Cloud-To-Ground Lightning Distribution and Its Relationship with Orography and Anthropogenic Emissions in the Po Valley

DECEMBER 2014 F E UDALE AND MANZATO 2651

Cloud-to-Ground Lightning Distribution and Its Relationship with Orography and Anthropogenic Emissions in the Po Valley

LAURA FEUDALE* AND AGOSTINO MANZATO Osservatorio Meteorologico Regionale/Agenzia per la Protezione dell’Ambiente del Friuli Venezia Giulia, Visco, Udine, Italy

(Manuscript received 16 January 2014, in ﬁnal form 17 July 2014)

ABSTRACT

The main object of this work is to study the lightning climatology in the Po Valley in Italy and how it varies in time (interannual, annual, weekly, and daily time scales) and space (sea coast, plains, and mountain areas) and how that is related to topographic characteristics and anthropogenic emissions. Cloud-to-ground (CG) lightning in the target area is analyzed for 18 yr of data (about 7 million records). It is found that the Julian Prealps of the Friuli Venezia Giulia region are one of the areas of maximum CG lightning activity across all of Europe. During spring lightning activity is more conﬁned toward the mountainous regions, whereas during summer and even more during autumn the lightning activity involves also the coastal region and the Adriatic Sea. This is due to different triggering mechanisms acting in different topographic zones and during different periods of the year and times of the day. In analogy to previous studies of lightning done in the United States, a weekly cycle is also identiﬁed in the area of interest, showing that on Friday the probability of thunderstorms reaches its maximum. After conducting a parallel analysis with monitoring stations of atmospheric particulates (diameter # 10 mm: PM10) and sounding-derived potential instability, the results presented herein seem to support the hypothesis that the weekly cycle in the thunderstorm activity may be due to anthropogenic emissions.

1. Introduction often) extensive forest fires (e.g., Podur et al. 2003). Therefore, accurate information regarding CG lightning– Lightning is one of the most powerful spectacles of na- prone areas is useful for safety purposes and for identi- ture, both for its danger and for its visual aspects. It also fying the best locations for electric, telecommunications, discharges a large amount of energy to Earth, produces and sensitive industrial infrastructures. significant chemical transformations (e.g., nitrogen oxides; Such accurate information has become available with Bond et al. 2002; Price and Rind 1994), and often has the introduction of the Lightning Location System (LLS), deadly effects (e.g., Holle et al. 2005; Ashley and Gilson which occurred in the late 1980s (Krider et al. 1981, 1989). 2009). Broadly speaking, lightning flashes may be grouped LLS has made it possible to compute the lightning densi- into two categories: those that strike the ground [cloud-to- ties, defined as the number of lightning flashes per a given ground (CG) lightning], and those that do not (cloud-to- unit of area and time. The most studied area is the United cloud or intracloud lightning). CG lightning is usually States (e.g., Fisher et al. 1993; Orville and Huffines 1999, the cause of interruptions in electric power transmissions 2001; Villarini and Smith 2013). Other frequently exam- and distribution, electromagnetic interference, and (quite ined areas are Canada (Herodotou et al. 1993; Nash and Johnson 1996), Brazil (Pinto et al. 1999), Japan (Asakawa et al. 1997; Shindo and Yokoyama 1998), Australia (Kilinc * Current affiliation: Department of Engineering and Architecture, and Beringer 2007), and, recently, some European coun- Naval Architecture and Ocean Engineering Division, University of triessuchasGermany(Finke and Hauf 1996), the United Trieste, Trieste, Italy. Kingdom (Holt et al. 2001), Italy (e.g., Bernardi and Ferrari 2004), Spain (Rivas Soriano et al. 2005), Austria Corresponding author address: Agostino Manzato, Osservatorio (Schulz et al. 2005), Romania (Antonescu and Burcea Meteorologico Regionale/Agenzia per la Protezione dell’Ambiente ák and Kyznarová 2011 del Friuli Venezia Giulia (OSMER/ARPA–FVG), via Oberdan 18/a, 2010), the Czech Republic (Nov ), Visco, UD 33040, Italy. Estonia (Enno 2011), Finland (Mäkelä et al. 2011), and E-mail: [email protected] Portugal (Ramos et al. 2011; Santos et al. 2012).

DOI: 10.1175/JAMC-D-14-0037.1

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Very recently, Anderson and Klugmann (2014) com- lightning counts versus 10-m wind gusts versus accumu- puted a lightning climatology across the full European lated rain-gauge observations every 6 h in the plain of the continent, using only 5 years of lightning data observed by Friuli Venezia Giulia region (hereinafter FVG, north- the new Arrival Time Differencing Network (ATDnet; eastern Italy). Manzato’s Fig. 3 shows a strong clustering Gaffard et al. 2008).Inaccordwiththatanalysis among heavy rain episodes, strong wind gusts, and very (Anderson and Klugmann 2014, their Fig. 4) it appears high CG lightning activity, while a similar kind of cluster- 2 2 that the main areas with more than 4 flashes km 2 yr 1 are ing occurs also for cases with weak rain, small maximum the Western Alps and northeastern (NE) Italy. In partic- winds, and a low number of CG lightning events. ular, over all of Europe they found only two spots with The target area of the present study is northeastern 2 2 lightning densities above 6.5 flashes km 2 yr 1 during the Italy, including the Po Valley, the central and eastern Alps, period of investigation. The largest area is located along and parts of the neighboring countries of Switzerland, the eastern Alps, while the second is located in the Ticino Austria, Slovenia, and Croatia. The Po ‘‘valley’’ is in reality region, on the border between Switzerland and Italy, and is a large plain basin oriented from west to east (for more than described in Nisi et al. (2014). That result is in very good 650 km) and surrounded on the northern and western sides agreement with the 1971–2008 precipitation climatology by the Alps, on the southern side by the Apennines, while across the Alps, derived by a very dense network of rain on the eastern side it is limited by the Adriatic Sea. This gauges, recently published by Isotta et al. (2014) (cf. with region has been found to contain one of the areas in central their Fig. 6). Even in that case, the largest rain-density area Europe most highly affected by CG lightning (Feudale et al. in the whole domain is on the Julian Alps, along the border 2013). The climatology of thunderstorms in the Po Valley between Italy and Slovenia. From these results NE Italy has been studied by Cacciamani et al. (1995), while Manzato appears to be a very active area from a meteorological (2007) and Manzato (2012) focused, respectively, on the point of view and deserves dedicated study. rain–thunderstorm and hail climatologies on the FVG plain By definition, a thunderstorm is a cumulonimbus as- subdomain. From these works and from the analysis of sociated with the presence of lightning and thunder, single-case studies (e.g., Rotunno and Ferretti 2003; Borga usually accompanied by strong gusts of wind and rain, et al. 2007; Davolio et al. 2009; Manzato et al. 2014), the sometimes also with hail (Glickman 2000). Storms char- meteorological role of the north Adriatic Sea in providing acterized by intense lightning activity have been as- moisture flux for feeding convection has been highlighted. sociated with strong convective events, like supercells, For this reason, the distance from the sea is also a param- squall lines, mesoscale convective systems, and tropical eter that will be investigated in relation with the CG cyclones (e.g., Goodman and MacGorman 1986; Branick lightning spatial distribution. Moreover, given the role that and Doswell 1992; Rutledge et al. 1993; MacGorman complex topography often plays in storm initiation and Burgess 1994). The complex interrelations between (Bourscheidt et al. 2009; Nisi et al. 2014), the relationships lightning and convective rainfall (e.g., Zipser 1994; with the topographic characteristics, like orography height, Petersen and Rutledge 1998; Tapia et al. 1998; Rivas altitude standard deviation, and its gradients [called di- Soriano et al. 2001), flash floods (Petersen et al. 1999), rectional slopes in Nisi et al. (2014)], will be considered. hail (Changnon 1992; Solomon et al. 2004; Soula et al. In addition, following the results of Bell et al. (2008, 2009) 2004), and high winds and tornadoes (MacGorman and and Rosenfeld and Bell (2011) regarding the possible con- Burgess 1994; Carey and Rutledge 2003; van den nection between thunderstorms and anthropogenic aerosols Broeke et al. 2005) have been investigated in the past, (atmospheric pollution), this study also investigates possible given the severe consequences and damage that they can relationships between lightning occurrence and anthropo- cause to society (Curran et al. 2000; Changnon 2001). genic emissions, carrying out analyses both on weekly It would be very interesting to also study in detail (high frequency) and annual (low frequency) time scales. these relations in NE Italy, but that is not easy because The article is organized as follows. After a brief de- of the many different triggering mechanisms (e.g., syn- scription of the data used, which is given in section 2, the optic cold fronts, convergence lines, thermal boundaries, spatial and temporal characteristics are described in orographic lifting) that can initiate and sustain storms in section 3, with some factors that may influence the CG this area and because of the complex microphysics un- lightning distribution discussed. In section 4, evidence of derlying cloud electrification and consequent lightning a relationship between the CG lightning distribution and discharges (Williams et al. 2005; Rakov and Uman 2007). topography is presented, and in section 5 the possible What has been learned from previous work is that the most relationship of the CG lightning distribution with the intense convective events produce more CG lightning, distribution of particulate diameter # 10 mm (PM10) together with heavy rain and strong winds. For example, and instability indices is discussed. The conclusions Fig. 3 in Manzato (2003) presents a 3D scatterplot of CG follow in section 6.

