| 项目编号: | 1341878
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| 项目名称: | Assimilation of Doppler Radar Data with an Ensemble-based Variational Method for Storm-scale Numerical Weather Prediction |
| 作者: | Jidong Gao
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| 承担单位: | University of Oklahoma Norman Campus
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| 批准年: | 2013
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| 开始日期: | 2014-03-01
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| 结束日期: | 2018-02-28
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| 资助金额: | USD480941
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Geosciences - Atmospheric and Geospace Sciences
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| 英文关键词: | storm-scale
; convective-scale
; convective-scale flow
; effective assimilation
; radar reflectivity
; larger-scale atmospheric flow
; efficient ensemble-based hybrid three-dimensional variational/ensemble kalman filter
; convective storm-scale dynamics
; storm-scale three-dimensional variational da system
; convective numerical weather prediction
; nsf/ncar weather research
; convective scale doppler radar
; dynamic prediction
; mesoscale ensemble forecast information
; severe thunderstorm
; thunderstorm-related hazard
; storm-scale da system
; convective-scale da
; doppler radar datum
; nationwide wsr-88d radar network
; weather phenomenon
; convective-scale datum assimilation
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| 英文摘要: | Most currently employed convective-scale data assimilation (DA) schemes were developed primarily for application to larger-scale atmospheric flows and weather phenomena, in which sharply contrasting balances and constraints are relevant. Moreover, at convective scales Doppler radar is the only widely available means of providing extensive observations of sufficiently high spatial and temporal resolution needed to facilitate dynamic prediction of high-impact weather phenomena such as severe thunderstorms. As such, the effective assimilation of Doppler radar data into convection-resolving models is of increasing importance, yet underutilized. Building upon their previous work on convective-scale DA, these researchers will explore new approaches to optimally assimilate operationally-collected WSR-88D Doppler radar data available from a newly-upgraded national network employing dual-polarization technology, and in particular will: (i) Determine how to best use reflectivity observations in addition to radial velocity data; (ii) examine the usefulness of the background tendency information in a storm-scale DA system; and (iii) implement an efficient ensemble-based hybrid three-dimensional variational/Ensemble Kalman Filter (3DVAR/EnKF) framework that incorporates existing mesoscale ensemble forecast information into a storm-scale three-dimensional variational DA system.
The Intellectual Merit of this effort centers upon developing a novel DA strategy that makes optimal use of both radar reflectivity and radial velocity fields, which are uniquely suitable for specifying rapidly evolving convective-scale flows, in order to provide initial conditions for high-resolution storm-scale NWP models such as the NSF/NCAR Weather Research and Forecasting (WRF) model--a system that enjoys use in both research and operational settings. This approach seeks to improve our physical understanding of convective storm-scale dynamics and is further aimed toward improved detection and anticipation of thunderstorm-related hazards as well as more accurate quantitative precipitation forecasts needed for hydrological applications. This work may also help to solve the initial "balance problem" that is inherent in convective numerical weather prediction (NWP), but has heretofore been largely overlooked by the research community. Broader Impacts of this work include deriving maximum benefit from the considerable U.S. investment in the nationwide WSR-88D radar network by accelerating the use of these data in both operational and research-based NWP. This project will embody educational benefits through mentoring of graduate students and a postdoctoral research associate, and emerging results will be integrated into teaching materials and publications reaching broad audiences. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/97257
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| Appears in Collections: | 影响、适应和脆弱性
气候减缓与适应
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| Recommended Citation: | |
Jidong Gao. Assimilation of Doppler Radar Data with an Ensemble-based Variational Method for Storm-scale Numerical Weather Prediction. 2013-01-01.
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