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The Impact of Analysis Correction-based Additive Inflation on subseasonal tropical prediction in the Navy Earth System Prediction Capability 基于分析修正的加法膨胀对海军地球系统预测能力中热带副季节预测的影响
IF 2.9 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2024-01-09 DOI: 10.1175/waf-d-23-0046.1
Stephanie S. Rushley, M. Janiga, William Crawford, Carolyn A. Reynolds, William Komaromi, J. McLay
Accurately simulating the Madden-Julian Oscillation (MJO), which dominates intraseasonal (30-90 day) variability in the tropics, is critical to predicting tropical cyclones (TCs) and other phenomena at extended-range (2-3 week) timescales. MJO biases in intensity and propagation speed are a common problem in global coupled models. For example, the MJO in the Navy Earth System Prediction Capability (ESPC), a global coupled model, has been shown to be too strong and too fast, which has implications for the MJO-TC relationship in that model.The biases and extended-range prediction skill in the operational version of the Navy ESPC are compared to experiments applying different versions of Analysis Correction-based Additive Inflation (ACAI) to reduce model biases. ACAI is a method in which time-mean and stochastic perturbations based on analysis increments are added to the model tendencies with the goals of reducing systematic error and accounting for model uncertainty. Over the extended boreal summer (May-November), ACAI reduces the root mean squared error (RMSE) and improves the spread-skill relationship of the total tropical and MJO-filtered OLR and low-level zonal winds. While ACAI improves skill in the environmental fields of low-level absolute vorticity, potential intensity, and vertical wind shear, it degrades the skill in the relative humidity, which increases the positive bias in the Genesis Potential Index (GPI) in the operational Navy ESPC. Northern Hemisphere integrated TC genesis biases are reduced (increased number of TCs) in the ACAI experiments, which is consistent with the positive GPI bias in the ACAI simulations.
马登-朱利安涛动(MJO)在热带季内(30-90 天)变率中占主导地位,准确模拟马登-朱利安涛动对于预测热带气旋(TC)和其他大范围(2-3 周)时间尺度的现象至关重要。MJO 在强度和传播速度上的偏差是全球耦合模式中的一个常见问题。例如,海军地球系统预报能力(ESPC)这一全球耦合模式中的 MJO 已被证明过强和过快,这对该模式中的 MJO-TC 关系产生了影响。海军 ESPC 运行版本中的偏差和远距离预报技能与应用不同版本的基于分析校正的加法膨胀(ACAI)以减少模式偏差的实验进行了比较。ACAI 是一种将基于分析增量的时间均值和随机扰动添加到模式趋势中的方法,目的是减少系统误差并考虑模式的不确定性。在延长的北方夏季(5 月至 11 月),ACAI 降低了均方根误差(RMSE),改善了热带和 MJO 滤波 OLR 总量以及低层带状风的传播-技能关系。虽然ACAI提高了低层绝对涡度、位势强度和垂直风切变等环境场的技能,但它降低了相对湿度的技能,从而增加了海军ESPC运行中的成因位势指数(GPI)的正偏差。在 ACAI 试验中,北半球综合热气旋成因偏差减小(热气旋数量增加),这与 ACAI 模拟中 GPI 的正偏差是一致的。
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引用次数: 0
Comparison of Clustering Approaches in a Multi-Model Ensemble for U.S. East Coast Cold Season Extratropical Cyclones 美国东海岸冷季外热带气旋多模式集合中的聚类方法比较
IF 2.9 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2024-01-09 DOI: 10.1175/waf-d-23-0017.1
Benjamin M. Kiel, B. Colle
Several clustering approaches are evaluated for 1–9-day forecasts using a multi-model ensemble that includes the GEFS, ECMWF, and Canadian ensembles. Six clustering algorithms and three clustering spaces are evaluated using mean sea-level pressure (MSLP) and 12-h accumulated precipitation (APCP) for cool-season extratropical cyclones across the Northeast United States. Using the MSLP cluster membership to obtain the APCP clusters is also evaluated, along with applying clustering determined at one lead time to cluster forecasts at a different lead time. Five scenarios from each clustering algorithm are evaluated using displacement and intensity/amount errors from the scenario nearest to the MSLP and 12-h APCP analyses in the NCEP GFS and ERA5, respectively. Most clustering strategies yield similar improvements over the full ensemble mean and are similar in probabilistic skill except that: (1) Intensity Displacement Space gives lower MSLP displacement and intensity errors; and (2) Euclidean Space and Agglomerative Hierarchical Clustering, when using either full or average linkage, struggle to produce reasonably sized clusters. Applying clusters derived from MSLP to 12-h APCP forecasts is not as skillful as clustering by 12-h APCP directly, especially if several members contain little precipitation. Use of the same cluster membership for one lead time to cluster the forecast at another lead time is less skillful than clustering independently at each forecast lead time. Finally, the number of members within each cluster does not necessarily correspond with the best forecast, especially at the longer lead times, when the probability of the smallest cluster being the best scenario was usually underestimated.
