FIGURE 3 - uploaded by Lei Cai
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A flowchart showing the main process of downloading, storing, and loading data managed by a DataSourced object.
Source publication
In the space physics community, processing and combining observational and modeling data from various sources is a demanding task because they often have different formats and use different coordinate systems. The Python package GeospaceLAB has been developed to provide a unified, standardized framework to process data. The package is composed of s...
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... subclass uses the attributes and methods of DatasetSourced as the abstracts and has its own functionality. Figure 3 shows the main process of querying, downloading, storing, and loading data by a DatasetSourced or its subclass object. The DatasetSourced object provides three modes to load a sourced dataset: "AUTO", "dialog", and "assigned". ...
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... of the values is assigned to the DatasetSourced attribute load_mode (see also Figure 2). For the mode "AUTO", the DatasetSourced object try to finish the entire process shown in Figure 3 automatically. First, it searches the associated data files that have been stored in the local directories. ...
Citations
... VISER automatically synthesized visual query language based on the data table provided by the user and part of the visualization as sample input. Recommend candidate visualization results with the same visualization form on the data table [36][37][38][39]. ...
With the rapid development of computer hardware and big data processing technology, the bottleneck of intelligent analysis of massive data has changed from "how to deal with massive data quickly" to "how to mine valuable information quickly and effectively from massive data". Visualization and visualization analysis based on human visual perception characteristics, combined with data analysis and human-computer interaction and other technologies, use visual charts to deconstruct the knowledge and rules contained in complex data. This technology runs through the whole life cycle of data science, known as the last kilometer in the field of big data intelligence, and has achieved remarkable results in many big data application analysis scenarios. Traditional visualization analysis is extremely dependent on the user's frequent active participation in the whole life cycle of visualization analysis, including data preparation, data conversion, visualization mapping, visual rendering, user interaction, visual analysis and other stages, which require high professional skills of users and low intelligence of the system. Therefore, the traditional visualization analysis mode and systems have the challenges of high threshold of visualization analysis, high cost of data preparation, high latency of interaction response, and low efficiency of interaction mode. Therefore, this paper introduces the application, challenges in visualization based on explainable AI.
... visualization.mpl.geomap.geodashboards, an open-source Python package to manage and visualize data in space physics (Cai et al. 2022). ...
One of the major processes that solar wind drives is the outflow and escape of ions from the planetary atmospheres. The major ion species in the upper ionospheres of both Earth and Mars is O ⁺ , and hence it is more likely to dominate the escape process. On Earth, due to a strong intrinsic magnetic field, the major ion outflow pathways are through the cusp, polar cap, and the auroral oval. In contrast, Mars has an induced magnetosphere, where the ionosphere is in direct contact with the shocked solar wind plasma. Therefore, physical processes underlying the ion energization and escape rates are expected to be different on Mars as compared to Earth. In the current work, we study the near-simultaneous ion outflow event from both Earth and Mars during the passage of a stream interaction region/high-speed stream (SIR/HSS) during 2016 May, when both the planets were approximately aligned on the same side of the Sun. The SIR/HSS propagation was recorded by spacecraft at the Sun–Earth L1 point and Mars Express at 1.5 au. During the passage of the SIR, the dayside and nightside ion outflows at Earth were observed by Van Allen Probes and Magnetospheric Multiscale Mission orbiters, respectively. At Mars, the ion energization at different altitudes was observed by the STATIC instrument on board the MAVEN orbiter. We observe evidence for the enhanced ion outflow from both Earth and Mars during the passage of the SIR, and identify the dominant drivers of the ion outflow.
... html. The open source Python package GeospaceLAB is used to search for EISCAT-DMSP conjugate events and to visualize the data (Cai et al., 2022). ...
Energetic particle precipitation is the major source of electron production that controls the ionospheric Pedersen and Hall conductances at high latitudes. Many studies use empirical formulas to estimate conductances. The particle precipitation spectra measured by the Defense Meteorological Satellite Program (DMSP) Special Sensor J are often used as the input to the empirical formulas. In this study, we evaluate the empirical formulas of ionospheric conductances during four different types of auroral precipitation conditions based on 63 conjugate events observed by DMSP and EISCAT. The conductances calculated from the DMSP data with the empirical formulas are compared with those based on EISCAT measurements with the standard equations. The best correlation between these two is found when the empirical Robinson formulas (Robinson et al., 1987, https://doi.org/10.1029/ja092ia03p02565) are used in the presence of diffuse electron precipitation without ions. In the presence of ion precipitation, the correlation coefficients are smaller, but the correlation improves when the Galand formulas (Galand & Richmond, 2001, https://doi.org/10.1029/1999ja002001) are used to estimate the contribution of ion precipitation to the conductances. We also found that pure ion precipitation can cause the increase of conductances up to 2–7 S for Pedersen and 2.5–10 S for Hall conductances, which is positively correlated with the auroral electrojet index. Overall, the empirical formulas applied to the DMSP particle spectra underestimate the ionospheric conductances.
