Lake-effect Snow: Understanding Predictability and Dynamics through Ensemble-Based Convective-Permitting Data Assimilation, Modeling, and Sensitivity Analysis

湖泊效应雪：通过基于集合的对流允许数据同化、建模和敏感性分析来了解可预测性和动力学

基本信息

批准号：
1745243
负责人：
Steven Greybush
金额：
$ 49.31万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-01-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1745243&HistoricalAwards=false
关键词：
Lake effect Snow Understanding Predictability

项目摘要

Lake-effect snow (LES), the result of a cold air mass being advected over relatively warm water, is responsible for some of the heaviest snowfall accumulations in the eastern half of the United States. These storms can generate intense snowfall rates of several inches (5-10 cm) per hour, leading to accumulations of more than a meter of snow in the course of a day, and can be accompanied by near zero visibility, strong winds, bitter cold, and even thunders. This project will investigate lake-effect precipitation over the Great Lakes region of the Northeastern U.S.: a multi-scale phenomena combining strong synoptic and mesoscale forcing with fine convective-scale structures that present a prediction challenge. This research will contribute to the graduate theses and dissertations of multiple students, as well as provide material for classroom exercises on numerical weather prediction and effective use of ensemble data. The NSF-sponsored Ontario Winter Lake-effect Systems (OWLeS) field campaign provides an excellent test environment for the evaluation of data assimilation techniques due to the rich observation dataset, including sounding systems, ground measurements, aircraft sensors, and mobile radars, as well as the rich, diverse scientific interests of a large group of collaborators investigating fundamental science. This project will implement and compare the most advanced four-dimensional ensemble and hybrid data assimilation systems, and evaluate their relative strengths and weaknesses for analysis and prediction of lake-effect snow. Radar products for winter weather will be assimilated, and their impact on timescales of predictability determined. Results will quantify the impacts of each observing system on forecast quality, establish the intrinsic and practical predictability of lake-effect snow, and assess the contributions of the lake surface boundary, model errors, and synoptic and mesoscale initial conditions and their underlying dynamics.An additional broader impact is the development and evaluation of the best data assimilation techniques. This has the potential to provide guidance to operations as the nation moves toward a national convective scale ensemble. The next-generation regional operational prediction systems will require kilometer-scale convective-permitting model resolution and rapid updates ingesting all available observations using the most effective four-dimensional ensemble and/or hybrid data assimilation techniques. Improved forecast lead time and accuracy for lake-effect events will have positive societal impacts on residents of lake-effect prone regions. Reanalysis fields produced by this research are a vital component to the analyses of collaborators investigating the structure and evolution of lake-effect snow bands and the role of upstream lake-atmosphere interactions.

湖效应雪（LES）是冷气团平流过相对温暖的水面的结果，是美国东半部一些最大降雪量的原因。这些风暴可产生每小时几英寸（5-10 厘米）的强烈降雪，导致一天内积雪超过一米，并可能伴随着能见度接近零、强风和严寒。，甚至还有雷声。该项目将研究美国东北部五大湖地区的湖泊效应降水：一种将强天气和中尺度强迫与精细对流尺度结构相结合的多尺度现象，这对预测提出了挑战。这项研究将为多名学生的研究生论文和学位论文做出贡献，并为数值天气预报和有效使用集合数据的课堂练习提供材料。 NSF 赞助的安大略冬季湖效应系统 (OWLeS) 实地活动为评估数据同化技术提供了良好的测试环境，因为观测数据集丰富，包括探测系统、地面测量、飞机传感器和移动雷达。作为研究基础科学的一大群合作者的丰富、多样的科学兴趣。该项目将实施和比较最先进的四维集合和混合数据同化系统，并评估它们在湖泊效应雪分析和预测方面的相对优势和劣势。针对冬季天气的雷达产品将被同化，并确定它们对可预测性时间尺度的影响。结果将量化每个观测系统对预报质量的影响，建立湖泊效应雪的内在和实际可预测性，并评估湖面边界、模型误差、天气和中尺度初始条件及其潜在动力学的贡献。其他更广泛的影响是最佳数据同化技术的开发和评估。随着国家走向全国对流规模整体，这有可能为行动提供指导。下一代区域业务预测系统将需要公里级的对流允许模型分辨率和快速更新，使用最有效的四维集合和/或混合数据同化技术吸收所有可用的观测结果。提高湖泊效应事件的预报提前时间和准确性将对湖泊效应易发地区的居民产生积极的社会影响。这项研究产生的再分析场是合作者研究湖泊效应雪带的结构和演化以及上游湖泊与大气相互作用的作用的分析的重要组成部分。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Elevated Mixed Layers during Great Lake Lake-Effect Events: An Investigation and Case Study from OWLeS

大湖效应事件期间的混合层升高：OWLeS 的调查和案例研究

DOI：
10.1175/mwr-d-22-0344.1
发表时间：
2024-01
期刊：
Monthly Weather Review
影响因子：
3.2
作者：
Greybush, Steven J.;Sikora, Todd D.;Young, George S.;Mulhern, Quinlan;Clark, Richard D.;Jurewicz, Michael L.
通讯作者：
Jurewicz, Michael L.

Lake-Effect Snowbands in Baroclinic Environments

斜压环境中的湖泊效应雪带

DOI：
10.1175/waf-d-18-0191.1
发表时间：
2019-10
期刊：
Weather and Forecasting
影响因子：
2.9
作者：
Eipper, Daniel T.;Greybush, Steven J.;Young, George S.;Saslo, Seth;Sikora, Todd D.;Clark, Richard D.
通讯作者：
Clark, Richard D.

Predicting the Inland Penetration of Long-Lake-Axis-Parallel Snowbands

预测长湖轴平行雪带向内陆的渗透

DOI：
10.1175/waf-d-18-0033.1
发表时间：
2018-10
期刊：
Weather and Forecasting
影响因子：
2.9
作者：
Eipper, Daniel T.;Young, George S.;Greybush, Steven J.;Saslo, Seth;Sikora, Todd D.;Clark, Richard D.
通讯作者：
Clark, Richard D.

Applications of the Geometry-Sensitive Ensemble Mean for Lake-Effect Snowbands and Other Weather Phenomena

几何敏感集合均值在湖效应雪带和其他天气现象中的应用

DOI：
10.1175/mwr-d-21-0212.1
发表时间：
2022-02
期刊：
Monthly Weather Review
影响因子：
3.2
作者：
Seibert, Jonathan J.;Greybush, Steven J.;Li, Jia;Zhang, Zhoumin;Zhang, Fuqing
通讯作者：
Zhang, Fuqing

The Lake-Effect Snow Ensemble Reanalysis Version 1.0 Dataset

湖效应雪系再分析 1.0 版数据集