The use of lightning data and Meteosat infrared imagery for the nowcasting of lightning activity

Atmospheric Research 168 (2016) 57–69

Contents lists available at ScienceDirect

Atmospheric Research journal homepage: www.elsevier.com/locate/atmos

The use of lightning data and Meteosat infrared imagery for the nowcasting of lightning activity Athanasios Karagiannidis ⁎, Konstantinos Lagouvardos, Vassiliki Kotroni National Observatory of Athens, Institute for Environmental Research and Sustainable Development, Vas. Pavlou & Metaxa, 15236 Athens, Greece

a r t i c l e

i n f o

Article history: Received 26 January 2015 Received in revised form 2 August 2015 Accepted 6 August 2015 Available online 2 September 2015 Keywords: Lightning activity nowcasting MSG IR imagery ZEUS lightning detection network data Greek mainland Summer period

a b s t r a c t The development and efficiency assessment of a lightning activity nowcasting tool is presented. The tool employs MSG IR imagery and real-time lightning data provided by ZEUS network to nowcast the manifestation of lightning activity over the Greek mainland for a time span of 1 h. The efficiency of the tool is assessed for 20 days with widespread lightning activity observed during the warm period of the year through a verification procedure that computes a collection of appropriate statistics for selected areas. The analysis of these statistics shows that the tool estimates successfully almost 80% of the upcoming activity. The false alarm rate is close to 40%, while a small overestimation is evident. Since the adverse effects of a case of missed activity are much more than that of a false alarm, the tool is considered successful and fit for operational use. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Lightning strikes are a known threat to human life and also to a variety of activities. Aviation industry suffers significant financial losses due to delayed departures, reroutings and postponed landings. Luckily the lightning related accidents are quite rare. Agriculture and stockrasing are also significantly affected by fires and livestock deaths caused by lightning strikes. Telecommunication quality can be deteriorated to the point of shutting down during thunderstorms while electrical power networks can suffer significant damages too. Over the last decades a series of ground-based operational lightning detection systems have been developed, e.g. the European based systems ATDNET (Gaffard et al., 2008), EUCLID, LINET (Betz et al., 2009) and ZEUS (Kotroni and Lagouvardos, 2008). These systems contribute significant information to the analysis of characteristics of lightning, and also create a solid database that can be used to evaluate the possible impact and economic losses caused by thunderstorms. The importance of lightning has been accepted by HyMeX (hydrology cycle in the Mediterranean experiment (Drobinski et al., 2014)) experiment that has developed PEACH, its Atmospheric Electricity component, dedicated to the observation of both lightning activity and electrical state of continental and maritime thunderstorms in the area of the Mediterranean Sea (Defer et al., 2015). The importance of forecasting lightning activity is of unquestionable value. Numerical Weather Prediction Models are employed to produce short and medium range forecasts of thunderstorms and lightning ⁎ Corresponding author. Tel.: +30 2108109203. E-mail address: [email protected] (A. Karagiannidis).

http://dx.doi.org/10.1016/j.atmosres.2015.08.011 0169-8095/© 2015 Elsevier B.V. All rights reserved.

manifestation (Yair et al., 2010; Lynn et al., 2012; Wong et al., 2013; Giannaros et al., 2015). However, these forecasts require a significant amount of time to run. In order to have a very short range forecast, of 1 h or less, other means should be utilized. This paper presents the methodology of development and also assesses the efficiency of a lightning activity nowcasting tool, which is based solely on Meteosat Second Generation (MSG) infrared imagery and on the ZEUS lightning detection records. The ability of geostationary satellites' infrared imagery to identify and examine convective systems has already been studied quite thoroughly. Lately the plausibility of estimation of convective initiation (CI) and lightning initiation (LI) using radar and satellite data has drawn the attention of the scientific community. In an early effort Feidas and Cartalis (2001) employed Meteosat images on the 5.7, 7.1, 10.5 and 12.5 μm channels in order to monitor mesoscale convective cloud systems (MSCs) associated with severe storms. They used the brightness temperature of the selected channels along with geometrical features to identify the cloud cells associated with two winter intense floods in Greece. Their algorithm was proven satisfactory enough since it was able to locate convective cloud cells, track them to the point of dissipation, and account for their splitting and merging during the lifetime of the convective system. Jirak et al. (2003) successfully tried to supplement prior studies on Mesoscale Convective Systems (MCS) classification using more comprehensive satellite and radar data. In their study the authors used the − 52 °C threshold value of infrared composite images in order to classify MCS. Morales and Anagnostou (2003), elaborated on the estimation of precipitation using geostationary infrared imagery and lightning observations, attempting to identify accurately the convective areas. In their

