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In the last 60 years, the largest displacement of the magnetic equator (by about 1100 km northwards) occurred in the Brazilian longitudinal sector. The magnetic equator passed by Tatuoca magnetic observatory (TTB) in northern Brazil in 2012 and continues to move northward. Due to the horizontal geomagnetic field geometry at the magnetic equator, enhanced electric currents in the ionosphere are produced – the so-called equatorial electrojet (EEJ). The magnetic effect of the EEJ is observed in the range of ±3 ° from the magnetic equator, where magnetic observatories record an amplified daily variation of the H component. In order to track the spatial and temporal variation of this phenomena, a new magnetometer station was installed in Macapá (MAA), which is about 350 km northwest of TTB. In this paper, we present the setup and data analysis of MAA station from November 2019 until September 2021. Because of its special configuration, we develop a method for temperature correction of the vector magnetometer data. [ABSTRACT FROM AUTHOR]

In the last 60 years, the largest displacement of the magnetic equator (by about 1100 km northwards) occurred in the Brazilian longitudinal sector. The magnetic equator passed by Tatuoca magnetic observatory (TTB) in northern Brazil in 2012 and continues to move northward. Due to the horizontal geomagnetic field geometry at the magnetic equator, enhanced electric currents in the ionosphere are produced - the so called equatorial electrojet (EEJ). The magnetic effect of the EEJ is observed in the range of ± 3 degrees from the magnetic equator, where magnetic observatories record an amplified daily variation of the H component. In order to track the spatial and temporal variation of this phenomena, a new magnetometer station was installed in Macapá (MAA), which is about 350 km northwest from TTB. In this paper, we present the setup and data analysis of MAA station from 11/2019 until 09/2021. Because of its special configuration, we develop a method for temperature correction of the vector magnetometer data. [ABSTRACT FROM AUTHOR]

MOSFiT (Magnetic Observatories and Stations Filtering Tool) is a Python package to visualize and filter data from magnetic observatories and magnetometer stations. The purpose of MOSFiT is to automatically isolate and analyze the secular variation (SV) information measured by geomagnetic observatory data. External field contributions may be reduced by selecting data according to local time and geomagnetic indices and by subtracting the magnetospheric field predictions of the CHAOS-7 model. MOSFiT calculates the SV by annual differences of monthly means, and geomagnetic jerk occurrence time and amplitude are automatically calculated by fitting two straight-line segments in a user-defined time interval of the SV time series. Here, we present the new Python package, validate it against independent results from previous publications and show its application. In particular, we quantify the RMS misfit between SV derived from processing schemes and the SV predicted by CHAOS-7. Analyzing the International Real-time Magnetic Observatory Network (INTERMAGNET) quasi-definitive data with MOSFiT allows for a timely investigation of SV, such as the detection of recent geomagnetic jerks. It can also be used for data selection for, e.g., external field studies or quality control of geomagnetic observatory data. [ABSTRACT FROM AUTHOR]

MOSFiT (Magnetic Observatories and Stations Filtering Tool) is a python package to visualize and filter data from magnetic observatories and magnetometer stations. The purpose of MOSFiT is to automatically isolate and analyze the secular variation (SV) information measured by geomagnetic observatories data. External field contributions may be reduced by selecting data according to local time and geomagnetic indices and by subtracting the magnetospheric field predictions of the CHAOS-7 model. MOSFiT calculates the SV by annual differences of monthly means and geomagnetic jerk occurrence time and amplitude are automatically calculated by fitting two straight-line segments in a user-defined time interval of the SV time series. Here, we present the new python package, validate it against independent results from previous publications, and show its application. In particular, we quantify the RMS misfit between SV derived from processing schemes and the SV predicted by CHAOS-7. Analysing the INTERMAGNET quasi-definitive data with MOSFIT allows for a timely investigation of SV such as the detection of recent geomagnetic jerks. It can also be used for data selection for e.g., external field studies as well as for quality control of geomagnetic observatory data. [ABSTRACT FROM AUTHOR]