利用包括 GEFS、ECMWF 和加拿大集合在内的多模式集合,对 1-9 天预报的几种聚类方法进行了评估。利用美国东北部冷季外热带气旋的平均海平面气压(MSLP)和 12 小时累积降水量(APCP),对六种聚类算法和三种聚类空间进行了评估。此外,还评估了使用 MSLP 聚类成员资格获得 APCP 聚类的情况,以及将一个提前期确定的聚类应用于不同提前期的聚类预测的情况。分别使用 NCEP GFS 和 ERA5 中最接近 MSLP 和 12 小时 APCP 分析的位移和强度/数量误差,对每种聚类算法的五个方案进行了评估。与全集合平均值相比,大多数聚类策略都有类似的改进,在概率技能方面也相似,但以下情况除外:(1)强度位移空间的 MSLP 位移和强度误差较小;(2)欧几里得空间和聚合分层聚类在使用完全或平均联系时,难以产生合理大小的聚类。将 MSLP 得出的聚类应用于 12 小时 APCP 预报,不如直接按 12 小时 APCP 进行聚类来得有效,特别是当几个成员的降水量很少时。使用一个前导时间的同一聚类成员对另一个前导时间的预报进行聚类,不如在每个预报前导时间进行独立聚类那么熟练。最后,每个集群内的成员数并不一定与最佳预报一致,特别是在较长的前导时间内,最小集群成为最佳方案的概率通常被低估。
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引用次数: 0
Collaborative Exploration of Storm-Scale Probabilistic Guidance for NWS Forecast Operations 为 NWS 预报业务提供风暴尺度概率指导的合作探索
IF 2.9 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2024-01-08 DOI: 10.1175/waf-d-23-0174.1
Katie A. Wilson, P. Burke, Burkely T. Gallo, Patrick S. Skinner, T. T. Lindley, Chad M. Gravelle, Stephen W. Bieda, Jonathan G. Madden, Justin W. Monroe, Jorge E. Guerra, Dale A. Morris
The operational utility of the NOAA National Severe Storm Laboratory’s storm-scale probabilistic Warn-on-Forecast System (WoFS) was examined across the watch-to-warning time frame in a virtual NOAA Hazardous Weather Testbed (HWT) experiment. Over four weeks, 16 NWS forecasters from local Weather Forecast Offices, the Storm Prediction Center, and the Weather Prediction Center participated in simulated forecasting tasks and focus groups. Bringing together multiple NWS entities to explore new guidance impacts on the broader forecast process is atypical of prior NOAA HWT experiments. This study therefore provides a framework for designing such a testbed experiment, including methodological and logistical considerations necessary to meet the needs of both local office and national center NWS participants. Furthermore, this study investigated two research questions: (1) How do forecasters envision WoFS guidance fitting into their existing forecast process? and (2) How could WoFS guidance be used most effectively across the current watch-to-warning forecast process? Content and thematic analyses were completed on flowcharts of operational workflows, real-time simulation interactions, and focus group activities and discussions. Participants reported numerous potential applications of WoFS, including improved coordination and consistency between local offices and national centers, enhanced hazard messaging, and improved operations planning. Challenges were also reported, including the knowledge and training required to incorporate WoFS guidance effectively and forecasters’ trust in new guidance and openness to change. The solutions identified to these challenges will take WoFS one step closer to transition, and in the meantime, improve the capabilities of WoFS for experimental use within the operational community.