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FIG. 1. Target area with topography (grayscale; m), EUCLID lightning detector locations (gray triangles), the Udine WMO 16044 sounding station (gray square), and labels identifying the PM10 stations (listed in Table 1).

2. Data a network of Vaisala, Inc., ‘‘IMPACT ESP’’ sensors, which detect the return stroke (usually considered the The domain of interest for this study is precisely de- largest discharge transfer during a lightning event) with fined as 448–488N and 98–158 E. As seen in Fig. 1, this a location accuracy of 500 m or better (e.g., http://www. domain includes a large part of the Po Valley and the hobeco.net/pdf/IMPACTESP_B210324en-A.pdf). central-northeastern part of the Alps and Prealps on the While cloud-to-cloud lightning accounts for the ma- northern side. The northern part of the Adriatic Sea, jority of the total lightning produced (e.g., Boccippio included on the southeast side of the domain, plays a et al. 2001; de Souza et al. 2009), the Italian CESI/SIRF role in mitigating the continental climate and is a source network measures only the CG lightning. Since the au- of moisture advected inland by southerly, southeasterly, thors are not aware of other LLS networks also detect- or southwesterly flows. In fact, the whole Adriatic Sea is ing cloud-to-cloud lightning and operating in Italy for at mostly parallel to the Apennines on the west side and to least 10 years, only the CG lightning data have been the Dinaric Alps on the east side, so that every flow with considered in the present work. Until 1999, the CESI/ a southerly component can be ‘‘channelized’’ inside and SIRF measurement network system was using up to 16 pushed toward the Po Valley. sensors located in Italy. Out of these, only 5 are inside The CG lightning data used in this analysis are pro- the study area or are less than 100 km from its borders. vided by the Centro Elettrotecnico Sperimentale Italiano– In 2000, SIRF entered in the EUCLID Network and Sistema Italiano Rilevamento Fulmini (CESI/SIRF; the lightning detection system was strengthened by the Bernardi and Ferrari 2004) within the framework of European collaboration. Another four sensors (near the the European Cooperation for Lightning Detection study domain) were added, for a total of nine sensors (EUCLID) Network. The dataset consists of all CG monitoring the target area (their locations are shown by lightning records, with both positive and negative po- the nine gray triangles in Fig. 1). Bernardi and Ferrari larities, observed between January 1995 and December (2004) and Feudale et al. (2013) found some small dif- 2012 (18 yr). These measurements are obtained through ferences in the network efficiency before and after 1999

