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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

Citizen Scientists Conduct Distributed Doppler

Measurement for Ionospheric Remote Sensing

Kristina Collins , Aidan Montare, Nathaniel Frissell, Member, IEEE, and David Kazdan, Senior Member, IEEE

Abstract— Doppler shift measurement using time standard 

stations as passive radar beacons is well-established as a means 

of estimating virtual ionospheric height. A community science 

experiment in distributed Doppler shift measurement was conducted in October 2019 using the time station WWV, on the 

event of that station’s centennial. Participants were asked to 

collect Doppler shift data from the WWV 5-MHz carrier via the 

open-source software program fdigi. This experiment garnered 

participation from stations across the country, demonstrating 

considerable volunteer interest: 45 recordings of WWV’s 5-MHz 

beacon were collected. The novel element of this study is the use of 

distributed low-fdelity sensors in this geophysical domain, and 

the robust participation of the amateur radio community was 

enabled by inexpensive and readily available instrumentation. 

In this letter, we present an initial correlation analysis of the 

resultant data and discuss its implications for future long-term 

distributed Doppler networks. The data collected are in good 

agreement and, when examined together, offer some insights into 

regional trends. Despite the variety of equipment used by the 

amateur community, this community science approach shows 

promise for addressing the problem of undersampling in the 

geospace system. 

Index Terms— Citizen science, distributed sensing, Doppler 

shift, GPS-disciplined oscillator (GPSDO), time standard. 

I. INTRODUCTION

AMATEUR radio is a proven resource for the collection of scientific data in ionospheric physics [1]. The

population of amateur radio operators and shortwave listeners is widely distributed, largely technically literate, and

socially inclined, making the community well-suited for citizen science. As amateur radio has become more computerized, the number of licensed operators has increased;

the same trend enables greater sophistication in scientific

data collection at a lower cost. The experiment discussed

herein, undertaken in October 2019, leveraged the instrumentation and goodwill of the amateur community for a

large-scale data collection exercise [2]. The event sought

citizen scientists from the amateur radio community as well

Manuscript received October 5, 2020; revised December 1, 2020 and

January 25, 2021; accepted February 26, 2021. This work was supported by

NSF under Grant AGS-1932997 and Grant AGS-2002278. (Corresponding

author: Kristina Collins.)

Kristina Collins, Aidan Montare, and David Kazdan are with the Department

of Electrical Engineering, Case Western Reserve University, Cleveland, OH

44106 USA (e-mail: kd8oxt@case.edu; kb3umd@case.edu; ad8y@case.edu).

Nathaniel Frissell is with the Department of Physics and Engineering, The

University of Scranton, Scranton, PA 18510 USA.

Color versions of one or more figures in this letter are available at

https://doi.org/10.1109/LGRS.2021.3063361.

Digital Object Identifier 10.1109/LGRS.2021.3063361

Fig. 1. Map of participants in North America, some international stations

not shown. Squares: stations with GPSDOs. Star: location of WWV.

as those with HF listening equipment. Instructions were provided at https://hamsci.org/wwv-centennial-festival-frequencymeasurements and publicized widely in the amateur radio

community. Data were made available in a public archive

[3]. This exercise was a pilot experiment for the Personal

Space Weather Station project, an effort by the Ham Radio

Science Citizen Investigation (HamSCI) collective to create a

distributed network of instruments that will gather scientificgrade observations of signals of opportunity across the HF

bands from volunteer citizen observers. This will complement

current work performing related measurements with dedicated

research instruments [4].

Although its daily and seasonal variations are wellcharacterized, the ionosphere remains undersampled on

regional and global scales. The placement and density of

instrumentation networks are limited by logistical and financial concerns, and active instruments such as ionosondes

are additionally limited by spectrum allocations. There is,

however, no practical limit on the density of receiver networks.

Furthermore, the range and density of the amateur community

exceeds that of any instrumentation network deployed for

research purposes. This volunteer-driven model is therefore

a powerful tool for deploying a data collection network in

short- or long-term science campaigns.