NOAA 国家强风暴实验室的风暴尺度概率预报系统(WoFS)在 NOAA 危险天气试验台(HWT)的虚拟试验中,对从观测到预警的整个时间段的实用性进行了检验。在四周时间里,来自当地天气预报办公室、风暴预报中心和天气预报中心的 16 名国家气象局预报员参加了模拟预报任务和焦点小组。将多个国家气象局实体聚集在一起探索新指南对更广泛预报流程的影响,这在 NOAA 之前的 HWT 实验中并不常见。因此,本研究提供了设计此类试验台实验的框架,包括满足地方办事处和国家中心 NWS 参与者需求所需的方法和后勤考虑因素。此外,本研究还探讨了两个研究问题:(1) 预报员如何设想将 WoFS 指导融入他们现有的预报流程?对业务工作流程图、实时模拟互动、焦点小组活动和讨论进行了内容和主题分析。与会者报告了 WoFS 的许多潜在应用,包括改善地方办事处和国家中心之间的协调性和一致性、加强灾害信息传递以及改善业务规划。与会者还报告了面临的挑战,包括有效采用 WoFS 指南所需的知识和培训,以及预报员对新指南的信任和对变革的开放性。针对这些挑战所确定的解决方案将使 WoFS 离过渡更近一步,与此同时,还将提高 WoFS 在业务界试验性使用的能力。
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引用次数: 0
Verification of the Global Forecast System, North American Mesoscale Forecast System, and High-Resolution Rapid Refresh Model Near-Surface Forecasts by use of the New York State Mesonet 利用纽约州中间网验证全球预报系统、北美中尺度预报系统和高分辨率快速刷新模式近地表预报
IF 2.9 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2023-12-29 DOI: 10.1175/waf-d-23-0094.1
Lauriana C. Gaudet, Kara J. Sulia, R. Torn, Nick P. Bassill
Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and High-Resolution Rapid Refresh (HRRR) 2-m temperature, 10-m wind speed, and precipitation accumulation forecasts initialized at 1200 UTC are verified against New York State Mesonet (NYSM) observations from 1 January 2018 through 31 December 2021. NYSM observations at 126 site locations are used to calculate standard error statistics (e.g., forecast error, root mean square error) for temperature and wind speed and contingency table statistics for precipitation across forecast hours, meteorological seasons, and regions. The majority of the focus is placed on the first 18 forecast hours to allow for comparison among all three models. A daily NYSM station-mean temperature error analysis identified a slight cold bias at temperatures below 25°C in the GFS, a cool-to-warm bias as forecast temperatures warm in the HRRR, and a warm bias at temperatures above 30°C in each model. Differences arise when considering temperature biases with respect to lead times and seasons. Wind speeds are over-forecast at all ranges in each season, and forecast wind speeds ≥ 18 m s−1 are rarely observed. Performance diagrams indicate overall good forecast performance at precipitation thresholds of 0.1–1.5 mm, but with a high frequency bias in the GFS and NAM. This paper provides an overview of deterministic forecast performance across NYS, with the aim of sharing common biases associated with temperature, wind speed, and precipitation with operational forecasters and is the first step in developing a real-time model forecast uncertainty prediction tool.
