| 项目编号: | 1632850
|
| 项目名称: | Developing a Climatology of Horizontal Convective Rolls over Oklahoma: Combining Observational Data Sets to Increase Understanding |
| 作者: | David Stensrud
|
| 承担单位: | Pennsylvania State Univ University Park
|
| 批准年: | 2016
|
| 开始日期: | 2016-08-01
|
| 结束日期: | 2019-07-31
|
| 资助金额: | 484146
|
| 资助来源: | US-NSF
|
| 项目类别: | Standard Grant
|
| 国家: | US
|
| 语种: | 英语
|
| 特色学科分类: | Geosciences - Atmospheric and Geospace Sciences
|
| 英文关键词: | hcr
; hcr climatology
; horizontal convective roll
; oklahoma mesonet observation
; horizontal grid spacing
|
| 英文摘要: | Horizontal convective rolls (HCRs) are a common occurrence in the warm season and influence planetary boundary layer (PBL) mixing, PBL structure and convection initiation. Yet very little is known about the climatology of HCRs. This oversight is worrisome as HCRs are present in convection-allowing models and studies suggest that these modeled HCRs can be caused by inaccuracies in PBL parameterization. The goal of the research is to use the Twin Lakes, Oklahoma (KTLX), WSR-88D to observe HCRs during the warm season and merge this information with ancillary data to develop a rich, multi-year data base for studying HCRs. From the KTLX observations we will pursue the following scientific objectives: 1) Develop a 10-year warm season climatology of HCR occurrence frequency and characteristics (time of onset and decay, wavelength, orientation angle, year-to-year variability) using observations from April to September 2005-2014; 2) Determine PBL depth and mean PBL winds and wind shear using Rapid Update Cycle (RUC) or Rapid Refresh (RAP) analyses, and hence calculate HCR aspect ratios, for each day with HCRs; 3) Use Oklahoma Mesonet observations from three surface stations underneath the KTLX clear-air radar volume to calculate sensible heat flux and other surface parameters associated with HCR formation and maintenance; 4) Test and identify a robust automated approach to HCR identification that could then be used in a subsequent studies to extend the HCR climatology nationally using all available WSR-88D observation locations. The resulting large data set on HCRs will then be used to examine the mechanisms of HCR formation and help improve our understanding of this common boundary layer phenomenon.
Intellectual Merit:
The created HCR data set will be novel in terms of the number of cases (several hundred) and the richness of the ancillary environmental data on surface fluxes and PBL structure, and will be used to define the warm season HCR frequency, aspect ratios, orientation angles, typical onset time and longevity for the first time ever. These data will be incredibly beneficial to compare with the frequency of HCR formation in convection-allowing numerical weather prediction models. This type of comparison will help determine how well PBL schemes function as horizontal grid spacing continues to decrease below 3 km.
Broader Impacts:
The project will enhance graduate, undergraduate, and precollege education, including the mentoring of a graduate student, graduate student participation in development of inquiry-based activities for undergraduate meteorology courses, and implementation of a week-long K-12 teacher workshop using adaptations of these activities followed by academic year support for K-12 classroom implementation. This will lead to enhanced K-12 teachers' and students' understanding of research strategies utilizing radar data to study mesoscale atmospheric phenomena and will increase graduate students' and K-12 teachers' understanding of the systems thinking involved in meteorological research, while also enhancing researchers' and graduate students' understanding of inquiry-based teaching practices and communication skills with non-technical audiences. |
| 资源类型: | 项目
|
| 标识符: | http://119.78.100.158/handle/2HF3EXSE/91620
|
| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
|
|
There are no files associated with this item.
|
| Recommended Citation: | |
David Stensrud. Developing a Climatology of Horizontal Convective Rolls over Oklahoma: Combining Observational Data Sets to Increase Understanding. 2016-01-01.
|
|
|