Monitoring precipitation and lightning via changes in
atmospheric gamma radiation
M. B. Greenfield, A. Domondon, S. Tsuchiya and G. Tomiyama
International Christian University, Mitaka, Japan
Abstract. Atmospheric γ−radiation has been measured since 19991 and recently at three elevations 220m from the first site to
ascertain position dependency and optimal elevation for observing γ-rays from radon and radon-progeny found in
precipitation. Radiation from time-independent and diurnal components was minimized in order to ascertain the reliability,
accuracy and practicality of determining precipitation rates from correlated γ-rates. Data taken with 4-12.9cm3 NaI detectors
at elevations above ground of 9.91, 14.2, 15.7, and 21.4 m were fit with a model assuming a surface and/or volume
deposition of radon progeny on/in water droplets during precipitation which predicts γ -ray rates proportional to the 2/5
and/or 3/5 power of rain rates, respectively. With mostly surface deposition and age corrections for radon progeny, the
correlation coefficients improved with elevation and reached a maximum at 0.95 around 20m. Atmospheric γ radiation
enables monitoring precipitation rates to 0.3 mm/h with time resolution limited only by counting statistics. High γ-ray rates,
decreasing with 40-minute half-life following lightning may be indirectly due to ions accelerated in electric field.
of RPR-GRR and SPR-GRR correlations would enable
real-time, remote sensing of precipitation rates solely
upon measurement of GRR. These rates are essentially
integrated and averaged over areas on the order of the
mean free path of γ-rays in air and are not subject to the
same collection or sampling difficulties associated with
standard precipitation gauges such as magnitude and
direction of wind velocity, evaporation or sublimation
in the case of snow1 (see below). Since increased GRR
during periods of precipitation has been observed in
widely varying (and remote) locations6,7,9-11,14,15,26,27 it
has been suggested1, and recently demonstrated, that
there is a sufficient pool of radon and progeny in the
upper atmosphere to enable the use of RPR and SPR
monitors in remote locations27. Since the correlations
depend on upper atmospheric progeny concentrations
which may be in secular equilibrium, they may be
globally applicable25,26 requiring only regional
renormalization.
INTRODUCTION
10
8
Count rate (Bq)
Relationships between time-dependent atmospheric γ
radiation, measured at a distance above ground level
(DAG) of 14.2 m, and both rain and snow precipitation
rates (herein designated as RPR and SPR, respectively)
have been established.1,2 The relationships between γray flux rates (GRR) and RPR or SPR, 0.18 ± 0.02
(mm/hr)/Bq and 4.02 ± 0.04 (mm/hr)/Bq, respectively,
were found to be reliable and reproducible. The unit
Bq rather than Bq/m3 is used as a convenient way to
refer to un-normalized decay rates which would give
rise to observed, relative, time-dependent, backgroundsubtracted GRR from the effective volumes viewed by
12.9cm3 NaI detectors with solid angles of about 2π and
with energy dependent efficiencies.1
The prospect of using these reproducible relationships
between RPR-GRR and SPR-GRR as a means of
reliably measuring precipitation rates has been noted.1
Increases in GRR during periods of precipitation have
been well documented.3-18 Time and position variations
in the rates at which radon is exhaled and accumulated
at ground level6,7,19-25 may have obscured and/or
discouraged attempts to quantify this effect in real time.
Observations at 15-20 m elevation over 32 months
suggest that RPR-GRR correlations are nearly
independent of local ground level radon concentrations
if GRR from the latter is treated as noise1. Figure 1
shows the background subtracted GRR at 15 m versus
time for 90 days in 2001. Peaks are correlated with
precipitation1 and other variations in GRR are relatively
minor1-18. The goals of this work were to find optimal
DAG for monitoring RPR and SPR and to understand
the physics of the process. If successful application
6
4
2
0
0
10
20
30
40
50
60
Time (days fromMarch 8, 2001)
70
80
90
Figure 1. Time dependent background subtracted gamma ray
rates as a function of time.
