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Analysis of Convection Observed in Dynamics of the Madden-Julian Oscillation (MJO) (DYNAMO)

马登-朱利安振荡 (MJO) 动力学中观测到的对流分析 (DYNAMO)

基本信息

批准号：
1649784
负责人：
Steven Rutledge
金额：
$ 67.06万
依托单位：
Colorado State University
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：
2017-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649784&HistoricalAwards=false
关键词：
Analysis Convection Observed Dynamics Madden

项目摘要

The Madden-Julian Oscillation (MJO), a term to describe slowly eastward moving complex convection systems that circumnavigate the tropics in about 30 to 60 days, has broad impacts on global weather and climate, including the variability of monsoon rainfall, the frequency and intensity of tropical cyclones, north American winter precipitation, and the frequency of tornadoes over the Great Plains of the U.S. Despite a few decades of studies, the MJO is not well understood and its prediction skill is limited. A field experiment called the Dynamics of the MJO (DYNAMO) was launched in 2011 in aiming to advance understanding of the MJO by collecting in situ observations over the central Indian Ocean where the MJO forms most often. DYNAMO deployed radiosondes, Doppler radars, oceanographic instrumentation, research aircraft and other sensors to study the spectrum of convection and ocean interactions associated with the MJO. This research project will use Doppler radar data and soundings from the DYNAMO field experiment over the Central Indian Ocean along with satellite observations, to elucidate the mesoscale flow structure and dynamical evolution associated with the MJO. The funded project will utilize the extensive Doppler radar and atmospheric sounding network data from DYNAMO to study how precipitating clouds organize as a function of the environment. A related effort will be directed towards synthesizing the data from the four DYNAMO Doppler radars to provide a comprehensive validation dataset for numerical modeling activities targeted at MJO prediction. The PI will also investigate how MJO precipitation and clouds change across the diurnal cycle, and how the diurnal cycle of precipitation interacts with the MJO. The third task of this research is to investigate physical processes associated with how shallow clouds transition to deep clouds during the initiation stage of the MJO. Long term satellite and reanalysis data will be used to determine the underlying large-scale and physical mechanisms responsible for specific mode of the transition such as gradual versus rapid transition. This research will contribute to the knowledge base regarding initiation of the MJO, which at present has major uncertainties. Significant efforts are being directed towards identifying the physics of MJO initiation based on numerical model simulations. The research project will develop a statistical database regarding organization, structure, and the diurnal cycle of MJO precipitating clouds spanning the MJO initiation, mature and dissipating stages. This dataset will provide a robust means for validating precipitation and clouds simulated by numerical models targeted at MJO. Carrying out research on the structure, organization, diurnal cycle, and transition of MJO precipitating clouds will provide a more physically based understanding of the MJO that ultimately should improve numerical model simulations of MJO initiation and propagation. Such knowledge and gained prediction skill may also improve understanding of the worldwide impacts of the MJO and eventually lead to better short and long term forecasts of global weather patterns.

马登-朱利安涛动(MJO)是一个描述缓慢东移的复杂对流系统在30-60天内绕热带飞行的术语，它对全球天气和气候产生了广泛的影响，包括季风降水的变异性、热带气旋的频率和强度、北美冬季降水以及美国大平原上龙卷风的频率。尽管经过了几十年的研究，MJO仍然没有被很好地理解，其预测技能也是有限的。2011年启动了一项名为MJO(发电机)的现场实验，旨在通过收集MJO最经常形成的印度洋中部的现场观测来促进对MJO的了解。迪纳摩公司部署了无线电探空仪、多普勒雷达、海洋学仪器、研究机和其他传感器，以研究与MJO有关的对流和海洋相互作用的频谱。这项研究项目将利用中印度洋迪纳摩现场试验的多普勒雷达资料和探测资料，以及卫星观测，来阐明与MJO有关的中尺度流动结构和动力学演变。资助的项目将利用迪纳摩的广泛的多普勒雷达和大气探测网络数据来研究降水云是如何作为环境的函数组织起来的。将开展一项相关工作，以综合四台迪纳摩多普勒雷达的数据，为针对MJO预测的数值模拟活动提供一个全面的验证数据集。PI还将调查MJO降水和云在日循环中的变化，以及降水的日循环如何与MJO相互作用。这项研究的第三个任务是研究在MJO初始阶段与浅云向深云转变相关的物理过程。将利用卫星和再分析的长期数据来确定导致过渡的具体模式的根本的大规模和物理机制，如逐步过渡和快速过渡。这项研究将有助于建立关于启动MJO的知识库，目前MJO具有重大不确定性。目前正在作出重大努力，以数值模式模拟为基础确定MJO引发的物理机制。该研究项目将开发一个关于MJO初始、成熟和消散阶段的MJO降水云的组织、结构和日循环的统计数据库。该数据集将为验证以MJO为目标的数值模式模拟的降水和云提供可靠的手段。对MJO降水云的结构、组织、日循环和过渡的研究将提供对MJO的更多物理基础的理解，最终将改进MJO发起和传播的数值模式模拟。这些知识和获得的预测技能还可能提高对MJO的全球影响的了解，并最终导致对全球天气模式的更好的短期和长期预报。