Unauthenticated | Downloaded 10/01/21 02:29 PM UTC 2654 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 53 that, in any case, should not affect the total derived CG TABLE 1. List of the 19 monitoring stations for PM10 analysis in lightning climatology. the Po Valley area. Columns show the identification name used in Following Feudale et al. (2013), the lightning dataset Fig. 1 (ID), the town of the station location (city), the latitude of the station (lat), the longitude of the station (lon), and the altitude of is first preprocessed to discard multiple strokes, con- the station (alt). sidering as a single lightning event all the flash records reported in the same second and in the same area of ID City Lat (8) Lon (8) Alt (m) 0.01830.018. After this preprocessing, the final number BG Bergamo 45.62 9.61 182 of useful records is about 7 million. Since Bernardi and BS Brescia 45.54 10.22 149 Tommasini (2012) suggest that, given the network ac- BZ Bolzano 46.48 11.34 247 CR Cremona 45.14 10.05 45 curacy of 500 m in lightning location, a minimum grid of FE Ferrara 44.84 11.61 8 2km 3 2 km should be considered, the data were re- GO Gorizia 45.94 13.62 50 gridded with a spatial resolution of 0.038 in longitude MN Mantova 45.14 10.80 18 (;2.41 km) and 0.028 in latitude (;2.13 km), so that OSO Osoppo 46.22 13.07 167 every grid box covers an area of about 5.13 km2 (the PC Piacenza 45.05 9.70 61 2 PD Padova 45.43 11.89 11 total domain area is about 205 300 km ). Finally, the PN Pordenone 45.96 12.66 28 gridded data were aggregated into hourly matrices. PR Parma 44.79 10.33 60 In the second part of this study, the PM10 particulate RE Reggio Emilia 44.69 10.66 50 matter data from 19 stations having time series of adequate RO Rovigo 45.07 11.78 7 length (from 2002 to 2010) are analyzed. The list of these TN Trento 46.06 11.13 203 TS Trieste 45.66 13.77 2 stations is shown in Table 1, where the identification (ID) UD Udine 46.07 13.23 100 label used to show their location inside the domain (see VE Venezia 45.43 12.31 1 Fig. 1) is defined. Data for the daily PM10 (usually accu- VR Verona 45.44 10.96 62 mulated from 0000 to 0000 local time) were retrieved through BRACE, an online air-quality database (http:// www.brace.sinanet.apat.it/web/struttura.html) created by Italy. In particular, there are many grid boxes with 24 21 22 the Agency for Environmental Protection and Technical maximum densities above 7 3 10 flashes h km on 1 services [APAT, now known as the Superior Institute for the southern flank of the Carnic and Julian Prealps. The Environmental Protection and Research (ISPRA)]. After core of this maximum (on the Julian Prealps) appears to this procedure, the correlation between the mean-domain be approximately 40 km to the southeast with respect to value for the lightning density and the PM10 values is the maximum area shown in Fig. 4 of Anderson and studied on annual and weekly time scales. Klugmann (2014). This shift may be due to their much Finally, the high-vertical-resolution Udine–Campoformido shorter time series (5 vs 18 yr) or to the lower spatial 2 [World Meteorological Organization (WMO) code resolution of the ATDnet network with respect to the 16044, latitude 5 46.038N, longitude 5 13.188E, altitude 5 EUCLID network. Also, the maximum density shown in 94 m; shown by a gray square in Fig. 1] radiosoundings Fig. 2 is much closer to the maximum in precipitation made by the Italian Aeronautica Militare during the shown in Fig. 6 of Isotta et al. (2014), even though the April–October 1995–2012 period were used to evaluate latter seems to be shifted by a few kilometers eastward. 24 21 22 the climatological relation between the lightning proba- Other areas with density above 5 3 10 flashes h km bility and two indices related to the atmosphere instability. lie on the southern flank of the Prealps of the Veneto In addition, the weekly cycle for the probability of having and Lombardia regions (just south of the 468 parallel) severe weather in the FVG plain (derived from lightning and on the southwestern flank of the Apennines in and FVG surface station observations) was derived and Tuscany (in the southwestern corner of the domain). compared with the other weekly cycles.

3. Temporal and spatial CG distribution 1 The Julian Prealps are located along the border between Italy a. Mean spatial distribution and Slovenia (north of the UD and GO labels in Fig. 1) and are WNW toward ESE oriented, while the Carnic Prealps are on their Figure 2 shows the CG lightning density (deﬁned as western side (north of the PN label in Fig. 1) and are SW–NE 2 2 lightning ﬂashes h 1 km 2) in the whole study domain oriented, so that the two form a bow-shaped barrier. 2 and period. As was already seen in a previous study on Anderson and Klugmann (2014) report a location accuracy of better than 5 km, an aggregation of multiple lightning strikes within a slightly smaller domain and with a slightly shorter data 20 km, and a regridding onto a 0.20830.208 grid (about 22 km 3 time series (Feudale et al. 2013), the CG lightning in this 14 km), which means a much coarser resolution than that used in case also has an arc-shaped maximum density in NE the present work.

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21 22 FIG. 2. Spatial distribution of lightning density deﬁned as the average number of CG ﬂashes h km during the period 1995–2012 (18 yr) on a 0.03830.028 grid. The scale is multiplied by a factor of 104 for convenience.

Isolated spikes such as one in Austria along the border Prealps. More generally, the southern slopes of moun- with Italy (46.588N, 13.468E) and another in Croatia tains have higher lightning densities than do the corre- (near the northern point of the island of Cres) corresponding northern sides. Finally, there seems to be an spond to transmitter antenna towers, which attract increasing gradient of density going from the west to- lightning discharges, as discussed in Feudale et al. ward the east. From this qualitative first analysis it ap- (2013). The highest-density records in the domain are pears that both topographic characteristics and the 2 2 2 above 10 3 10 4 flashes h 1 km 2, corresponding to distance of peaks from the sea (which is a major source 2 2 about 9 flashes yr 1 km 2. of moisture when southerly winds blow) are key factors On the other hand, there is an interesting minimum in explaining the observed lightning density. of lightning density in the region downstream of the b. Mean temporal distribution Apennines chain, between the cities of Piacenza and Reggio Emilia (respectively, PC and RE in Fig. 1). The To evaluate the temporal characteristics of the light- other large area of minimum density is between eastern ning distribution and to see if there is some kind of pe- Switzerland (Grisons region) and western Austria riodic pattern of behavior in the lightning activity, and (Vorarlberg area and the southern part of Nordtirol), hence in the thunderstorm activity, the lightning density including the small state of Liechtenstein. mean value in the study domain of Fig. 2 is computed Comparing Fig. 2 with Fig. 1, it is possible to see a and analyzed on different time scales. general correlation between orography and lightning Figure 3a displays the interannual mean value of CG density. At fine scales lightning density seems to show lightning in the target domain, indicating large year-to- the ‘‘fingerprint’’ of the orography. In fact, many small year variability, with a maximum in 2002 greater than 2 2 2 alpine valleys have lower densities than the surrounding 3.4 3 10 4 flashes h 1 km 2, followed by 2009, 2007, peaks. But in different parts of the domain this relation and 2008, with respective values greater than 3 3 2 2 2 is more complex than what one can see at first glance. 10 4 flashes h 1 km 2. Even though there is a positive For example, the Italian Prealpine peaks have much apparent trend toward a higher frequency of CG lightning higher densities than do the Alpine peaks or Austrian activity, the result of a linear regression (straight line)

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FIG. 3. Domain means of the (a) CG lightning annual distribution and the 18-yr linear trend (straight line); (b) CG lightning monthly distribution (thin line) with the 610-day moving average (thick line); (c) CG lightning weekly distribution, where the vertical lines are for 2 2 the PM1 standard error; and (d) CG lightning daily cycle distribution. Units are number of flashes h 1 km 2 3 104. shows that this positive trend is not statistically robust During the spring, convection is forced by frequent (R 5 0.05 and p value 5 0.39). Atlantic cold fronts, while during the summer this pat- Figure 3b depicts the monthly mean value of CG tern is often prevented by strong Mediterranean anti- lightning flashes on a yearday scale, and the thick line cyclones and hence the convection is more due to stronger is the smoothed 610-day moving average, which filters radiative warming, which produces larger potential in- high-frequency values. Figure 3b shows that the active stability (in this case triggering is often provided by valley period starts approximately in April, reaches its or sea breezes, orographic lifting, and convergence lines). maximum during the months of July and August Finally, during autumn there are fewer anticyclone situ- 2 2 2 (around 8 3 10 4 flashes h 1 km 2), and fades there- ations (with more cold fronts from the Atlantic Ocean), after until October–November, when the lightning and while the potential instability is lower than in sum- activity, though small, still persists. In the FVG plain, mer (Manzato 2007, his Fig. 5), the Adriatic Sea is much Manzato (2007) has found a similar pattern of be- warmer than in spring, so that there is more available havior for the ‘‘strongest thunderstorms’’ (see the ‘‘fuel’’ for convection, sometimes embedded in larger ‘‘CALCA6h. 0.79’’ histogram in his Fig. 4a) and has precipitating structures. In addition, Costa et al. (2001) explained it as follows. state that one of the most representative synoptic