全球预报系统(GFS)、北美中尺度预报系统(NAM)和高分辨率快速预报系统(HRRR)在 1200 UTC 时初始化的 2 米气温、10 米风速和降水累积预报与纽约州中间网(NYSM)从 2018 年 1 月 1 日至 2021 年 12 月 31 日的观测数据进行了验证。126 个站点的 NYSM 观测数据被用于计算温度和风速的标准误差统计(如预报误差、均方根误差),以及不同预报时段、气象季节和地区降水的或然率统计。大部分重点放在前 18 个预报小时,以便对所有三种模式进行比较。通过对纽约气象站每日平均气温误差的分析,发现 GFS 在气温低于 25°C 时有轻微的冷偏差,HRRR 在预报气温升高时有由冷到暖的偏差,而每个模式在气温高于 30°C 时都有暖偏差。当考虑温度偏差与提前期和季节有关时,就会出现差异。在每个季节的所有范围内,风速都预报过高,很少观测到预报风速≥ 18 m s-1。性能图显示,在降水量阈值为 0.1-1.5 毫米时,预报性能总体良好,但 GFS 和 NAM 的频率偏差较大。本文概述了纽约州的确定性预报性能,目的是与业务预报员分享与温度、风速和降水相关的共同偏差,这也是开发实时模式预报不确定性预测工具的第一步。
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引用次数: 0
The influence of time varying sea-ice concentration on Antarctic and Southern Ocean numerical weather prediction 海冰浓度时变对南极和南大洋数值天气预报的影响
IF 2.9 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2023-12-28 DOI: 10.1175/waf-d-22-0220.1
Zhaohui Wang, Alexander D. Fraser, Phil Reid, Richard Coleman, S. O’Farrell
Although operational weather forecasting centres are increasingly using global coupled atmosphere-ocean-ice models to replace atmosphere-only models for short- and medium-range (10-day) weather forecasting, the influence of sea ice on such forecasting has yet to be fully quantified, especially in the Southern Ocean. To address this gap, a polar-specific version of the Weather Research and Forecasting model is implemented with a circumpolar Antarctic domain to investigate the impact of daily updates of sea-ice concentration on short- and medium- range weather forecasting. A statistically-significant improvement in near-surface atmospheric temperature and humidity is shown from +24 hours to +192 hours when updating the daily sea-ice concentration in the model. The forecast skill improvements for 2 m temperature and dewpoint temperature are enhanced from June to September, which is the period of late sea-ice advance. Regionally, model improvement is shown to occur in most sea-ice regions, although the improvement is strongest in the Ross Sea and Weddell Sea sectors. The surface heat budget also shows remarkable improvement in outgoing radiative heat fluxes and both sensible and latent heat fluxes. This idealised research demonstrates the non-negligible effect of including more accurate time-varying sea-ice concentration in numerical weather forecasting.
尽管业务天气预报中心越来越多地使用全球大气-海洋-冰层耦合模式来取代纯大气模式进行中短期(10 天)天气预报,但海冰对这种预报的影响尚未完全量化,特别是在南大洋。为了弥补这一不足,我们在南极环极域实施了极地专用版天气研究和预报模式,以研究海冰浓度每日更新对中短期天气预报的影响。在模型中每日更新海冰浓度后,近地面大气温度和湿度从+24 小时到+192 小时都有统计意义上的显著改善。6 月至 9 月是海冰后期推进期,这一时期 2 米气温和露点温度的预报技能得到提高。从区域上看,大多数海冰区的模式改进,尽管罗斯海和威德尔海区的改进最强。地表热量预算也显示出外向辐射热通量以及显热和潜热通量的显著改善。这项理想化研究表明,将更准确的时变海冰浓度纳入数值天气预报具有不可忽视的作用。
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引用次数: 0
Severe convective storms in limited instability organized by pattern and distribution 按模式和分布组织的有限不稳定性中的强对流风暴
IF 2.9 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2023-12-26 DOI: 10.1175/waf-d-23-0130.1
Trevor A. Campbell, G. Lackmann, Maria J. Molina, Matthew D. Parker
Severe convection occurring in high-shear, low-CAPE (HSLC) environments is a common cool-season threat in the Southeastern United States. Previous studies of HSLC convection document the increased operational challenges that these environments present compared to their high-CAPE counterparts, corresponding to higher false-alarm ratios and lower probability of detection for severe watches and warnings. These environments can exhibit rapid destabilization in the hours prior to convection, sometimes associated with the release of potential instability. Here, we use self-organizing maps (SOMs) to objectively identify environmental patterns accompanying HSLC cool season severe events and associate them with variations in severe weather frequency and distribution. Large scale patterns exhibit modest variation within the HSLC subclass, featuring strong surface cyclones accompanied by vigorous upper-tropospheric troughs and northward-extending regions of instability, consistent with prior studies. In most patterns, severe weather occurs immediately ahead of a cold front. Other convective ingredients, such as lower-tropospheric vertical wind shear, near-surface equivalent potential temperature (θe) advection, and the release of potential instability, varied more significantly across patterns. No single variable used to train SOMs consistently demonstrated differences in the distribution of severe weather occurrence across patterns. Comparison of SOMs based on upper and lower quartiles of severe occurrence demonstrated that the release of potential instability was most consistently associated with higher-impact events in comparison to other convective ingredients. Overall, we find that previously developed HSLC composite parameters reasonably identify high-impact HSLC events.
