I-Han Chen, Judith Berner, Christian Keil, Ying-Hwa Kuo, George C. Craig
This study uses the convective adjustment time scale to identify the climatological frequency of equilibrium and non-equilibrium convection in different parts of the Contiguous United States (CONUS) as modeled by the operational convection-allowing High-Resolution Rapid Refresh (HRRR) forecast system. We find a qualitatively different climatology in the northern and southern domains separated by the 40°N parallel. The convective adjustment time scale picks up the fact that convection over the northern domains is governed by synoptic flow (leading to equilibrium) while locally forced, non-equilibrium convection dominates over the southern domains. Using a machine learning algorithm, we demonstrate that the convective adjustment timescale diagnostic provides a sensible classification that agrees with the underlying dynamics of equilibrium and non-equilibrium convection. Furthermore, the convective adjustment time scale can indicate the model quantitative precipitation forecast (QPF) quality, as it correctly reflects the higher QPF skill for precipitation under strong synoptic forcing. This diagnostic based on the strength of forcing for convection will be employed in future studies across different parts of CONUS to objectively distinguish different weather situations and explore the potential connection to warm-season precipitation predictability.
{"title":"Classification of Warm-Season Precipitation in High-Resolution Rapid Refresh (HRRR) model forecasts over the Contiguous United States","authors":"I-Han Chen, Judith Berner, Christian Keil, Ying-Hwa Kuo, George C. Craig","doi":"10.1175/mwr-d-23-0108.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0108.1","url":null,"abstract":"This study uses the convective adjustment time scale to identify the climatological frequency of equilibrium and non-equilibrium convection in different parts of the Contiguous United States (CONUS) as modeled by the operational convection-allowing High-Resolution Rapid Refresh (HRRR) forecast system. We find a qualitatively different climatology in the northern and southern domains separated by the 40°N parallel. The convective adjustment time scale picks up the fact that convection over the northern domains is governed by synoptic flow (leading to equilibrium) while locally forced, non-equilibrium convection dominates over the southern domains. Using a machine learning algorithm, we demonstrate that the convective adjustment timescale diagnostic provides a sensible classification that agrees with the underlying dynamics of equilibrium and non-equilibrium convection. Furthermore, the convective adjustment time scale can indicate the model quantitative precipitation forecast (QPF) quality, as it correctly reflects the higher QPF skill for precipitation under strong synoptic forcing. This diagnostic based on the strength of forcing for convection will be employed in future studies across different parts of CONUS to objectively distinguish different weather situations and explore the potential connection to warm-season precipitation predictability.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"53 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139251528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicolas Bruneau, T. Loridan, Nic Hannah, Eugene Dubossarsky, Mathis Joffrain, John Knaff
While Tropical Cyclone (TC) risk is a global concern, high regional differences exist in the quality of available data. This paper introduces InCyc, a globally consistent database of high-resolution full-physics simulations of historical cyclones. InCyc is designed to facilitate analysis of TC wind risk across basins and is made available to research institutions. We illustrate the value of this database with a case study focused on key wind risk parameters, namely the location and intensity of peak winds for the North Atlantic and western North Pacific basins. A novel approach based on random forest algorithms is introduced to predict the full distribution of these TC wind risk parameters. Based on a leave-one-storm-out evaluation, the analysis of the predictions shows how this innovative approach compares to other parametric models commonly used for wind risk assessment. We finally discuss why capturing the full distribution of variability is crucial as well as the broader use in the context of TC risk assessment systems (i.e. “catastrophe models”).