II. METHODOLOGY

A. Doppler Measurement via WWV

WWV is a radio station of the U.S. National Institute of

Standards and Technology (NIST) that transmits standard time

and frequency information. Located in Fort Collins, Colorado,

since 1966, WWV provides a stable, reliable, and traceable

reference for frequency and time that can be received with

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
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Fig. 2. Conceptual illustration of Doppler measurement of changes in virtual

ionospheric height.

simple equipment. Signals from HF time standards stations

like WWV propagate via the ionosphere, and the path length

of the signal changes with ionospheric conditions. These

variations occur on a daily basis, with the virtual ionospheric

height at a given location dropping during the day and rising during the night; they also undergo variation on longer

(e.g., seasonal) and shorter (e.g., minute) timescales. The

frequency of the received signal is affected by Doppler shift,

as shown in Fig. 2. For a ray undergoing a single hop,

a movement upward in the virtual ionospheric height at the

point of reflection results in a drop in received frequency as

the wavefronts are stretched farther apart. Conversely, if the

virtual height decreases, the wavefronts move closer together

and the received frequency is increased. Since WWV’s carrier

is extraordinarily precise, the change in received frequency

may be used as a proxy for change in path length, and

therefore change in virtual ionospheric height at the point

where the signal reflects off the ionosphere before reaching

the receiver. The propagation-induced Doppler shifts are very

small. The typical average frequency excursions are less than

1 Hz out of carrier frequencies in the 2.5 to 15 MHz range, and

measurement precision is limited by the frequency calibration

and stability of the receiver’s local oscillator. At standard

temperature, a typical crystal oscillator is guaranteed by the

manufacturer to be one part in 106. However, temperature

swings may cause it to exhibit drift on the order of tens

of hertz [5]. A GPS disciplined oscillator (GPSDO) removes

these limitations by imparting the accuracy and stability of the

atomic clocks used in GPS satellites [6]. With atomic clock

accuracy on both ends of the transmission path, frequency

deviation measurements can be attributed solely to propagation

effects.

The technique of measuring Doppler shifts is wellestablished and has been in use since the inception of

ionospheric physics [7]. However, until now, the availability

of stations was limited by the cost of instrumentation and

data collection apparatus. In particular, single-board computers

such as the Raspberry Pi and inexpensive GPS-disciplined

(and therefore NIST-traceable) oscillators like the Leo Bodnar

(approx. $100 USD) have reduced the barrier to entry for

making and disseminating precision frequency measurements.

B. The Festival of Frequency Measurement

In 2019, a gathering was held in Fort Collins to celebrate the

centennial of the awarding of WWV’s callsign [8]. Because of

its long tenure, wide reach, and 24-h programming, WWV is

fondly regarded among the international community of amateur radio operators and shortwave listeners. The festival of

frequency measurement was conceived as a way to showcase

WWV’s scientific utility as a beacon for propagation research.

Participants were asked to make recordings using the opensource radio program fldigi [9]. Its frequency analysis mode

uses a phase accumulation method which begins with the

user’s initial best guess of frequency and then tracks the

possibly varying signal. Added to fldigi since the Festival

is an additional mode titled FMT, which performs a similar

function but is based on a frequency estimation algorithm by

Tsui and Reisenfeld [10]. This method estimates a received

frequency by an efficient discrete Fourier transform method

with a faster approach to the Rao–Cramer lower bound than

the phase accumulation method. This mode was specifically

designed for use in the American radio relay league frequency

measuring test (FMT). The FMT is a metrology exercise in

which a frequency-precise, stable carrier is transmitted and

participants attempt to estimate that frequency as closely as

possible. Doppler shift due to changes in the path length is a

source of error in the FMT, but since WWV’s precise carrier

frequency is known a priori, the same method may be used

to determine that Doppler shift. Since fldigi is already widely

adopted among amateur radio operators, this approach enabled

wide participation among the community.

Of WWV’s five beacon frequencies (2.5, 5, 10, 15, and

20 MHz), the 5-MHz beacon was selected as the one most

likely to have favorable propagation range across the United

States without being significantly affected by local noise at

receiving stations. It is below the Maximum Usable Frequency

for 3000 km paths (MUF3000) typically observed by midlatitude ionosondes both day and night, which is in agreement

with the IRI climatological model. Participants were asked to

collect data for the 24-h period corresponding to the UTC

day of October 1, and to begin data collection before and

end after that period if possible. A test-run with several

stations was held before the experiment in order to ensure the

effectiveness of the data collection process [11]. After that,

no significant guidance was provided to participating stations.