发生在高切变、低CAPE(HSLC)环境中的强对流是美国东南部冷季常见的威胁。以往对 HSLC 对流的研究表明,与高 CAPE 环境相比,这些环境带来的运行挑战更大,相应的误报率更高,严重天气监视和警报的探测概率更低。这些环境在对流发生前的几个小时内会表现出快速的不稳定,有时还与潜在不稳定性的释放有关。在此,我们使用自组织地图(SOM)客观地识别伴随 HSLC 冷季严重事件的环境模式,并将它们与严重天气频率和分布的变化联系起来。大尺度模式在 HSLC 亚类中表现出适度的变化,其特点是强表面气旋伴随着强烈的上对流层槽和向北延伸的不稳定区域,这与之前的研究一致。在大多数模式中,恶劣天气会紧接着冷锋出现。其他对流成分,如低对流层垂直风切变、近地表等效潜在温度(θe)平流和潜在不稳定性的释放,在不同模式中的变化更为显著。用于训练 SOM 的单个变量在不同模式的恶劣天气发生分布方面都没有持续的差异。基于严重天气发生率上下限四分位数的 SOMs 比较表明,与其他对流成分相比,潜在不稳定性的释放与影响较大的事件关联最为一致。总之,我们发现以前开发的 HSLC 复合参数可以合理地识别高影响 HSLC 事件。
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引用次数: 0
Relating Tropical Cyclone Intensification Rate to Precipitation and Convective Features in the Inner Core 热带气旋加强率与内核降水和对流特征的关系
IF 2.9 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2023-12-19 DOI: 10.1175/waf-d-23-0155.1
Xinxi Wang, Haiyan Jiang, Oscar Guzman
Using Tropical Rainfall Measuring Mission Microwave Imager observations of global tropical cyclones (TCs) from 1998 to 2013, relationships between TC intensification rate and inner-core convective and precipitation parameters are examined by decoupling the dependency of these parameters on TC intensity and that on TC intensification rate. Sixteen TC intensity change-intensity categories are categorized based on the initial intensity and 24-h future intensity change. The results show that the TC inner-core mean rain rate, convective intensity, and stratiform rain occurrence, and axisymmetric index of convective intensity increase significantly with TC intensification rate for each TC intensity category. The symmetry of rain rate and stratiform rainfall occurrence also increase significantly with TC intensification rate for each intensity category, except from slowly intensifying (SI) to rapidly intensifying (RI) group when the initial intensity is major hurricane. The RI major hurricanes have significantly more asymmetric rainfall distribution and distribution of stratiform rainfall occurrence than those of SI major hurricanes. For TCs with initial intensity in tropical depression, tropical storm, and major hurricane categories, the RI group has a significantly more asymmetric pattern of shallow precipitation/convection occurrence in the inner core than the SI group, while it has a significantly more symmetric pattern of deep convection occurrence than the SI group. The inner-core size, as quantified by the radius of maximum azimuthal mean rainfall decreases with both TC intensification rate and TC intensity.
