Weather and Climate Extremes最新文献_第9页

The Unprecedented Late-Summer 2023 Heatwave in Southeastern South America: Attribution and future projection of similar events 2023年南美洲东南部史无前例的夏末热浪：类似事件的归因和未来预测

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-05-08 DOI: 10.1016/j.wace.2025.100772

Woon Mi Kim , Isla R. Simpson , Laurent Terray , Soledad Collazo

In March 2023, southeastern South America (SESA) experienced a severe heatwave with its maximum intensity exceeding four standard deviations from the climatological mean. The timing of the occurrence was also unusual, as it occurred in the late summer. This study examines the contributing factors to the March 2023 SESA heatwave using a dynamical adjustment approach based on constructed atmospheric circulation analogs from the ERA5 reanalysis. Additionally, we assess changes in March heatwaves in the Coupled Model Intercomparison Project 6 (CMIP6) Shared Socioeconomic Pathways 3-7.0 climate simulations using the same method.

The dynamical adjustment indicates that the largest contributors to the heatwave are circulation anomalies (on average 33%, 2.72

° C

) and thermodynamic effect (58%, 4.75

° C

), primarily linked to soil-temperature feedback. This result supports that extremely dry soil from the ongoing multi-year drought played a role in amplifying the heatwave intensity. The persistence of the circulation anomalies is also noticeable during the period. The contribution of the long-term temperature trend is 9% (0.78

° C

).

In CMIP6 future simulations, the number of March heatwaves increases, but the relative frequency of March-2023-like dry-hot heatwaves decreases, largely due to projected increases in soil moisture. The contributions of the temperature trends and circulation anomalies are larger, while the thermodynamic effects related to soil-temperature feedback are reduced. The finding suggests that future March heatwaves are driven by increases in temperatures with reduced roles of soil moisture. However, uncertainty exists in future soil moisture projections, indicating the need for more understanding of changes in heatwaves in the region.

2023年3月，南美洲东南部（SESA）经历了一次强烈的热浪，其最大强度超过了气候平均值的4个标准差。这次事件发生的时间也很不寻常，因为它发生在夏末。本文采用基于ERA5再分析模拟大气环流的动力调整方法，探讨了2023年3月SESA热浪的影响因素。此外，我们在耦合模式比较项目6 （CMIP6）共享社会经济路径3-7.0气候模拟中使用相同的方法评估了3月份热浪的变化。动力调整表明，环流异常（平均占33%，2.72°C）和热力学效应（58%,4.75°C）对热浪的贡献最大，主要与土壤温度反馈有关。这一结果支持了持续多年的干旱造成的极度干燥的土壤在放大热浪强度方面发挥了作用。在此期间，环流异常的持续也很明显。长期温度趋势的贡献率为9%（0.78°C）。在CMIP6未来的模拟中，3月热浪的次数增加，但类似2023年3月的干热热浪的相对频率减少，这主要是由于预估的土壤湿度增加。温度趋势和环流异常的贡献较大，而与土壤温度反馈相关的热力学效应减弱。这一发现表明，未来3月的热浪是由温度升高和土壤湿度作用减弱所驱动的。然而，未来土壤湿度预测存在不确定性，这表明需要更多地了解该地区热浪的变化。

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引用次数: 0

Future projection of East Asian atmospheric rivers in high-resolution climate models 东亚大气河流在高分辨率气候模式中的未来预估

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-05-07 DOI: 10.1016/j.wace.2025.100776

Yeeun Kwon, Seok-Woo Son

Atmospheric rivers (ARs) play a critical role in extreme precipitation in East Asia during the East Asian summer monsoon. While ARs are projected to increase in a warming climate, their regional changes in East Asia remain unclear partly due to the use of relatively coarse models. This study investigates future changes in East Asian ARs using high-resolution climate model simulations. The results show a robust increase in AR frequency and associated precipitation in East Asia in the near future (2025–2050). ARs are also projected to become more intense and persistent with a considerable increase in extreme precipitation, although the quantitative change slightly differs depending on the AR detection algorithms. Such changes are primarily driven by thermodynamic processes, with dynamic processes playing a secondary role. However, the dynamic processes, especially low-frequency circulation changes, contribute significantly to the inter-model spread, determining the uncertainty in the future projections of East Asian ARs. This finding helps to better understand future changes in AR and associated extreme precipitation in East Asia.