CP680, Application of Accelerators in Research and Industry: 17th Int'l. Conference, edited by J. L. Duggan and I. L. Morgan
© 2003 American Institute of Physics 0-7354-0149-7/03/$20.00
820
The amplitude of the diurnal variation in GRR is
shown in Fig 2. At ground level the amplitude of this
cycle, which is proportional to (and may be used as a
monitor of) local radon concentration1, is comparable to
the time independent background. It was observed to
be a factor of about 20 less on top of the 14.2 m Natural
Science Building (NSB). This effect is thought to be
due to pooling and subsequent mixing and transport to
the upper atmosphere of radon and radon progeny
originally exhaled from the ground into the lower
atmosphere.1,23,26,28,29 The diurnal amplitude decreases
with DAG (due to decreasing radon concentration23,25)
thus the signal to noise ratio for GRR associated with
RPR at increasing DAG should improve. It is well
known that local radon exhalation rates are greatly
affected by changes in meteorological conditions.21-24
The presence of radon and radon-progeny in rain and
snow may be a complex function of atmospheric radon
concentration8,28-30, the manner and history of the cloud
formation31, the distance the local cloud has traversed
in its formation, the length of time the radio-isotopes
were resident in the clouds prior to precipitation, radonprogeny concentrations in the lower atmosphere during
precipitation1,23,29,30, the type (rain and/or snow)1,2 and
relative surface area to volume.1,2 The precipitation
type is especially important since snow yields an order
of magnitude more radiation per unit mass than rain1,2,
and the radioactivity of snow has been observed to
change by a factor of four depending on the “wetness”
or relative surface area of the snowflake. Similar
complexities exist in the scavenging of acidic aerosols
by rain and snow.
Although the multi-faceted nature of this problem
makes it virtually impossible to isolate and
independently measure each of these factors it is useful
to measure how the sensitivity and reliability may be
optimized. This work focuses on using radon progeny
as a tracer of precipitation rates, but progeny could as
well be used as tracers for radon exhalation rates1,23
evaporation rates, and physics of cloud formation.30,31
Figure 3 and 4 show the cross-correlations between
the GRR obtained during periods of precipitation for
detectors positioned at different positions and
elevations, respectively, indicating that the phenomena
were reproducible over distances of hundreds of meters
and elevations on the order of tens of meters.
It was originally reported that the RPR-GRR and SPRGRR correlations were linear1 for rain rates in excess of
8 mm/h. The relatively small change in GRR with
change in RPR (for RPR greater than 8mm/h) obscured
the fact that the relationship was non-linear. Radon
progeny are in secular equilibrium26,30 until falling to
the ground after which the activity decays with the halflife of the dominant progeny.3,24,30 Using an “age
adjustment” to correct for this2 and a surface model
determinations of RPR and SPR to a few tenths of mm
per hour during storms with rain rates of up to 20 mm/h
and of durations up to 20 hours were possible.
EXPERIMETAL METHOD
One goal of this work is to determine the practicality
and economy of using this method to monitor RPR and
SPR, so relatively small 12.9 cm3 NaI detectors34 were
used, the geometry and efficiency of which are given
3.5
3
NSB GRR (Bq)
2.5
2
1.5
1
0.5
0
-0.5
0
0.5
1
1.5
Level 1 GRR (Bq)
2
2.5
Figure 3. Correlation (r coefficient = 0.96) between GRR
during precipitation at locations separated by 220 m.
0 .8
2.5
0 .6
2
0 .5
0 .4
Level 1 GRR (Bq)
Count rate (Bq)
0 .7
0 .3
0 .2
0 .1
0
38
3 8 .5
39
3 9 .5
40
4 0 .5
1.5
1
0.5
0
41
T im e (d a y s afte r 8 M a rc h 2 0 0 1 )
-0.5
0
Figure 2. Diurnal variation of GRR at 14.2 m above ground.
The curve is: GRR=a+bcos(t/τ+φ), where a, b, τ, and φ are
adjustable and t is the time. The best fit is for τ =1.01 ± 0.03
days, and phase φ yields maximum amplitude at sunrise.
