Chrome Extension
WeChat Mini Program
Use on ChatGLM

Prediction of the Structure of the Corona for the 2024 Total Solar Eclipse:  A Continuously Updated Model

crossref(2024)

Cited 0|Views21
Abstract
Total solar eclipses offer an unparalleled opportunity to observe the low and middle corona.  As is our tradition, the solar physics team at Predictive Science is predicting the structure of the solar corona for the April 8, 2024 total solar eclipse, using a magnetohydrodynamic (MHD) model of the corona.  The model incorporates thermodynamic transport terms and employs a wave-turbulence-driven (WTD) description of coronal heating and solar wind acceleration.  Our previous coronal predictions employed relaxed MHD solutions corresponding to a boundary condition based on a single photospheric magnetic map, incorporating data that at best was measured 10 to 14 days prior to the eclipse. This year, we introduce a new paradigm:  A continuously updated prediction based on a time-evolving model.  To accomplish this near-real time description, we have incorporated 3 new elements:  (1) a time-evolving MHD model driven by evolution of the photospheric magnetic field,  (2) an automated method for energizing the non-potential corona near polarity inversion lines that evolve in time, and (3) The Open-source Flux Transport (OFT) model, that assimilates near-real time surface magnetic flux observations from SDO HMI as well as low-latency observations from the Solar Orbiter PHI instrument made away from the Sun–-Earth line. This presentation will give an overview of the entire prediction effort and describe the time-dependent coronal dynamical features that appear in the solutions. Research Supported by NASA and NSF.  Computational resources provided by the NSF ACCESS program and the NASA Advanced Supercomputing division at Ames.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文提出了一种持续更新的预测模型,利用磁流体动力学(MHD)模型预测2024年4月8日发生的日全食期间的太阳冠层结构,引入了时间演化的模型新范式。

方法】:研究采用了一种包含热力学传输项的MHD模型,并利用波动-湍流驱动(WTD)描述冠层加热和太阳风加速。

实验】:实验通过结合三个新元素实现实时描述:时间演化的MHD模型、自动化的非势冠层能量方法以及开源通量传输(OFT)模型,并使用了SDO HMI和Solar Orbiter PHI仪器提供的近实时表面磁场观测数据,以预测日全食期间冠层动态特征。