在东亚夏季风期间，大气河流在东亚极端降水中起着关键作用。虽然预估ar在气候变暖时会增加，但其在东亚的区域变化仍不清楚，部分原因是使用了相对粗糙的模式。本研究利用高分辨率气候模式模拟研究东亚ar的未来变化。结果表明，在近期（2025-2050年）东亚地区的AR频率和相关降水将强劲增加。随着极端降水的大量增加，AR也将变得更加强烈和持续，尽管AR检测算法的数量变化略有不同。这种变化主要是由热力学过程驱动的，动力过程起次要作用。然而，动力过程，特别是低频环流变化，对模式间传播有重要贡献，决定了东亚未来ARs预测的不确定性。这一发现有助于更好地理解东亚AR和相关极端降水的未来变化。

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引用次数: 0

Joint estimation of trend in bulk and extreme daily precipitation in Switzerland 瑞士大量降水和极端日降水趋势的联合估计

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-05-07 DOI: 10.1016/j.wace.2025.100769

Abubakar Haruna, Juliette Blanchet, Anne-Catherine Favre

Precipitation is crucial for water supply and energy generation in the Alps. However, heavy precipitation can also lead to natural disasters. It is therefore essential to understand the changes in both mean and extreme precipitation in order to develop effective adaptation and mitigation strategies. This study jointly models the observed long-term trends in both the bulk and extremes of daily precipitation distribution in Switzerland by employing a non-stationary version of the Extended Generalized Pareto distribution (EGPD). The EGPD allows us to model the entire non-zero precipitation range while remaining consistent with extreme value theory in its lower and upper tails.. We incorporated the non-stationarity by allowing the parameters of the distribution to vary with two covariates, time and sea surface temperature, and used a bootstrap approach for uncertainty assessment and to assess the significance of the modeled trends. The results indicate that extreme precipitation has increased in all seasons, while mean precipitation has only significantly increased in winter in northern Switzerland. This increase in winter precipitation is attributed to both a positive trend in the frequency and in the intensity of wet days precipitation.

降水对阿尔卑斯山的供水和发电至关重要。然而，强降水也会导致自然灾害。因此，必须了解平均和极端降水的变化，以便制定有效的适应和缓解战略。本研究采用扩展广义帕累托分布（EGPD）的非平稳版本，联合模拟了观测到的瑞士日降水分布的总体和极值的长期趋势。EGPD使我们能够模拟整个非零降水范围，同时在其上下尾保持与极值理论的一致。我们通过允许分布参数随两个协变量（时间和海面温度）变化而变化来纳入非平稳性，并使用自举方法进行不确定性评估和评估模型趋势的重要性。结果表明，极端降水在所有季节都有所增加，而平均降水仅在冬季显著增加。冬季降水的增加是由于湿日降水的频率和强度都呈正趋势。

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引用次数: 0

Synoptic-based model for reconstructing and forecasting high-frequency sea-level extremes in the mediterranean 重建和预测地中海高频率海平面极端事件的天气学模型

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-05-06 DOI: 10.1016/j.wace.2025.100775

P. Zemunik Selak , I. Vilibić , C. Denamiel , P. Pranić

This paper evaluates the performance of a synoptic index-based model designed to predict extreme non-seismic sea-level oscillations at tsunami timescales (NSLOTTs) across 32 tide-gauge stations in the Mediterranean Sea, where NSLOTTs can contribute up to 50 % of the total sea-level range. The model employs percentile-determined threshold exceedance criteria to define extreme NSLOTT events. A part of the time series containing half of extreme NSLOTT events is used for model training, while the rest is used for performance assessing. The baseline model integrates seven synoptic variables previously identified for a known NSLOTT hotspot and available within atmospheric reanalysis products. Various model configurations and modifications were tested to evaluate adaptability and robustness in forecasting and detecting extreme NSLOTT events. Results indicate that the model success in forecasting extreme events slightly outweighs its success in detecting observed extreme events. For stations where the baseline model performs well, this proficiency remains consistent across different configurations. However, the uncertainty in model performance is greater for these stations compared to those with poorer performance, which show minimal improvement despite configuration adjustments. Sub-basin analysis reveals that tide-gauge stations located in the eastern Adriatic Sea exhibit the best performance on average. These findings provide valuable insights for optimizing the model setup, enhancing its predictive capabilities, and improving its application in projecting extreme NSLOTT events in future climates. Ultimately, this work may contribute to coastal hazard and flooding mitigation, as well as resilience-building efforts, where extreme NSLOTT events could play a substantial role.

