Book contents
- Applications of Data Assimilation and Inverse Problems in the Earth Sciences
- Series page
- Applications of Data Assimilation and Inverse Problems in the Earth Sciences
- Copyright page
- Contents
- Contributors
- Preface
- Acknowledgements
- Part I Introduction
- Part II ‘Fluid’ Earth Applications: From the Surface to the Space
- Part III ‘Solid’ Earth Applications: From the Surface to the Core
- 11 Trans-Dimensional Markov Chain Monte Carlo Methods Applied to Geochronology and Thermochronology
- 12 Inverse Problems in Lava Dynamics
- 13 Data Assimilation for Real-Time Shake-Mapping and Prediction of Ground Shaking in Earthquake Early Warning
- 14 Global Seismic Tomography Using Time Domain Waveform Inversion
- 15 Solving Larger Seismic Inverse Problems with Smarter Methods
- 16 Joint and Constrained Inversion as Hypothesis Testing Tools
- 17 Crustal Structure and Moho Depth in the Tibetan Plateau from Inverse Modelling of Gravity Data
- 18 Geodetic Inversions and Applications in Geodynamics
- 19 Data Assimilation in Geodynamics: Methods and Applications
- 20 Geodynamic Data Assimilation: Techniques and Observables to Construct and Constrain Time-Dependent Earth Models
- 21 Understanding and Predicting Geomagnetic Secular Variation via Data Assimilation
- 22 Pointwise and Spectral Observations in Geomagnetic Data Assimilation: The Importance of Localization
- Index
- References
21 - Understanding and Predicting Geomagnetic Secular Variation via Data Assimilation
from Part III - ‘Solid’ Earth Applications: From the Surface to the Core
Published online by Cambridge University Press: 20 June 2023
- Applications of Data Assimilation and Inverse Problems in the Earth Sciences
- Series page
- Applications of Data Assimilation and Inverse Problems in the Earth Sciences
- Copyright page
- Contents
- Contributors
- Preface
- Acknowledgements
- Part I Introduction
- Part II ‘Fluid’ Earth Applications: From the Surface to the Space
- Part III ‘Solid’ Earth Applications: From the Surface to the Core
- 11 Trans-Dimensional Markov Chain Monte Carlo Methods Applied to Geochronology and Thermochronology
- 12 Inverse Problems in Lava Dynamics
- 13 Data Assimilation for Real-Time Shake-Mapping and Prediction of Ground Shaking in Earthquake Early Warning
- 14 Global Seismic Tomography Using Time Domain Waveform Inversion
- 15 Solving Larger Seismic Inverse Problems with Smarter Methods
- 16 Joint and Constrained Inversion as Hypothesis Testing Tools
- 17 Crustal Structure and Moho Depth in the Tibetan Plateau from Inverse Modelling of Gravity Data
- 18 Geodetic Inversions and Applications in Geodynamics
- 19 Data Assimilation in Geodynamics: Methods and Applications
- 20 Geodynamic Data Assimilation: Techniques and Observables to Construct and Constrain Time-Dependent Earth Models
- 21 Understanding and Predicting Geomagnetic Secular Variation via Data Assimilation
- 22 Pointwise and Spectral Observations in Geomagnetic Data Assimilation: The Importance of Localization
- Index
- References
Summary
Abstract: Geomagnetic data assimilation is a recently established research discipline in geomagnetism. It aims to optimally combine geomagnetic observations and numerical geodynamo models to better estimate the dynamic state of the Earth’s outer core, and to predict geomagnetic secular variation. Over the past decade, rapid advances have been made in geomagnetic data assimilation on various fronts by several research groups around the globe, such as using geomagnetic data assimilation to understand and interpret the observed geomagnetic secular variation, estimating part of the core state that is not observable on the Earth’s surface, and making geomagnetic forecasts on multi-year time scales. In parallel, efforts have also been made on proxy systems for understanding fundamental statistical properties of geomagnetic data assimilation, and for developing algorithms tailored specifically for geomagnetic data assimilation. In this chapter, we provide a comprehensive overview of these advances, as well as some of the immediate challenges of geomagnetic data assimilation, and possible solutions and pathways to move forward.
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- Publisher: Cambridge University PressPrint publication year: 2023