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.

这个为期三年的光泽项目旨在开发数据同化技术，以在太阳周期的规模上基于物理学的太阳活动预测。 预计该项目将提高我们的建模能力，以预测太阳周期，并提高我们对太阳能发电机和太阳周期性质的了解。 应用于复杂的发电机模型的数据同化技术将使广泛的太阳能物理社区受益。 该项目的科学结局对于地球上的高层大气层的研究以及长期的气候可能很重要，这对当前和未来的太空任务和社会都会有益。 这个为期三年的光泽项目的研究计划包括以下任务：（i）研究模型预测对各种数据同化方法的观察数据中不确定性的敏感性以及在动力学系统配方中的各种减少的发电机模型； （ii）制定程序来估计模型参数，系统状态及其不确定性；通过将它们应用于模拟数据和先前的太阳周期观测值来验证和测试数据同化程序； （iii）使用当前的观察数据，计算周期25的黑子数量以及总磁场和环形磁场成分的预测，并提供不确定性和置信区间； （iv）通过使用2D发电机模型，天气磁图以及来自太阳能天文台和基于地面的概要网络（如锣和索利斯）的2D磁性模型，以及子午流量测量结果，为太阳活动的长期概括预测制定了数据同化程序。 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能活动的重要知识，这是太阳能，地球际和星际环境中高能干扰的主要来源。 这种知识对于从太阳能表面到地球及以后的太空天气条件进行准确的建模和预测至关重要。 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。