Unauthenticated | Downloaded 10/01/21 02:29 PM UTC DECEMBER 2014 F E UDALE AND MANZATO 2657 situations for producing heavy rainfall in northern Italy, in distribution presents different spatial patterns on particular during the spring and autumn, is the presence of a month-by-month basis. Starting gradually in May (not a trough in the western Mediterranean that brings mid- shown), the June CG lightning mainly affect the Prealps tropospheric moist southwesterly flow toward the Alps. area, in particular across the eastern part of the domain Decreasing the temporal scale of analysis, Fig. 3c (Carnic and Julian Prealps; Fig. 4a). They become more shows the characteristic CG lightning activity on a day of widespread (but predominantly confined in the 45.28 # the week time scale. Following work done by Bell et al. latitude # 47.08 band) and reach their maximum fre- (2009) and Rosenfeld and Bell (2011), the horizontal quency during July, as shown in Fig. 4b. In August, CG weekly scale is replicated, to get a better picture of the lightning also begins to affect quite frequently the transition during the weekend. Above the mean value coastal areas near the Adriatic Sea and the Ligurian Sea computed for each day of the week using all of the (the southwestern corner in Fig. 4c), while the activity in available data from 1995 to 2012, a 6 1 standard error is the inner central Alps is much lower than in July. Fi- also shown to give an idea of the data dispersion. nally, in September (Fig. 4d) the CG lightning distri- Figure 3c shows that there is a gradual increase in the bution is almost totally centered on the northern CG lightning activity starting from Sunday (the day with Adriatic Sea (around the Gulf of Trieste, near the TS the minimum CG lightning activity) and reaching a label in Fig. 1, and the Kvarner Gulf, near Cres), with maximum on Friday, followed by a rapid drop on Sat- a secondary peak near the Ligurian Sea. urday. There is also a local minimum observed on This migration from the Alpine–Prealpine area during Wednesday, but the error bars are large, so that it is not May–July toward the coastal area (in particular those clear if it is statistically significant. Apart from that, the that have steep reliefs in the vicinity, like the Apennines weekly periodicity seen in Fig. 3c could be potentially or the Veneto and FVG Prealps in Italy, or the northern related to anthropic activities, like increased pollution part of the Dinaric Alps in Slovenia and Croatia) in during working days, as will be investigated in more August–September clearly indicates the importance of detail in section 5. the interaction between the warm sea of late summer Potential instability strongly depends on solar heating and initial autumn [e.g., sea surface temperature mean and hence varies with time of day. This is reflected in the values shown in Fig. 4b of Manzato (2007)] and the thunderstorm activity as a function of the period of the orography. day, as clearly evident in Fig. 3d, showing the mean daily distribution of CG lightning after averaging over the 4. CG relationship with topography entire domain in hourly steps. The behavior is vaguely sinusoidal, with maximum activity during the afternoon, As shown at the end of the previous section and also 2 2 2 (peak greater than 4 3 10 4 flashes h 1 km 2 between found by previous studies (Rivas Soriano et al. 2005; de 1600 and 1700 UTC, i.e., between 1800 and 1900 local Souza et al. 2009; Antonescu and Burcea 2010), CG time during summer) and a minimum during the lightning density is also related to topography. To 2 2 2 morning (about 1 3 10 4 flashes h 1 km 2 between 0700 quantify this more objectively, in this section the anal- and 1000 UTC). The temporal interval separating the ysis of the lightning distribution will be stratified for morning minimum from the afternoon maximum is only three different areas, characterized by their altitude 7 h, indicating that the semidiurnal component is large. range. These seasonal (Fig. 3b) and daily cycle (Fig. 3d)out- Figure 5c shows a ‘‘density plot’’ created to evaluate comes are expected since thunderstorm activity in Eu- the domain grid-box distribution based on their number rope generally reaches its maximum during summer, with of CG lightning flashes and altitude ranges. Along the a peak in the afternoon due to the maximum radiative abscissa the altitude levels in 20-m steps are shown, heating (e.g., Finke and Hauf 1996; Holt et al. 2001; Rivas while along the ordinate the mean number of lightning Soriano et al. 2005; Manzato 2007; Antonescu and Burcea flashes is presented. The x–y colored field is the loga- 2010; Novák and Kyznarová 2011; Gladich et al. 2011). It rithm of the number of domain grid boxes that have CG is not always so: for example, Santos et al. (2012) have lightning counts and an altitude in that given range of found the maximum lightning activity in Portugal to be lightning frequency and orography altitude range. It is during the month of September, followed by April, with possible to see a general trend of decreasing density a bimodal distribution. (which should not be confused with the lightning density field) with higher lightning frequency and with in- c. Spatial distribution for different months creasing altitude. The highest density is found for areas In the previous section only the mean value of the with altitudes less than 60 m and in particular in the first whole domain was studied, but the CG lightning 0–20-m bar near the ordinate (orange/yellow points),

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FIG. 4. CG lightning spatial distribution during (a) June, (b) July, (c) August, and (d) September, calculated on an 18-yr basis (1995–2012). 2 2 The scale shows the flashes h 1 km 2 3 104. while another relative maximum occurs above 400 m of the remaining 59% of the domain). For each of these altitude. three areas, we computed the mean of the CG lightning flashes total number and evaluated its moving-average a. CG lightning distribution across three topographic annual trend by yearday (thick line in Figs. 6a,c,e) and zones its moving-average daily cycle (Figs. 6b,d,f). Based on the ‘‘altitude–frequency’’ analysis in Fig. 5c, Figure 6 illustrates the different lightning character- the lightning characteristics will be stratified into three istics in the three subareas. In particular, while in Fig. 6a altitude ranges, chosen for convenience using the 5- and the 610-day moving average of the lightning probabil- 400-m thresholds. The first area is called the sea–coastal ity from mid-June to July is always lower than 6 3 2 2 2 area, identified by grid boxes covering areas with alti- 10 4 flashes h 1 km 2, the opposite occurs in Fig. 6e and, tudes below 5 m MSL (i.e., about 17% of the entire from late June, also in Fig. 6c. On the contrary, while domain), including all of the seashore region and over- the lightning mean density in the sea–coastal area 2 2 2 sea zone (cyan area in Fig. 5d). The second area, cor- (Fig. 6a) remains above 6 3 10 4 flashes h 1 km 2 until responding to the ‘‘plains’’ and ‘‘foothills’’ region, is mid-September, that happens only until mid-August for identified by grid boxes with altitudes in the 5–400-m the mountain area (Fig. 6e). range (green area in Fig. 5d, which covers about 24% of This analysis provides a clear example of how con- our domain); finally, the third area, called for convenience vection moves from the internal mountain areas in late the mountains area even though mountains usually have spring to the coastal–sea regions in the late summer and higher elevations, is identified by grid boxes with alti- autumn. Apart from the synoptic discussion already tudes above 400 m (maroon area in Fig. 5d, representing presented concerning the frequency of cold fronts (higher

