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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-朱利安振荡（MJO）是一个描述缓慢向东移动的复杂对流系统的术语，它在大约30到60天内环绕热带，对全球天气和气候有广泛的影响，包括季风降雨的变化，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并没有得到很好的理解，其预测能力也很有限。2011年启动了一项名为MJO动力学的实地实验，旨在通过收集MJO最常形成的印度洋中部的现场观测数据来增进对MJO的了解。海洋观测组织部署了无线电探空仪、多普勒雷达、海洋学仪器、研究飞机和其他传感器，以研究与MJO有关的对流和海洋相互作用的频谱。该研究项目将利用多普勒雷达数据和沿着卫星观测的中印度洋上空的赤道涛动实地试验的探测结果，阐明与赤道涛动有关的中尺度流结构和动力演变。受资助的项目将利用来自气象组织的广泛的多普勒雷达和大气探测网络数据，研究降水云如何作为环境的函数组织起来。一个相关的努力将针对合成的数据从四个多普勒雷达MJO的预测提供一个全面的验证数据集的数值模拟活动。PI还将研究MJO降水和云在昼夜周期中的变化，以及降水的昼夜周期如何与MJO相互作用。本研究的第三个任务是调查与MJO初始阶段浅云如何过渡到深云相关的物理过程。将使用长期卫星和再分析数据来确定造成特定过渡模式（如逐渐过渡还是快速过渡）的基本大规模物理机制。这项研究将有助于建立关于启动司法部长办公室的知识库，目前这一知识库还存在很大的不确定性。目前正在作出重大努力，根据数值模型模拟确定MJO启动的物理过程。该研究项目将开发一个关于MJO降水云的组织、结构和日循环的统计数据库，涵盖MJO的开始、成熟和消散阶段。该数据集将为验证针对MJO的数值模型模拟的降水和云提供一个强大的手段。对MJO降水云的结构、组织、日周期和过渡进行研究将提供对MJO的更物理基础的理解，最终将改善MJO启动和传播的数值模型模拟。这些知识和获得的预测技能也可能提高对MJO全球影响的理解，并最终导致对全球天气模式的更好的短期和长期预测。