Improving Vertical Velocity Retrievals from Doppler Radar Observations of Convection

改进多普勒雷达对流观测的垂直速度反演

• 批准号: 1623626
• 负责人: Alan Shapiro
• 金额: $ 59.94万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1623626&HistoricalAwards=false
• 关键词: Improving; Vertical; Velocity; Retrievals; Doppler

Weather radars have a variety of functions, from sensing severe weather to providing estimates of rainfall. One of the main duties of Doppler radar is to retrieve the horizontal wind field. However, in meteorology it is very important to know the vertical winds to determine how quickly air is rising or descending. Radar meteorologists have used a variety of methods to estimate vertical winds from radar data, but there is room for improvement. The researchers in this project will develop advanced techniques to improve analysis of the three dimensional wind field. The new techniques will benefit research studies on severe weather topics such as supercell thunderstorms and tornadoes as well as potentially improving the initialization of numerical weather models. The project will also help to train the next generation of scientists by including students in the data analysis.The research team will address several long-standing issues related to the processing and utilization of radar meteorology products. This award is focused on minimizing the errors due to non-simultaneous data collection and the derivation of vertical velocity from radar measurements. Data will be collected from a variety of radar and profiler systems at the Department of Energy's Atmospheric Radiation Measurement site in Oklahoma to address the following research tasks: 1) Designing a new spatially-variable advection-correction/analysis procedure to reduce errors in radial wind analyses, 2) Investigating the utility of the anelastic vertical vorticity equation as a constraint in variational dual-Doppler wind analysis, and 3) Investigating the advantages and disadvantages of ensemble Kalman filter (EnKF) wind analysis versus variational dual-Doppler wind analysis.

气象雷达具有多种功能，从感知恶劣天气到提供降雨量估计。 多普勒雷达的主要任务之一是反演水平风场。 然而，在气象学中，了解垂直风以确定空气上升或下降的速度是非常重要的。 雷达气象学家使用了各种方法从雷达数据估计垂直风，但仍有改进的余地。 该项目的研究人员将开发先进的技术来改善三维风场的分析。 这些新技术将有利于对超级单体雷暴和龙卷风等恶劣天气主题的研究，并可能改善数值天气模型的初始化。 该项目还将通过让学生参与数据分析来帮助培训下一代科学家，研究小组将解决与雷达气象产品的处理和利用有关的几个长期存在的问题。 该奖项的重点是最大限度地减少由于非同步数据收集和从雷达测量中推导垂直速度而产生的误差。 将从位于俄克拉荷马州的能源部大气辐射测量站的各种雷达和剖面仪系统收集数据，以解决以下研究任务：1）设计一种新的空间变量平流校正/分析程序，以减少径向风分析中的误差，2）研究滞弹性垂直涡度方程作为变分双多普勒风分析中的约束的效用，（3）比较了集合卡尔曼滤波（EnKF）风场分析与变分双多普勒风场分析的优缺点。

项目成果

High-Resolution, Rapid-Scan Dual-Doppler Retrievals of Vertical Velocity in a Simulated Supercell

模拟超级细胞中垂直速度的高分辨率、快速扫描双多普勒反演

• DOI: 10.1175/jtech-d-18-0211.1
• 发表时间: 2019
• 期刊: Journal of Atmospheric and Oceanic Technology
• 影响因子: 2.2
• 作者: Dahl, Nathan A.;Shapiro, Alan;Potvin, Corey K.;Theisen, Adam;Gebauer, Joshua G.;Schenkman, Alexander D.;Xue, Ming
• 通讯作者: Xue, Ming

Spatially Variable Advection Correction of Doppler Radial Velocity Data

多普勒径向速度数据的空间可变平流校正

• DOI: 10.1175/jas-d-20-0048.1
• 发表时间: 2021
• 期刊: Journal of the Atmospheric Sciences
• 影响因子: 3.1
• 作者: Shapiro, Alan;Gebauer, Joshua G.;Dahl, Nathan A.;Bodine, David J.;Mahre, Andrew;Potvin, Corey K.
• 通讯作者: Potvin, Corey K.