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FIG. 5. (a) Meridional variation of the orography standard deviation (south–north direction) (m). (b) Map of the distance from the coast (8). (c) Density of grid points in the corresponding range of CG lightning frequency (log) and orography altitude (m). (d) The target area divided into three main zones: the coastal–sea (cyan), the plains–foothills (green), and the mountain (maroon). in spring and autumn than during summer), another im- a much stronger daily cycle. Note that similar trends portant role can be played by the different triggering were found in Fig. 6 of Tuomi and Mäkelä (2008) for the mechanisms acting during the storm season. In particular, land/sea areas of Finland. during spring the orographic lifting of low-level moist air To explain the different pattern of behavior in the by fronts or valley breezes seems to prevail (more storms diurnal cycle, a different triggering mechanism can be in the mountain region), whereas during late summer and more appropriate than different synoptic patterns, be- autumn (more storms near the sea) other mechanisms are cause the latter usually have longer time scales than do implicated, such as sea-breeze- or mesoscale-induced the short variations during the period of the day. In moisture ﬂuxes coming from the sea, which during au- particular, in the coastal–sea area, nighttime convection tumn is also still warm, because it has a higher heat ca- (Parker 2008; French and Parker 2010) seems to be pacity than does the inland territory. much more frequent than in the two other areas, which Looking at the diurnal cycle of Figs. 6b,d,f, the situa- provides further evidence that different triggering tion is even more different in the different areas. While mechanisms, such as convergence lines, low-level moist in the sea–coastal area (Fig. 6b) there is not a strong jets, sea–land boundaries, bores, etc., play an important daily cycle of thunderstorm activity (just lower activity role in this area in particular during the night, when the during late morning to early afternoon, between 0900 low levels are more stable than during afternoon, and and 1500 UTC), in the plains–foothills area (Fig. 6d) and the convection can exist with a more elevated cloud even more so in the mountain region (Fig. 6f) there is base. On the other hand, over the plain and mountain

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FIG. 6. (a) Time series of index lightning per yearday and (b) its daily behavior during the 24 h in the coastal–sea area (area 1, height between 0 and 5 m). (c),(d) and (e),(f) As in (a) and (b), but for the internal–foothill area (area 2, height between 5 and 400 m) and the mountains area (area 3, height above 400 m), respectively. The thick curves in (a),(c), and (e) represent the 610-day moving average.

Unauthenticated | Downloaded 10/01/21 02:29 PM UTC DECEMBER 2014 F E UDALE AND MANZATO 2661 areas, the classical theory based on potential instability and crossing northward toward the orographic barrier. It (lifting the low levels with high equivalent potential tem- is worth noting that these Prealps have peaks as high as perature Qe) seems to be the main mechanism for thun- 2000 m and are only 70–80 km away from the shore. derstorm development, because it follows the solar heating and reaches its maximum values during the afternoon hours. 5. The CG relationship with anthropogenic The present analysis reveals that the different spatial emissions distributions of convection between the coastal area (occurring primarily during the evening–nighttime in In section 3 we discussed the general temporal char- late summer) and the mountain area (occurring pri- acteristics of the lightning distribution and found that marily in the afternoon during spring and summer) are a weekly cycle seemed to arise from the analysis (see probably related to different characteristics of the trig- Fig. 3c). Evidence of weather weekly cycles has been gering mechanisms, which depend also on the distance reported previously in studies by Bell et al. (2008, 2009) from the Adriatic Sea and on its mean temperature. in the United States, especially with respect to precipitation, lightning, and storm occurrence. However, b. CG lightning correlation with topographic for Europe, Laux and Kunstmann (2008) analyzed and characteristics of the area identified a weekly cycle only with respect to tempera- To investigate in more detail the origin of the different ture (which to a first approximation is similar to the timings of thunderstorm activity in the different target trend shown in Fig. 3c), while they found no significant areas described in Fig. 6, relations with a few topo- weekly trend in the precipitation field. The hypothesis graphic characteristics have been studied. First, for each proposed by Bell et al. (2008, 2009) and Rosenfeld and grid box of the domain we computed the standard de- Bell (2011) (hereinafter called the Bell and Rosenfeld viation of the orography in the neighborhood (related to hypothesis) predicts that there is a dependence of thun- the slope of the orography and hence to the orographic derstorm occurrence on a particular day of the week lifting mechanism) with 50 resolution, in order to capture (therefore, a weekly cycle) that is due to the variation of the main structure on the larger scale, avoiding ‘‘noisy’’ pollution with the day of the week. In fact, consistent with effects due to a higher resolution. Subsequently, also fol- the weekly cycles of meteorological variables, a weekly lowing Nisi et al. (2014), we examined possible pointwise periodicity of air pollutants and aerosols was also found correlations of the CG lightning mean probability with the (e.g., Gong et al. 2007). following quantities for all of the 50 3 50 grid boxes: Any such weekly cycle may be considered evidence of anthropogenic influences on the weather in that area, 1) the simple orography height, because, for the Bell and Rosenfeld hypothesis, the 2) the standard deviation (std) of the orography, weekly cycle of working weekdays and resting Sunday 3) the meridional (Fig. 5a) gradient of the orography should be associated with weekly varying levels of par- std, ticulate air pollution. Pernigotti et al. (2012) found that 4) the zonal gradient of the orography std, the Po Valley is a hot-spot area in Europe for its high 5) the sum of the meridional and the zonal gradients of concentration of pollutants (especially during the winter the orography std, season) and that the pollutant concentration levels in 6) the distance of each grid point from the sea (Fig. 5b), this area are expected to remain problematic even after and applying antipollution strategies. 7) the combined correlation of the distance from the sea To see if the Bell and Rosenfeld hypothesis could be with one of the other quantities listed before (from applied in this particular area, some representative points 1–5). monitoring stations distributed in the target domain High correlation was found with the distance from the were chosen. They are listed in Table 1 and their loca- 2 sea (Fig. 5b), R 5 0.55 (p value , 2 3 10 16), followed tions are shown on the map in Fig. 1. For these 19 sta- by the meridional gradient of the orography std tions the daily PM10 data were downloaded from the (Fig. 5a), R 5 0.43. All of the other quantities had R BRACE dataset. The data were available from 2002–03 below 0.3. However, the highest correlation of 0.60 (p to 2010, but for the stations of Padova and Cremona they 2 value , 2 3 10 16) was obtained by linearly combining were available only from 2006 (time series length re- the distance from the sea and the meridional gradient of duced to 5 yr). the orography std, suggesting that the mean spatial a. PM10 cluster analysis thunderstorm distribution, having the highest peaks in the Carnic and Julian Prealps, is explained on average by Surface PM10 is a highly variable field in space and the meridional moist flow coming from the Adriatic Sea time and for this reason it is difficult to relate its value