本文评估了一个基于天气指数的模型的性能，该模型旨在预测地中海32个潮汐测量站在海啸时间尺度上的极端非地震海平面振荡（nslots），其中nslots可以贡献高达总海平面范围的50%。该模型采用百分位数确定的阈值超出标准来定义极端NSLOTT事件。包含一半极端NSLOTT事件的时间序列的一部分用于模型训练，而其余部分用于性能评估。基线模式整合了先前为已知的NSLOTT热点确定的七个天气变量，这些变量在大气再分析产品中可用。对各种模型配置和修改进行了测试，以评估预测和检测极端NSLOTT事件的适应性和稳健性。结果表明，该模型预测极端事件的成功率略高于探测观测到的极端事件的成功率。对于基线模型表现良好的站点，这种熟练程度在不同的配置中保持一致。然而，与那些性能较差的站点相比，这些站点的模型性能的不确定性更大，尽管配置调整，但这些站点的改进很小。分流域分析表明，位于亚得里亚海东部的测潮站平均表现最好。这些发现为优化模型设置、增强模型预测能力以及改进模型在预测未来极端nslot事件中的应用提供了有价值的见解。最终，这项工作可能有助于减轻沿海灾害和洪水，以及在极端NSLOTT事件可能发挥重要作用的复原力建设工作。

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引用次数: 0

Predictability assessment of marine heatwaves in the Northeast Pacific based on SEAS5 基于SEAS5的东北太平洋海洋热浪可预测性评估

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-04-25 DOI: 10.1016/j.wace.2025.100773

Zhouhong Liu , Boni Wang , Haixia Shan

Marine Heatwaves (MHWs), extreme ocean warming events, have attracted global attention. This research utilizes forecast data from SEAS5 (Seasonal Forecasting System 5) and OISST (Optimum Interpolation Sea Surface Temperature), applying a range of evaluation metrics from both deterministic and probabilistic forecasting viewpoints. It assesses the forecasting performance of the SEAS5 in the Northeast Pacific (NEP) over the period from 1994 to 2021, examining both spatial and temporal dimensions. The midwest of the NEP exhibits subpar performance in terms of both deterministic and probabilistic predictions when compared to other regions. Furthermore, the SEAS5's forecast skill for MHWs is significantly influenced by the El Niño-Southern Oscillation (ENSO) and seasonal variations. This study establishes probability thresholds for MHWs' occurrences to assess MHWs' predictability using SEAS5, demonstrating that forecasting effectiveness across NEP subregions strongly depends on probability thresholds. To evaluate model performance in terms of reliability and resolution, the research concentrates on the Brier Score decomposition, revealing that the southeastern NEP region exhibits superior reliability and resolution. Additionally, the study focuses on not only the comprehensive efficacy of SEAS5 forecast on the NEP as a whole, but also on the specific performance across different regions. The proposed reliability categorization of MHWs indicates that the majority of regions within the NEP fall into Category 3 and above (at least marginally useful) across all lead times. The SEAS5 has shown high predictability in forecasting the occurrences of MHWs in the NEP, exhibiting diverse forecasting accuracy for MHWs across various maritime regions.

海洋热浪（MHWs）是海洋极端变暖事件，已引起全球关注。本研究利用来自季节预报系统5 （SEAS5）和最佳插值海面温度（OISST）的预测数据，从确定性和概率预测的角度应用了一系列评估指标。从空间和时间两个维度评估了1994年至2021年期间东北太平洋（NEP）第5季的预报性能。与其他地区相比，新经济政策的中西部地区在确定性和概率预测方面表现欠佳。此外，厄尔尼诺Niño-Southern涛动（ENSO）和季节变化显著影响了SEAS5对强震的预报能力。本研究建立了mhw发生的概率阈值，利用SEAS5评估mhw的可预测性，表明NEP次区域的预报有效性强烈依赖于概率阈值。为了评价模型在可靠性和分辨率方面的性能，研究集中在Brier Score分解上，结果表明新经济政策东南部地区具有较好的可靠性和分辨率。此外，本研究不仅关注了SEAS5预测对新经济政策整体的综合效果，还关注了不同区域的具体表现。建议的mhw可靠性分类表明，NEP内的大多数地区在所有交货时间内都属于第3类及以上（至少是边际有用的）。在预测NEP中mhw的发生方面，SEAS5显示出很高的可预测性，对不同海洋区域的mhw表现出不同的预测精度。

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引用次数: 0

Editorial: Australia's Tinderbox Drought 社论：澳大利亚的火药箱干旱

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-04-21 DOI: 10.1016/j.wace.2025.100766

