Cloud electrification and related lightning activity in thunderstorms have their origin in the charge separation and resulting distribution of charged iced particles within the cloud. So far, the ice distribution within convective clouds has been investigated mainly by means of ground-based meteorological radars. In this paper we show how the products from Cloud Profiling Radar (CPR) on board CloudSat, a polar satellite of NASA's Earth System Science Pathfinder (ESSP), can be used to obtain information from space on the vertical distribution of ice particles and ice content and relate them to the lightning activity. The analysis has been carried out, focusing on 12 convective events over Italy that crossed CloudSat overpasses during significant lightning activity. The CPR products considered here are the vertical profiles of cloud ice water content (IWC) and the effective radius (ER) of ice particles, which are compared with the number of strokes as measured by a ground lightning network (LINET). Results show a strong correlation between the number of strokes and the vertical distribution of ice particles as depicted by the 94 GHz CPR products: in particular, cloud upper and middle levels, high IWC content and relatively high ER seem to be favourable contributory causes for CG (cloud to ground) stroke occurrence.
Lightning is an effect of cloud electrification, but there is no consensus in
the scientific community on the relative importance of the different
mechanisms that cause the charge separation in clouds (MacGorman and Rust,
1998; Sherwood et al., 2006). However, the importance of cloud microphysical
structure, in particular the ice water content, in this process is generally
accepted and is confirmed by experimental studies and numerical modelling.
Considering the various mechanisms, the non-inductive ice–ice interaction is
the most efficient process for cloud electrification (Mansell et al., 2005).
This mechanism requires the presence of large ice hydrometeors (i.e. graupel
or hail pellets) that collide with ice crystals in a suspension of
supercooled water droplets (Reynolds et al., 1957; Takahashi, 1978; Berdeklis
and List, 2001; Adamo et al., 2007). Laboratory studies by Takahashi (1978)
and Jayaratne et al. (1983) also showed that the charge sign is positive or
negative as a function of the cloud temperature and liquid water content
(LWC). Further laboratory studies have investigated the impact of cloud
conditions on electrification (see Saunders et al., 2006), which also underline
the importance of mixing solid and liquid hydrometeors. Observational studies
indicate that positive cloud-to-ground (
Actually, a large part of electrification should be the result of colliding
ice particles. Even if the mechanisms of electrification are still largely
debated, it is agreed that the convective updraught plays a key role, by
increasing the liquid water content, the number of supercooled water
droplets and the number and size of ice crystals available in the so-called
charging zone, namely the region in which the charge transfer takes place.
After collisions between rimed particles and smaller ice particles, the
larger particles take one charge while the smaller ice crystals take the
opposite charge. Larger particles such as hail and graupel remain suspended in the
updraught and fall out when their terminal velocity exceeds the updraught, while
the lighter ice particles are lifted to the upper regions of the cloud,
establishing an electric field within the cloud. This generates the in-cloud
electric field distribution that initiates lightning when the field exceeds
values of 100–250 kV m
Since the relationship between lightning and microphysical cloud structure is widely accepted, meteorological radar has frequently been used to investigate thunderclouds. Much research (since Kinzer, 1974) has been devoted to the observation of radar reflectivity and lightning considering the horizontal structure of the thundercloud. Rutledge and Petersen (1994) confirmed with their observations the bipolar model described by Orville et al. (1988): the majority of the negative CG flashes are located in the region of higher radar reflectivity while the positive ones are found in areas of weaker reflectivity, in coincidence with the stratiform rainfall region. Moreover, the number of cloud-to-ground flashes is highly correlated with the vertical radar profile, showing the contribution of the mixed-phase region to the non-inductive charging mechanism. Zipser and Lutz (1994) analysed the vertical profiles of radar reflectivity and the known differences in lightning frequencies for three different regimes (oceanic, monsoon and continental), confirming the relationship between the ice water content (IWC) and the effectiveness of charge separation.
More recently, Katsanos et al. (2007) observed the correlation between
reflectivity and lightning, finding a vertical profile with values greater
than 53 dBZ in the low levels, of
Radar reflectivity gradient is also a good indicator for strong updraughts
(Zipser and Lutz, 1994; Hondl and Eilts, 1994), which contribute to the
creation of opposite charge regions. Some clues in the same direction came
also from satellite measurements. Petersen et al. (2005) pointed out that a
solid relationship can be found between lightning and cloud ice content. In
their study, they used the ice water path (IWP) integrated from cloud top to
the
However, PR uses a frequency (13.8 GHz) that is not optimal for taking into account the contribution to the IWC due to small ice crystals in the upper cloud portion, which is due to its relatively high sensitivity threshold (17 dBz). This information can instead be provided by the so-called cloud radars, which are active sensors at higher frequency, with much higher sensitivity. Ground-based cloud radars generally provide a limited amount of information about the ice in convective clouds due to the signal attenuation caused by the underlying liquid particles and rain layers. On the contrary, satellite-based cloud radars like the 94 GHz Cloud Profiling Radar (CPR) on CloudSat, and planned on upcoming EarthCARE, have demonstrated their full potential in profiling IWC and ER in the upper cloud layers, where attenuation due to small, solid hydrometeors can be assumed negligible (Stephens et al., 2008; Austin et al., 2009).