Unauthenticated | Downloaded 10/01/21 02:29 PM UTC 2662 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 53 with local meteorological phenomena such as thunder- from the list of stations used to compute the ‘‘mean’’ PM10 storms. The approach followed in this work is to look for value for our Po Valley domain. an average value representative of the entire domain, to b. CG lightning and PM10 annual trend be related to the mean value of lightning probability in the whole domain. Both fields are studied only from In this section the temporal characteristics of the April to October, avoiding the high values of PM10 re- mean PM10 concentration are analyzed in parallel with corded during the winter season. To extract a coherent the temporal characteristics of the mean CG lightning mean PM10 signal from the 19 different surface stations, distribution to highlight possible correlations. The two analyses were performed. The first one is a k-means analysis of daily values (24 h accumulated) is divided clustering analysis based on the mean and standard into two temporal scales: an annual scale, based on deviation of the annual PM10 value. From Fig. 7a it is a low-pass-filtered time series with respect to the year- possible to identify three groups: the high mean value day (moving average of 610 days), and a weekly scale, group consists only of two stations (Verona and which also takes into consideration the high-frequency Mantova), while the low value group has mainly stations part of the signal. near foothills (Gorizia, Osoppo, Udine, and Pordenone) or Figure 8a shows the annual trend with a yearday res- in the mountains (Trento), located on the northeast side. olution, after a low-pass filter of 610 days, for the mean The second analysis is a hierarchical clustering CG lightning distribution (black line) and the PM10 dendrogram (Fig. 7b) using as a metric (i.e., measure of distribution, averaged on the selected 16 monitoring sta- distance between stations) the mutual linear correlation tions (gray line). As already described in section 3,the between the daily PM10 values of each pair of stations. CG lightning distribution is more active between April While the first analysis is a low-frequency analysis, (day 91) and October (day 304). The PM10 mean con- looking at the annual first two moments, the second one is centration for the same area shows a complementary more of a high-frequency analysis, looking for the daily trend: higher concentrations are recorded during the coherence among different stations. In Fig. 7b,stations winter and the autumn. A drastic decrease occurs in mid- with high correlations are joined in the bottom part of the April, moving to low concentrations in spring and sum- dendrogram and groups having larger distances apart mer, and then increasing again in September. In fact, in (lower correlation) are joined in subsequent steps. The autumn and winter high anthropogenic emissions (due level with three groups (the three gray boxes) shows to the building heating systems), in combination with different results than those found with the k-means a higher frequency of stagnant atmospheric conditions, clustering. In particular, there are three stations that join cause very high PM10 concentrations. at a very large distance: Trieste (coastal station) for the Since we are interested in studying the relation with first group and Trento and Bolzano (stations inside the thunderstorms, we have analyzed only the ‘‘stormy season,’’ Alps region) for the third cluster. which is the April–October period. The correlation be- To evaluate the strength of the correlation between tween the mean PM10 and lightning density in the the lightning probability and a spatially averaged PM10 April–October period (yeardays 91–304) is R 520.64 2 signal, the result of the second clustering method is (p value 5,2 3 10 16). Even removing September considered more appropriate. Based on the results of the and October (which have steep increases in PM10) hierarchical analysis, the stations of Trieste, Trento, and and limiting the period to yeardays 91–250,3 the two Bolzano were excluded from the following analysis, variables are anticorrelated, with R 520.40. because they are considered to be outliers with respect c. CG lightning and PM10 weekly cycle to the features described by the other stations, which are mainly located on the plain or foothills. Also, it should The following analysis on a weekly time scale is also be noted that inside the small valley with steep mountain restricted to just April–October, but in this case the surroundings (like the alpine valleys of Trento and Bol- high-frequency part of the signal is retained, because zano) the dispersion of pollution is more difficult than in a 610-day moving average would remove the day-to- the open plain. Moreover, in the mountainous part of the day variations. Figure 8b shows the mean CG lightning domain it is more likely that the emission of anthropogenic anomalies (detrended with respect to the mean value of pollution is lower than in the plain area, where most of the each yearday, black line) and the mean (over 16 industry and human population are concentrated. On the contrary, Trieste is located along the coast and is ventilated (e.g., frequent sea breezes or even strong bora winds), so 3 Note that in Italy there is a law forbidding the use of building thatpollutioncanveryeasilybedispersed.Thesearead- heating systems in the northeast region of the country from 15 ditional reasons to remove Trieste, Trento, and Bolzano April to 15 October.

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FIG. 7. (a) Result of the cluster analysis on the monitoring stations by the k-means method with the choice of three clusters, using as a discretization, the mean and the standard deviation of the PM10 concentration; the plus sign, circle, and triangle represent the centroid of each cluster. (b) Dendogram of the results of the cluster analysis on the monitoring stations, using as the distance measure the correlation between their PM10 concentrations.

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FIG. 8. (a) Yearday (‘‘Julian day’’) distribution for the domain mean of CG lightning (black line) and for the PM10 concentration averaged on the monitoring stations (gray line). (b) Weekly cycle of the domain-mean value for lightning (black) and PM10 (gray) during April–October 2002–10, after being detrended with respect to the yearday to ﬁlter the low-frequency variability. Bars show 61 standard error. Units for lightning are number of ﬂashes 2 2 h 1 km 2 3 104. Units for PM10 are milligrams per meter cubed.

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FIG. 9. The weekly trends of the probability of having signiﬁcant thunderstorms in the plain of the FVG region (deﬁned as at least one 6-h period with CALCA $ 0.7; Manzato 2003) for the period April–October 1995–2012 (black line with circles), maximum Qe in the lowest 250 hPa (medium-gray line with plus signs), and the difference in temperature with the environment (DT500, light-gray line with triangles) for the initial parcel lifted pseudoadiabatically at 500 hPa. The last two indices have been derived from the April–October 1995–2012 Udine–Campoformido radiosoundings made at 1200 UTC and have been detrended to remove the mean value for each yearday before stratifying for the different days

