HF (high-frequency, 3-30 MHz) Doppler sounding is an established means of observing the bottomside ionosphere. Its principle of operation is straightforward: a shift in signal path length effects a corresponding Doppler shift. This information may be integrated with other ionospheric measurements to examine ionospheric variability resulting from geophysical events.
The Doppler shift f D in a received signal may be expressed as the time derivative of the phase path of the radio Published by Copernicus Publications.
signal. After Chum et al. (2018),
where c is the speed of light, n is the real part of refractive index for electromagnetic waves, N is the electron (plasma) density, and z R is the height of reflection. This methodology is well established in the scientific literature (Breit and Tuve, 1925;Davies et al., 1962;Jacobs and Watanabe, 1966).
In recent years, enabling technologies have become prevalent which reduce the barriers to performing precise Doppler measurements. In particular, single-board computing greatly reduces the expense and difficulty of data logging, and readily available GPS-disciplined oscillators (GPSDOs) allow for precision timing at a price point on the order of USD 100. The price burden for this method is also reduced by the use of existing time standard stations, such as WWV, WWVH, and CHU. These stations broadcast national standard time via AM signals with precisely controlled carriers, providing ideal signals of opportunity.
Accordingly, it is now tenable to create distributed systems of HF Doppler receivers which serve as a meta-instrument for the observation of ionospheric disturbances, either in short-term campaigns such as the one recorded in Collins et al. (2022) or in long-term data collection such as in the dataset presented herein. Such systems are readily supported by citizen scientists in the amateur radio and shortwave listening communities (Collins et al., 2021;Frissell et al., 2022b).
These data are useful to geospace scientists seeking to build a more complete picture of short-term events (lasting hours to days) which occurred during the recorded time frame, such as solar flares and geomagnetic storms. Today, frontier science investigations in these fields generally rely on combining observations from multiple instrument platforms, including total electron content estimations derived from the Global Navigation Satellite System (GNSS TEC) (Vierinen et al., 2016), incoherent scatter radar (ISR) (Nicolls and Heinselman, 2007;Zhang et al., 2021), Super Dual Auroral Radar Network (SuperDARN) radar (Nishitani et al., 2019), and vertical ionosondes (Hunsucker, 1991;Scotto et al., 2012), among others. Oblique HF sounders such as the ones used in this dataset represent one of many tools for the multi-instrument observer and can provide direct benefit to these investigations. To wit, satellite measurements (e.g., GNSS TEC) produce height-integrated measurements from the bottomside to topside of the ionosphere, whereas the Personal Space Weather Station (PSWS) measures bottomside variability. ISRs yield range-resolved measurements of plasma parameters throughout the ionosphere but have limited geographic coverage and cannot run constantly, primarily due to high cost of both installation and operation. While SuperDARN radars are well established and measure parameters of the bottomside ionosphere that cannot be measured by the PSWS, SuperDARN is a pulsed system and typically has at best a 1 min cadence. Ionosondes, too, generally have slower cadence (3-15 min). Vertical ionosondes produce bottomside vertical profiles for a single site. Oblique ionosondes share a measurement geometry with the Grape but sweep in frequency, whereas the Grape monitors a single frequency with essentially continuous time resolution, which allows for monitoring short-timescale ionospheric variability along a single path. A key advantage of the PSWS is its low cost, which allows for flexible and dynamic deployment of stations in regions of interest. It is also the most analogous to an HF communication system, which supports applicationdriven monitoring of propagation conditions.
Further insights may also be developed by examination of multiyear trends. As discussed in Sect. 4.3, seasonal variations are clearly evident in the longest datasets collected at the time of writing. As observations continue throughout solar cycle 25, we expect that these Doppler data, recorded at a greater level of coordination in the long term than has generally been achieved in the past, will support or yield novel analyses of seasonal ionospheric variability.