58

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

study they delineated the cloud area using the − 15 °C isoline of IR brightness temperature (TB). Lensky and Rosenfeld (2003) developed an algorithm of delineation of night-rain based on the brightness temperature difference between a thermal IR channel (11 μm) and a mid-IR channel (3.7 μm) which performed very well both for maritime clouds over ocean and for warm clouds over land. Roberts and Rutledge (2003) analyzed the capability of convective initiation nowcasting using radar and satellite imagery. In their work, they exploited cloud growth information, as expressed by the cloud top temperature differences on subfreezing levels between consecutive satellite images. They showed that the use of satellite data to determine the cloud growth rate through the differentiation of cloud top temperature rate contributes significantly to the overall estimation efficiency. Chronis et al. (2004) in an effort to estimate thunderstorm rainfall, used Meteosat infrared data and lightning data, and they delineated the cloud area using the −18 °C isoline of IR brightness temperature. Mecikalski and Bedka (2006) employed visual and infrared GOES imagery to create 8 predictor fields to forecast convective initiation and achieved a relatively accurate forecast of 45 min ahead. The convective initiation point was defined as the time that the radar reflectivity reaches 35 dBZ. The 8 predictors focus on the potential of a cumulus cloud to reach that reflectivity. Three of these predictors (hereafter called Interest Fields (IF)), are single and multichannel brightness temperatures, while the remaining 5 are time trend related parameters. The first 3 fields are indicative of the cloud top height, and depth of a cumulus cloud, while the next 5 are indicative of the cloud growth rate. In general, tall, opaque, fast growing clouds are expected to reach the 35 dBZ threshold. Based on the assumption that when 7 of the 8 IF reach the criteria, a pixel is characterized as positive for convective initiation, their analysis showed that it is possible to achieve a 60–70% success, for summer time convective cases. Mecikalski et al. (2010) conducted a comprehensive survey of the Infrared Channels from MSG, aiming to identify those that are more suitable to identify CI. Sixty seven IFs that contained information on cloud depth, updraft strength, and cloud-top glaciation were assessed. Using correlation and principal component analyses, they selected 21 IFs as those which contain the least amount of redundant information. Siewert et al. (2010) tried to use the MSG IFs proposed by Mecikalski et al. (2010) and the GOES IFs proposed by Mecikalski and Bedka (2006) in order to estimate the efficiency of CI nowcasting over Europe and north Africa, based on MSG IR imagery. They used a total of 19 IFs, and characterized a pixel as of CI threat when at least 16 IFs met the threshold value, while in the case that 18 IFs met the threshold value, the pixel was characterized as a strong indicator of CI. Harris et al. (2010) examined the behavior of 10 IFs based on GOES-12 infrared imagery, regarding their convective and lightning initiation nowcasting ability. These fields were similar to those proposed by previous studies (Mecikalski and Bedka (2006) and Siewert et al. (2010)) including two newly introduced. By examining 172 summertime convection cases the authors showed that a 35 min average lead time in CI and LI is plausible using the 8 of the 10 IFs. The selected interest fields, their description and the proposed critical values are presented in Table 1. Feidas and Giannakos (2011) used multispectral infrared data and developed adequate techniques to identify precipitation clouds and distinguish them from non-precipitating clouds. The proposed schemes are based on the characteristics of the 10.8 μm brightness temperature and the brightness temperature differences between 10.8 and 12.1 μm, 8.7 and 10.8 μm, and finally 6.2 and 10.8 μm. The best estimations were produced when all of the available information was used. Recently, Kolios and Feidas (2013) developed an automated nowcasting methodology for mesoscale convective systems over the Mediterranean basin using MSG IR imagery. In their analysis the MSCs were identified as areas of at least 100 km2 of horizontal extend with brightness temperature lower than −45 °C. A hybrid method for automated thunderstorm observation by tracking and monitoring of electrically charged cells, called ec-TRAM, is presented in the work of Meyer et al. (2013). This system