0.5
1
1.5
Level 2 GRR (Bq)
2
2.5
Figure 4. Correlation (r coefficient = 0.98) between GRR
during precipitation at locations separated by 6m in elevation.
821
elsewhere.1,35 One of these was placed on top of the
Natural Science Building (NSB) at the International
Christian University (ICU) in Tokyo, Japan in the same
position (35o 42’ 18” N, 139o 31’ 44” E and 14.2 m
above ground) as that for which extensive data were
taken and reported upon in 1999-2000.1,2,27 Three other
detectors were positioned at 21.4 m (level 1), 15.7 m
(level 2), and 9.9 m (level 3) above ground within the
bell tower (IBT) of the ICU church (35o 42’ 12” N and
139o 31’ 48” E) which is located 220 m from NSB2.
Those detectors were above one another and fully
exposed to the atmosphere except for lead shielding
between the detectors and the concrete tower wall.
Each viewed an unshielded solid angle of a bit less than
2π, comparable to, but oriented at right angles to, that
viewed by the detector positioned on top of (NSB).
The purpose of these measurements was to ascertain
the optimum elevation for maximizing the signal from
γ-radiation in rain relative to the noise from the diurnal
component1,27 fed by exhalation of radon from soil and
building materials. Measurements on top of moderately
tall buildings would be more practical than those made
at significantly higher DAG. Thus, if increasing DAG
beyond 15-20m does not improve the signal to noise
ratio, then measuring there might outweigh any
advantages obtained by increasing DAG without limit.
Continuity38 insures that the density of precipitation
in a unit volume of atmosphere is independent of the
wind velocity. Since the flux of γ-rays from a given
volume of air is isotropic, the detector geometries
should yield the same atmospheric GRR from rain even
though they are viewing the atmosphere at right angles
to each other. The time independent backgrounds
(from the ground, buildings and cosmic rays) were the
same to within 10% for all geometries and measured
with sufficient statistical accuracy (about 0.8%) such
that they could be subtracted from the time varying
component without contributing significant error.
All GRR in excess of 300 KeV were integrated over 6min intervals at level 3 and 30-min intervals at levels 1,
2, and NSB. Level 3 data revealed relatively short time
fluctuations and summed over 5 successive bins could
be compared with data from the other stations. The
structures observed in the level 3 data (see figure 5)
have better GRR response time to RPR than the rain
gauges used by most weather bureaus. Precipitation,
integrated over 30-minute intervals, was measured to an
accuracy of 0.5 mm36. The RPR and SPR were assumed
to be the same at level 1, 2, and 3 detectors as for the
NSB detector, positioned 6 meters from the rain gauge.
than 8 mm/h. In recent work2 a model assuming that
radon progeny producing the increase in GRR are
deposited on the surface and/or within the volume of
raindrops was established and enabled GRR - RPR
correlations with as little as a few tenths of mm/h.
The correlation coefficient (r = 0.96) for the NSBlevel 1 and similar correlations between NSB and the
other levels for RPR (SPR) verify that GRR response
to a given RPR (SPR) is not location dependent up to
220m, and the coefficient (r = 0.98) between changes in
GRR measured during precipitation between level 1
and level 2 (6m directly below it) along with similar
correlations (0.93<r<0.95) between the other detector
pair combinations (not shown) indicate that features of
GRR during precipitation are elevation correlated.
The rain flux entering any volume will be equivalent
to that leaving irrespective of the speed and direction of
the raindrops through the unit volume. If evaporation
and condensation below the cloud base31 are neglected,
the quantity of radon progeny per unit volume of
atmosphere is proportional to the total surface area of
droplets within that volume, the expected GRR would
then be proportional to the product of the number of
drops per unit volume (n), the surface density of
radioisotopes (σr), and the average surface area (Aave)
of the raindrops at any moment. The instantaneous
GRR observed by a detector viewing a constant volume
of atmosphere would then be given by:
GRR = C1 n σr Aave = C1 n σr (4πrave2)
(1)
Where C1 is a time independent normalization constant.