Jason P. Evans , Nerilie J. Abram

引用次数: 0

Influence of inner-core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble model 内核对称对热带气旋快速增强的影响及其机器学习集成模型预测

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-04-17 DOI: 10.1016/j.wace.2025.100770

Jiali Zhang , Qinglan Li , Liguang Wu , Qifeng Qian , Xuyang Ge , Sam Tak Wu Kwong , Yun Zhang , Xinyan Lyu , Guanbo Zhou , Gaozhen Nie , Pak Wai Chan , Wai Kin Wong , Linwei Zhu

This study proposed a novel quantitative index, the Symmetric Ratio, derived from satellite observations to depict Tropical Cyclone (TC) inner-core symmetry. This index is found to be significantly influential in TC Rapid Intensification (RI). We applied four machine learning (ML) models—Decision Tree, Random Forest, Light Gradient Boosting Machine, and Adaptive Boosting to forecast TC RI in the Northwestern Pacific (WNP) and North Atlantic (NA) basins from 2005 to 2023, with lead times of 12 and 24 hours. An ensemble model integrated these ML models to further enhance prediction accuracy. Model training used TC best track and reanalysis data from 2005 to 2020, with validation from 2021 to 2022. Independent forecasting tests from 2016 to 2023 applied real-time TC track data from the Automated Tropical Cyclone Forecasting system and environmental data from the Global Forecast System. Compared with the best deterministic model with the detection probability (POD) of 21 % and false alarm rate (FAR) of 50 % for 24-h RI forecasts in the NA basin during 2016–2020, our ensemble model demonstrated significant improvements, achieving a POD of 0.27 and an FAR of 0.18 for the same period. For 2021–2023, the ensemble model obtained POD values of 0.24 and 0.41, and FAR values of 0.33 and 0.45 for 24-h predictions in the NA and WNP basins, respectively. Key predictors identified include maximum wind speed tendency, vertical wind shear, potential intensity, and Symmetric Ratio. These findings advance our understanding of TC RI mechanisms and improve prediction accuracy.

本文提出了一种新的量化指标——对称比，该指标来源于卫星观测，用于描述热带气旋（TC）内核的对称性。该指标对TC快速强化（RI）有显著影响。我们应用决策树、随机森林、光梯度增强机和自适应增强四种机器学习模型对西北太平洋（WNP）和北大西洋（NA）流域2005 - 2023年的TC RI进行了预测，预估时间分别为12和24小时。一个集成模型集成了这些机器学习模型，以进一步提高预测精度。模型训练使用了2005年至2020年的TC最佳跟踪和再分析数据，验证时间为2021年至2022年。2016 - 2023年的独立预报试验应用了来自自动热带气旋预报系统的实时TC轨迹数据和来自全球预报系统的环境数据。与最佳确定性模型（2016-2020年NA盆地24 h RI预测POD为21%，虚警率为50%）相比，我们的集合模型在同一时期的POD为0.27，FAR为0.18，具有显著的改进。在2021-2023年，集合模型在NA和WNP流域的24 h预测POD值分别为0.24和0.41，FAR值分别为0.33和0.45。确定的关键预测因子包括最大风速趋势、垂直风切变、势强和对称比。这些发现促进了我们对TC - RI机制的理解，提高了预测的准确性。

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引用次数: 0

Exploring synoptic patterns contributing to extreme rainfall from landfalling tropical cyclones in China 探讨热带气旋登陆中国导致极端降雨的天气模式

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-04-17 DOI: 10.1016/j.wace.2025.100768

Xiaoting Fan , Dajun Zhao , Ying Li , Xin Zhang , Yiyun Xie , Lianshou Chen

Extreme rainfall resulted from landfalling tropical cyclones (ERLTC) can lead to severe disasters and enormous economic losses across China, highlighting the critical need to improve ERLTC forecasting accuracy for disaster prevention and mitigation. This study examines 789 ERLTC days in China from 1979 to 2019. These ERLTC days are classified into four dominant synoptic patterns (P1 to P4) using the self-organizing map method based on the configurations of landfalling tropical cyclones (TCs), the western Pacific subtropical high (WPSH), and associated low-level water vapor transport. These four patterns are characterized as follows: P1 (33.0 %) reflects a typical mid-summer circulation with WPSH and south Asian high (SAH) ridge lines around 25°N; P2 (30.0 %) exhibits a northward-shifted WPSH and SAH with double TC circulations; P3 (20.3 %) features a southward-shifted WPSH and SAH with the weakest TC and monsoon circulation; P4 (16.7 %) shows an eastward-shifted WPSH and SAH with the strongest TC circulation. Compared to P1, TCs are positioned further north in P2, further south in P3, and further east in P4. The intensity centers of ERLTC correspond closely with regions of column-integrated water vapor flux convergence and high-level divergence, located in southern coast areas under P1 and P3, southeast mainland and around Bohai Bay under P2, and along eastern coastal areas under P4. Meanwhile, ERLTC is confined to regions south of 30°N in P3 due to weaker water vapor transport. These findings offer valuable insights to comprehensively understand ERLTC events in China.

