SHINE: Prediction of Solar Activity Using Non-linear Dynamo Models and Data Assimilation Approach

SHINE：使用非线性发电机模型和数据同化方法预测太阳活动

• 批准号: 1622341
• 负责人: Irina Kitiashvili
• 金额: $ 34.35万
• 依托单位: Bay Area Environmental Research Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1622341&HistoricalAwards=false
• 关键词: SHINE; Prediction; Solar; Activity; Using

This 3-year SHINE project is aimed at developing data assimilation techniques for physics-based predictions of the solar activity on the scale of the solar cycle. The project is expected to improve our modeling capabilities to predict the solar cycle, and to advance our knowledge about the solar dynamo and the nature of the solar cycle. The data assimilation techniques applied to the sophisticated dynamo models would benefit the broad solar physics community. The scientific outcome of this project would be important for the studies in the heliosphere, the Earth's upper atmosphere, and possibly climate in the long-term, and it would be beneficial for current and future space missions and society. The research plan of this 3-year SHINE project includes the following tasks: (i) investigate the sensitivity of model predictions to uncertainties in observational data for various data assimilation methods and various reduced dynamo models in a dynamical system formulation; (ii) develop procedures to estimate the model parameters, system state, and their uncertainties; verify and test data assimilation procedures by applying them to simulated data and previous solar cycle observations; (iii) using current observational data, calculate predictions of the sunspot number and total poloidal and toroidal magnetic field components for Cycle 25, and provide uncertainties and confidence intervals; and (iv) develop a data assimilation procedure for long-term synoptic forecasts of solar activity by using 2D dynamo models, synoptic magnetograms, and meridional flow measurements from the Solar Dynamics Observatory and ground-based synoptic networks such as GONG and SOLIS. The project is directly relevant to the NSF's SHINE program, because it will provide important knowledge about the global solar activity, which is the major source of high-energy disturbances in the solar, heliospheric, and interplanetary environment. Such knowledge is critical for accurate modeling and prediction of space weather conditions from the solar surface to the Earth and beyond. The research and EPO agenda of this project supports the Strategic Goals of the AGS Division in discovery, learning, diversity, and interdisciplinary research.

这一为期3年的“阳光”项目旨在开发数据同化技术，以便在太阳周期尺度上对太阳活动进行基于物理学的预测。 该项目预计将提高我们预测太阳周期的建模能力，并提高我们对太阳发电机和太阳周期性质的认识。 应用于复杂发电机模型的数据同化技术将使广大的太阳物理学界受益。 该项目的科学成果将对日光层、地球高层大气以及可能的长期气候研究具有重要意义，并将有益于当前和未来的空间飞行任务和社会。 这个为期3年的SHINE项目的研究计划包括以下任务：（i）调查各种数据同化方法和动力系统公式中各种简化发电机模型的模式预测对观测数据中不确定性的敏感性;（ii）开发估计模式参数、系统状态及其不确定性的程序;通过将数据同化程序应用于模拟数据和以前的太阳周期观测，验证和测试这些程序; ㈢利用目前的观测数据，计算第25周期太阳黑子数目和总极向和环向磁场分量的预测值，并提供不确定性和置信区间;以及（iv）通过使用二维发电机模型、天气磁图和太阳动力学观测站和地面天气网络（如GONG和SOLIS）的纬向流测量值，开发用于太阳活动长期天气预报的数据同化程序。 该项目与NSF的SHINE计划直接相关，因为它将提供有关全球太阳活动的重要知识，这是太阳，日光层和行星际环境中高能扰动的主要来源。 这类知识对于准确建模和预测从太阳表面到地球及其以外的空间气象条件至关重要。 该项目的研究和EPO议程支持AGS部门在发现，学习，多样性和跨学科研究方面的战略目标。