{"title":"Modelling Variability in Tropical Cyclone Maximum Wind Location and Intensity using InCyc: A Global Database of High-Resolution Tropical Cyclone Simulations","authors":"Nicolas Bruneau, T. Loridan, Nic Hannah, Eugene Dubossarsky, Mathis Joffrain, John Knaff","doi":"10.1175/mwr-d-22-0317.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0317.1","url":null,"abstract":"While Tropical Cyclone (TC) risk is a global concern, high regional differences exist in the quality of available data. This paper introduces InCyc, a globally consistent database of high-resolution full-physics simulations of historical cyclones. InCyc is designed to facilitate analysis of TC wind risk across basins and is made available to research institutions. We illustrate the value of this database with a case study focused on key wind risk parameters, namely the location and intensity of peak winds for the North Atlantic and western North Pacific basins. A novel approach based on random forest algorithms is introduced to predict the full distribution of these TC wind risk parameters. Based on a leave-one-storm-out evaluation, the analysis of the predictions shows how this innovative approach compares to other parametric models commonly used for wind risk assessment. We finally discuss why capturing the full distribution of variability is crucial as well as the broader use in the context of TC risk assessment systems (i.e. “catastrophe models”).","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"1 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139270216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Derek R. Stratman, N. Yussouf, Christopher A. Kerr, B. Matilla, John R. Lawson, Yaping Wang
The success of the National Severe Storms Laboratory’s (NSSL) experimental Warn-on-Forecast System (WoFS) to provide useful probabilistic guidance of severe and hazardous weather is mostly due to the frequent assimilation of observations, especially radar observations. Phased-array radar (PAR) technology, which is a potential candidate to replace the current U.S. operational radar network, would allow for even more rapid assimilation of radar observations by providing full-volumetric scans of the atmosphere every ~1 min. Based on previous studies, more frequent PAR data assimilation can lead to improved forecasts, but it can also lead to ensemble underdispersion and suboptimal observation assimilation. The use of stochastic and perturbed parameter methods to increase ensemble spread is a potential solution to this problem. In this study, four stochastic and perturbed parameter methods are assessed using a 1-km-scale version of the WoFS and include the stochastic kinetic energy backscatter (SKEB) scheme, the physically-based stochastic perturbation (PSP) scheme, a fixed perturbed parameters (FPP) method, and a novel surface-model scheme blending (SMSB) method. Using NSSL PAR observations from the 9 May 2016 tornado outbreak, experiments are conducted to assess the impact of the methods individually, in different combinations, and with different cycling intervals. The results from these experiments reveal the potential benefits of stochastic and perturbed parameter methods for future versions of the WoFS. Stochastic and perturbed parameter methods can lead to more skillful forecasts during periods of storm development. Moreover, a combination of multiple methods can result in more skillful forecasts than using a single method.
美国国家强风暴实验室（NSSL）的试验性预报预警系统（WoFS）之所以能够成功地为恶劣和危险天气提供有用的概率指导，主要归功于对观测数据，特别是雷达观测数据的频繁同化。相控阵雷达（PAR）技术是取代当前美国业务雷达网络的潜在候选技术，通过每隔约 1 分钟对大气层进行全容积扫描，可以更快速地同化雷达观测数据。根据以往的研究，更频繁的 PAR 数据同化可以改善预报，但也可能导致集合分散不足和观测同化不理想。使用随机和扰动参数方法来增加集合扩散是解决这一问题的潜在办法。在这项研究中，使用 1 公里尺度的 WoFS 版本评估了四种随机和扰动参数方法，包括随机动能反向散射（SKEB）方案、基于物理的随机扰动（PSP）方案、固定扰动参数（FPP）方法和新型地表-模式方案混合（SMSB）方法。利用 2016 年 5 月 9 日龙卷风爆发时的 NSSL PAR 观测数据进行了实验，以评估这些方法单独、不同组合和不同循环间隔的影响。这些实验结果揭示了随机和扰动参数方法对未来版本 WoFS 的潜在好处。随机参数法和扰动参数法可以在风暴发展期做出更准确的预报。此外，与使用单一方法相比，将多种方法结合使用可获得更高水平的预报。
{"title":"Testing stochastic and perturbed parameter methods in an experimental 1-km Warn-on-Forecast system using NSSL’s phased-array radar observations","authors":"Derek R. Stratman, N. Yussouf, Christopher A. Kerr, B. Matilla, John R. Lawson, Yaping Wang","doi":"10.1175/mwr-d-23-0095.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0095.1","url":null,"abstract":"The success of the National Severe Storms Laboratory’s (NSSL) experimental Warn-on-Forecast System (WoFS) to provide useful probabilistic guidance of severe and hazardous weather is mostly due to the frequent assimilation of observations, especially radar observations. Phased-array radar (PAR) technology, which is a potential candidate to replace the current U.S. operational radar network, would allow for even more rapid assimilation of radar observations by providing full-volumetric scans of the atmosphere every ~1 min. Based on previous studies, more frequent PAR data assimilation can lead to improved forecasts, but it can also lead to ensemble underdispersion and suboptimal observation assimilation. The use of