TY - JOUR A1 - Pick, Leonie A1 - Korte, Monika T1 - An annual proxy for the geomagnetic signal of magnetospheric currents on Earth based on observatory data from 1900–2010 JF - Geophysical Journal International N2 - We introduce the Annual Magnetospheric Currents index as long-term proxy for the geomagnetic signal of magnetospheric currents on Earth valid within the time span 1900–2010. Similar to the widely used disturbance storm time and ‘Ring Current’ indices, it is based on geomagnetic observatory data, but provides a realistic absolute level and uncertainty estimates. Crucial aspects to this end are the revision of observatory crustal biases as well as the implementation of a Bayesian inversion accounting for uncertainties in the main field estimate, both required for the index derivation. The observatory choice is based on a minimization of index variance during a reference period spanning 1960–2010. The new index is capable of correcting observatory time series from large-scale external signals in a user-friendly manner. At present the index is only available as annual mean values. An extension to hourly values for the same time span is in progress. KW - Magnetic field variations through time KW - Satellite magnetics KW - Inverse theory KW - Statistical methods KW - Time-series analysis Y1 - 2017 U6 - https://doi.org/10.1093/gji/ggx367 SN - 1365-246X SN - 0956-540X VL - 211 IS - 2 SP - 1223 EP - 1236 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Pick, Leonie A1 - Korte, Monika A1 - Thomas, Yannik A1 - Krivova, Natalie A1 - Wu, Chi-Ju T1 - Evolution of Large-Scale Magnetic Fields From Near-Earth Space During the Last 11 Solar Cycles JF - Journal of Geophysical Research: Space Physics N2 - We use hourly mean magnetic field measurements from 34 midlatitude geomagnetic observatories between 1900 and 2015 to investigate the long-term evolution and driving mechanism of the large-scale external magnetic field at ground. The Hourly Magnetospheric Currents index (HMC) is derived as a refinement of the Annual Magnetospheric Currents index (HMC, Pick & Korte, 2017, https://doi.org/10.1093/gji/ggx367). HMC requires an extensive revision of the observatory hourly means. It depends on three third party geomagnetic field models used to eliminate the core, the crustal, and the ionospheric solar-quiet field contributions. We mitigate the dependency of HMC on the core field model by subtracting only nondipolar components of the model from the data. The separation of the residual (dipolar) signal into internal and external (HMC) parts is the main methodological challenge. Observatory crustal biases are updated with respect to AMC, and the solar-quiet field estimation is extended to the past based on a reconstruction of solar radio flux (F10.7). We find that HMC has more power at low frequencies (periods = 1 year) than the Dcx index, especially at periods relevant to the solar cycle. Most of the slow variations in HMC can be explained by the open solar magnetic flux. There is a weakly decreasing linear trend in absolute HMC from 1900 to present, which depends sensitively on the data rejection criteria at early years. HMC is well suited for studying long-term variations of the geomagnetic field. KW - geomagnetic indices KW - geomagnetic observatories Y1 - 2019 U6 - https://doi.org/10.1029/2018JA026185 SN - 2169-9402 SN - 0148-0227 SP - 2527 EP - 2540 PB - Union CY - Washington, DC ER - TY - JOUR A1 - Pick, Leonie A1 - Effenberger, Frederic A1 - Zhelavskaya, Irina A1 - Korte, Monika T1 - A Statistical Classifier for Historical Geomagnetic Storm Drivers Derived Solely From Ground-Based Magnetic Field Measurements JF - Earth and Space Science N2 - Solar wind observations show that geomagnetic storms are mainly driven by interplanetary coronal mass ejections (ICMEs) and corotating or stream interaction regions (C/SIRs). We present a binary classifier that assigns one of these drivers to 7,546 storms between 1930 and 2015 using ground‐based geomagnetic field observations only. The input data consists of the long‐term stable Hourly Magnetospheric Currents index alongside the corresponding midlatitude geomagnetic observatory time series. This data set provides comprehensive information on the global storm time magnetic disturbance field, particularly its spatial variability, over eight solar cycles. For the first time, we use this information statistically with regard to an automated storm driver identification. Our supervised classification model significantly outperforms unskilled baseline models (78% accuracy with 26[19]% misidentified interplanetary coronal mass ejections [corotating or stream interaction regions]) and delivers plausible driver occurrences with regard to storm intensity and solar cycle phase. Our results can readily be used to advance related studies fundamental to space weather research, for example, studies connecting galactic cosmic ray modulation and geomagnetic disturbances. They are fully reproducible by means of the underlying open‐source software (Pick, 2019, http://doi.org/10.5880/GFZ.2.3.2019.003) KW - geomagnetic observatory data KW - geomagnetic storm drivers KW - historical geomagnetic storms KW - supervised machine learning Y1 - 2019 U6 - https://doi.org/10.1029/2019EA000726 SN - 2333-5084 VL - 6 SP - 2000 EP - 2015 PB - American Geophysical Union CY - Malden, Mass. ER - TY - GEN A1 - Pick, Leonie A1 - Effenberger, Frederic A1 - Zhelavskaya, Irina A1 - Korte, Monika T1 - A Statistical Classifier for Historical Geomagnetic Storm Drivers Derived Solely From Ground-Based Magnetic Field Measurements T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Solar wind observations show that geomagnetic storms are mainly driven by interplanetary coronal mass ejections (ICMEs) and corotating or stream interaction regions (C/SIRs). We present a binary classifier that assigns one of these drivers to 7,546 storms between 1930 and 2015 using ground‐based geomagnetic field observations only. The input data consists of the long‐term stable Hourly Magnetospheric Currents index alongside the corresponding midlatitude geomagnetic observatory time series. This data set provides comprehensive information on the global storm time magnetic disturbance field, particularly its spatial variability, over eight solar cycles. For the first time, we use this information statistically with regard to an automated storm driver identification. Our supervised classification model significantly outperforms unskilled baseline models (78% accuracy with 26[19]% misidentified interplanetary coronal mass ejections [corotating or stream interaction regions]) and delivers plausible driver occurrences with regard to storm intensity and solar cycle phase. Our results can readily be used to advance related studies fundamental to space weather research, for example, studies connecting galactic cosmic ray modulation and geomagnetic disturbances. They are fully reproducible by means of the underlying open‐source software (Pick, 2019, http://doi.org/10.5880/GFZ.2.3.2019.003) T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 982 KW - geomagnetic observatory data KW - geomagnetic storm drivers KW - historical geomagnetic storms KW - supervised machine learning Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-474996 SN - 1866-8372 IS - 982 SP - 2000 EP - 2015 ER -