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

GSTDTAP

项目编号	1745243
	Lake-effect Snow: Understanding Predictability and Dynamics through Ensemble-Based Convective-Permitting Data Assimilation, Modeling, and Sensitivity Analysis
	Steven Greybush
主持机构	Pennsylvania State Univ University Park
项目开始年	2018
	2018
项目结束日期	2020-12-31
资助机构	US-NSF
项目类别	Standard Grant
项目经费	493141(USD)
国家	美国
语种	英语
英文摘要	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.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/72230
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Steven Greybush.Lake-effect Snow: Understanding Predictability and Dynamics through Ensemble-Based Convective-Permitting Data Assimilation, Modeling, and Sensitivity Analysis.2018.