stochastic and perturbed parameter methods to increase ensemble spread is a potential solution to this problem. In this study, four stochastic and perturbed parameter methods are assessed using a 1-km-scale version of the WoFS and include the stochastic kinetic energy backscatter (SKEB) scheme, the physically-based stochastic perturbation (PSP) scheme, a fixed perturbed parameters (FPP) method, and a novel surface-model scheme blending (SMSB) method. Using NSSL PAR observations from the 9 May 2016 tornado outbreak, experiments are conducted to assess the impact of the methods individually, in different combinations, and with different cycling intervals. The results from these experiments reveal the potential benefits of stochastic and perturbed parameter methods for future versions of the WoFS. Stochastic and perturbed parameter methods can lead to more skillful forecasts during periods of storm development. Moreover, a combination of multiple methods can result in more skillful forecasts than using a single method.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"152 11","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139268167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Greybush, T. Sikora, George S. Young, Quinlan Mulhern, Richard D. Clark, Michael L. Jurewicz
Data from rawinsondes launched during intensive observation periods (IOPs) of the Ontario Winter Lake-effect Systems (OWLeS) field project reveal that elevated mixed layers (EMLs) in the lower troposphere were relatively common near Lake Ontario during OWLeS lake-effect events. Conservatively, EMLs exist in 193 of the 290 OWLeS IOP soundings. The distribution of EML base pressure derived from the OWLeS IOP soundings reveals two classes of EML, one that has a relatively low-elevation base (900 – 750 hPa) and one that has a relatively high-elevation base (750 – 500 hPa). It is hypothesized that the former class of EML, which is the focus of this research, is, at times, the result of mesoscale processes related to individual Great Lakes. WRF reanalysis fields from a case study during the OWLeS field project provide evidence of two means by which low-elevation base EMLs can originate from the lake-effect boundary layer convection and associated mesoscale circulations. First, such EMLs can form within the upper-level outflow branches of mesoscale solenoidal circulations. Evacuated Great Lake-modified convective boundary layer air aloft then lies above ambient air of a greater static stability, forming EMLs. Second, such EMLs can form in the absence of a mesoscale solenoidal circulation when Great Lake-modified convective boundary layers overrun ambient air of a greater density. The reanalysis fields show that EMLs and layers of reduced static stability tied to Great Lake-modified convective boundary layers can extend downwind for hundreds of kilometers from their areas of formation. Operational implications and avenues for future research are discussed.
{"title":"Elevated Mixed Layers during Great Lake Lake-effect Events: An Investigation and Case Study from OWLeS","authors":"S. Greybush, T. Sikora, George S. Young, Quinlan Mulhern, Richard D. Clark, Michael L. Jurewicz","doi":"10.1175/mwr-d-22-0344.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0344.1","url":null,"abstract":"Data from rawinsondes launched during intensive observation periods (IOPs) of the Ontario Winter Lake-effect Systems (OWLeS) field project reveal that elevated mixed layers (EMLs) in the lower troposphere were relatively common near Lake Ontario during OWLeS lake-effect events. Conservatively, EMLs exist in 193 of the 290 OWLeS IOP soundings. The distribution of EML base pressure derived from the OWLeS IOP soundings reveals two classes of EML, one that has a relatively low-elevation base (900 – 750 hPa) and one that has a relatively high-elevation base (750 – 500 hPa). It is hypothesized that the former class of EML, which is the focus of this research, is, at times, the result of mesoscale processes related to individual Great Lakes. WRF reanalysis fields from a case study during the OWLeS field project provide evidence of two means by which low-elevation base EMLs can originate from the lake-effect boundary layer convection and associated mesoscale circulations. First, such EMLs can form within the upper-level outflow branches of mesoscale solenoidal circulations. Evacuated Great Lake-modified convective boundary layer air aloft then lies above ambient air of a greater static stability, forming EMLs. Second, such EMLs can form in the absence of a mesoscale solenoidal circulation when Great Lake-modified convective boundary layers overrun ambient air of a greater density. The reanalysis fields show that EMLs and layers of reduced static stability tied to Great Lake-modified convective boundary layers can extend downwind for hundreds of kilometers from their areas of formation. Operational implications and avenues for future research are discussed.