Table 1 The GOES IFs proposed by Harris et al. (2010) and the critical values that indicate possible lightning initiation. Interest Field

Description

10.7 μm TB

Cloud tops cold enough to support supercooled water and ice mass growth; cloud-top glaciation Cloud growth rate (vertical)

10.7 μm TB 15

15–30 min threshold b0 °C b−6 °C

min trend 6.5–10.7-μm TB difference 13.3–10.7-μm TB difference 6.5–10.7-μm TB 15 min trend 13.3–10.7-μm TB 15 min trend 3.9–10.7-μm TB difference 3.9-mm fraction reflectance

Cloud-top height relative to mid/upper troposphere

N−30 °C

Cloud-top height relative to mid/upper troposphere; better indicator of early cumulus development but sensitive to cirrus Cloud growth rate (vertical) toward dry air aloft

N−13 °C

Cloud growth rate (vertical) toward dry air aloft

N4 °C

N5 °C

N17 °C

Cloud-top glaciation Cloud top consists of ice (ice is poorer reflector than water at 3.9 μm)

b0.11

utilizes cell tracking algorithms that are based on those developed for the Cb-TRAM system (Zinner et al., 2008) and exploit radar and lightning data. Tested on a database covering the period from May to September 2008 over south Germany, the method proved to be fast and reliable. One of the main advantages of the method is the combined use of radar and lightning data, working complementary to each other. Mecikalski et al. (2013) extended the Harris et al. (2010) study by comparing GOES cloud-top properties and radar reflectivities regarding their ability to nowcast first-flash LI. It was found that although convective and lightning initiation can be nowcasted using radar or satellite data separately, it is more effective to use radar reflectivities to improve the satellite driven nowcasts. Finally, Matthee and Mecikalski (2013) focused on the possibility of nowcasting lightning initiation using MSG and lightning data. They used the IFs already introduced by the previous mentioned researchers and examined their behavior in lightning and non-lightning producing convective clouds. Eight out of the ten examined IR MSG fields showed different average values for lightning and non-lightning clouds, implying that they can be used in lightning nowcasting systems. These IFs along with the physical processes that they describe and the proposed critical values for cloud-to-ground

Table 2 The MSG IFs proposed by Matthee and Mecikalski (2013) and the critical values that indicate possible initiation of CG lightning. Channel differencing and time trends 15 min 6.2–7.3 μm trend

Physical process

Updraft strength 30 min 6.2–7.3 μm trend Updraft strength 15 min 10.8 μm trend Updraft strength 30 min 10.8 μm trend Updraft strength 6.2–7.3 μm Cloud depth 6.2–10.8 μm Cloud depth 8.7–10.8 μm Cloud-top glaciation 15 min 8.7–10.8 μm trend Cloud-top glaciation [(8.7–10.8) − (10.8–12.0) μm Cloud-top glaciation 15 min [(8.7–10.8) − Cloud-top glaciation (10.8–12.0)] μm trend

Critical value for CG lightning initiation Positive trends for ≥30 min with ≥2 °C increase during this time Positive trends for ≥30 min with ≥4 °C increase during this time ≤−10 °C ≤−20 °C ≥−5 °C ≥−10 °C ≥0.5 °C N/Aa ≥0.5 °C N/Aa

a Results from interest field was not significantly different at the 95% confidence level, no critical value was obtained.