Understanding ionospheric variability remains a frontier topic in the space physics community. This variability is key not only to understanding ionospheric dynamics in its own right but also as a means to understanding the coupled geospace system as a whole, which includes the ionosphere's connection to both space above and the neutral atmosphere below. Ionospheric variability takes on many forms and arises from many sources. Some forms are better understood than others. Sources of variability from space include solar flares that last minutes (e.g., Dellinger, 1937;Benson, 1964;andChakraborty et al., 2018, 2021), substorms that last a few hours (e.g., Gjerloev et al., 2007;Blagoveshchenskii, 2013;and Hori et al., 2018), and ionospheric and geomagnetic storms that can last days (e.g., Buonsanto, 1999;Prölss, 2008;and Thomas et al., 2016). Sources of variability from below include traveling ionospheric disturbances (TIDs) associated with atmospheric gravity waves (AGWs) (e.g., Hines, 1960, andHunsucker, 1982). These are associated with terrestrial weather patterns and may be caused by events such as tornadoes (Nishioka et al., 2013), tsunamis (Galvan et al., 2011;Huba et al., 2015), or high-latitude sources (Grocott et al., 2013;Frissell et al., 2016).
To understand this variability, it is important to measure over both large spatial and temporal domains and with high resolution. While many large-scale professional ionospheric sensing networks exist, the ionosphere remains significantly undersampled. To help address this undersampling issue, members of the Ham Radio Science Citizen Investigation (HamSCI) collective are working to develop the Personal Space Weather Station (PSWS), a modular, multiinstrument, ground-based space science observation platform that can be operated and afforded by individuals, as described in Collins et al. (2021Collins et al. ( , 2022)). The low-cost version of the PSWS is known as the Grape, documented by Gibbons et al. (2022). The Grape is a narrowband, high-frequency (HF) receiver that observes ionospheric variability by measuring the Doppler shift of signals emitted by highly stable transmitters, such as WWV and WWVH operated by the US National Institute of Standards and Technology (NIST) and CHU operated by the Institute for National Measurement Standards of the National Research Council of Canada.
The Doppler shift mechanism is illustrated in Fig. 1. Here, WWV transmits an HF signal that is refracted by the ionosphere back to Earth, where it is received by station W8EDU. Ionospheric variability related to peak layer height, peak layer electron density, and/or layer thickness can cause changes in the propagation path that are sensed as positive Doppler shifts for decreasing path lengths (blueshifts) and negative Doppler shifts for increasing path lengths (redshifts) (Lynn, 2009). Doppler shift variations can also be used to measure the period, wavelength, and direction of TIDs (Georges, 1968;Crowley and Rodrigues, 2012;Chilcote et al., 2015;Trop et al., 2021;Trop, 2021;Romanek et al., 2022).
The majority of stations in this dataset use the purpose-built Grape V1, a low-cost receiver described by Gibbons et al. (2022). This is a low-to intermediate-frequency receiver optimized for Doppler measurements.
It is also possible to use the software components with other hardware: as noted in the nodelist.csv file in the software repository, which is included in abridged form in Table A1, some of the registered nodes collect data using commercial off-the-shelf (COTS) amateur radio receivers which are capable of accepting an external frequency input from, e.g., GPS-disciplined oscillators. Citizen scientists from the amateur radio and shortwave listening communities can therefore leverage their existing hardware to contribute to the PSWS network at no additional cost and with no licensure requirement. The data processing framework of the Personal Space Weather Station network is robust to the addition and modification of new nodes and to data outages. Data collected up to 1 June 2022 are represented in the data inventory shown in Fig. 4.
The process of data curation is depicted in Fig. 2. Each station collects 24 h datasets according to an established standard and uploads them on a daily basis to a central FTP server. Test files, corrupted files, and spurious uploads are eliminated, and the data are consolidated into a single *.zip file, which is posted to the data repository (Collins, 2022). While the size of the final *.zip file varies according to the number of stations collecting data, the efficiency of compression, and other factors, it is on the order of a few gigabytes. The updated dataset can then be downloaded from Zenodo to a subdirectory in the code repository (Frissell et al., 2022a) and used to create updated versions of the visualizations discussed in this paper.
An example file is shown in Appendix C, which shows a file with the corresponding filename 2020-07-09TT002940Z_N0000001 _G1_EN91fh_FRQ_WWV5.csv. This filename includes, in order, the date the data were collected and the UTC time at which that collection began; the node number, corresponding to the list in Table A1; the type of radio being used (e.g., "G1" indicates Grape version 1); the Maidenhead grid square in which the data were recorded; and the time standard station being measured. A detailed description of the file format and upload process is available from Gibbons et al. (2021). Metadata at the beginning of each file record station information, including room for comments. The main table has three columns: UTC time, estimated frequency, and received power.