... The multiple arc events examined in this study are a subset of the DMSP SSUSI TPA data set in Kullen et al. (2023), and have been processed with GeospaceLAB (https://github.com/JouleCai/geospacelab), a python package for managing and visualizing data in space physics (Cai et al., 2022). All TPA events in Kullen et al. (2023) have been selected by visual inspection of DMSP SSUSI images in the LBH short wavelength range from F16, F17, F18 and F19 according to the selection criteria described above. ...
Plain Language Summary
Occasionally, multiple auroral arcs form far poleward of the main auroral oval. These multiple transpolar arcs were so far believed to almost always be conjugate (appearing in both hemispheres simultaneously). We show for the first time that more than half of them appear in only one hemisphere. Conjugate multiple arcs appear when the magnetic field in the solar wind (IMF) is strongly northward. Non‐conjugate multiple arcs show a less strong dependency on northward IMF. Interestingly, we found a clear correlation between non‐conjugate multiple arc events and IMF BX. This is unexpected, as in general, polar auroral arcs do not show any clear dependence on BX. Non‐conjugate multiple arcs appear mainly in the southern hemisphere when the IMF points sunward, and in the northern hemisphere when it points tailward. As IMF BX is known to introduce an interhemispheric asymmetry in the field‐line topology close to the reconnection sites, this may affect the formation of multiple arcs differently in the two hemispheres, and thus might explain the non‐conjugacy of those events.
... For the new dataset, TPAs are identified in auroral images measured by the SSUSI onboard four DMSP-Block 5D3 satellites (F16, F17, F18, and F19). The SSUSI image data have been processed and visualized using the Python package GeospaceLAB (Cai et al., 2022). The package allows to overplot various space-based and Earth-bound datasets from the high-latitude ionosphere. ...
... and processed by GeospaceLAB version v0.5.2. The latter is a python package for visualization space data, developed by Cai et al. (2022), which is available online at https://doi.org/10.5281/zenodo.5377318. Solar wind data was downloaded from the OMNIWeb: https://omniweb.gsfc.nasa.gov/. ...
In this work, we investigate the interhemispheric transpolar arc (TPA) conjugacy using four previously published datasets based on Polar UV images, DMSP particle data, and IMAGE images, and a new TPA list based on DMSP SSUSI images during 1 Sep to 15 Oct 2015. IMF Bx ${B}_{\mathrm{x}}$ and the Earth's dipole tilt have often been suggested to influence the TPA conjugacy, as both induce a north‐south asymmetry on the magnetosphere. However, by comparing these parameters at TPA formation with the background distribution for each dataset, we find that neither the dipole tilt nor Bx ${B}_{\mathrm{x}}$ plays a major role for the TPA conjugacy in four of the five datasets. The well‐known correlation between initial TPA location and IMF By ${B}_{\mathrm{y}}$ appears in all datasets with information about the TPA formation. In addition, we find that a minority of dawnside TPAs form during the “wrong” By ${B}_{\mathrm{y}}$ sign. In the northern (southern) hemisphere, dawn TPAs appear also during weakly duskward (dawnward) IMF. Due to the polar orbit of DMSP satellites, TPA conjugacy and location can be examined on a case‐by‐case basis with the new dataset. The results show that at least 73% of TPAs appear in both hemispheres simultaneously. IMF Bx ${B}_{\mathrm{x}}$ and dipole tilt values for conjugate TPAs do not differ from those for non‐conjugate TPAs. Most conjugate (isolated) TPAs appear on opposite oval sides in each hemisphere (57%). Interestingly, in case northern and southern hemisphere TPAs form on the same oval side, they appear typically at dawn during weak IMF By ${B}_{\mathrm{y}}$.
Solar cycles 24–25 were quiet until a geomagnetic storm with a Sym‐H index of −170 nT occurred in late March 2023. On March 23–24, a Fabry‐Perot interferometer (FPI; 630 nm) in Tromsø, Norway, recorded the highest thermospheric wind speed of over 500 m/s since 2009. Comparisons with magnetometer readings in Scandinavia showed that a large amount of electromagnetic energy was transferred to the ionosphere‐thermosphere system. Total electron content maps suggested an enlarged auroral oval and revealed that the FPI observed winds near the polar cap instead of inside the oval for a long period during the storm main phase. The FPI wind had a strong equatorward component during the storm, likely because of the powerful anti‐sunward ionospheric plasma flow in the polar cap. The positive Y‐component of the IMF for 6 days before the storm caused a successive westward component of the FPI‐measured wind during the storm main phase. On March 24, the first day of the storm recovery phase, thermospheric wind disturbed and the ionospheric density decreased significantly at high latitudes. This density depression lasted for several days, and a large amount of electromagnetic energy during the storm modified the thermospheric dynamics and ionospheric plasma density.