The value rave is the average radius of the raindrops or
the average of the root-mean-squared radius, rrms-ave if
the raindrops are not spherical. Drop size spectra are
complex and time dependent with large drops
overtaking and combining with slower, smaller drops.
The shape and time dependence of rave is expected to be
small and neglected in this model.
The rain precipitation rate (RPR) is proportional to
the product of the average volume of the raindrops
(Vave), the number density of drops, and the average
Level 3 GRR (Bq)
2
1.5
1
0.5
0
DATA AND ANALYSIS
0
5
10
15
20
25
Time after noon (h)
In earlier work1 RPR and SPR were correlated with
background-subtracted GRR, but only for RPR of less
Figure 5. GRR (error bars reflect statistical errors) integrated
over 5 min as a function of time during precipitation
822
stopped acquiring progeny). Correlations between
GRR and powers of the adjusted RPR data were varied
between 2/5 and 3/5 to determine the best fit to the
data.2 Good fits to the data were obtained with
exponents of 0.45 ± 0.05. This suggests progeny were
mostly adsorbed on the surface as per equation (5) and
to a lesser extent absorbed within the volume as per (7).
In earlier work it was reported that each unit of
melted snow yielded up to 20 times more GRR (most
likely do to its increased surface/volume) than for an
equivalent volume of rain.1,2 This number was found to
be 4-5 times that for rain in a recent measurement (see
figure 6), since it was a wetter snow presumably with
less surface per volume. This is consistent with a
surface model since snow has considerably more
surface area per unit volume than rain, and dry snow
more than wet snow. The SPR indicated in figure 6 in
mm/h of equivalent melted snow are measured with a
bin size of 0.5mm/h. Since the surface area per melted
volume and fall speed vary significantly for different
types of snow, a power law like that established for rain
which has a well-defined surface area per unit volume
cannot be established. The linear correlation shown as
the solid line yields a correlation coefficient, r =0.97.
Figure 7 shows a plot of GRR and the age adjusted2
(RPR)0.45 for the NSB detector as a function of time
during a lengthy rain storm. The GRR are joined by a
smooth curve. Similar plots for the GRR of each level
as well as that averaged over all four detectors with the
terminal velocity of the raindrops (νterm) which
according to Stokes Law39 is a constant for a given
average raindrop surface area. Thus RPR is given by:
RPR = C2 n νterm Vave = C2 n νterm (4πrave3/3)
(2)
where C2 is a normalization constant (theoretically
equal to one) and independent of the size of the
raindrops and the rate at which they are falling. The
terminal velocity can be expressed according to Stokes’
Law as follows:
νterm = 2/9 (rave2g/η) (ρw − ρa)
(3)
where g is the acceleration due to gravity, η is the
viscosity of air, ρw the density of water and ρa the
density of air, which are all approximately constant at a
given elevation, pressure, and temperature. Eliminating
νterm from equations (2) and (3) one obtains:
RPR = C2 n 2/9(rave2g/η) (ρw −ρa)(4πrave3/3)
= C2 n 8πg/27η (ρw −ρa) ( rave5)
(4)
Eliminating rave from equations 1 and 4 one gets:
GRR=(RPR)2/5[(C28g/27η)(ρw−ρa)]-2/54C1π3/5n3/5σr (5)
If one instead assumes that progeny are uniformly
mixed throughout the volume of the raindrop with
density (ρr) then equation (1) may be modified:
GRR = C1 n ρr Vave = C1 n ρr (4πrave /3)
3.5
3
(6)
2.5
SPR (mm/h)
3
Eliminating rave from equations (4) and (6) one gets:
GRR=(RPR)3/5[(C28g/27η)(ρw−ρa)]-3/5
C14π2/5n2/5ρr/3
2
1.5
1
0.5
(7)
0
0
Due to the consistency of renormalized, backgroundsubtracted GRR per unit RPR observed since 1999 (and
with detectors having separation of 220 m and DAG
differences of up to 12 m) the product of the terms to
the right of RPR in equations (5) and (7) is observed to
be time independent. Since the terms in the square
brackets are constant, the product of the 3/5th or 2/5th
power of the number density of raindrops with the
progeny surface or volume densities, respectively, must
not vary radically within a given rain storm. Thus a
larger numbers of droplets each will consume a smaller
fraction of the available radon progeny per unit time.