In this work, we propose a novel approach for studying the relationship between lightning and the vertical distribution of IWC and ER, characterizing the charged cloud regions. We report and discuss the results obtained by selecting 12 convective events over Italy. Section 2 describes the CPR products used in this study and the LINET sensor data, while in Sect. 3 one storm, optimally observed by CPR, is analysed in detail to extract the cloud structure characteristics favourable to lightning production and to better focus the statistical analysis of the 12 events, described in Sect. 4. In Sect. 5 we summarize and discuss the results.
CloudSat is a NASA Earth Sciences Systems Pathfinder mission that started in
2006 and flies in formation with other Earth Sciences missions, taking part
in a constellation of sun-synchronous satellites called “A-train”
(Stephens et al., 2008). The overlap between the fields of view of the
satellites leads to a multi-satellite observing system for studying
different aspects of the atmosphere. In particular, the objective of
CloudSat is to measure, for the first time, the vertical structure of the
clouds in order to improve their characterization in global models. The
CloudSat instrument is the 94 GHz nadir-pointing CPR, which measures the
power backscattered by targets as a function of distance from the radar. The
CPR provides 2-D atmospheric slices with a vertical resolution of 240 m, a
1.7
For this study, we used two 2B-level CloudSat products: the cloud geometrical profile (2B-GEOPROF) and the radar-only version of the cloud ice and liquid water content (2B-CWC-RO). The 2B-GEOPROF contains the measured reflectivity of the vertical column, after a screening performed with the MODIS cloud mask to filter out non-cloudy profiles and a correction for gaseous absorption. The 2B-CWC-RO provides vertical profiles of IWC, LWC and cloud particle effective radius: the retrieval algorithms work on profiles already classified as cloudy and assume log-normal cloud particles size distribution, also using the vertical temperature profile as estimated by ECMWF analysis (Stephens et al., 2008). Other CloudSat products (i.e. 2B-CWC-RVOD and 2C-ICE) use synergies with other space-borne instruments to provide more accurate IWC and ER estimates, but their availability is limited to the 2006–2011 time frame, due to spacecraft orbit modifications. We preferred to use nominally less accurate products, but considered a longer time record.
Radiation at 94 GHz is strongly attenuated by the interaction with
hydrometeors, especially large ice and liquid/melting particles.
Experimental data reported average two-way attenuation rates of 4.8 dB km
Lightning strokes acquired by LINET (Betz et al., 2007, 2009) have been used in the study. LINET is a lightning detection network developed at the University of Munich in 2006 and consists of about 130 sensors ensuring a 200–250 km baseline in 17 European countries (Betz and Meneux, 2014).
The 10.8
LINET works in VLF/LF (very low frequency/low frequency) band, but a lightning detection algorithm to
discriminate IC from CG is applied as well. Even if CG and IC signals tend
to dominate respectively at high and low currents (Betz et al., 2009), a
discrimination based on amplitude considerations is not reliable due to their
overlap (Betz et al., 2007; Nag and Rakov, 2007). For this
reason a 3-D-method called “time of arrival” (TOA) has been developed. This
method is based on the different origins of VLF emission between IC and CG:
the corresponding differences in travel times are calculated by the TOA
locating algorithm and give the height of the lightning emission. TOA
requires a maximum sensor baseline of 250 km and location accuracy
sufficient to appreciate the difference between the two travel distances. The
location accuracy has been verified by strikes into towers of known position
(Betz et al., 2009) and reaches an average accuracy around 150 m. The
comparison with other lightning networks has revealed a good
time coincidence (Loboda et al., 2009) and a higher capacity to discriminate
IC from CG (Lagouvardos et al., 2009). The good sensitivity of the antenna,
which detects signals smaller than 5 kA, attributes a total lightning
quality to the network. The magnetic flux of the lightning signal is
detected by means of two orthogonal loops directly as a function of time in
a frequency range between 1 and 200 kHz, while a GPS clock achieves the
signal timing with accuracy better than 100 ns (Betz et al., 2009). This
characteristic is useful for a variety of research purposes, such as cell
tracking (Betz et al., 2008), recognition of severe weather conditions
(Casella et al., 2012) and the study of lightning induced chemical processes
(Tuck, 1976), and it guarantees an input for the improvement of models which
describe convective processes. The algorithm analyses a window of 512
In this Section we present one of the 12 case studies as an example, to make the reader acquainted with the information that the instruments and data described in the previous section can provide if optimally observed by CPR. For this purpose, we selected one mesoscale-organized, long-lasting convective system that occurred in northern Italy on the night between 12 and 13 August 2010. The convective episode started in the late afternoon (LT) of 12 August above the western Po Valley and moved eastward in the following hours, reaching the maximum development around 23:30 UTC the same day. This event produced a long time record in terms of the number of strokes in northern Italy.
