Bootstrapping Swarm and observatory data to generate candidates for the DGRF and IGRF-13

Earth, Planets and Space

Santiago Marsal¹  J. Miquel Torta¹  J. Manuel Tordesillas²  Manuel Catalán³  F. Javier Pavón-Carrasco⁴  Fátima Martín-Hernández⁴ 

[1] Observatori de l’Ebre (OE), Univ. Ramon Llull - CSIC, Ctra. de l’Observatori, 2, 43520, Roquetes, Spain;Observatorio Geofísico de Toledo, Instituto Geográfico Nacional (IGN), Av. Adolfo Suárez, km 4, 45005, Toledo, Spain;Real Observatorio Geofísico de la Armada (ROA), C/Cecilio Pujazón s/n, 11110, San Fernando, Spain;Universidad Complutense de Madrid (UCM) and Geoscience Institute IGEO (CSIC - UCM), Avda. Complutense, s/n, 28040, Madrid, Spain;

关键词: Geomagnetic field;    Geomagnetic field modelling;    Secular variation;    IGRF;    Swarm;    Geomagnetic observatories;   

DOI  :  10.1186/s40623-020-01198-y

来源: Springer

【 摘 要 】

As posted by the Working Group V of the International Association of Geomagnetism and Aeronomy (IAGA), the 13th generation of the International Geomagnetic Reference Field (IGRF) has been released at the end of 2019. Following IAGA recommendations, in this work we present a candidate model for the IGRF-13, for which we have used the available Swarm satellite and geomagnetic observatory ground data for the last year. In order to provide the IGRF-13 candidate, we have extrapolated the Gauss coefficients of the main field and its secular variation to January 1st, 2020. In addition, we have generated a Definitive Geomagnetic Reference Field model for 2015.0 using the same modelling approach, but focussed on a 1-year time window of data centred on 2015.0. To jointly model both satellite and ground data, we have followed the classical protocols and data filters applied in geomagnetic field modelling. Novelty arrives from the application of bootstrap analysis to solve issues related to the inhomogeneity of the spatial and temporal data distributions. This new approach allows the estimation of not only the Gauss coefficients, but also their uncertainties.

【 授权许可】

CC BY