Progeny densities in and on rain decay in time with the
weighted average mean half-life of the dominant radon
progeny after they are deposited upon any surface. Thus
the GRR observed per unit volume of rain depends
upon the “age” of the water (the time since the droplets
0.2
0.4
0.6
0.8
Level 1 GRR (Bq)
1
1.2
1.4
Figure 6. Snow precipitation (error bars reflect gauge
resolution) versus GRR (error bars reflect statistical errors)
NSB GRR (Bq) and [RPR(mm/h)]
0.45
3.5
3
2.5
2
1.5
1
0.5
0
0
5
10 after noon
15(h)
Time
20
25
Figure 7. GRR (diamonds - error bars reflect statistical error)
and RPR (open circles - error bars reflect rain gauge
resolution) as a function of time during precipitation.
823
electric fields associated with positive lightning.
Further data and the details of such processes will be
discussed in detail in future work37.
3
2.5
2
1.5
SUMMARY AND CONCLUSIONS
1
0.5
The elevation and position dependence for observing
γ-rays from radon-progeny in rain was determined by
measuring γ-radiation at three equally spaced distances
above ground and a fourth location separated from the
first three by 220 m. The response of RPR to GRR was
correlated at all locations. The GRR-RPR correlations
were consistent with a model assuming radon progeny
are adsorbed predominantly on the surface of raindrops
(see equations (5) and (7)). Excellent correlations
between the 0.45 power of RPR (age adjusted for the
radon progeny half-life) and GRR measured at 30 min.
intervals were obtained at all four locations. Although
one cannot uniquely convert GRR (effect) to RPR
(cause), inverse correlations between GRR to the 1/0.45
= 2.22 power and age adjusted RPR give self-consistent
determinations of RPR from GRR.
Signal to noise ratios and resulting correlations
improved with elevation (r from about 0.8 to 0.9). The
0
- 0.5
- 0.5
0
0.5
1
1.5
2
2.5
3
NS B GRR (Bq)
Figure 8. Correlation between (RPR)0.45 and GRR for the
NSB, with correlation coefficient r = 0.94
adjusted (RPR)0.45 were of similar quality. The two
rates vary similarly with time not only for the single
NSB detector, which was 6 m from the rain gauge but
also for the IBT detectors 220 m from the rain gauge.
Figure 8 (above) shows the correlation between
(RPR)0.45 and GRR for the NSB, for which a correlation
coefficient of 0.94 was obtained. Vertical errors reflect
the rain gauge binning and horizontal errors reflect the
statistical error of the NSB detector.
Similar plots for the levels 1, 2, and 3 detectors (not
shown) yield coefficients of 0.87, 0.86, and 0.84,
respectively. Changes in RPR of a few tenths of mm/h
are observable by tracking corresponding changes in
the GRR. In order to reproduce the amplitude and the
fine structure for small RPR it was necessary to use the
surface model, and to get the proper phase relation it
was necessary to use age corrections mentioned above.
The signal to noise ratio and the resulting quality of
the RPR-GRR correlations improved with DAG. The
IBT correlations were poorer at lower elevations and
were observed to approach the best value at about 20 m
above ground. Thus relatively inexpensive NaI
detectors position on the rooftops of moderately sized
buildings may be used for measuring RPR and SPR.
Atmospheric GRR was observed in the detector at the
NSB shortly after a period of lightning without the
presence of significant precipitation. This activity
(concurrently observed in an IBT detector) decayed
with a half-life of about 40 minutes as indicated by the
arbitrarily normalized exponential decay of radiation
shown as the solid curve in figure 9.