Data sets are identified by participants’ amateur radio callsigns

(e.g., W8EDU), a unique identifier that is associated with the

bearer’s name and mailing address. Most participants uploaded

data to the repository at www.zenodo.org, while some sent it

to the organizers directly.

III. ANALYSIS

A. Initial Review of Data

To gain an overview of the data, we applied a 300 point

(5 min) low pass filter and plotted each station’s Doppler

measurements as shown in Fig. 3.

We removed several data sets as follows:

1) Too short: W6MSU, VK3ZAZ, K2LYV.
 

  

 

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COLLINS et al.: CITIZEN SCIENTISTS CONDUCT DISTRIBUTED DOPPLER MEASUREMENT

Fig. 3. All Doppler measurements submitted by participants for 5 MHz, filtered and ordered by longitude east to west, following the path of the terminator.

Shown to the right of each plot is information about the station which made the measurement. The dotted line indicates local sunrise at each station

location, the solid line local sunset. Culled data sets are appended at the end, beginning with VK3ZAZ. A few stations lacked an accurate timebase in the

receiver or soundcard, and therefore have a constant offset error. To adjust for this, the plots for these data sets (W1KU, K4BSE, KM4YMI, WX4US, and

VE3OAT) have been offset so that the data are shown while keeping the scale the same. Note the presence of some features which appear to align across

many submissions, as well as some features that indicate noise, equipment, or data collection errors.

2) Noise indicating a ground loop: VE6IXD, WB4HIR,

AC6SL.

3) Frequency drift: N7QNM, NU0C.

4) Miscellaneous errors: XE3I, VE3YX, W8UM,

KA6WKE.

5) Too close to show skywave propagation: K0ANS,

W9INE.

The absolute value of data submitted by N2MD was flipped to

account for the fact that it had been measured using a lower

sideband rather than an upper sideband. These adjustments are

reflected in Fig. 3.

B. Geographical Trends

We sorted stations by region to help illuminate possible

correlations since strongly correlated features between nearby

stations support the hypothesis that these features are geophysical in nature. In some cases, correlation between data

sets was immediately evident: for example, the traces for

K7KMQ, WA7BNM, and W6OQI, all stations in California

with GPSDOs, show a pronounced similarity.

C. Correlation Analysis

We used the corr() function in the Python library pandas

[12], [13] to perform Pearson correlations (1) between the data

sets for each pair of stations

cov X Y

X Y = ( (1)

X Y
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Fig. 4. Illustration of centroid calculations, where A and B represent a pair

of stations and W represents the beacon station, WWV.

Fig. 5. Correlation versus distance (AB, in Fig. 4) for each pair of stations,

separated by presence of GPSDOs. Marker size indicates centroid distance

CW.

Fig. 6. Correlation versus centroid distance (CW, in Fig. 4) for each pair of

stations, with marker size indicating the distance between receiving stations.

Two distances were used, as illustrated in Fig. 4: the distance

AB between any two stations, and the centroid distance CW1

between the center point of those stations and the beacon,

WWV. The former was calculated according to the geodesic

distance using the geopy() library; the latter was calculated

via the haversine method. Results for the filtered, retained data

sets are shown in Figs. 5 and 6. Each point on the plot indicates

a set of two stations whose frequency data were correlated in

this way. (Amplitude data was omitted from this analysis, but

is included with the original data set.) In the first case, shown

in Fig. 5, two stations that are very close together should

have very strongly correlated data sets, and two stations that

are far apart should not be as well correlated. We see this trend

appear clearly when we restrict the set of stations examined

to only those with GPSDOs.

1Although C is not the literal centroid, CW is termed the “centroid distance,”

as its distance from WWV maps linearly to the distance between WWV and

the centroid of the three points.

Fig. 7. Center points (C in Fig. 4), with darker colormap suggesting stronger

correlation. (Left) for pairs of stations where neither station has a GPSDO;

center, where one station does and one does not (Right) where both do.