The visualization code in Frissell et al. (2022a) allows for the dynamic visualization of station availability and datasets. Results can be examined on scales ranging from seconds to years, for one station in isolation or in comparison to others. Examples of this visualization code are given below. Section 4.1 describes the map and Gantt chart which summarize where and when station data are available for a given period of time. Section 4.2 and 4.3, respectively, demonstrate short-and long-term analyses of data from a single station. Subsequent sections focus on the detection of geophysical signatures: Sect. 4.4 demonstrates the detection of signatures consistent with traveling ionospheric disturbances, while Sect. 4.5 showcases the detection of solar flares by multiple Grape stations.
A map of stations to date is shown in Fig. 3. Stations were chosen on a volunteer basis, with some (e.g., Node 18 in California) specifically recruited to improve coverage. Clusters of stations are evident around universities involved in the project: Case Western Reserve University in Cleveland, the University of Scranton in Pennsylvania, and the New Jersey Institute of Technology each have a collection of nodes belonging to researchers. An additional cluster is generated by volunteers of the New England amateur radio community. There are also nodes close to WWV in the Fort Collins area, Colorado (e.g., Node 13), which are within the transmitter's radio horizon and can be used to confirm that trends in the data originate with the ionosphere and not the radio transmitters.
Several stations are registered as nodes but do not have data included in the dataset reported at the time of writing. This may be for one of three reasons: first, the station may have data recorded but not uploaded to the FTP server; second, the station may be in the process of installing a node; and third, the station may be used for experimentation with new data collection methods, including spectrum sampling and other frequency analysis algorithms. A central aspect of this work is its architecture as a living dataset, i.e., a dataset into which new stations and historic data may be easily incorporated.
Figure 4 shows the data collected by each node over time. The network is modular: new stations can easily be added, and data analysis procedures are tolerant of outages and changes in frequency for each node.
As illustrated in Fig. 1, electron density in the ionosphere increases during the day as a result of photoionization and decreases at night due to recombination (Davies, 1990), producing a recognizable trend in Doppler plots. Confirming this diel variation (i.e., checking for a sunrise peak) is recommended by Gibbons et al. (2022) as a benchmark for an operator to ensure that the trends observed in their station's data are geophysical in nature.
The plotting routine automatically computes the local sunrise and sunset for a given station location and shades the background accordingly. An example of data collected by Node 1 is shown in Fig. 5. The output produces two plots: Doppler shift on the top and amplitude on the bottom. In each case, the raw data, scatter-plotted in blue, undergo filtering to produce the filtered result, which is overlaid in yel-
One category of ionospheric phenomena of particular interest for the PSWS network is medium-scale traveling ionospheric disturbances (MSTIDs), defined by Hunsucker (1982) as wavelike perturbations of ionospheric plasma with wavelengths of hundreds of kilometers, phase velocities of hundreds of meters per second, and periods between 10 min and 1 h. While MSTIDs may be associated with either atmospheric gravity waves (AGWs) from the neutral atmosphere (e.g., Hines, 1960;Bristow et al., 1994;Frissell et al., 2016) or from electrodynamic processes (e.g., Kelley, 2011;Atilaw et al., 2021), the source of MSTIDs is still not well understood due to their ubiquitous nature and the complexities of atmosphere-ionosphere coupling. Trop et al. (2021) and Trop (2021) developed a technique to estimate TID period, speed, propagation direction, and velocity from a network of AM broadcast band Doppler receivers described by Chilcote et al. (2015); at the time of writing, this technique is being developed for use with HF data from the PSWS network by Romanek et al. (2022). Figure 8a is generated using the same standard processing as Fig. 5. Next, the data are interpolated and filtered with a 0.5-1.2 mHz (T = 14-33 min) bandpass to isolate the dominant MSTID, similar to the approach used in Sect. 3.1.2 of Frissell et al. (2014). The output is then separated into 4 h bins with a 90 % overlap and plotted as a spectrogram in Fig. 8b, which shows signatures consistent with MSTIDs.