of the week. The unit for the Qe and DT500 anomalies is kelvins. On the right side, the scale indicates the probability of having a corresponding day of the week with at least one 6-h period with significant storm in the FVG plain. stations) PM10 anomalies (gray line) on a weekly time Sanchez-Lorenzo et al. (2008) for daily maximum tempera- scale, together with their standard errors. Both trends ture anomalies in Spain. Here, the standard errors are much are computed only during the April–October 2002–10 larger than those for PM10, but the maximum on Friday is period, which explains why the lightning weekly cycle is clearly outside the standard error bars of all the other days. slightly different from that shown in Fig. 3c (which The shift in the maximum could also be explained if one covers the 1995–2012 period). As expected, the PM10 thinks of the thunderstorms as an efficient way to remove distribution shows a clear weekly signal, with lower PM10, because of the rain and strong wind produced. Hence, PM10 concentrations during Sunday, rapidly increasing on Friday the PM10 can be lower than on Thursday just to the middle of the week (up to Thursday), and starting because there is a maximum activity of thunderstorm during to decrease from Friday up to Sunday. In general, this that day. The correlation between the seven pairs of this can reflect the anthropic activity measured by the air weekly cycle is R 5 0.48, but it is not very significant because pollution due to the industrial activity and daily com- of the low number of points (p value 5 0.28). muting, even though Friday is still a working day. Note d. Discussion on the lightning and PM10 correlations that the standard errors for PM10 are relatively small, so that the day-to-day variations seem statistically robust. The two previous analyses of CG lightning distribution The CG lightning anomaly distribution seems to reflect in comparison with the PM10 concentration distribution a similar trend, except for Wednesday (which shows the during the April–October period seem to give contrasting lowest lightning activity) and for the shift in the maximum, results: anticorrelation in the yearday analysis and cor- which occurs on Friday instead of on Thursday. It is relation in the weekly analysis. But in the analysis of noted that a similar local minimum during Wednesday is yeardays 91–304, averaging for each day of the year re- also shown in Fig. 1 of Laux and Kunstmann (2008) for moves the day-of-the-week effect, since the same yearday the temperature anomalies in Germany and in Fig. 1b of has different days of the week in different years. Moreover,

Unauthenticated | Downloaded 10/01/21 02:29 PM UTC 2666 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 53 using the 610-day moving average, the high-frequency accumulated rain, and wind gusts (the last two measured signal has been removed in the analysis of the 214 yeardays by a network of 15 FVG surface stations). If there is no and the day-of-the-week analysis (with periodicity of 7 lightning, CALCA6h is 0 by default, while its historical days) can be considered a high-frequency signal. So, maximum value is 1. The values above 0.79 are considered aquestionarises:whyduringtheApril–Octoberpe- the tail of the most severe thunderstorms in the FVG plain. riod does the low-frequency signal anticorrelate, while In the present study we took all and only the days with at the high-frequency signal seems to correlate? least one 6-h period with CALCA6h $ 0.7 (called sig- Our hypothesis is that, from a low-frequency point of nificant storms) during the period April–October 1995– view, the mean emission of PM10 in the April–October 2012. Counting the number of these cases for each day of period can be considered to be to a first approximation the week, we computed the weekly probability of having constant, and hence, when the surface stations measure a significant thunderstorm in the FVG plain. low concentrations, there are, on average, more PM10 Figure 9 shows the probability of having at least one particles at higher elevations. That can lead to two pos- 6-h period with CALCA6h $ 0.7 during the different sible scenarios. The first one is the Bell and Rosenfeld days of the week (black line), together with the anom- hypothesis, that is, that the PM10 transported at high aly of two instability indices derived by the Udine– elevations can act as—or be correlated with the pres- Campoformido soundings (WMO code 16044, latitude 5 ence of—cloud condensation nuclei (CCN, particles 46.038N, longitude 5 13.188E, altitude 5 94 m) at typically in the 0.02–0.2-mm range,4 on which water va- 1200 UTC.5 The dark gray line with the plus signs is the por may condense) and a higher concentration of CCN equivalent potential temperature of the most unstable clearly increases the thunderstorm occurrence proba- parcel (maximum Qe in the lowest 250 hPa), which is used bility, for microphysical reasons. The second scenario is as the initial parcel for pseudoadiabatic lifting when eval- that the PM10 is transported to higher altitudes during uating the sounding instability. The light gray line with the situations with more convection (more vertical triangles represents the potential instability. In fact, DT500 transport in the lower planetary boundary layer, PBL) (Manzato 2003)issimilartotheclassicalliftedindex and hence during the situations that are already more (Galway 1956), but uses as the initial parcel the 30-hPa- prone to thunderstorm development. In this second thick most unstable parcel, so it corresponds to the most case, the anticorrelation between the low-frequency unstable lifted index (MULI). Negative values of DT500 PM10 and lightning would not be a direct relation but mean potential instability (the initial parcel lifted to would be mediated by the PBL instability. Note that 500 hPa is lighter than the environmental air), while posi- these two explanations are not mutually exclusive. tive values signify a potentially stable profile. On the other hand, from a high-frequency point of It is possible to see that during April–October 1995– view, the weekly cycle can track the true day-to-day 2012 the maximum probability of having significant high-frequency variation in the total PM10 emission: storms in the FVG plain is well peaked on Friday, while Sunday there is a lower production of anthropogenic the minima are on Wednesday and Sunday. This trend is PM10 and hence there are fewer CCN available at very similar to the April–October 2002–10 weekly light- higher levels and a lower lightning probability, accord- ning cycle seen in Fig. 8b, which is computed on a much ing to the Bell and Rosenfeld hypothesis. Within this larger domain, a period half as long, and also does not context, the minimum probability of lightning on consider the observed rain and wind gust variables. Wednesday shown in Fig. 8b is not well explained by the Hence, the two similar weekly cycles can be considered to PM10 weekly cycle, apart from the fact that the standard be at least partially independent. error bars are very large. The potential instability measured by DT500 shows a maximum (negative anomaly) on Tuesday and a mini- e. Potential instability weekly cycle mum (positive anomaly) on Sunday, followed by Monday To test the role of potential instability in the weekly and Wednesday. The standard error bars are such that the cycle of lightning, another test has been done. Manzato maximum on Tuesday and minimum on Sunday do not (2003) defined a 6-h thunderstorm ‘‘intensity’’ (called overlap. The linear correlation with probability of signifi- CALCA6h) for the FVG plain, having an area of only cant thunderstorms is R 520.58 (p value 5 0.17). The 1 /46 of our study domain, based on CG lightning, weekly cycle of the most unstable parcel Qe shows that the minimum Qe anomaly occurs during Sunday and Monday,

4 Note that PM10 stations measure all particles with diameters #10 mm; therefore, particles that are much smaller than 10 mm are 5 Since the PM10 concentration and lightning density describe daily accumulated. mean values (0000–0000), only the midday sounding was selected.