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

lightning initiation are summarized in Table 2. Although not included in Table 2, the 10.8 μm brightness temperature was also shown to be quite different between the two cases. Two of the IFs did not present statistical differences between lightning and non-lightning convective clouds and therefore they are not proposed for utilization. Summarizing the methods and conclusions of the aforementioned studies, it could be stated that CI and LI nowcasting using satellite imagery is based primarily on three distinct features of cumulus clouds: the degree of cloud top glaciation, the cloud depth and the cloud growth rate. These features, expressed by various IFs, are proven to be the most effective indicators of CI and LI. The present work is devoted to the development of an operational tool for nowcasting lightning activity over Greece. This tool is based on a synthesis of the existing state of the art methodologies on nowcasting CI and LI and uses only MSG IR imagery and real-time lightning data as input. For the verification of the developed tool, 20 days with widespread lightning activity over the study area that were observed during the warm period of the years 2010–2013 have been used. The innovative part of this work is that the tool attempts to nowcast lightning activity regardless of thunderstorm development stage, cirrus contamination or preexisting convection in the area. In previous studies, since their aim was to examine and quantify the characteristics of the IR imagery before convective or lightning initiation, the research usually focused on cases of convective cells which were not masked by cirrus clouds or other cloud masses. However, an operational nowcasting tool has to perform continuously and produce reliable estimations for all kinds of cloud forms and synoptic settings. The tool that is presented in this paper is a first effort toward this goal.

59

The study is organized as follows: Section 2 presents the study area, the data and the methodology used to develop and verify the efficiency of the nowcasting tool. Section 3, illustrates the results of the tool development and its verification. Finally Section 4 summarizes the conclusions of this work and also discusses the most important findings and points that deserve further development.

2. Data and methodology 2.1. The data The Meteosat Second Generation is a series of geostationary satellites, carrying a 12-channel imager, called “Spinning Enhanced Visible and Infrared Imager” (SEVIRI). It observes the full disk of the Earth and has a repeat cycle of 15 min. SEVIRI has eight spectral channels in the thermal infrared and three channels in the solar spectrum with a maximum sampling resolution of 3 km. It also includes a broadband high resolution visible channel which provides images at 1 km sampling resolution. More about the characteristics of Meteosat satellites channels and the way they are used for the observation and analysis of atmospheric features like clouds, surface temperatures and water vapor can be found in Aminou et al. (1997); Levizzani et al. (2001), Schmetz et al. (2002), Mecikalski and Bedka (2006), Ricciardelli et al. (2008) and Feidas and Giannakos (2011). The brightness temperatures of the 6.2 and 10.8 μm MSG channels (hereafter TB6.2 and TB10.8) are utilized for the nowcasting tool. It should be noted here that a rescaling of the horizontal resolution was

Fig. 1. Graphic illustration of the division of the Greek mainland into 17 areas. The 7 areas used for verification purposes are highlighted in red. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

60

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

performed prior to the analysis, resulting in a horizontal grid of 0.05° in latitude and longitude. The images are available every 15 min. The archive of ZEUS VLF lightning detection system is also utilized to improve the estimation efficiency and perform the verification of the nowcasting methodology. Cloud-to-ground lightning strokes data were extracted from the archived database, as latitude-longitude and time event points. ZEUS is a Very Low Frequency lightning detection network with 6 detectors installed in Europe (Birmingham in the United Kingdom, Roskilde in Denmark, Iasi in Romania, Larnaca in Cyprus, Athens in Greece and Mazagon in Spain). The network is operated by the National Observatory of Athens since 2005, while details on the system can be found in Kotroni and Lagouvardos (2008) and Lagouvardos et al. (2009). The ZEUS VLF system is less efficient during the night. However, despite the fact that some strokes may be missed, a storm as an entity is barely missed. The developed tool aims at nowcasting the upcoming activity without regard to its severity. Based on that, it is believed that the reduced efficiency of the ZEUS system during the nighttime, will not affect substantially the tool's efficiency. Moreover, it should be noted that most of the warm period thunderstorms are triggered by surface overheating, which takes place only during the daytime and reach maximum development in the afternoon. Both datasets (MSG imagery and ZEUS strokes data) are confined in the examined area which is defined as the area delineated from 16 to 30° eastern longitude and from 34 to 44° northern longitude. The temporal range of the data used in the development and verification of the tool extends from 2010 to 2013.