On other occasions GRR in addition to that which
was expected (as per equations 5 and 7 above) was
observed during thunderstorms shortly after lightning
(an example of which is shown in figure 10). The
additional activity decayed with half-life similar to that
associated with lightning without precipitation.
If an arbitrarily normalized exponential decay tail of
45 min half-life is subtracted from the GRR due to the
observed precipitation, the fit shown as the solid curve
in figure 11 is quite good. The lightning related activity
may be indirectly related to ions accelerated in the high
NSB gamma rate (Bq)
10
8
6
4
2
0
1
2
3
4
5
6
time after midnight April 4, 2002 (h)
Figure 9. Exponential decay of activity observed in the NSB
detector shortly after lightning. The solid curve is a
(t1/2=40min) arbitrarily normalized exponential decay.
GRR (Bq) and RPR (mm/h)
7
6
5
lightning
<-->
4
3
2
1
0
6
8
10
12
14
16
18
time after noon August 1, 2002 (h)
Figure 10. GRR are closed circles (error bars reflecting
statistical errors) and RPR are open circles (error bars
reflecting rain gauge resolution) as a function of time.
Lightning occurred during period indicated by arrow.
824
radioisotopes, and GRR/(RPR)2/5 and/or GRR/(RPR)3/5,
respectively, along with geometric constants27,40-42. The
amplitude of the diurnal cycle of GRR may be used as a
monitor of local radon concentrations and exhalation
rates which may give insight into the substratum and
tectonic activity.1,43-45 An international collaboration is
exploring all of the above in dispersed locations.27
We acknowledge the Univ. of Auckland Physics Dept.
for hosting one of us (MBG), Prof. G. Austin for his
comments and Prof. K. Kitahara for his encouragement.
7
6
5
lightning
<-->
4
3
2
1
0
6
8
10
12
14
16
18
time after no o n A ugust 1, 2002 (h)
Figure 11. The closed circles are [GRR – Ce-t/τ] τ is
ln2x45min (errors reflect statistics) and the open circles SPR
(errors reflect rain gauge resolution) as a function of time.
Lightning occurred during time indicated by arrow. The solid
curve is a smooth line through the closed circles. The dashed
curve is a smooth line drawn through the uncorrected GRR.
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best DAG for monitoring rain via GRR from adsorbed
progeny was 15-20m, the height of a 3-4 story building.
The remarkable reproducibility and reliability of these
correlations for rain rates up to 25 mm/h and 20 hours
duration enables real time determination of RPR (SPR)
to within 0.3 mm/h (0.2 mm/h). The time resolution
depends only on GRR statistics and was measured to
within better than 6 min. The total integrated rainfall
(snowfall) is obtainable under nearly all conditions.
For large wind shear this method, which is independent
of the magnitude of the horizontal speed of rain
droplets, might be more reliable than standard gauges.
For snow, neither wind shear nor the sublimation of
snow (before it is melted) contributes significant error.
All the GRR reported herein were obtained using only
small volume (12.9 cm3), relatively inexpensive NaI
detectors. An array of such detectors would provide a
reliable, practical, and cost effective way to remotely
monitor precipitation rates under most conditions. The
atmospheric GRR data suggest that real response time
is better than 6 minutes and is only limited in this work
by counting statistics (which may be improved by
merely increasing the size of the detectors employed).
Increased GRR during periods of precipitation has
been observed in widely varying locations6-11,14,15,27
suggesting that there is a sufficient pool of radon
progeny in the upper atmosphere to enable the use of
such RPR and SPR monitors in remote locations.1
Since the upper atmospheric progeny concentrations are
in secular equilibrium25,26 this method is likely to be
globally applicable, requiring only periodic, regional
renormalization. If so, such a setup might be used in
deserts and/or polar-regions because they are durable
enough to serve as remote, unmanned monitors.
Work in progress includes the measurement and
explanation of anomalous GRR during thunderstorms.37
It may also be possible to determine the number density
and other properties of moderate rainfalls by
determining surface σr and/or volume ρr densities of
825