Vertical cross section of the CPR reflectivity along the path shown in Fig. 1. In the bottom panel, the number of CG strokes recorded by LINET for each CPR profile are reported.
Figure 1 shows the METOSAT-9 10.8
In Fig. 2, the CPR reflectivity is plotted in colour shades along the path
shown in Fig. 1, while the bars below indicate the number of CG strokes
registered by LINET for each corresponding CPR profile. For each cloud
profile, a neighbourhood of 1.5 km radius is searched for strokes in the
LINET database, within an interval of
Vertical profiles of IWC (black line, bottom axis) and ER (red
line, top axis) for four CPR profiles, namely
Lightning activity occurs only on the convective part of the cloud, while no strokes are detected on the anvil or in the growing convective cell at around profile no. 30 along the track. However, there is a relatively large region (between profile no. 98 and profile no. 110) where no strokes are recorded despite the CPR reflectivity showing a thick cloud layer. This suggests that not only is the high ice content a key factor in developing strokes, but also that the vertical distribution of IWC has an impact. Out of the total number of strokes detected (82), only three are positive, and they are detected in the area of weakest reflectivity, which corresponds to the stratiform rainfall region, confirming the model described by Orville (Orville et al., 1988).
To better understand the role of IWC vertical distribution, a more detailed
analysis is made by selecting profiles with and without lightning and in the
convective part of the cloud. In Fig. 3, the vertical profiles of CPR
products IWC and ER are presented in case of lightning detected (top panel)
and no lightning detected (bottom panel) within the profiles. Figure 3a
shows the profile no. 96 from the start of the cross section reported in
Fig. 2, where the LINET registered 13 strokes, while 12 strokes are
reported for the profile no. 118, shown in Fig. 3b. For Fig. 3c (profile
no. 106) and 3d (profile no. 124), still in the active part of the cloud, no
strokes are reported. The cloud structures in these four profiles present
some similarities: a thick ice layer between 5 and 11 km above the ground with
peak concentration larger than 1 g m
For this case, the presence of lightning is favoured by a dense ice layer at the cloud top, where relatively smaller ice particles are present, coupled with a second layer of high IWC, characterized by larger hydrometeors that, given the known structure of thunderstorm, can be identified as large graupel. This structure is coherent with the graupel–crystal charging mechanism.
Lucky CloudSat overpasses over storms, like the one described in the previous section, are not common. This is mainly due to the narrow observation of CPR, coupled with the low Earth-orbiting satellite sampling. However, we attempt to extend the statistics by collecting other significant storms over Italy and sufficiently sampled by at least one CloudSat overpass in their core convective structure. We limited our research to five convective seasons starting from 2009, when the LINET sensors network reached the baseline necessary for optimal lightning detection, also in Italy. In this way we found 12 case studies in this period with the necessary characteristics. Each of them was analysed in detail, similarly to what shown in the previous section. In the 12 cases, we counted a total of 301 CG strokes assigned to 97 profiles, out of a total of 1701 cloud profiles collected by CPR. We consider this resulting data set quite representative of the variety of profiles that occurred during the Italian storms in the convective season. Given the limitations of the CPR-retrieved quantities discussed in Sect. 2.1, we will present our results with a caution to only consider the numerical values valid within a CloudSat analysis, performed with the 2B-CWC-RO data product.
An overview of CPR profiles composing the data set is presented in Fig. 4. The mean characteristics of the IWC and ER vertical profiles are plotted, separating the class of profiles with and without CG lightning. Note that we count only CG strokes, since they propagate vertically. IC often propagate horizontally and cannot be directly associated to a single cloud profile, so they have not been included in this study focusing on vertical structures. A clear difference between the classes is evident. Not only is the IWC larger, especially in the upper levels (about 10 km) where lightning is detected, but ER profiles also evidence an overall increase, especially around 5–6 km.