Table 3 POD, FAR, CSI and BIAS for 7 TB10.8 threshold values. Threshold (°C)

−28

−24

−20

−12

−18

−16

−8

POD FAR CSI BIAS

0.59 0.39 0.43 0.98

0.64 0.41 0.44 1.09

0.67 0.44 0.44 1.21

0.75 0.49 0.43 1.49

0.69 0.45 0.44 1.28

0.71 0.46 0.44 1.34

0.80 0.54 0.41 1.74

procedure, the verification is performed for the aforementioned set of days and areas. Finally, in the 3rd stage the use of the lightning realtime data of the preceding 15 minute period is introduced. The CL is reduced by 1 when no lightning activity is evident in the area during the previous 15 min. Again the verification procedure is performed in the same areas and days to assess the degree of success of the developed tool. 2.2.1. Stage 1 Following Mecikalski and Bedka (2006), Matthee and Mecikalski (2013), Mecikalski et al. (2010), Mecikalski et al. (2013), Roberts and Rutledge (2003), Harris et al. (2010) and Siewert et al. (2010), 3 IFs were selected for their efficiency as lightning estimation indicators. These IFs are the TB10.8 (indicative of the cloud top glaciation), the TB6.2–TB10.8 difference (indicative of the cloud depth) and the TB10.8 15 minute trend, which will be referenced as “TB10.8trend” (indicative

2.2. The development and verification methodology of the nowcasting tool The nowcasting tool is built on the premise that MSG IR imagery and real time lightning data should be able to predict lightning activity without use of any other information. The referenced literature supports that notion, although it also suggests that such a tool can be improved with the use of radar or other sources of data. As it will be shown, a nowcasting tool based merely on satellite imagery and lightning data can produce satisfactory estimations for a time window of 1 h, when radar data are not available. For the needs of this work, Greece is divided in a number of areas according to thunderstorm and lightning climatology. For each area, a nowcast will be issued every 15 min. This nowcast (hereafter Certainty Level — “CL”) will be valid for the following hour and alert for the level of certainty for lightning occurrence. A CL equal to 0 is an indication that no lightning activity is expected, while every level above 0 will indicate an increasing certainty for upcoming lightning activity. As shown by Kotroni and Lagouvardos (2014) and Galanaki et al. (2015), the lightning activity of the Greek area is concentrated over the mainland during summer and over the sea during winter. The protection from lightning over land is of particular interest because more than 85% of the population is concentrated there along with most of the economic and social activities. Indeed, Papagiannaki et al. (2013) in their study on the high impact events over Greece reported 19 fatal incidents of lightning during the period 2001–2011, the majority of which occurred over land and during the warm period of the year. Therefore, it was decided to start the calibration, verification and application of the tool for the warm period of the year (May to September). The development of the tool is realized in 3 distinct stages. In the first stage the MSG IFs that will be used are selected along with their appropriate threshold values. At this stage a preliminary screening of all available IFs is performed. Three of the mostly used IFs are selected and undergo a verification procedure which aims at defining the optimum threshold values. The verification is performed over seven mainland areas (red boxes in Fig. 1) for 20 days of the years 2010–2013 with significant lightning activity. In the second stage a combined use of the three IFs is performed. According to their level of agreement, a CL is issued for every area. To assess the efficiency of the nowcasting

Fig. 2. (a) POD and FAR, (b) CSI and (c) BIAS for 7 TB10.8 threshold values.