登陆热带气旋（ERLTC）导致的极端降雨会给中国各地带来严重的灾害和巨大的经济损失，这凸显了提高热带气旋（ERLTC）预报精度以预防和减轻灾害的迫切需要。本研究考察了1979年至2019年中国789个ERLTC日。利用登陆热带气旋（tc）、西太平洋副热带高压（WPSH）和相关低层水汽输送的配置，采用自组织图方法将这些ERLTC日划分为4个主要天气型（P1 ~ P4）。这四种模式的特征如下：P1（33.0%）反映了一个典型的盛夏环流，在25°N附近有西太平洋高压和南亚高压脊线；P2（30.0%）表现为北移的西太平洋高度和南亚高压，并伴有双TC环流；P3（20.3%）以副高和南亚高压南移为主，TC和季风环流最弱；P4（16.7%）表现为西太平洋高压和南亚高压东移，TC环流最强。与P1相比，tc在P2中位置更靠北，在P3中位置更靠南，在P4中位置更靠东。ERLTC的强度中心与水汽通量辐合和高层辐散区密切对应，位于P1和P3下的南部沿海地区，P2下的东南大陆和渤海湾周边地区，以及P4下的东部沿海地区。同时，由于水汽输送较弱，在P3中ERLTC被限制在30°N以南地区。这些发现为全面了解中国的ERLTC事件提供了有价值的见解。

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引用次数: 0

The enhanced integration of proven techniques to quantify the uncertainty of forecasting extreme flood events based on numerical weather prediction models 在数值天气预报模式的基础上，加强整合已证实的技术，以量化极端洪水事件预测的不确定性

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-04-16 DOI: 10.1016/j.wace.2025.100767

Mitra Tanhapour , Jaber Soltani , Hadi Shakibian , Bahram Malekmohammadi , Kamila Hlavcova , Silvia Kohnova , Peter Valent

Skillful forecasting of reservoir inflow is one of the main prerequisites for determining reservoir operation and management policies. This research incorporates proven techniques in a novel way to develop a comprehensive framework for forecasting event-based inflow floods with sub-daily time steps (6-h intervals), considering the uncertainty of Numerical Weather Prediction (NWP) models. Accordingly, raw precipitation forecasts were extracted for six extreme flood events in the Dez River basin, Iran. A Multi-Model Ensemble (MME) system was developed using the Group Method of Data Handling (GMDH) and Weighted Average-Weighted Least Square Regression (WA-WLSR) models to post-process raw precipitation forecasts. We thereupon proposed an approach that combined the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model with the Long-Short Term Memory (LSTM) network (HBV-LSTM model) to enhance flood forecasting. Moreover, a comparative analysis was performed between the modeling approaches, i.e., probabilistic inflow forecasting and deterministic inflow forecasting. The results revealed that the forecasting skill of the MME model built using the WA-WLSR model was higher than that of the GMDH model. Accordingly, the highest Continuous Ranked Probability Skill Scores (CRPSS) of 0.61 and 0.67 were achieved by the GMDH and WA-WLSR models, respectively, based on a precipitation threshold of 10 mm. Additionally, both the HBV-LSTM model and the LSTM network outperformed the individual HBV model in producing inflow flood hydrographs. Based on the best flood forecasting approach, i.e., the HBV-LSTM model, the

NSE

exceeded 0.95, and the

NRMSE

remained below 0.09 for various flood events. The outcomes indicated a variability of 2–10 % in the relative peak error using the HBV-LSTM approach for different flood events. Our findings provide valuable insights for determining the key elements of reservoir operations and enhancing management strategies under flood conditions.