利用热带降雨测量任务微波成像仪对 1998 年至 2013 年全球热带气旋(TC)的观测数据,通过将这些参数对热带气旋强度的依赖性与对热带气旋增强率的依赖性分离,研究了热带气旋增强率与内核对流和降水参数之间的关系。根据初始强度和 24 小时未来强度变化,划分了 16 个 TC 强度变化-强度类别。结果表明,在每个TC强度类别中,TC内核平均雨率、对流强度和层状雨发生率以及对流强度轴对称指数随着TC增强率的增加而显著增加。除初始强度为大飓风时从缓慢增强组(SI)到快速增强组(RI)外,各强度组的雨率对称性和层状雨出现率也随 TC 增强率的增加而显著增加。与 SI 大飓风相比,RI 大飓风的降雨分布和层状降雨出现率分布明显更不对称。对于初始强度为热带低压、热带风暴和大飓风的热带气旋,RI 组内核浅层降水/对流出现的非对称模式明显多于 SI 组,而其深层对流出现的对称模式则明显多于 SI 组。以最大方位角平均降雨量半径量化的内核大小随着热气旋加强率和热气旋强度的增加而减小。
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引用次数: 0
The Pacific Northwest Heat Wave of 25-30 June 2021: Synoptic/Mesoscale Conditions and Climate Perspective 2021 年 6 月 25-30 日的西北太平洋热浪:综合/中尺度条件和气候展望
IF 2.9 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2023-12-18 DOI: 10.1175/waf-d-23-0154.1
Clifford F. Mass, David Ovens, John Christy, Robert Conrick
An unprecedented heatwave occurred over the Pacific Northwest and southwest Canada on 25-30 June 2021, resulting in all-time temperature records that greatly exceeded previous record maximum temperatures. The impacts were substantial, including several hundred deaths, thousands of hospitalizations, a major wildfire in Lytton, British Columbia, and severe damage to regional vegetation. Several factors came together to produce this extreme event: a record-breaking mid-tropospheric ridge over British Columbia in the optimal location, record-breaking mid-tropospheric temperatures, strong subsidence in the lower atmosphere, low-level easterly flow that produced downslope warming on regional terrain and the removal of cooler marine air, an approaching low-level trough that enhanced downslope flow, the occurrence at a time of maximum solar insolation, and drier than normal soil moisture. It is shown that all-time record temperatures have not become more frequent and that annual high temperatures are only increased at the rate of baseline global warming. Although anthropogenic warming may have contributed as much as 1°C to the event, there is little evidence of further amplification from increasing greenhouse gases. Weather forecasts were excellent for this event, with highly accurate predictions of the extreme temperatures.
2021 年 6 月 25 日至 30 日,太平洋西北部和加拿大西南部出现了前所未有的热浪,气温大大超过了以往的最高气温记录。热浪造成了巨大影响,包括数百人死亡、数千人住院治疗、不列颠哥伦比亚省莱顿发生大面积野火以及地区植被严重受损。造成这次极端事件的因素有几个:不列颠哥伦比亚省上空的对流层中脊位于最佳位置,创下了历史记录;对流层中脊温度创下历史记录;大气低层强烈下沉;低层偏东气流使该地区地形下坡升温,并带走了较冷的海洋空气;低层低谷正在逼近,加强了下坡气流;发生在太阳日照最强的时候;土壤水分比正常情况下干燥。结果表明,历史最高气温纪录并没有变得更加频繁,年最高气温仅以基线全球变暖的速度增加。虽然人类活动导致的气候变暖可能造成了 1 摄氏度的高温,但几乎没有证据表明温室气体的增加会进一步加剧气候变暖。这次事件的天气预报非常出色,对极端气温的预测非常准确。
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引用次数: 0
Predicting Tropical Cyclone Formation with Deep Learning 利用深度学习预测热带气旋的形成
IF 2.9 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2023-12-14 DOI: 10.1175/waf-d-23-0103.1
Quan Nguyen, Chanh Kieu
Exploring new techniques to improve the prediction of tropical cyclone (TC) formation is essential for operational practice. Using convolutional neural networks, this study shows that deep learning can provide a promising capability for predicting TC formation from a given set of large-scale environments at certain forecast lead times. Specifically, two common deep-learning architectures including the residual net (ResNet) and UNet are used to examine TC formation in the Pacific Ocean. With a set of large-scale environments extracted from the NCEP/NCAR reanalysis during 2008–2021 as input and the TC labels obtained from the best track data, we show that both ResNet and UNet reach their maximum forecast skill at the 12–18 hour forecast lead time. Moreover, both architectures perform best when using a large domain covering most of the Pacific Ocean for input data, as compared to a smaller subdomain in the western Pacific. Given its ability to provide additional information about TC formation location, UNet performs generally worse than ResNet across the accuracy metrics. The deep learning approach in this study presents an alternative way to predict TC formation beyond the traditional vortex-tracking methods in the current numerical weather prediction.