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

of the cloud growth rate). The latter is defined as the difference between two successive 15 minute images of the TB10.8. In order to delineate cloud areas that are expected to manifest lightning activity, the value of each pixel for each IF is compared to a pre-specified threshold value. TB10.8 represents the degree of glaciation at the cloud top. Previous studies (Latham et al., 2007; Deierling et al., 2008) indicate that the presence of precipitation and nonprecipitation sized ice fluxes at the upper levels of the clouds is necessary to the charge-separation processes, which leads to the build-up of electrical fields and eventually lightning within the thunderstorms. According to these studies, there are indications that a positive relation between the degree of glaciation and the total flash rate exists. Since the TB10.8 appears to be inversely proportional to the strength of electrical phenomena inside the cloud it is assumed that TB10.8 should be lower than a pre-specified threshold to allow lightning occurrence inside the cloud. The cloud depth, as expressed by the difference TB6.2–TB10.8, is expected to be greater when the TB6.2 is significantly higher than the TB10.8. Following that notion, a high positive TB6.2–TB10.8 difference indicates a deep cloud. The potential for electrification increases when this difference is higher than the predefined threshold value. A rapidly growing cumuliform cloud appears with a continuously reduced cloud top temperature. The 15 minute trend of the TB10.8 must acquire negative values when a cloud is actually growing. In fact, TB10.5trend should be lower than the pre-set threshold because a strong negative trend value is associated to a rapidly growing cloud. The selection of the adequate threshold values is not a straightforward task. The values proposed by the

61

Table 4 POD, FAR, CSI and BIAS for 6 TB6.2–TB10.8 threshold values. Threshold (°C)

−35

−30

−25

−20

−15

−10

POD FAR CSI BIAS

0.89 0.59 0.39 2.18

0.83 0.51 0.45 1.69

0.75 0.46 0.46 1.39

0.69 0.42 0.46 1.19

0.62 0.38 0.44 1.00

0.53 0.33 0.41 0.80

literature cannot be used as such, mainly due to two important reasons. The first is that the geographical region where the methods are used is different, a fact which results in different convective activity and lightning climatology. The second and equally important reason is that a nowcasting system like the one developed here, should be able to estimate lightning activity regardless of the development stage of the cumulus clouds. In other words, the system should be able to estimate not just the “first lightning” but all lightning activity during the full thunderstorm life cycle. This is rather difficult, mostly because during the mature and dissipation stage of a thunderstorm, the completely glaciated anvils that are formed and carried away from the cumulonimbus cells form extensive cloud masses that obscure the line-of-sight of the detector. In some cases these extensive cloud masses are perceived as convective clouds, mainly in terms of glaciation and cloud depth, and therefore can create false alarms of expected lightning activity. The optimum threshold values for these 3 IFs are selected after performing a verification analysis. The idea is to estimate the presence

Fig. 3. The spatial distribution of the TB10.8 along with the actual lightning strokes of the next hour. The optimum threshold value isoline is also shown.

62

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

of lightning activity during the following hour using the IFs produced for a series of time instants, and then compare the estimations with the actual lightning activity as it was recorded by the ZEUS network. The Greek mainland is divided to seventeen continental areas according to geographical and climatological criteria. These areas are rectangular but not equal in size. As already mentioned, seven of them are selected to act as test areas in the verification procedure (Fig. 1). Each one is characterized to be expecting lightning activity when at least 10% of its pixels meet the threshold value for the examined IF. For example, since all IR images and subsequently all IF fields, have a 0.05 × 0.05° horizontal grid, a 1 × 1° area comprises 400 pixels, and therefore the area is expected to present lightning activity if at least 40 pixels meet the threshold value. Using the archived ZEUS data, the total number of lightning strokes of the following hour is calculated. When at least 2 strokes are found inside the verification area, the area is considered to present lightning activity within the examined hour. Following that rationale, there are: (i) cases that a test area is estimated to present lightning activity in the next hour and this estimation is proven correct (hereafter a “hit” case), (ii) cases that a test area is estimated to present lightning activity but this estimation is proven wrong (“false alarm” case), (iii) cases that a test area was not estimated to present lightning activity but according to the ZEUS data it does (“miss” case) and finally (iv) cases that a test area was not estimated to present lightning activity and this estimation is proven correct (“correct negative” case). A contingency table was then created for every area for the 20 analyzed days. Based on the contingency table, the following statistical scores were computed for each area: Probability of Detection; POD ¼ False Alarm Rate; FAR ¼

