The Kalmag model as a candidate for IGRF-13

Earth, Planets and Space

Vincent Lesur¹  Julien Baerenzung²  Matthias Holschneider²  Sabrina Sanchez³  Johannes Wicht³ 

[1] Institut de Physique du Globe, Paris, France;Institute for Mathematics, University of Potsdam, Potsdam, Germany;Max Planck Institute for Solar System Research, Göttingen, Germany;

关键词: Geomagnetic field;    Secular variation;    Assimilation;    Kalman filter;    Machine learning;   

DOI  :  10.1186/s40623-020-01295-y

来源: Springer

【 摘 要 】

We present a new model of the geomagnetic field spanning the last 20 years and called Kalmag. Deriving from the assimilation of CHAMP and Swarm vector field measurements, it separates the different contributions to the observable field through parameterized prior covariance matrices. To make the inverse problem numerically feasible, it has been sequentialized in time through the combination of a Kalman filter and a smoothing algorithm. The model provides reliable estimates of past, present and future mean fields and associated uncertainties. The version presented here is an update of our IGRF candidates; the amount of assimilated data has been doubled and the considered time window has been extended from [2000.5, 2019.74] to [2000.5, 2020.33].

【 授权许可】

CC BY