探索改进热带气旋(TC)形成预测的新技术对业务实践至关重要。本研究利用卷积神经网络表明,深度学习可以在一定的预报准备时间内,从一组给定的大尺度环境中预测热带气旋的形成。具体来说,包括残差网(ResNet)和 UNet 在内的两种常见深度学习架构被用于研究太平洋的热带气旋形成。我们使用从 2008-2021 年 NCEP/NCAR 再分析中提取的一组大尺度环境作为输入,并使用从最佳路径数据中获得的 TC 标签,结果表明 ResNet 和 UNet 在 12-18 小时的预报准备时间内都达到了最大预报技能。此外,与西太平洋较小的子域相比,当使用覆盖太平洋大部分区域的大域作为输入数据时,这两种架构的性能最佳。鉴于 UNet 能够提供有关热带气旋形成位置的更多信息,因此在准确度指标上,UNet 的表现普遍不如 ResNet。本研究中的深度学习方法是目前数值天气预报中传统涡旋跟踪方法之外预测 TC 形成的另一种方法。
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引用次数: 0
Jumpiness in ensemble forecasts of Atlantic tropical cyclone tracks 大西洋热带气旋路径集合预报的跳跃性
IF 2.9 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2023-12-13 DOI: 10.1175/waf-d-23-0113.1
David S. Richardson, H. Cloke, John A. Methven, F. Pappenberger
We investigate the run-to-run consistency (jumpiness) of ensemble forecasts of tropical cyclone tracks from three global centers: ECMWF, the Met Office and NCEP. We use a divergence function to quantify the change in cross-track position between consecutive ensemble forecasts initialized at 12-hour intervals. Results for the 2019-2021 North Atlantic hurricane season show that the jumpiness varied substantially between cases and centers, with no common cause across the different ensemble systems. Recent upgrades to the Met Office and NCEP ensembles reduced their overall jumpiness to match that of the ECMWF ensemble. The average divergence over the set of cases provides an objective measure of the expected change in cross-track position from one forecast to the next. For example, a user should expect on average that the ensemble mean position will change by around 80-90 km in the cross-track direction between a forecast for 120 hours ahead and the updated forecast made 12 hours later for the same valid time. This quantitative information can support users’ decision making, for example in deciding whether to act now or wait for the next forecast. We did not find any link between jumpiness and skill, indicating that users should not rely on the consistency between successive forecasts as a measure of confidence. Instead, we suggest that users should use ensemble spread and probabilistic information to assess forecast uncertainty, and consider multi-model combinations to reduce the effects of jumpiness.
我们研究了来自三个全球中心的热带气旋路径集合预报的运行间一致性(跳跃性):ECMWF、气象局和 NCEP。我们使用发散函数来量化以 12 小时间隔初始化的连续集合预报之间的交叉路径位置变化。2019-2021年北大西洋飓风季节的结果表明,不同情况和中心之间的跳跃性差异很大,不同集合系统之间没有共同的原因。最近对气象局和 NCEP 集合的升级降低了其整体跳跃性,使其与 ECMWF 集合相匹配。案例集的平均偏离度提供了一个客观指标,用于衡量从一个预报到下一个预报的横道位置的预期变化。例如,用户应平均预期,在同一有效时间内,120 小时前的预报与 12 小时后的更新预报之间,集合平均位置将在横轨方向上变化约 80-90 公里。这些定量信息可以帮助用户做出决策,例如决定是现在行动还是等待下一次预报。我们没有发现跳跃性和技能之间有任何联系,这表明用户不应依赖连续预报之间的一致性来衡量信心。相反,我们建议用户使用集合传播和概率信息来评估预测的不确定性,并考虑多模式组合来减少跳跃性的影响。
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Weather and Forecasting
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