Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13

Tangborn, Andrew; Kuang, Weijia; Sabaka, Terence J.; Yi, Ce

doi:10.1186/s40623-020-01324-w

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…For the predictive secular variation models covering 2020.0 to 2025.0, the candidates broadly fall into two main categories: (1) computing SV solely from the latest available satellite and ground data (Alken et al 2020a;Pavón-Carrasco et al 2020;Huder et al 2020;Petrov and Bondar 2020;Rother et al 2020), or (2) applying physics-based modeling, combined with recent satellite and ground data to forecast future field changes based on underlying core dynamics (Brown et al 2020;Fournier et al 2020;Minami et al 2020;Metman et al 2020;Sanchez et al 2020;Tangborn et al 2020 et al 2020). Accurately forecasting the temporal evolution of the main geomagnetic field is nontrivial, due to challenges such as the low resolution of the recoverable magnetic field at the core-mantle boundary, the occurrence of unpredictable geomagnetic jerks or the uncertainty associated with diffusion of the field over short timescales (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of candidate models for the 13th generation International Geomagnetic Reference Field

Alken

Thébault

Beggan

et al. 2021

Earth Planets Space

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In December 2019, the 13th revision of the International Geomagnetic Reference Field (IGRF) was released by the International Association of Geomagnetism and Aeronomy (IAGA) Division V Working Group V-MOD. This revision comprises two new spherical harmonic main field models for epochs 2015.0 (DGRF-2015) and 2020.0 (IGRF-2020) and a model of the predicted secular variation for the interval 2020.0 to 2025.0 (SV-2020-2025). The models were produced from candidates submitted by fifteen international teams. These teams were led by the British Geological Survey (UK), China Earthquake Administration (China), Universidad Complutense de Madrid (Spain), University of Colorado Boulder (USA), Technical University of Denmark (Denmark), GFZ German Research Centre for Geosciences (Germany), Institut de physique du globe de Paris (France), Institut des Sciences de la Terre (France), Pushkov Institute of Terrestrial Magnetism, Ionosphere and Radio Wave Propagation (Russia), Kyoto University (Japan), University of Leeds (UK), Max Planck Institute for Solar System Research (Germany), NASA Goddard Space Flight Center (USA), University of Potsdam (Germany), and Université de Strasbourg (France). The candidate models were evaluated individually and compared to all other candidates as well to the mean, median and a robust Huber-weighted model of all candidates. These analyses were used to identify, for example, the variation between the Gauss coefficients or the geographical regions where the candidate models strongly differed. The majority of candidates were sufficiently close that the differences can be explained primarily by individual modeling methodologies and data selection strategies. None of the candidates were so different as to warrant their exclusion from the final IGRF-13. The IAGA V-MOD task force thus voted for two approaches: the median of the Gauss coefficients of the candidates for the DGRF-2015 and IGRF-2020 models and the robust Huber-weighted model for the predictive SV-2020-2025. In this paper, we document the evaluation of the candidate models and provide details of the approach used to derive the final IGRF-13 products. We also perform a retrospective analysis of the IGRF-12 SV candidates over their performance period (2015–2020). Our findings suggest that forecasting secular variation can benefit from combining physics-based core modeling with satellite observations.

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Section: Introductionmentioning

confidence: 99%

Evaluation of candidate models for the 13th generation International Geomagnetic Reference Field

Alken

Thébault

Beggan

et al. 2021

Earth Planets Space

View full text Add to dashboard Cite

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“…CM6 was used to generate main field candidates for epochs 2015.0 and 2020.0. Tangborn et al (2021) present a secular variation forecast for 2020 to 2025 combining a geodynamo model with an ensemble Kalman filter. Various geomagnetic field models are used as inputs to the data assimilation scheme.…”

Section: Open Accessmentioning

confidence: 99%

Special issue “International Geomagnetic Reference Field: the thirteenth generation”

Alken

Thébault

Beggan

et al. 2022

Earth Planets Space

167

144

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“…The two families of physical models (based on core surface flows or on dynamos) are themselves uncertain; in an attempt to take, at least partly, this uncertainty into account, most groups now resort to ensemble approaches (Minami et al 2020;Sanchez et al 2020;Brown et al 2021;Fournier et al 2021;Tangborn et al 2021) for steps (2) and (3) in Fig. 4.…”

Section: Physical Modelsmentioning

confidence: 99%

Physics-based secular variation candidate models for the IGRF

Fournier

Aubert

Lesur

et al. 2021

Earth Planets Space

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Each International Geomagnetic Reference Field (IGRF) model released under the auspices of the International Association of Geomagnetism and Aeronomy comprises a secular variation component that describes the evolution of the main magnetic field anticipated for the 5 years to come. Every Gauss coefficient, up to spherical harmonic degree and order 8, is assumed to undergo its own independent linear evolution. With a mathematical model of the core magnetic field and its time rate of change constructed from geomagnetic observations at hand, a standard prediction of the secular variation (SV) consists of taking the time rate of change of each Gauss coefficient at the final time of analysis as the predicted rate of change. The last three generations of the IGRF have additionally witnessed a growing number of candidate SV models relying upon physics-based forecasts. This surge is motivated by satellite data that now span more than two decades and by the concurrent progress in the numerical modelling of Earth’s core dynamics. Satellite data reveal rapid (interannual) geomagnetic features whose imprint can be detrimental to the quality of the IGRF prediction. This calls for forecasting frameworks able to incorporate at least part of the processes responsible for short-term geomagnetic variations. In this letter, we perform a retrospective analysis of the performance of past IGRF SV models and candidates over the past 35 years; we emphasize that over the satellite era, the quality of the 5-year forecasts worsens at times of rapid geomagnetic changes. After the definition of the time scales that are relevant for the IGRF prediction exercise, we cover the strategies followed by past physics-based candidates, which we categorize into a “‘core–surface flow” family and a “dynamo” family, noting that both strategies resort to “input” models of the main field and its secular variation constructed from observations. We next review practical lessons learned from our previous attempts. Finally, we discuss possible improvements on the current state of affairs in two directions: the feasibility of incorporating rapid physical processes into the analysis on the one hand, and the accuracy and quantification of the uncertainty impacting input models on the other hand.

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Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13

Cited by 4 publications

References 33 publications

Evaluation of candidate models for the 13th generation International Geomagnetic Reference Field

Evaluation of candidate models for the 13th generation International Geomagnetic Reference Field

Special issue “International Geomagnetic Reference Field: the thirteenth generation”

Physics-based secular variation candidate models for the IGRF

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