Kalmag: a high spatio-temporal model of the geomagnetic field

被引:13
作者
Julien, Baerenzung [1 ]
Matthias, Holschneider [2 ]
Saynisch-Wagner, Jan [1 ]
Thomas, Maik [1 ]
机构
[1] Geoforschungszentrum Potsdam, Sect Earth Syst Modelling 1 3, Potsdam, Germany
[2] Univ Potsdam, Inst Math, Potsdam, Germany
来源
EARTH PLANETS AND SPACE | 2022年 / 74卷 / 01期
关键词
Geomagnetic field; Lithospheric field; Secular variation; Magnetospheric field; Induced field; Assimilation; Kalman filter; Machine learning; EARTHS MAGNETIC-FIELD; QUIET-TIME; SAC-C; SATELLITE; CHAMP; CURRENTS; WAVES;
D O I
10.1186/s40623-022-01692-5
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
We present the extension of the Kalmag model, proposed as a candidate for IGRF-13, to the twentieth century. The dataset serving its derivation has been complemented by new measurements coming from satellites, ground-based observatories and land, marine and airborne surveys. As its predecessor, this version is derived from a combination of a Kalman filter and a smoothing algorithm, providing mean models and associated uncertainties. These quantities permit a precise estimation of locations where mean solutions can be considered as reliable or not. The temporal resolution of the core field and the secular variation was set to 0.1 year over the 122 years the model is spanning. Nevertheless, it can be shown through ensembles a posteriori sampled, that this resolution can be effectively achieved only by a limited amount of spatial scales and during certain time periods. Unsurprisingly, highest accuracy in both space and time of the core field and the secular variation is achieved during the CHAMP and Swarm era. In this version of Kalmag, a particular effort was made for resolving the small-scale lithospheric field. Under specific statistical assumptions, the latter was modeled up to spherical harmonic degree and order 1000, and signal from both satellite and survey measurements contributed to its development. External and induced fields were jointly estimated with the rest of the model. We show that their large scales could be accurately extracted from direct measurements whenever the latter exhibit a sufficiently high temporal coverage. Temporally resolving these fields down to 3 hours during the CHAMP and Swarm missions, gave us access to the link between induced and magnetospheric fields. In particular, the period dependence of the driving signal on the induced one could be directly observed. The model is available through various physical and statistical quantities on a dedicated website at https://ionocovar. agnld.uni-potsdam.de/Kalmag/.
引用
收藏
页数:22
相关论文
共 50 条
  • [41] Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering
    Jurek, Marcin
    Katzfuss, Matthias
    STATISTICS AND COMPUTING, 2022, 32 (01)
  • [42] Spatial-temporal change of the geomagnetic field: environmental aspect
    Orlyuk, M., I
    Romenets, A. A.
    GEOFIZICHESKIY ZHURNAL-GEOPHYSICAL JOURNAL, 2020, 42 (04): : 18 - 38
  • [43] Exploring Spatio-temporal Movements for Intelligent Mobility Services
    Gruner, Tobias
    Frey, Soren
    Nahm, Jens
    Reichardt, Dirk
    PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON VEHICLE TECHNOLOGY AND INTELLIGENT TRANSPORT SYSTEMS (VEHITS), 2020, : 123 - 128
  • [44] Pulse of the City: Spatio-Temporal Twitter Content Analysis
    Lu, Yunan
    Kusmik, William A.
    Turaga, Deepak S.
    2020 IEEE 36TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOPS (ICDEW 2020), 2020, : 33 - 39
  • [45] Spatio-Temporal Analysis and Prediction of Cellular Traffic in Metropolis
    Wan, Xu
    Zhou, Zimu
    Xiao, Fu
    Xing, Kai
    Yang, Zheng
    Liu, Yunhao
    Peng, Chunyi
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2019, 18 (09) : 2190 - 2202
  • [46] Spatio-temporal Urban Growth Modeling of Jaipur, India
    Dadhich, Pran Nath
    Hanaoka, Shinya
    JOURNAL OF URBAN TECHNOLOGY, 2011, 18 (03) : 45 - 65
  • [47] Optical solitons in birefringent fibers with spatio-temporal dispersion
    Bhrawy, A. H.
    Alshaery, A. A.
    Hilal, E. M.
    Savescu, Michelle
    Milovic, Daniela
    Khan, Kaisar R.
    Mahmood, Mohammad F.
    Jovanoski, Zlatko
    Biswas, Anjan
    OPTIK, 2014, 125 (17): : 4935 - 4944
  • [48] Deep Learning for Spatio-Temporal Data Mining: A Survey
    Wang, Senzhang
    Cao, Jiannong
    Yu, Philip S.
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2022, 34 (08) : 3681 - 3700
  • [49] Spatio-temporal traffic queue detection for uninterrupted flows
    Bae, Bumjoon
    Liu, Yuandong
    Han, Lee D.
    Bozdogan, Hamparsum
    TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2019, 129 : 20 - 34
  • [50] Spatio-temporal interaction of bacteria mixture within biofilms
    Li, Y.
    Kim, K. S.
    Deschamps, J.
    Briandet, R.
    Trubuil, A.
    SPATIAL STATISTICS CONFERENCE 2015, PART 1, 2015, 26 : 11 - 18