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

GSTDTAP

项目编号	1622341
	SHINE: Prediction of Solar Activity Using Non-linear Dynamo Models and Data Assimilation Approach
	Irina Kitiashvili
主持机构	Bay Area Environmental Research Institute
项目开始年	2016
	2016-09-15
项目结束日期	2019-08-31
资助机构	US-NSF
项目类别	Continuing grant
项目经费	109245(USD)
国家	美国
语种	英语
英文摘要	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.
来源学科分类	Geosciences - Atmospheric and Geospace Sciences
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/70396
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Irina Kitiashvili.SHINE: Prediction of Solar Activity Using Non-linear Dynamo Models and Data Assimilation Approach.2016.