NSWP: Data-based Forecasting of the Geomagnetic Field with High Resolution in Space

NSWP：基于数据的空间高分辨率地磁场预测

• 批准号: 0817333
• 负责人: Mikhail Sitnov
• 金额: $ 30万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-12-01 至 2011-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0817333&HistoricalAwards=false
• 关键词: NSWP; Data; based; Forecasting; Geomagnetic

The magnetic field is a fundamental parameter that governs the structure of the magnetosphere and its storm-time dynamics. Achieving its timely, accurate, and reliable forecasting is one of principal goals of the National Space Weather Program. It is especially important for the inner magnetosphere, where magnetic storms and radiation belt disturbances occur, and where the capabilities of the present-day first-principle models are most limited. In particular, the dynamics of the magnetic field is a key factor controlling the radial transport and acceleration of the radiation belts. A recently developed technique based on an extensible model for the field of equatorial currents that uses large sets of spacecraft data has been shown to dramatically improve the spatial resolution of the empirical picture of the magnetospheric magnetic field. Since the data accumulation, necessary for high resolution in space, may be too long and smear out important dynamical effects, a new nonlinear data-binning technique has been devised, where the spatial structure of each state of the magnetosphere is described by fitting the model to a local subset of data. It includes both the actual data obtained for the given state and data from other time intervals (e.g., similar phases of other magnetic storms), neighboring the present state in the space of global parameters, solar wind electric field, geomagnetic activity index Sym-H, and its time derivative. Initial results for magnetic storm structure and dynamics made with the model are consistent with in situ geosynchronous data, IMAGE spacecraft observations and the picture of field-aligned currents inferred from the Iridium constellation data, indicating that the technique offers a powerful new way to extract important new information on the storm-time currents and magnetic field from the past events. The goal of the project is to transform the present high-resolution model into a fully-fledged forecasting tool by using the interplanetary medium data as the only model input. Missing information on the state of the magnetosphere, available in the current model through the Sym-H index, will be provided in its forecasting version through a predicted Sym-H and through a more detailed description of the solar wind and IMF parameters and their time histories. The project will be done in three steps. First, the predicted Sym-H or Dst indices, already available from existing global forecasting models, will be used as a proxy of the actual Sym-H index. Second, a new data-fitting procedure will be elaborated, in which only solar wind and IMF data are used together with their time histories. Third, the new tool will be validated and optimized using in situ data and the already available high-resolution model based on the actual Sym-H index for the full range of storms. The proposed study uses the largest assembled database of in-situ space magnetic field data and concurrent interplanetary medium data ever compiled for empirical modeling studies, based on 11 years of GOES, IMP 8, Polar, Geotail, Cluster, ACE, and Wind spacecraft observations. When available, data from the new THEMIS mission will also be added to the data set. The final product of the study will be a set of space weather forecasting codes specifying the magnetospheric magnetic field with the resolution in space of a few Earth radii and the temporal resolution up to substorm time scales. To provide fast predictions the new codes will be parallelized and tested on local clusters and supercomputers.

磁场是控制磁层结构及其风暴时间动态的基本参数。实现及时、准确和可靠的预报是国家空间天气计划的主要目标之一。这对内磁层尤其重要，因为磁暴和辐射带扰动发生在那里，而目前的第一原理模型的能力也是最有限的。特别是，磁场的动力学是控制辐射带的径向传输和加速的关键因素。 最近开发的一种技术以赤道电流场的可扩展模型为基础，使用了大量航天器数据集，已证明这种技术大大提高了磁层磁场经验图的空间分辨率。由于空间高分辨率所需的数据积累可能太长，会掩盖重要的动力学效应，因此设计了一种新的非线性数据分箱技术，通过将模型拟合到局部数据子集来描述磁层每个状态的空间结构。它既包括为给定状态获得的实际数据，也包括来自其他时间间隔的数据（例如，其他磁暴的相似阶段），在全球参数、太阳风电场、地磁活动指数Sym-H及其时间导数的空间中与当前状态相邻。用该模型得到的磁暴结构和动力学的初步结果与现场地球同步数据、IMAGE航天器观测和从铱星座数据推断的场向电流图片一致，表明该技术提供了一种强有力的新方法，可以从过去的事件中提取关于磁暴时电流和磁场的重要新信息。该项目的目标是将行星际介质数据作为唯一的模型输入，将目前的高分辨率模型转变为成熟的预测工具。目前的模型通过Sym-H指数提供了关于磁层状态的缺失信息，而在其预测版本中，将通过预测的Sym-H以及通过对太阳风和国际货币基金组织参数及其时间历史的更详细描述提供这些信息。该项目将分三个步骤进行。首先，现有全球预测模型中已有的Sym-H或Dst指数预测值将被用作实际Sym-H指数的替代值。第二，将制定一个新的数据拟合程序，其中只有太阳风和IMF数据与它们的时间历史一起使用。第三，将利用现场数据和现有的高分辨率模型，根据实际的Sym-H指数，对整个风暴范围进行验证和优化。拟议的研究使用了最大的现场空间磁场数据和并行行星际介质数据的汇编数据库，这些数据是基于11年的GOES，IMP 8，Polar，Geotail，Cluster，ACE和Wind航天器观测而编制的。新的THEMIS使命的数据如果可以获得，也将加入数据集。这项研究的最后产品将是一套空间气象预报代码，具体说明磁层磁场，空间分辨率为几个地球半径，时间分辨率可达亚暴时间尺度。为了提供快速预测，新代码将在本地集群和超级计算机上进行并行化和测试。