Unauthenticated | Downloaded 10/01/21 02:29 PM UTC DECEMBER 2014 F E UDALE AND MANZATO 2667 while the maximum Qe anomaly occurs on Thursday, fol- periods: while in the mountains the afternoon low-level- lowed by Friday. The linear correlation with probability of based potential instability and the orographic lifting signiﬁcant thunderstorms is slightly lower than for the mechanism seem to play a major role, over the sea and potential instability: R 5 0.53 (p value 5 0.23). coastal area it seems that other triggering mechanisms

In this case also, the decrease of Qe on Friday could be (possible candidates include convergence lines, low- seen as a consequence of the maximum thunderstorm level moist jets, sea–land boundaries, and bores) that activity, if one considers that thunderstorms take their are efficient also during the night and with less low-level energy from the high-Qe air ingested and produce low-Qe potential instability acquire more importance, in par- output with their downdrafts.6 More generally, the ticular during the end of the summer, when the sea weekly cycle of the maximum Qe in the lowest 250 hPa is reaches its maximum sea surface temperature values quite similar to the PM10 weekly cycle seen in Fig. 8b (Manzato 2007, his Fig. 4b). In the foothills area the CG (apart from the local minima on Wednesday) and could lightning distribution behaves with a cycle that is about support the hypothesis that there is more PM10 at high halfway between those of the coastal and the mountain altitudes (where it can act as CCN) during the days with regions, on both annual and daily time scales. more thermals (convective activity), but it does not ex- Considering the spatial distribution, the mountainous plain if PM10 is a cause of thunderstorm or if there is just part of the FVG region (NE Italy) seems to be the area an indirect correlation with instability and PBL mixing. affected by the maximum CG lightning occurrence, 2 2 Nevertheless, we cannot imagine any natural effect able reaching values of about 10 flashes km 2 yr 1. This dis- to produce a weekly trend in the maximum Qe in the tribution shows good correspondence with the orography lowest 250 hPa; therefore, it is possible that this thermal and the land–sea surface type and in particular with the anomaly is also related to human activities and hence to presence of steep mountains at only 70–80 km from the anthropogenic emissions. Marano–Grado Lagoon (a source of high-Qe air), fol- The local minimum in thunderstorm activity on lowed by the Adriatic Sea. The CG lightning density Wednesday could be due to the maximum instability linearly correlates well with the meridional gradient of the anomaly of the day before, because those thunderstorms orography standard deviation and the distance from the remove the thermodynamic energy already accumu- sea used together. This result may be interpreted as a lated. But in that case the standard error bars are quite qualitative description of a typical situation favoring large, so that is just a speculation. thunderstorm occurrence: convection is mainly fed by a strong meridional (south, southwesterly, or southeasterly) flow, advecting moist air from the sea inland, where the 6. Conclusions Carnic or Julian Prealps act to produce orographic lifting. The present study focuses on the climatological char- Cloud-to-ground lightning exhibits a weekly cycle, acteristics of CG lightning in northeastern Italy and the with a maximum on Friday and a minimum during surrounding regions, observing the different patterns of Wednesday and Sunday. A similar pattern of behavior is behavior related to the topography and time scale. It is found for different periods and areas and also when shown that CG lightning activity varies strongly with the considering rain and wind gusts produced by thunder- period of the year and the time of the day and that it storms, so it can be regarded as quite a robust signal. varies within three different areas having different types Also, the PM10 measured at the surface by 16 stations of topography. In the areas close to the coast, where the shows a weekly cycle, with a minimum during Sunday proximity with the sea is a key factor, the CG lightning and Monday and a maximum on Thursday, so that there activity peaks in the second half of the summer (August is a 1-day delay between the lightning and the PM10 to mid-September) and much more often during the maxima. A similar trend is found also for the most un- evening/nighttime. A different trend is observed in the stable parcel Qe in the lowest 250 hPa derived from the mountain areas, where the CG lightning activity peaks Udine 1200 UTC soundings, while potential instability during midsummer (from mid-June to mid-August), (DT500) is highest on Tuesday, followed by Thursday, especially during the afternoon, when the surface Friday, and Saturday. During April–October 2002–10 heating produces the maximum potential instability. the mean PM10 and lightning weekly cycles in the Po The foregoing finding indicates that the main triggering Valley correlate with R 5 0.48. During April–October mechanism is quite different in the different areas and 1995–2012 the probability of having significant storms in

the FVG plain and potential instability or maximum Qe have correlations of 20.58 and 0.53, respectively. 6 Basically, a thunderstorm is an engine that converts thermal Regarding the PM10 effect, no clear conclusions can energy into kinetic energy and water vapor into condensed water. be derived by the actual results, because of the many

Unauthenticated | Downloaded 10/01/21 02:29 PM UTC 2668 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 53 feedbacks relating PM10, instability (and PBL thermals Boccippio, D. J., K. L. Cummins, H. J. Chiristian, and S. J. activity), and thunderstorms. One possible interpretation Goodman, 2001: Combined satellite and surface based esti- of the results is that, on average, there is an anthropic mation of the intracloud-cloud-to-ground lightning ratio over the continental United States. Mon. Wea. Rev., 129, 108–122, effect that accumulates during the week (starting from doi:10.1175/1520-0493(2001)129,0108:CSASBE.2.0.CO;2. Sunday), increasing anomalies of PM10 and of maximum Bond, D. W., S. Steiger, R. Zhang, X. Tie, and R. E. Orville, 2002: The Qe in the lowest 250 hPa until Thursday, while also in- importance of NOx production by lightning in the tropics. Atmos. creasing the probability of triggering thunderstorms on Environ., 36, 1509–1519, doi:10.1016/S1352-2310(01)00553-2. Friday, followed by Saturday. These thunderstorms re- Borga, M., P. Boscolo, F. Zanon, and M. Sangati, 2007: Hydro- meteorological analysis of the 29 August 2003 flash flood in the move this excess of PM10 (through rainfall and wind) and eastern Italian Alps. J. Hydrometeor., 8, 1049–1067, doi:10.1175/ Q of e (because storms convert the environmental thermal JHM593.1. energy into kinetic energy), restoring the initial condi- Bourscheidt, V., O. Pinto, K. Naccarato, and I. Pinto, 2009: The tions on Sunday. Of course, if this anthropic effect is real, influence of topography on the cloud-to-ground lightning it is not the primary cause of thunderstorm occurrence in density in south Brazil. Atmos. Res., 91, 508–513, doi:10.1016/ j.atmosres.2008.06.010. the Po Valley, but rather it modulates the natural vari- Branick, M. L., and C. A. Doswell III, 1992: An observation of ability of these events. For example, the maximum vari- the relationship between supercell structure and lightning ation in the probability of having a significant storm in the ground-strike polarity. Wea. Forecasting, 7, 143–149, doi:10.1175/ FVG plain during the different days of week is 3%, with 1520-0434(1992)007,0143:AOOTRB.2.0.CO;2. respect to a mean probability of 12%. Cacciamani, C., F. Battaglia, P. Patruno, L. Pomi, A. Selvini, and S. Tibaldi, 1995: A climatological study of thunderstorm activity in the Po Valley. Theor. Appl. Climatol., 50, 185–203, Acknowledgments. This research was carried out un- doi:10.1007/BF00866116. der the support of Project 2CE120P3 INCA–CE, funded Carey, L. D., and S. A. Rutledge, 2003: Characteristics of cloud-to- by the program ETC Central Europe FESR. We thank ground lightning in severe and nonsevere storms over the Dr. Marina Bernardi and the CESI/SIRF group for central United States from 1989–1998. J. Geophys. 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