A testbed for geomagnetic data assimilation

Author:

Gwirtz K1,Morzfeld M1,Kuang W2,Tangborn A3

Affiliation:

1. Cecil H. and Ida M. Green Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California, San Diego, CA, 92037, USA

2. Geodesy and Geophysics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA

3. Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD, 21228, USA

Abstract

SUMMARY Geomagnetic data assimilation merges past and present-day observations of the Earth’s magnetic field with numerical geodynamo models and the results are used to initialize forecasts. We present a new ‘proxy model’ that can be used to test, or rapidly prototype, numerical techniques for geomagnetic data assimilation. The basic idea for constructing a proxy is to capture the conceptual difficulties one encounters when assimilating observations into high-resolution, 3-D geodynamo simulations, but at a much lower computational cost. The framework of using proxy models as ‘gate-keepers’ for numerical methods that could/should be considered for more extensive testing on operational models has proven useful in numerical weather prediction, where advances in data assimilation and, hence, improved forecast skill, are at least in part enabled by the common use of a wide range of proxy models. We also present a large set of systematic data assimilation experiments with the proxy to reveal the importance of localization and inflation in geomagnetic data assimilation.

Funder

NASA

Office of Naval Research

Publisher

Oxford University Press (OUP)

Subject

Geochemistry and Petrology,Geophysics

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Impact of localization and inflation on geomagnetic data assimilation;Physics of the Earth and Planetary Interiors;2024-10

2. High‐Dimensional Covariance Estimation From a Small Number of Samples;Journal of Advances in Modeling Earth Systems;2024-08-30

3. A Theory for Why Even Simple Covariance Localization Is So Useful in Ensemble Data Assimilation;Monthly Weather Review;2023-03

4. Extending ensemble Kalman filter algorithms to assimilate observations with an unknown time offset;Nonlinear Processes in Geophysics;2023-02-07

5. A low-rank ensemble Kalman filter for elliptic observations;Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences;2022-10

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