Monthly Weather Review最新文献_第2页

Leveraging deterministic weather forecasts for in-situ probabilistic temperature predictions via deep learning 通过深度学习利用确定性天气预报进行现场概率温度预测

IF 3.2 3区地球科学

Monthly Weather Review Pub Date : 2024-06-04 DOI: 10.1175/mwr-d-23-0273.1

David Landry, A. Charantonis, C. Monteleoni

{"title":"Leveraging deterministic weather forecasts for in-situ probabilistic temperature predictions via deep learning","authors":"David Landry, A. Charantonis, C. Monteleoni","doi":"10.1175/mwr-d-23-0273.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0273.1","url":null,"abstract":"\u0000We propose a neural network approach to produce probabilistic weather forecasts from a deterministic numerical weather prediction. Our approach is applied to operational surface temperature outputs from the Global Deterministic Prediction System up to ten-day lead times, targeting METAR observations in Canada and the United States. We show how postprocessing performance is improved by training a single model for multiple lead times. Multiple strategies to condition the network for the lead time are studied, including a supplementary predictor and an embedding. The proposed model is evaluated for accuracy, spread, distribution calibration, and its behavior under extremes. The neural network approach decreases CRPS by 15% and has improved distribution calibration compared to a naive probabilistic model based on past forecast errors. Our approach increases the value of a deterministic forecast by adding information about the uncertainty, without incurring the cost of simulating multiple trajectories. It applies to any gridded forecast including the recent machine learning-based weather prediction models. It requires no information regarding forecast spread and can be trained to generate probabilistic predictions from any deterministic forecast.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387334","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}

引用次数: 0

Examining the Impact of Assimilating Surface, PBL, and Free Atmosphere Observations from TORUS on Analyses and Forecasts of Two Supercells on 8 June 2019 研究从 TORUS 同化地表、PBL 和自由大气观测数据对 2019 年 6 月 8 日两个超级暴风雪的分析和预测的影响

IF 3.2 3区地球科学

Monthly Weather Review Pub Date : 2024-06-03 DOI: 10.1175/mwr-d-23-0247.1

Matthew B. Wilson, A. Houston

{"title":"Examining the Impact of Assimilating Surface, PBL, and Free Atmosphere Observations from TORUS on Analyses and Forecasts of Two Supercells on 8 June 2019","authors":"Matthew B. Wilson, A. Houston","doi":"10.1175/mwr-d-23-0247.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0247.1","url":null,"abstract":"\u0000This study describes data-denial experiments conducted to examine the impact of assimilating subsets of data from the TORUS project on storm-scale ensemble forecasts of two supercells on 8 June 2019. Assimilated data from TORUS includes mobile mesonet, UAS, and radiosonde observations. The TORUS data are divided into three spatial subsets to evaluate the importance of observing different parts of the atmosphere on forecasts of this case: the SFC subset consisting of just the near-surface mobile mesonet observations, the PBL subset consisting of UAS observations and radiosonde profiles below 762 m, and the FREE subset consisting of radiosonde profiles above 762 m. Data denial experiments are then conducted by comparing analyses and free forecasts generated using a cycled EnKF data assimilation system assimilating conventional observations, radar observations, and all of the TORUS observations at once with experiments where one of the three subsets is removed in turn as well as a control experiment assimilating only conventional and radar observations. Our results show that assimilating all of the TORUS observations at once in the ALL experiment improves the storm-scale ensemble forecasts much more often than it degrades them, and that no one subset of the TORUS data was consistently most important for improving the analyses or forecasts.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141270648","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}

引用次数: 0

Development of Multi-Scale EnKF within GSI and Its Applications to Multiple Convective Storm Cases with Radar Reflectivity Data Assimilation Using the FV3 Limited Area Model 在 GSI 中开发多尺度 EnKF 及其在使用 FV3 有限区域模式的雷达反射率数据同化的多重对流风暴案例中的应用