More hints on the profiles statistical properties are illustrated in Fig. 5. To provide a rough assessment of the probability distribution for the
quantities of our interest we adopted the histograms, in which the frequencies
of observations occurring in certain ranges of values are shown. In Fig. 5a the histogram of mean IWC for cloud profiles with at least one CG
stroke is shown (the histograms of profiles without lightning are simply the
complement). A vertical mean IWC value of 0.75 g m
Distribution of profiles with strokes (red circles, with radius
proportional to the number of strokes) and without strokes (black circles),
with respect to
Distribution of profiles with strokes (red circles, with radius
proportional to the number of strokes) and without strokes (black circles),
with respect to mean IWC in the
The second CloudSat product we analysed in order to characterize the cloud profiles is the ER of the ice particles, defined as the ratio between third- and second-order moments of the particle size distribution, i.e. the area-weighted mean of the particle radius (Hansen and Travis, 1974). It is worth noting that, when non-spherical ice particles are involved, the particle radius means the radius of the equivalent mass ice sphere. Similarly to what has already been done for IWC, in Fig. 6a and b we show the histograms of mean ER and maximum ER for profiles with at least one CG stroke. A relatively high mean value of ER (greater than 0.85 mm) or a high maximum (greater than 1.1 mm) seems to be a necessary condition for CG strokes. CPR is not designed to classify hydrometeor types, but large mean or maximum values of ER are compatible with the presence of large ice particles (probably graupel) in a significant part of the cloud profile. Histograms 6c and 6d illustrate where the ER maxima are vertically located. As expected, the region in convective storms where we can generally find the maximum of ER is the charging zone, where the charge separation occurs. However, a significant number of profiles, in this case without a clear statistical preference between profile with and without lightning, have their ER maximum in the upper cloud layers, clue of strong updraught in the profiles that makes the presence of suspended large particles possible.
To further investigate the outcomes of Figs. 5 and 6, for each
profile we computed the average values of IWC and ER values along the two
vertical regions evidenced, hereafter referred to as the “charging zone” (CZ)
0 to
As for the effective radius, it seems that lightning occurs for any value of UC mean ER in the range 0.4–1.2 mm, while a relatively high value (ER > 0.9 mm) of CZ ER is needed to have lightning.
To fully understand the meaning of the numerical values reported (especially
of IWC), it has to be clarified how IWC is computed by 2B-CWC-RO within the
melting layer, including in the CZ layers in our scheme. The IWC is computed
assuming the entire profile is ice and its value is set to zero in the
portion of the profile with temperatures lower than the freezing level, as
estimated by ECMWF analysis. Then, in the region between
From in-depth analysis of the 12–13 August 2010 case study, we have
identified two distinct regions in the vertical cloud structure that are significant
for lightning production. The uppermost region, found for Italy at an altitude
of about 10 km a.s.l., is characterized by high values of IWC (above 1 g m
Despite the difficulties in picking out CloudSat overpasses intersecting
active convective cells over Italy, the combined use of CG lightning data
and the profiles of IWC and ER provided by the CPR for the data set
has substantially confirmed the case study findings concerning the vertical
distribution of IWC and ER necessary for lightning. We can resume as
follows:
Statistically, the IWC UC has demonstrated its importance for describing
lightning occurrences. High IWC values can be caused by strong on-going
updraughts or the results of long-lasting cumulative effects of small charged
ice particles, resulting from collisions that occurred in the charging zone and
were transported upward to build up a usually positive charged layer. In both
cases they favour the presence of CG lightning. A well-known necessary condition for CG lightning occurrence is the
presence of a sensible amount (middle to high IWC) of large particles (high
ER values) in the CZ. They form the usually negative CZ layer and their effect
on the electric field can be further strengthened for induction by the presence
of the UC layer. When the two layers are clearly separated (see Fig. 3b and d), showing two
relative maxima, the in-cloud electric field can reach and exceed the high
values of 100–250 kV m
The combined effect of the two layers is evident in Fig. 8, where we chose
to connect the UC IWC and the CZ ER in one scatter plot. Apart from a few
outliers, the profiles with lightning are distributed around the blue
tendency line.
However, the position of the upper cloud layer of charged ice particles with respect to the CZ is also deeply influenced by high-altitude wind regimes, storm rotation, etc. (Stolzenburg et al., 1998). The CPR observation is composed by vertical narrow beams having the effect of slicing the storm. Hence, each beam captures information related only with the microphysics and the electric structure of a part of the whole storm. This is one of the reasons for the observed spreading in the obtained statistics.
Anyway, the best exploitation of CPR data is the single-case analysis. When, like in Sect. 3, one storm has been sampled enough in its core convective part, we can more easily recognize the different regions of the storm, make a specific interpretation of the signature of the eventual presence of the double layer and verify the presence of corresponding lightning activity.
The CloudSat Standard Data Products used in this work are provided by
the CloudSat Data Processing Center and are available after free
registration at
LINET data are available from Nowcast GmhB (
CloudSat Standard Data Products are distributed by the CloudSat Data
Processing Center, located at the Cooperative Institute for Research in the
Atmosphere at Colorado State University in Fort Collins, within the NASA
CloudSat Project. LINET data have been provided by Nowcast GmhB
(
The comments of two anonymous reviewers greatly increased the quality and significance of the paper. Edited by: G. Vulpiani Reviewed by: two anonymous referees