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"34 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139266618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Densely-observed remote sensing data such as radars and satellites generally contain significant spatial error correlations. In data assimilation, the observation error covariance matrix is usually assumed to be diagonal, and the dense data are thinned or spatially averaged to compensate for neglecting the spatial observation error correlation. However, in theory, including the spatial observation error correlation in data assimilation can make better use of the dense data. This study performs perfect model observing system simulation experiments (OSSE) using the non-hydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF) to assess the impact of assimilating horizontally dense and error-correlated observations. The condition number of the observation error covariance matrix, defined as the ratio of the largest to smallest eigenvalues, is important for the numerical stability of the LETKF computation. A large condition number makes it difficult to compute the ensemble transform matrix correctly. Reducing the condition number by reconditioning is found effective for stable computation. The results show that including the horizontal observation error correlation with reconditioning makes the analysis more accurate but requires six times more computations than the case with the diagonal observation error covariance matrix.
{"title":"Including the horizontal observation error correlation in the ensemble Kalman filter: idealized experiments with NICAM-LETKF","authors":"Koji Terasaki, Takemasa Miyoshi","doi":"10.1175/mwr-d-23-0053.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0053.1","url":null,"abstract":"Abstract Densely-observed remote sensing data such as radars and satellites generally contain significant spatial error correlations. In data assimilation, the observation error covariance matrix is usually assumed to be diagonal, and the dense data are thinned or spatially averaged to compensate for neglecting the spatial observation error correlation. However, in theory, including the spatial observation error correlation in data assimilation can make better use of the dense data. This study performs perfect model observing system simulation experiments (OSSE) using the non-hydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF) to assess the impact of assimilating horizontally dense and error-correlated observations. The condition number of the observation error covariance matrix, defined as the ratio of the largest to smallest eigenvalues, is important for the numerical stability of the LETKF computation. A large condition number makes it difficult to compute the ensemble transform matrix correctly. Reducing the condition number by reconditioning is found effective for stable computation. The results show that including the horizontal observation error correlation with reconditioning makes the analysis more accurate but requires six times more computations than the case with the diagonal observation error covariance matrix.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"55 40","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134901687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yang Li, Yubao Liu, Yueqin Shi, Baojun Chen, Fanhui Zeng, Zhaoyang Huo, Hang Fan
Abstract Convective initiation (CI) nowcasting is crucial for reducing losses of human life and property caused by severe convective weather. A novel deep learning method based on the U-net model (named as CIUnet) was developed for forecasting CI during the warm season with eight interest fields of Himawari-8 Advanced Himawari Imager (AHI) and terrain height. The results showed that the CIUnet model produced probability forecasts of CI occurrence location and time with POD (probability of detection) at 93.3±0.3% and FAR (false alarm ratio) at 18.3±0.4% at a lead time of 30-min. Sensitivity and permutation importance experiments on the input fields of the CIUnet model revealed that the differences in brightness temperature for spectral channels were more critical for CI nowcasts than the original infrared channel brightness temperatures. The brightness temperature difference between Band10 (7.3 μm ) and Band13 (10.4 μm ), which represents the cloud-top height relative to the lower-troposphere, is identified as the most important input fields for CI nowcasting. The tri-spectral brightness temperature difference (TTD), which represents cloud-top glaciation, is ranked the second and it significantly reduced the FAR of the CI forecast. Using terrain heights as an extra input feature improved the POD, but slightly overestimated CI over complex terrain. In addition, a layer-wise relevance propagation (LRP) analyses was performed, and confirmed that the CIUnet model can effectively identify the crucial regions and features of the input fields for accurate CI prediction. Therefore, both permutation importance experiments and LPR analyses are useful for improving the CIUnet model and advancing the understanding of CI mechanisms.
