@article{PickKorte2017,
  author    = {Pick, Leonie and Korte, Monika},
  title     = {An annual proxy for the geomagnetic signal of magnetospheric currents on Earth based on observatory data from 1900-2010},
  series = {Geophysical Journal International},
  volume    = {211},
  journal   = {Geophysical Journal International},
  number    = {2},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1365-246X},
  doi       = {10.1093/gji/ggx367},
  pages     = {1223 -- 1236},
  year      = {2017},
  abstract  = {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.},
  language  = {en}
}
@article{PickKorteThomasetal.2019,
  author    = {Pick, Leonie and Korte, Monika and Thomas, Yannik and Krivova, Natalie and Wu, Chi-Ju},
  title     = {Evolution of Large-Scale Magnetic Fields From Near-Earth Space During the Last 11 Solar Cycles},
  series = {Journal of Geophysical Research: Space Physics},
  journal   = {Journal of Geophysical Research: Space Physics},
  publisher = {Union},
  address   = {Washington, DC},
  issn      = {2169-9402},
  doi       = {10.1029/2018JA026185},
  pages     = {2527 -- 2540},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{PickEffenbergerZhelavskayaetal.2019,
  author    = {Pick, Leonie and Effenberger, Frederic and Zhelavskaya, Irina and Korte, Monika},
  title     = {A Statistical Classifier for Historical Geomagnetic Storm Drivers Derived Solely From Ground-Based Magnetic Field Measurements},
  series = {Earth and Space Science},
  volume    = {6},
  journal   = {Earth and Space Science},
  publisher = {American Geophysical Union},
  address   = {Malden, Mass.},
  issn      = {2333-5084},
  doi       = {10.1029/2019EA000726},
  pages     = {2000 -- 2015},
  year      = {2019},
  abstract  = {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)},
  language  = {en}
}
@misc{PickEffenbergerZhelavskayaetal.2019,
  author    = {Pick, Leonie and Effenberger, Frederic and Zhelavskaya, Irina and Korte, Monika},
  title     = {A Statistical Classifier for Historical Geomagnetic Storm Drivers Derived Solely From Ground-Based Magnetic Field Measurements},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {982},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-47499},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-474996},
  pages     = {2000 -- 2015},
  year      = {2019},
  abstract  = {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)},
  language  = {en}
}