做好水库入库预测是制定水库运行管理政策的重要前提之一。考虑到数值天气预报（NWP）模式的不确定性，本研究以一种新颖的方式结合成熟的技术，开发了一个综合框架，用于以次日时间步长（6小时间隔）预测基于事件的入流洪水。据此，提取了伊朗德兹河流域6次极端洪水事件的原始降水预报。利用分组数据处理方法（GMDH）和加权平均加权最小二乘回归（WA-WLSR）模型建立了多模式集成（MME）系统，对原始降水预报进行后处理。因此，我们提出了一种将Hydrologiska byr Vattenbalansavdelning （HBV）水文模型与长短期记忆（LSTM）网络（HBV-LSTM模型）相结合的方法来增强洪水预报。并对概率入流预测和确定性入流预测两种建模方法进行了对比分析。结果表明，利用WA-WLSR模型建立的MME模型的预测能力高于GMDH模型。因此，基于10 mm降水阈值，GMDH和WA-WLSR模型分别获得了最高的连续排名概率技能分数（CRPSS），分别为0.61和0.67。此外，HBV-LSTM模型和LSTM网络在生成流入洪水水文图方面都优于个体HBV模型。以HBV-LSTM模型作为最佳洪水预报方法，各洪水事件的NSE均超过0.95，NRMSE均低于0.09。结果表明，对于不同的洪水事件，使用HBV-LSTM方法的相对峰值误差的可变性为2 - 10%。我们的研究结果为确定水库运行的关键要素和加强洪水条件下的管理策略提供了有价值的见解。

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引用次数: 0

Sub-hourly precipitation and rainstorm event profiles in a convection-permitting multi-GCM ensemble 允许对流的多gcm集合中的次小时降水和暴雨事件廓线

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-04-10 DOI: 10.1016/j.wace.2025.100764

Marie Hundhausen , Hayley J. Fowler , Hendrik Feldmann , Joaquim G. Pinto

Extreme precipitation on short, sub-hourly time scales has the potential to trigger flash floods, is a particular threat to urban areas, and is expected to increase with climate change. However, little is known about sub-hourly precipitation extremes in convection-permitting climate models (CPMs). We investigate sub-hourly precipitation in the KIT-KLIWA ensemble — a CPM climate ensemble driven by 3 CMIP5 GCMs coupled to the regional climate model COSMO-CLM. The domain is centred over Germany with a grid resolution of 0.025 °(2.8 km). In an event-based analysis, we compare extreme precipitation down to 5-min resolution for a historical simulation (1971–2000) with the dense radar and raingauge observation network in Germany. We find that 5-min CPM precipitation data adequately reproduces the frequency distribution from radar measurements. To improve the understanding of the precipitation bias in CPM simulations we propose an event-based analysis, that reveals a tendency for the CPM to overestimate the occurrence of longer events, and for a simulation bias in the representation of heavy and short, likely convective, precipitation events. The CPM historical simulations mostly reproduce the event precipitation sum for events leading to 1 h and 6 h annual maxima. Maximum 5-min peak intensities of these extreme precipitation events agree with spatially-aggregated radar data but are well below intensity maxima observed in station data. A dominant (very) front-loaded shape for precipitation events leading to 1 h annual maxima is reproduced by the CPM ensemble. The demonstration that CPMs effectively capture the key features of rainstorm profiles, opens up opportunities for climate change studies and their application in hydrological modelling.

短时间、小时以下的极端降水有可能引发山洪暴发，对城市地区是一个特别的威胁，预计将随着气候变化而增加。然而，在允许对流的气候模式（cpm）中，人们对次小时极端降水知之甚少。本文研究了由3个CMIP5 gcm与COSMO-CLM区域气候模式耦合驱动的CPM气候集合KIT-KLIWA的次小时降水。该区域以德国为中心，网格分辨率为0.025°（2.8公里）。在基于事件的分析中，我们将历史模拟（1971-2000）的5分钟分辨率的极端降水与德国密集雷达和雨量观测网进行了比较。我们发现5min CPM降水数据充分再现了雷达测量的频率分布。为了提高对CPM模拟降水偏差的理解，我们提出了一种基于事件的分析方法，该方法揭示了CPM高估较长时间事件发生的趋势，以及在表示重降水事件和短降水事件（可能是对流降水事件）时的模拟偏差。CPM历史模拟主要再现了导致1 h和6 h年最大值的事件降水总和。这些极端降水事件的最大5分钟峰值强度与空间聚合雷达资料一致，但远低于台站资料观测到的最大强度。CPM集合再现了导致1 h年极大值的降水事件的主要（非常）前负荷形状。cpm有效捕捉暴雨剖面的主要特征，为气候变化研究及其在水文模拟中的应用提供了机会。

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引用次数: 0