{"title":"Probabilistic Convective Initiation Nowcasting Using Himawari-8 AHI with Explainable Deep Learning Models","authors":"Yang Li, Yubao Liu, Yueqin Shi, Baojun Chen, Fanhui Zeng, Zhaoyang Huo, Hang Fan","doi":"10.1175/mwr-d-22-0216.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0216.1","url":null,"abstract":"Abstract Convective initiation (CI) nowcasting is crucial for reducing losses of human life and property caused by severe convective weather. A novel deep learning method based on the U-net model (named as CIUnet) was developed for forecasting CI during the warm season with eight interest fields of Himawari-8 Advanced Himawari Imager (AHI) and terrain height. The results showed that the CIUnet model produced probability forecasts of CI occurrence location and time with POD (probability of detection) at 93.3±0.3% and FAR (false alarm ratio) at 18.3±0.4% at a lead time of 30-min. Sensitivity and permutation importance experiments on the input fields of the CIUnet model revealed that the differences in brightness temperature for spectral channels were more critical for CI nowcasts than the original infrared channel brightness temperatures. The brightness temperature difference between Band10 (7.3 μm ) and Band13 (10.4 μm ), which represents the cloud-top height relative to the lower-troposphere, is identified as the most important input fields for CI nowcasting. The tri-spectral brightness temperature difference (TTD), which represents cloud-top glaciation, is ranked the second and it significantly reduced the FAR of the CI forecast. Using terrain heights as an extra input feature improved the POD, but slightly overestimated CI over complex terrain. In addition, a layer-wise relevance propagation (LRP) analyses was performed, and confirmed that the CIUnet model can effectively identify the crucial regions and features of the input fields for accurate CI prediction. Therefore, both permutation importance experiments and LPR analyses are useful for improving the CIUnet model and advancing the understanding of CI mechanisms.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"112 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Veloso-Aguila, Kristen L. Rasmussen, Eric D. Maloney
Abstract A multiscale analysis of the environment supporting tornadoes in Southeast South America (SESA) was conducted based on a self-constructed database of 74 reports. Composites of environmental and convective parameters from ERA5 were generated relative to tornado events. The distribution of the reported tornadoes maximizes over the Argentine plains, while events are rare close to the Andes and south of Sierras de Córdoba. Events are relatively common in all seasons except in winter. Proximity environment evolution shows enhanced instability, deep-layer vertical wind shear, storm-relative helicity, reduced convective inhibition, and a lowered lifting condensation level before or during the development of tornadic storms in SESA. No consistent signal in low-level wind shear is seen during tornado occurrence. However, a curved hodograph with counterclockwise rotation is present. The Significant Tornado Parameter (STP) is also maximized prior to tornadogenesis, most strongly associated with enhanced CAPE. Differences in the convective environment between tornadoes in SESA and the U.S. Great Plains are discussed. On the synoptic scale, tornado events are associated with a strong anomalous trough crossing the southern Andes that triggers lee cyclogenesis, subsequently enhancing the South American Low-Level Jet (SALLJ) that increases moisture advection to support deep convection. This synoptic trough also enhances vertical shear that, along with enhanced instability, sustains organized convection capable of producing tornadic storms. At planetary scales, the tornadic environment is modulated by Rossby wave trains that appear to be forced by convection near northern Australia. Madden-Julian oscillation phase 3 preferentially occurs one to two weeks ahead of tornado occurrence.