IF 3.2 3区地球科学

Monthly Weather Review Pub Date : 2024-05-23 DOI: 10.1175/mwr-d-23-0235.1

Chong‐Chi Tong, Ming Xue, Chengsi Liu, Jingyao Luo, Youngsun Jung

{"title":"Development of Multi-Scale EnKF within GSI and Its Applications to Multiple Convective Storm Cases with Radar Reflectivity Data Assimilation Using the FV3 Limited Area Model","authors":"Chong‐Chi Tong, Ming Xue, Chengsi Liu, Jingyao Luo, Youngsun Jung","doi":"10.1175/mwr-d-23-0235.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0235.1","url":null,"abstract":"\u0000To improve the representation of all relevant scales in initial conditions for large-domain convection-allowing models, a new multi-scale ensemble Kalman filter (MEnKF) algorithm is developed and implemented within the GSI data assimilation framework coupled with the FV3 limited area model. The algorithm utilizes ensemble background error covariances filtered to match the observations assimilated. This is realized in a sequential manner: 1) When assimilating coarse-resolution observations such as radiosondes, ensemble background perturbations are filtered to remove scales smaller than those the observations can represent, along with relatively large horizontal localization radii to ensure low-noise and balanced analysis increments. 2) The resulting ensemble analyses from the first step then serve as the background to assimilate denser observations such as radar data with smaller localization radii. Several passes can be taken to assimilate all observations. In this paper, vertically increasing horizontal filter scales are used when assimilating rawinsonde and surface observations together while radar data are assimilated in the second step.\u0000The algorithm is evaluated through six convective storm cases during May 2021, with cycled assimilation of either conventional data only or with additional radar reflectivity followed by 24-h ensemble forecasts. Overall, positive impacts of the MEnKF on forecasts are obtained regardless of reflectivity data; its advantage over the single-scale EnKF is most significant in surface humidity and temperature forecasts up to at least 12 hours. More accurate hourly precipitation forecasts with MEnKF can last up to 24 hours for light rain. Furthermore, MEnKF forecasts higher ensemble probabilities for the observed hazardous events.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141105816","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}

引用次数: 0

A Quantile-Conserving Ensemble Filter Framework. Part III: Data Assimilation for Mixed Distributions with Application to a Low-Order Tracer Advection Model 量子守恒集合滤波器框架。第三部分：混合分布数据同化在低阶示踪平流模型中的应用

IF 3.2 3区地球科学

Monthly Weather Review Pub Date : 2024-05-23 DOI: 10.1175/mwr-d-23-0255.1

Jeffrey Anderson, Chris Riedel, Molly Wieringa, Fairuz Ishraque, Marlee Smith, Helen Kershaw

{"title":"A Quantile-Conserving Ensemble Filter Framework. Part III: Data Assimilation for Mixed Distributions with Application to a Low-Order Tracer Advection Model","authors":"Jeffrey Anderson, Chris Riedel, Molly Wieringa, Fairuz Ishraque, Marlee Smith, Helen Kershaw","doi":"10.1175/mwr-d-23-0255.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0255.1","url":null,"abstract":"\u0000The uncertainty associated with many observed and modeled quantities of interest in Earth system prediction can be represented by mixed probability distributions that are neither discrete nor continuous. For instance, a forecast probability of precipitation can have a finite probability of zero precipitation, consistent with a discrete distribution. However, nonzero values are not discrete and are represented by a continuous distribution; the same is true for rainfall rate. Other examples include snow depth, sea ice concentration, amount of a tracer or the source rate of a tracer. Some Earth system model parameters may also have discrete or mixed distributions. Most ensemble data assimilation methods do not explicitly consider the possibility of mixed distributions. The Quantile Conserving Ensemble Filtering Framework (Anderson 2022, 2023) is extended to explicitly deal with discrete or mixed distributions. An example is given using bounded normal rank histogram probability distributions applied to observing system simulation experiments in a low-order tracer advection model. Analyses of tracer concentration and tracer source are shown to be improved when using the extended methods. A key feature of the resulting ensembles is that there can be ensemble members with duplicate values. An extension of the rank histogram diagnostic method to deal with potential duplicates shows that the ensemble distributions from the extended assimilation methods are more consistent with the truth.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141104448","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}