Figure 9 shows the response of the network to solar flares on 28 October 2021, providing an example of a multi-instrument measurement which demonstrates how the PSWS may augment and be validated by existing professional networks. The top plot gives the X-ray irradiance as measured by NOAA's GOES-17 spacecraft, with two flares marked: an X-class solar flare with a maximum at 15:35 UTC and a smaller, C-class flare about 2 h later. Figure 9b shows the Doppler shift for eight stations from the day in question, color-mapped by station longitude; below it, Fig. 9c shows the relative received power for the same stations. A longitude-dependent Doppler flash (Chakraborty et al., 2021) is observed in the frequency plot in conjunction with each flare, and a radio blackout following the X1 flare is observed in the power plot. (The lone exception, Node 13, is the groundwave station near WWV.) This Doppler flash was also measured by the SuperDARN at Fort Hays, KS, as shown in Fig. 10, albeit at a slower cadence than the Grape measurements.
By default, no scaling is applied in the received power plot of Fig. 9c. As discussed in Sect. 5.1, received signal strength varies with the antenna but may not impact the accuracy of the estimated frequency. Additionally, the PSWS nodes which use commercial off-the-shelf (COTS) hardware rather than Grape receivers (see Table A1) may have an automatic gain control (AGC) which impacts the utility of the power measurement. Therefore, users are encouraged to begin by examining the raw data from an event of interest before applying scaling.
WWV's transmitter is well characterized and inherently accurate, with a measured carrier stability below one part in 10 12 (Lombardi, 2023). Allan deviation analysis by Lombardi (2022) demonstrates that the Grape V1 receiver recovers frequency with an upper bound of 2 ppb (2 × 10 -10 ). Further, Lombardi (2022) performs calibration of the Leo Bodnar GPSDO recommended by Gibbons et al. (2022) and demonstrates that, with a frequency stability of one part in 10 12 over a 1 d interval, it contributes no discernible measurement uncertainty.
Between transmitter and receiver, the received power varies according to location, antenna gain, and atmospheric attenuation. For example, in Fig. 6, the antenna replacement which took place at that station in August 2021 distinctly impacts the power plot but has relatively little impact on the frequency estimation. Because the frequency and power are logged together in the raw data, the end user may elect to discard or replace frequencies where the logged power is below a threshold of their choosing. Even a low-amplitude signal may yield viable frequency estimation data, however, as shown in Fig. 5. Trends observed by the network may therefore reasonably be considered to be of geophysical origin, albeit the result of multiple causes. Quantifying these ionospheric propagation effects is beyond the scope of this paper. Instead, this paper will allow investigations of these effects in the future through comparisons with other instruments and data-model comparisons.
We have provided comparison to prior products (e.g., Breit and Tuve, 1925;Davies et al., 1962;Jacobs and Watanabe, 1966;Collins et al., 2022), event-based validation from alternate sources (GOES-17 and SuperDARN, per Figs. 9 and 10 respectively), comparison of measured diel variation to model outcome (Figs. 6 and7), and initial records of sensors (see Figs. 5,9).
The definition of citizen science has evolved over time, and the PSWS network, which invites significant personal investment and involvement from network participants, hews more closely to the "co-created" models of intensive citizen science projects, in which participants play an active role in shaping all levels of the work, than it does to "contributory" models which emphasize crowdsourced data collection (Wiggins and Crowston, 2011).
The Personal Space Weather Station exemplifies the key elements of citizen science projects identified by Pandya and Dibner (2018). They emphasize that participants in citizen science projects are primarily not project-relevant scientists; indeed, the majority of nodes in Table A1 are maintained by volunteers with no financial or academic connection to the project. Pandya and Dibner (2018) also note that citizen science projects actively engage participants, engage those participants with data, and enable those participants to derive benefit from their participation: these aspects of the PSWS are attested to by Benedict and Waugh (2021). Finally, they note that citizen science projects use a systematic approach to producing reliable knowledge, help advance science, and communicate results, all aspects which are supported by this paper.