{"title":"Tornadoes in Southeast South America: Mesoscale to Planetary-scale Environments","authors":"Daniel Veloso-Aguila, Kristen L. Rasmussen, Eric D. Maloney","doi":"10.1175/mwr-d-22-0248.1","DOIUrl":"https://doi.org/10.1175/mwr-d-22-0248.1","url":null,"abstract":"Abstract A multiscale analysis of the environment supporting tornadoes in Southeast South America (SESA) was conducted based on a self-constructed database of 74 reports. Composites of environmental and convective parameters from ERA5 were generated relative to tornado events. The distribution of the reported tornadoes maximizes over the Argentine plains, while events are rare close to the Andes and south of Sierras de Córdoba. Events are relatively common in all seasons except in winter. Proximity environment evolution shows enhanced instability, deep-layer vertical wind shear, storm-relative helicity, reduced convective inhibition, and a lowered lifting condensation level before or during the development of tornadic storms in SESA. No consistent signal in low-level wind shear is seen during tornado occurrence. However, a curved hodograph with counterclockwise rotation is present. The Significant Tornado Parameter (STP) is also maximized prior to tornadogenesis, most strongly associated with enhanced CAPE. Differences in the convective environment between tornadoes in SESA and the U.S. Great Plains are discussed. On the synoptic scale, tornado events are associated with a strong anomalous trough crossing the southern Andes that triggers lee cyclogenesis, subsequently enhancing the South American Low-Level Jet (SALLJ) that increases moisture advection to support deep convection. This synoptic trough also enhances vertical shear that, along with enhanced instability, sustains organized convection capable of producing tornadic storms. At planetary scales, the tornadic environment is modulated by Rossby wave trains that appear to be forced by convection near northern Australia. Madden-Julian oscillation phase 3 preferentially occurs one to two weeks ahead of tornado occurrence.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"90 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135091745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kristen L. Axon, Adam L. Houston, Conrad L. Ziegler, Christopher C. Weiss, Erik N. Rasmussen, Michael C. Coniglio, Brian Argrow, Eric Frew, Sara Swenson, Anthony E. Reinhart, Matthew B. Wilson
Abstract On 28 May 2019, a tornadic supercell, observed as part of TORUS (Targeted Observation by UAS and Radars of Supercells) produced an EF-2 tornado near Tipton, KS. The supercell was observed to interact with multiple preexisting airmass boundaries. These boundaries and attendant air masses were examined using unoccupied aircraft system (UAS), mobile mesonets, radiosondes, and dual-Doppler analyses derived from TORUS mobile radars. The cool side air mass of one of these boundaries was found to have higher equivalent potential temperature and backed winds relative to the warm side air mass; features associated with MAHTEs (mesoscale air masses with high theta-E). It is hypothesized that these characteristics may have facilitated tornadogenesis. The two additional boundaries were produced by a nearby supercell and appeared to weaken the tornadic supercell. This work represents the first time that UAS have been used to examine the impact of preexisting airmass boundaries on a supercell, and it provides insights into the influence environmental heterogeneities can have on the evolution of a supercell.
{"title":"The potential roles of preexisting airmass boundaries on a tornadic supercell observed by TORUS on 28 May 2019","authors":"Kristen L. Axon, Adam L. Houston, Conrad L. Ziegler, Christopher C. Weiss, Erik N. Rasmussen, Michael C. Coniglio, Brian Argrow, Eric Frew, Sara Swenson, Anthony E. Reinhart, Matthew B. Wilson","doi":"10.1175/mwr-d-23-0007.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0007.1","url":null,"abstract":"Abstract On 28 May 2019, a tornadic supercell, observed as part of TORUS (Targeted Observation by UAS and Radars of Supercells) produced an EF-2 tornado near Tipton, KS. The supercell was observed to interact with multiple preexisting airmass boundaries. These boundaries and attendant air masses were examined using unoccupied aircraft system (UAS), mobile mesonets, radiosondes, and dual-Doppler analyses derived from TORUS mobile radars. The cool side air mass of one of these boundaries was found to have higher equivalent potential temperature and backed winds relative to the warm side air mass; features associated with MAHTEs (mesoscale air masses with high theta-E). It is hypothesized that these characteristics may have facilitated tornadogenesis. The two additional boundaries were produced by a nearby supercell and appeared to weaken the tornadic supercell. This work represents the first time that UAS have been used to examine the impact of preexisting airmass boundaries on a supercell, and it provides insights into the influence environmental heterogeneities can have on the evolution of a supercell.