引用次数: 0

Using satellite data assimilation techniques to combine infrasound observations and a full ray-tracing model to constrain stratospheric variables 利用卫星数据同化技术将次声观测和完整的射线追踪模型结合起来，以制约平流层变量

IF 3.2 3区地球科学

Monthly Weather Review Pub Date : 2024-05-21 DOI: 10.1175/mwr-d-23-0186.1

Javier Amezcua, S. P. Näsholm, Ismael Vera-Rodriguez

{"title":"Using satellite data assimilation techniques to combine infrasound observations and a full ray-tracing model to constrain stratospheric variables","authors":"Javier Amezcua, S. P. Näsholm, Ismael Vera-Rodriguez","doi":"10.1175/mwr-d-23-0186.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0186.1","url":null,"abstract":"\u0000Infrasound waves generated at the Earth’s surface can reach high altitudes before returning to the surface to be recorded by microbarometer array stations. These waves carry information about the propagation medium, in particular, temperature and winds in the atmosphere. It is only recently that studies on the assimilation of such data into atmospheric models have been published. Intending to advance this line of research, we here use the Modulated Ensemble Transform Kalman Filter (METKF) –commonly used in satellite data assimilation– to assimilate infrasound-related observations in order to update a column of three vertically varying variables: temperature and horizontal wind speeds. This includes stratospheric and mesospheric heights, which are otherwise poorly observed. The numerical experiments on synthetic data but with realistic reanalysis product atmospheric specifications (following the Observing System Simulation Experiment paradigm) reveal that a large ensemble is capable of reducing errors, especially for the wind speeds in stratospheric heights close to 30 – 60 km. While using a small ensemble leads to incorrect analysis increments and large estimation errors, the METKF ameliorates this problem and even achieves error reduction from the prior to the posterior mean estimator.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141117707","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}

引用次数: 0

Improving short-term, near-surface temperature forecasts by integrating weather pattern information into Model Output Statistics 将天气模式信息纳入模型输出统计，改进短期近地面温度预报

IF 3.2 3区地球科学

Monthly Weather Review Pub Date : 2024-05-21 DOI: 10.1175/mwr-d-23-0134.1

Matthias Zech, L. von Bremen

{"title":"Improving short-term, near-surface temperature forecasts by integrating weather pattern information into Model Output Statistics","authors":"Matthias Zech, L. von Bremen","doi":"10.1175/mwr-d-23-0134.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0134.1","url":null,"abstract":"\u0000Dynamical numerical weather prediction has remarkably improved over the last decades. Yet, postprocessing techniques are needed to calibrate forecasts which are based on statistical and Machine Learning techniques. With recent advances in the derivation of year-round, large-scale atmospheric circulations, or weather regimes, the question arises of whether this information can be valuable within forecast postprocessing methods. This paper investigates this by proposing a bias correction scheme to integrate the atmospheric circulation state derived from empirical orthogonal functions, referred to as weather patterns, for deterministic short-term, near-surface temperature forecasts based on LASSO regression. We propose a computational study which first evaluates different weather pattern definitions (spatial domain) to improve temperature forecasts in Europe. As a bias could be associated with the weather pattern at the model initialization time or at the realization time of the forecast, both variants are tested in this study. We show that forecasted weather patterns with the identical spatial domain as the forecast show best skill reaching Mean Squared Error Skill improvements of up to 3% (day-ahead) or 1% respectively (week ahead). Only considering land surface improvements in Europe, improvements of 4-6% for day-ahead and 1 to 5% for week-ahead forecasts are observable. We believe that this study not only introduces a simple yet effective tool to reduce bias in temperature forecasts but also contributes to the active discussion of how valuable weather patterns are and how to use them within forecast calibration techniques.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113936","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}

