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.

Madden-ulian振荡（MJO）是一个术语，该术语可缓慢向东移动复杂的对流系统，在大约30天到60天内绕过热带地区，对全球天气和气候产生了广泛的影响，包括季风降雨的变化，北美冬季的频率，北美季节的频率，以及托恩多的频率，不太了解托恩的频率，而不是托恩的频率。它的预测技能是有限的。 2011年启动了一个名为MJO（Dynamo）动力学的现场实验，目的是通过收集MJO中部中部的原位观察结果来促进对MJO的理解。发电机部署了辐射，多普勒雷达，海洋仪器，研究飞机和其他传感器，以研究与MJO相关的对流和海洋相互作用的范围。该研究项目将使用印度洋中部的迪纳摩场实验的多普勒雷达数据以及卫星观测，以阐明与MJO相关的中尺度流量结构和动力学演化。资助的项目将利用从Dynamo的广泛的多普勒雷达和大气发声网络数据来研究沉淀云如何随环境的函数而组织。相关的工作将针对从四个发电机多普勒雷达中综合数据，以提供针对MJO预测的数值建模活动的全面验证数据集。 PI还将研究MJO降水和云在整个昼夜周期中的变化，以及降水的昼夜周期如何与MJO相互作用。这项研究的第三个任务是调查与MJO启动阶段浅云如何过渡到深云相关的物理过程。长期卫星和重新分析数据将用于确定负责过渡的特定模式的基本大规模和物理机制，例如逐渐的过渡与快速过渡。这项研究将有助于有关MJO启动的知识基础，目前具有主要的不确定性。正在为基于数值模型仿真确定MJO启动的物理学的重大努力。该研究项目将开发一个统计数据库，涉及跨MJO启动，成熟和耗散阶段的MJO的组织，结构和昼夜周期。该数据集将提供一种可靠的手段，用于验证降水和云，该降水和云由针对MJO的数值模型模拟。对MJO沉淀云的结构，组织，昼夜周期和过渡进行研究将提供对MJO的更基于物理的理解，最终应改善MJO启动和传播的数值模型模型。这种知识并获得了预测技能也可以提高对MJO在全球影响的影响，并最终导致对全球天气模式的更好和长期预测。