IV. CONCLUSION

A. Human Factors

The data collection process highlighted several human factors issues, which are being addressed for future experiments.

1) Having participants upload data directly to the Zenodo

repository made the data more difficult to retrieve.

In later events, this was replaced by an upload page.

2) At the time of the experiment, fldigi’s Frequency Analysis mode recorded only timestamps, without noting the

date. This deficiency has since been addressed in a software patch to fldigi as a direct result of this experiment.

B. Instrumentation

The instrumentation used to collect data for the Festival

varied widely. Volunteers used different makes and models

of radios, different antennas, and different oscillators. As discussed above, the use of GPSDOs was a major distinguishing

factor between consistent and inconsistent data sets. This pilot

experiment demonstrated two important points for future work:

First, that valid data can be collected via existing amateur radio

apparatus, despite its variety; second, that data sets obtained

with precise local oscillators such as GPSDOs may support

data sets collected without. The correlation analysis would be

improved by a longer data collection period, to reduce the

impact of daily variability, and by a more intentional distribution of stations. As shown in Figs. 1 and 7, the volunteer
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COLLINS et al.: CITIZEN SCIENTISTS CONDUCT DISTRIBUTED DOPPLER MEASUREMENT

distribution for this experiment was uneven, particularly with

regard to GPSDOs.

C. Geophysical Observations

Two likely geophysical features are apparent in Fig. 3: First,

a rise in frequency can be seen following local sunrise, coinciding with the drop in ionospheric height at the control point

(point of reflection) due to ionization during the night/day

transition. Second, the shared geophysical features between

nearby stations at night are likely indicative of nighttime

traveling ionospheric disturbances (TIDs). For example, a set

of shared signatures is apparent in the data from NA0B,

N2MD, and KG4ARN, and another in the data from W6OQI,

WA7BNM, and K7KMQ. These oscillations between 3:00 and

15:00 UTC have a period of approximately 1 h. This is

consistent with the period of a large-scale TID [14].

An underlying assumption of the frequency analysis

mode—the presence of a single carrier—is flawed for this

application, since stations may receive X and O mode signals along different propagation paths, as well as signals

from other standards stations. In particular, WWV’s sister

station WWVH, situated on the island of Kauai in Hawaii,

broadcasts on the same carrier frequencies as WWV with

slightly different modulation. Since the analysis herein is

concerned primarily with the trends among stations, the effects

of receiving both signals are outside the scope of this letter.

However, their implications are significant for future research

and will impact the procedures of future experiments.

V. FUTURE WORK

The internal consistency of this data set having been verified, we will next turn our attention to data assimilation with

existing ionosonde networks. In particular, the consistency

of Doppler shift observations with virtual ionospheric height

measurements is a topic of current research.

The lessons learned from this analysis are being employed

in both short- and long-term experiments. We have used the

event as a template for similar experiments to collect data

from stations worldwide during the solar eclipses of June and

December 2020, collecting data for longer periods of time and

streamlining the data submission process. Longer-term pilot

experiments are also being conducted with a combination of

commercial radios and prototype PSWS stations, all equipped

with GPSDOs, along great circle paths from WWV across

the United States. Before the North American solar eclipse

of 2024, we intend to have a robust distributed network

of Doppler stations in place, stemming from the knowledge

gained through this initial experiment. As the network of

receivers becomes denser, we will examine the layers of

formation of the ionosphere in greater detail using this method.

ACKNOWLEDGMENT

The authors would like to thank the Case Amateur

Radio Club W8EDU, the HamSCI organization, the WWV

Staff, and all operators who collected data for this experiment. They would like to thank the WWV Amateur Radio

Club WW0WWV and NIST for making the event possible,

as well as all participants in the WWV centennial celebration (http://wwv100.com/wwv100.com). Frequency measurement data was recorded with fldigi, available at w1hkj.com,

and locations for amateur stations were retrieved from

https://www.qrz.com/QRZ.com. They would also like to thank

P. J. Erickson W1PJE, S. Cerwin WA5FRF, and W. Liles

NQ6Z for the advice and contributions. This work made use

of the High Performance Computing Resource in the Core

Facility for Advanced Research Computing at Case Western

Reserve University.

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