false alarms false alarms þ hits

Critical Success Index; CSI ¼ Bias; BIAS ¼

hits hits þ misses

hits hits þ misses þ false alarms

hits þ false alarms : hits þ misses

upcoming lightning activity. When 1, 2 or all 3 IFs estimate upcoming lightning activity in the examined area during the next hour then the CL issued has the value 1, 2 or 3 respectively, indicating an increasing certainty for lightning. The MSG combined estimation efficiency is also assessed through a verification methodology identical to that applied for each separate IF. 2.2.3. Stage 3 The final step is the utilization of the ZEUS archive lightning data to improve the estimation ability. The absence of lightning inside a cloud during the preceding 15 minute period can be considered as an indication of absence of significant electrical phenomena. Therefore it may be used as a negative indicator of upcoming activity. To represent that, the CL of the combined estimation (hereafter “MSG-ZEUS combined estimation”) is reduced by a level when no lightning activity is detected inside the examined area during the last 15 min preceding the estimation. The verification procedure is once again performed to assess the efficiency of the nowcasting tool. 3. Results Following the rationale that was illustrated in the previous section the results will be presented for each stage separately. Taking into account the general characteristics of the estimation efficiency statistics and the special features of the undergoing study, a general rule of thumb was set in the selection of the optimum set of statistics. This

ð1Þ ð2Þ ð3Þ ð4Þ

The perfect POD score is 1 when all the positive estimations are proven correct, while a score of 0 means that all the positive estimations are proven wrong. A FAR score of 1 means that all the positive estimations are wrong while a score of 0 means that no positive estimations of lightning activity is proved wrong. CSI represents the fraction of the successfully estimated cases over the total number of cases, excluding the correct negatives. A score of 1 indicates a perfect estimation, while a score of 0 indicates complete failure. Finally, the BIAS score ranges from 0 to infinite. When the score is over 1 then the lightning activity is overestimated and when the score is under 1 the lightning activity is underestimated. After the computation of the statistics for every test area over all the available 20 days, an average value among all test areas is computed for every statistical score. This procedure is repeated for different threshold values for every IF. After the examination of the averaged estimation efficiency statistics the optimum threshold value for each IF is selected. 2.2.2. Stage 2 The lightning activity estimation of each separate IF can be used as stand-alone tool. However, aiming at achieving better results, it was decided to combine the three IFs and the cloud features they represent into a unified estimation. This estimation (hereafter “MSG combined estimation”) takes into consideration the three separate nowcasts for every area and produces a CL as an algebraic summation of the three separate IFs. The CL ranges from 0 to 3. The CL0 is issued when none of the 3 IFs suggests the manifestation of lightning activity for the next hour. By definition CL0 is equivalent of a negative estimation of

Fig. 4. (a) POD and FAR, (b) CSI and (c) BIAS for 6 TB6.2–TB10.8 threshold values.

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

63

Fig. 5. The spatial distribution of the TB6.2–TB10.8 along with the actual lightning strokes of the next hour. The optimum threshold value isoline is also shown.

rule aims at selecting a group of statistics that provides increased rate of detection of the upcoming activity, despite the fact that in some cases the frequency of false alarms might be increased. To quantify that, POD is expected to be over 0.7 and FAR lower than 0.5. The CSI statistic must have its higher value while BIAS should indicate small over- or under-estimation, with values as close to 1 as possible.