The indispensable participation of the amateur radio and shortwave listening communities in these efforts is part of a citizen science legacy in those communities which dates back to the dawn of radio (Yeang, 2013) and continues to the present day (Collins et al., 2021).
1. We present a living dataset of HF Doppler measurements made by citizen scientists. These measurements are conducted using time standard stations' carrier signals as precise HF beacons. The amplitude and estimated Doppler shift are recorded at approximately a 1 s cadence by each station. Outages and nonstandard start times are automatically handled within the file format.
2. A modular framework is presented for the visualization and analysis of these data. Per the "Code and data availability" section, the code used to prepare the figures in this paper is made available for the reader's use. This code may be used to visualize future versions of the dataset as well. Additional nodes may be added to the primary dataset by coordination with the authors.
3. Doppler data reveal both short-term and multiyear trends in ionospheric variability. Exemplars include Figs. 6 and 9 above. These data may be used in conjunc-tion with other measurements to address frontier questions in geospace science using a multi-instrument approach.
The figures in this paper were produced using Python code and Jupyter Notebooks available at https://github.com/ HamSCI/hamsci_psws (last access: 17 March 2023) and https://doi.org/10.5281/zenodo.6654901 (Frissell et al., 2022a). The data are available at https://doi.org/10.5281/zenodo.6590283 (Collins, 2022). The Grape V1 hardware is fully documented in Gibbons et al. (2022), and the files to reproduce that hardware are available at https://doi.org/10.17632/NBBHY2YXMZ.1 (Gibbons et al., 2021).
To date, the PSWS network comprises a growing, selfsustaining community of station maintainers. The authors foresee two means of extending this network in the future, both of which have been instrumental in fostering it to date: first, the grassroots adoption of the system by self-motivated participants, generally through amateur radio clubs; and second, the targeted recruitment of station maintainers in regions of interest, particularly ahead of upcoming solar eclipses.
At the time of writing, the majority of stations are in the continental United States, but there is no inherent limitation of the system that dictates its range. The network is not limited only to Grape V1 hardware, nor to the exclusive use of WWV or other time standard stations as beacon signals. The flexible metadata format described above allows for independent signals on the amateur radio bands to be used in participatory campaigns and for these data to be integrated seamlessly into future versions of this dataset.
Efforts are also underway to develop multichannel versions of the Grape hardware, as well as wider spectral recording to support the analysis of multiple carrier signals associated with multiple simultaneous propagation paths.
By making these data permanently accessible to professional and citizen scientists, and by continuing data collection with a growing network of stations through cycle 25 and beyond (MacDonald et al., 2022), we hope to produce a record of short-term events and seasonal variability which will inform future studies of solar flare responses, MSTIDs, and other phenomena and which will form a benchmark for the validation of simulated Doppler shift in ionospheric models.
Registered nodes at the time of writing are listed in Table A1. et al., 2013, 2018, 2022). The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, the United Kingdom, and the United States of America. We thank the reviewers for evaluating and improving this paper. Mike Lombardi, K0WWX, made significant contributions during the revision phase. Finally, we thank the staff of WWV, WWVH, and CHU, without whom this work would not be possible.
Financial support. This research has been supported by the Division of Atmospheric and Geospace Sciences (grant nos. AGS-1932972, AGS-1932997, AGS-2002278, AGS-2230345, and AGS-2230346).
Review statement. This paper was edited by David Carlson and reviewed by two anonymous referees.
Earth Syst. Sci.Data, 15, 1403Data, 15, -1418Data, 15, , 2023 https://doi.org/10.5194/essd-15-1403-2023
https://doi.org/10.5194/essd-15-1403-2023Earth Syst. Sci.Data, 15, 1403Data, 15, -1418Data, 15, , 2023
Earth Syst. Sci. Data, 15, 1403-1418, 2023 https://doi.org/10.5194/essd-15-1403-2023 K. Collins et al.: Crowdsourced Doppler measurements of time standard stations
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March 2023). We thank our volunteers for giving generously of their time and expertise, particularly the maintainers of the first https://doi.org/10.5194/essd-15-1403-2023Earth Syst. Sci.Data, 15, 1403Data, 15, -1418Data, 15, , 2023