet (WJ); the cold (conveyor belt) jet (CJ); cold frontal convection (CFC); strong cold-sector (CS) winds; and, in some cases, the sting jet (SJ). The timing within the cyclone's life cycle, the location relative to the cyclone core and further characteristics differ between these features and, hence, likely also their associated forecast errors. In Part 1 of this study (Eisenstein et al., 2022a), we introduced the objective and flexible identification tool RAMEFI (RAndom-forest-based MEsoscale wind Feature Identification), which distinguishes between the WJ, CFC and CS as well as CJ and SJ combined. RAMEFI is based on a probabilistic random forest trained on station observations of 12 storm cases over Europe. Being independent of spatial distribution, RAMEFI can also be applied to gridded data. Here, we use RAMEFI to compile a climatology over 19 extended winter seasons (October–March 2000–2019) based on high-resolution regional reanalyses of the German Consortium for Small-scale Modelling (COSMO) model over Europe. This allows the first ever long-term objective statistical analysis of the mesoscale wind features, including their occurrence frequency, geographical distribution and characteristics. For western and central Europe, we demonstrate that the CS is prominent in most winter storms, while CFC is the least common cause of high winds, both in terms of frequency and affected area. However, probably due to convective momentum transport, CFC is on average the cause of the highest gusts after the CJ and has the highest gust factor. As expected, CFC high-wind areas show high levels of humidity and overcast conditions. In contrast, the CS is characterised by sunnier conditions interspersed by patchy cumulus clouds, leading to a broader cloud cover distribution than for other features. The WJ produces the weakest winds on average but affects a larger area than CJ. Central Europe is more strongly affected by WJ and CFC winds, while the CJ usually occurs farther north over the North and Baltic seas, northern Germany, Denmark and southern Scandinavia. System-relative composites show that the WJ and CFC tend to occur earlier in the cyclone life cycle than the CJ and CS. Consistently, the CS is the most common cause of high winds over eastern Europe, where cyclones tend to occlude, represented by a narrowing warm sector and weakening cold front. The WJ mostly occurs within the south-eastern quadrant of a cyclone bordered by the narrow CFC in the west. However, the location of CFC varies greatly between cases. The CS occurs in the south-western quadrant, while the CJ appears closer to the cyclone centre, sometimes stretching into the south-eastern quadrant. This objective climatology largely confirms previous, more subjective investigations but puts these into climatological context. It allows a more detailed analysis of feature properties and provides a solid foundation for model assessment and forecast evaluation in future studies.