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) 是一个术语，描述缓慢向东移动的复杂对流系统，在大约 30 至 60 天内环绕热带地区，对全球天气和气候产生广泛影响，包括季风降雨的变化、频率和强度热带气旋、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，人们对 MJO 的了解还不够深入，而且它的预测能力有限。 2011 年启动了一项名为“MJO 动力学”(DYNAMO) 的现场实验，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加深对 MJO 的了解。 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中印度洋 DYNAMO 现场实验的探测数据以及卫星观测数据，来阐明与 MJO 相关的中尺度流动结构和动力学演化。该资助项目将利用 DYNAMO 提供的广泛多普勒雷达和大气探测网络数据来研究降水云如何根据环境进行组织。相关工作将致力于综合来自四个 DYNAMO 多普勒雷达的数据，为 MJO 预测的数值建模活动提供全面的验证数据集。 PI 还将研究 MJO 降水和云在昼夜周期中如何变化，以及降水的昼夜周期如何与 MJO 相互作用。这项研究的第三个任务是研究 MJO 启动阶段浅层云如何过渡到深层云的相关物理过程。长期卫星和再分析数据将用于确定负责特定过渡模式（例如渐进与快速过渡）的潜在大规模和物理机制。这项研究将有助于建立有关 MJO 启动的知识库，目前该知识库还存在很大的不确定性。人们正在努力根据数值模型模拟来确定 MJO 引发的物理原理。该研究项目将开发一个有关 MJO 降水云的组织、结构和昼夜周期的统计数据库，涵盖 MJO 起始、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可靠的方法。对 MJO 降水云的结构、组织、昼夜周期和转变进行研究将为 MJO 提供更加基于物理的理解，最终改善 MJO 引发和传播的数值模型模拟。这些知识和获得的预测技能还可以增进对 MJO 全球影响的理解，并最终更好地预测全球天气模式的短期和长期。