引用次数: 0

Estimating wind speeds in tornadoes using debris trajectories of large compact objects 利用大型紧凑物体的碎片轨迹估算龙卷风中的风速

IF 3.2 3区地球科学

Monthly Weather Review Pub Date : 2024-05-20 DOI: 10.1175/mwr-d-23-0251.1

Connell S. Miller, Gregory A. Kopp, D. Sills, Daniel G. Butt

{"title":"Estimating wind speeds in tornadoes using debris trajectories of large compact objects","authors":"Connell S. Miller, Gregory A. Kopp, D. Sills, Daniel G. Butt","doi":"10.1175/mwr-d-23-0251.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0251.1","url":null,"abstract":"\u0000Currently, the Enhanced Fujita scale does not consider the wind-induced movement of various large compact objects such as vehicles, construction equipment, farming equipment / haybales, etc. that are often found in post-event damage surveys. One reason for this is that modelling debris in tornadoes comes with considerable uncertainties since there are many parameters to determine, leading to difficulties in using trajectories to analyze wind speeds of tornadoes. This paper aims to develop a forensic tool using analytical tornado models to estimate lofting wind speeds based on trajectories of large compact objects. This is accomplished by implementing a Monte Carlo simulation to randomly select the parameters and plotting cumulative distribution functions showing the likelihood of lofting at each wind speed. After analyzing the debris lofting from several documented tornadoes in Canada, the results indicate that the method provides threshold lofting wind speeds that are similar to the estimated speeds given by other methods. However, the introduction of trajectories produces estimated lofting wind speeds that are higher than the EF-scale rating given from the ground survey assessment based on structural damage. Further studies will be required to better understand these differences.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121325","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}

引用次数: 0

Diagnostics for Imbalance on the Convective Scale 对流尺度失衡诊断法

IF 3.2 3区地球科学

Monthly Weather Review Pub Date : 2024-05-20 DOI: 10.1175/mwr-d-23-0291.1

Theresa Diefenbach, L. Scheck, Martin Weissmann, George C. Craig

{"title":"Diagnostics for Imbalance on the Convective Scale","authors":"Theresa Diefenbach, L. Scheck, Martin Weissmann, George C. Craig","doi":"10.1175/mwr-d-23-0291.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0291.1","url":null,"abstract":"\u0000The analyses produced by a data assimilation system may be unbalanced, that is dynamically inconsistent with the forecasting model, leading to noisy forecasts and reduced skill. While there are effective procedures to reduce synoptic-scale imbalance, the situation on the convective scale is less clear because the flowon this scale is strongly divergent and non-hydrostatic. In this studywe compare three measures of imbalance relevant to convective-scale data assimilation: (i) surface pressure tendencies, (ii) vertical velocity variance in the vicinity of convective clouds, and (iii) departures from the vertical velocity prescribed by the weak temperature gradient (WTG) approximation. These are applied in a numerical weather prediction system, with three different data assimilation algorithms: 1. Latent Heat Nudging (LHN), 2. Local Ensemble Transform Kalman Filter (LETKF), and 3. LETKF in combination with incremental analysis updates (IAU). Results indicate that surface pressure tendency diagnoses a different type of imbalance than the vertical velocity variance and theWTG departure. The LETKF induces a spike in surface pressure tendencies, with a large-scale spatial pattern that is not clearly related to the precipitation pattern. This anomaly is notably reduced by the IAU. LHN does not generate a pronounced signal in the surface pressure, but produces the most imbalance in the other two measures. The imbalances measured by the partitioned vertical velocity variance andWTG departures are similar, and closely coupled to the convective precipitation. Between these two measures, the WTG departure has the advantage of being simpler and more economical to compute.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121915","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}

引用次数: 0

Diagnosing Radial Ventilation in Dropsonde Observations of Hurricane Sam (2021) 从飓风萨姆（2021 年）的垂吊观测数据中诊断径向通风情况

