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

麦登-朱利安涛动（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。人们对MJO的认识不深，其预测能力有限。一项名为MJO动力学（DYNAMO）的野外实验于2011年启动，旨在通过收集MJO最常形成的印度洋中部的现场观测数据，提高对MJO的理解。DYNAMO部署了无线电探空仪、多普勒雷达、海洋仪器、研究飞机和其他传感器来研究与MJO相关的对流频谱和海洋相互作用。该研究项目将利用多普勒雷达数据和DYNAMO在印度洋中部的现场实验的探测数据以及卫星观测，阐明与MJO相关的中尺度流结构和动力演变。该项目将利用来自DYNAMO的广泛的多普勒雷达和大气探测网络数据来研究降水云如何组织作为环境的功能。相关工作将直接针对四个DYNAMO多普勒雷达的数据进行综合，为针对MJO预测的数值模拟活动提供全面的验证数据集。该项目还将研究MJO降水和云在日循环中如何变化，以及降水的日循环如何与MJO相互作用。本研究的第三个任务是研究在MJO开始阶段与浅云向深云过渡有关的物理过程。长期卫星数据和再分析数据将用于确定造成特定转变模式的潜在大尺度和物理机制，例如渐变与快速转变。该研究将有助于建立MJO启动的知识库，目前MJO的启动存在很大的不确定性。目前正在进行大量的努力，以确定基于数值模型模拟的MJO起始的物理性质。本研究计划将建立横跨MJO形成、成熟及消散阶段的MJO降水云的组织、结构及日循环的统计资料库。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。开展对MJO降水云的结构、组织、日循环和转变的研究将提供对MJO更基于物理的理解，最终将改进MJO产生和传播的数值模型模拟。这些知识和获得的预测技能也可以提高对MJO全球影响的认识，并最终导致更好的全球天气模式的短期和长期预报。

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.