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"8 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135390972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In this study, downscaling, ensemble of data assimilation, time-lagging, and their combination were used to generate initial condition (IC) perturbations for 12-h convection-permitting ensemble forecasting for heavy-rainfall events over South China during the rainy season in 2013–2020. These events were classified as weak- and strong-forcing cases based on synoptic-scale forcing during the presummer rainy season and as landfalling tropical cyclone (TC) cases. This study investigated the impacts of various IC perturbation methods on multiscale characteristics of perturbations and the forecast performance for both nonprecipitation and precipitation variables. These perturbation methods represented different-source IC uncertainties and thus differed in multiscale characteristics of perturbations in vertical structures, horizontal distributions, and time evolution. Combination of various IC perturbation methods evidently increased perturbations or spreads of precipitation in both magnitude and location and thus improved the forecast-error estimation. Such an improvement was most and least evident for TC cases during the early and late forecasts, respectively, and was more evident for strong- than weak-forcing cases beyond 6 h. Combination of various IC perturbation methods generally improved both the ensemble-mean and probabilistic forecasts with case-dependent improvements. For heavy rainfall forecasting, 1–6-h improvements were most prominent for TC cases in terms of discrimination and accuracy, while 7–12-h improvements were least prominent for weak-forcing cases in terms of reliability and accuracy. In particular, the improvements in predicting weak-forcing cases increased with spatial errors. In contrast, for strong-forcing cases, the improvements were least and most prominent before and beyond 6 h, respectively.
{"title":"Different Initial Condition Perturbation Methods for Convection-Permitting Ensemble Forecasting over South China during the Rainy Season","authors":"Xubin Zhang, Jingshan Li","doi":"10.1175/mwr-d-23-0093.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0093.1","url":null,"abstract":"Abstract In this study, downscaling, ensemble of data assimilation, time-lagging, and their combination were used to generate initial condition (IC) perturbations for 12-h convection-permitting ensemble forecasting for heavy-rainfall events over South China during the rainy season in 2013–2020. These events were classified as weak- and strong-forcing cases based on synoptic-scale forcing during the presummer rainy season and as landfalling tropical cyclone (TC) cases. This study investigated the impacts of various IC perturbation methods on multiscale characteristics of perturbations and the forecast performance for both nonprecipitation and precipitation variables. These perturbation methods represented different-source IC uncertainties and thus differed in multiscale characteristics of perturbations in vertical structures, horizontal distributions, and time evolution. Combination of various IC perturbation methods evidently increased perturbations or spreads of precipitation in both magnitude and location and thus improved the forecast-error estimation. Such an improvement was most and least evident for TC cases during the early and late forecasts, respectively, and was more evident for strong- than weak-forcing cases beyond 6 h. Combination of various IC perturbation methods generally improved both the ensemble-mean and probabilistic forecasts with case-dependent improvements. For heavy rainfall forecasting, 1–6-h improvements were most prominent for TC cases in terms of discrimination and accuracy, while 7–12-h improvements were least prominent for weak-forcing cases in terms of reliability and accuracy. In particular, the improvements in predicting weak-forcing cases increased with spatial errors. In contrast, for strong-forcing cases, the improvements were least and most prominent before and beyond 6 h, respectively.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"18 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135634331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kelsey Malloy, Michael K. Tippett, William J. Koshak
Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden-Julian Oscillation (MJO) and El Niño-Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous U.S. (CONUS) using environmental variables (convective available potential energy and precipitation) andNational Lightning Detection Network data for 2003–2016. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO-lightning relationship at daily resolution and the winter-spring ENSO-lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the Coastal Southeast U.S. during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River Valley during winter.
{"title":"ENSO and MJO Modulation of U.S. Cloud-to-ground Lightning Activity","authors":"Kelsey Malloy, Michael K. Tippett, William J. Koshak","doi":"10.1175/mwr-d-23-0157.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0157.1","url":null,"abstract":"Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden-Julian Oscillation (MJO) and El Niño-Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous U.S. (CONUS) using environmental variables (convective available potential energy and precipitation) andNational Lightning Detection Network data for 2003–2016. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO-lightning relationship at daily resolution and the winter-spring ENSO-lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the Coastal Southeast U.S. during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River Valley during winter.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":"43 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135820181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：