IF 3.2 3区地球科学

Monthly Weather Review Pub Date : 2024-05-17 DOI: 10.1175/mwr-d-23-0224.1

Brian H. Tang, Rosimar Rios-Berrios, Jun A. Zhang

{"title":"Diagnosing Radial Ventilation in Dropsonde Observations of Hurricane Sam (2021)","authors":"Brian H. Tang, Rosimar Rios-Berrios, Jun A. Zhang","doi":"10.1175/mwr-d-23-0224.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0224.1","url":null,"abstract":"\u0000This study presents a method to diagnose radial ventilation, the horizontal flux of relatively low-θe air into tropical cyclones, from dropsonde observations. We used this method to investigate ventilation changes over three consecutive sampling periods in Hurricane Sam (2021), which underwent substantial intensity changes over three days. During the first and last periods, coinciding with intensification, the ventilation was relatively small due to a lack of spatial correlation between radial flow and θe azimuthal asymmetries. During the second period, coinciding with weakening, the ventilation was relatively large. The increased ventilation was caused by greater shear associated with an upper-level trough, tilting the vortex, along with dry, low-θe air wrapping in upshear. The spatial correlation of the radial inflow and anomalously low-θe air resulted in large ventilation at mid-to-upper levels. Additionally, at low-to-mid levels, there was evidence of mesoscale inflow of low-θe air in the stationary band complex. The location of these radial ventilation pathways and their effects on Sam’s intensity are consistent with previous idealized and real-case modeling studies. More generally, this method offers a way to monitor ventilation changes in tropical cyclones, particularly when there is full-troposphere sampling around and within a tropical cyclone’s core.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140966139","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}

引用次数: 0

Structure and maintenance of a quasi-linear mesoscale convective system that caused torrential rain in southern Kyushu, Japan on 11 July 2021 2021 年 7 月 11 日造成日本九州南部暴雨的准线性中尺度对流系统的结构和维持情况

IF 3.2 3区地球科学

Monthly Weather Review Pub Date : 2024-05-15 DOI: 10.1175/mwr-d-23-0209.1

Wataru Mashiko

{"title":"Structure and maintenance of a quasi-linear mesoscale convective system that caused torrential rain in southern Kyushu, Japan on 11 July 2021","authors":"Wataru Mashiko","doi":"10.1175/mwr-d-23-0209.1","DOIUrl":"https://doi.org/10.1175/mwr-d-23-0209.1","url":null,"abstract":"\u0000A quasi-linear mesoscale convective system that remained nearly stationary (hereafter referred to as a stationary QLCS) for almost 8 h in southern Kyushu, Japan, caused torrential rainfall exceeding 350 mm on 11 July 2021. The stationary QLCS consisted of several rainbands organized by back-building convection. As the cold pool intensified, the system attained a more widespread structure, leaning on the upshear side. To elucidate the mechanism responsible for the upshear tilt, numerical simulations with 250-m horizontal grid spacing were conducted, including sensitivity experiments in which the evaporative cooling rates from rain were reduced to modify the cold pool intensity. Results show that the cold pool is critical to the organization of the QLCS, and the structure normal to it is mainly governed by the balance between the low-level shear magnitude and cold pool intensity, supporting the application of so-called Rotunno-Klemp-Weisman (RKW) theory to this event. The cause of the weak updraft that accompanies the leaning system over the strong cold pool was also investigated, analyzing the trajectories, vertical momentum equation, and pressure perturbation field using the anelastic equation. It is revealed that the updraft travels a longer distance through the tilted system experiencing more mixing with the ambient air, which results in less thermal buoyancy. In addition, the updraft is decelerated by the downward perturbation gradient force owing to the vertical buoyancy gradient around the sloping surface of the cold pool and the dynamical effect caused by the baroclinity-associated strong horizontal vorticity around the cold pool leading edge.","PeriodicalId":18824,"journal":{"name":"Monthly Weather Review","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140974731","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}

引用次数: 0