3.1. Stage 1 3.1.1. The cloud top glaciation IF, TB10.8 As stated earlier, areas considered positive for upcoming lightning activity should have TB10.8 lower than a certain threshold value. Various threshold values that have already been proposed in the referenced literature were presented in the Introduction section. These values extend widely below 0 °C because glaciation requires temperatures below freezing level. In the present work temperatures ranging from 0 to −30 °C were examined. Table 3 presents the average estimation statistics for the threshold values of − 8, − 12, − 16, − 18, − 20, − 24 and − 28 °C, because the best estimations were produced within this range. Fig. 2 illustrates the statistical results. POD and FAR are continuously increasing with the increase of the threshold value, reaching 0.71 and 0.46 at − 16 °C. CSI, although it does not fluctuate significantly, reaches its maximum value (0.44) in the range from − 16 to − 24°. The overestimation that is evident for

the higher threshold values is reduced to 1.34 for −16 °C, a BIAS score that is quite acceptable. Summarizing, the threshold temperature of −16 °C can be considered as the optimum choice because CSI presents its maximum value, BIAS is close to 1, POD is high enough (0.71) and FAR is acceptable (0.49), all in alignment to the general rule of thumb set in the beginning of the section. Fig. 3 illustrates the TB10.8 field at 13:00UTC, 17 June 2011, along with the −16 °C isotherm and the observed lightning of the following hour. There are clear indications of expected lightning activity in the next 60 min for large parts of continental Greece. These estimations are proven to be correct in most areas, since numerous lightning strokes were recorded in the immediate vicinity of these areas. In some cases however, strokes that were not nowcasted were recorded (Case 1 in the figure) while positive estimations were proven wrong (Case 2 in the figure). Such weaknesses are to be expected for three main reasons.

Table 5 POD, FAR, CSI and BIAS for 6 TB10.8trend threshold values. Threshold (°C)

−12

−10

−8

−6

−4

−2

POD FAR CSI BIAS

0.27 0.30 0.25 0.39

0.39 0.33 0.32 0.58

0.52 0.37 0.40 0.84

0.67 0.43 0.44 1.20

0.81 0.51 0.44 1.71

0.93 0.61 0.38 2.46

64

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

Firstly, the ZEUS network tracks cloud-to-ground strokes which are only a part of the total lightning activity. It is possible that lightning activity within a convective cell may temporarily be manifested only as cloud-to-cloud strokes that are not recorded. If that is the case, although the upcoming lightning activity is correctly estimated, it is never recorded by the ZEUS network, and the estimation is perceived as a false alarm. Secondly, when trying to nowcast lightning activity 1 h ahead, there is always a possibility that cloud top glaciation is achieved 15, 30 or even 45 min after the estimation time. In this case, lightning activity that is manifested later in the hour may be estimated based on any of the following IR images but not on the present one. Finally, the brightness temperature of the 10.8 μm channel is indicative of the water phase of the cloud top. Negative values are associated with glaciation, but glaciation of the cloud top can be also found for example in thin cirrus clouds or towering cumulus or cumulonimbus cloud anvils. Cirrus clouds are not associated with convective activity and therefore they are not associated with lightning activity. Towering cumulus clouds in the development stage, although sometimes glaciated at the top, are not certain to manifest lightning activity. Regarding cumulonimbus anvils, they are expected to give false estimations of expected lightning activity because they are usually forming in the dissipating stage of a thunderstorm, when lightning activity in the cloud is limited or even absent. 3.1.2. The cloud depth IF, TB6.2–TB10.8 After the preliminary examination of a wide range of threshold values the best ones were identified within the range between − 10 and − 35 °C. Table 4 and Fig. 4 present the results of the analysis for this range of thresholds. POD and FAR are increasing significantly with the decrease of the threshold value and reach 0.75 and 0.46 respectively at − 25 °C. The CSI does not fluctuate significantly, however the higher values (0.46) are achieved when the −20 and −25 °C threshold values are used. Finally the BIAS, which also increases with the decrease of the threshold value, indicates large overestimation below −30 °C. Overall, the threshold value of −25 °C is considered as the optimum selection because CSI takes its highest values, POD, FAR have acceptable values and BIAS is relatively close to 1. Fig. 5 presents the TB6.2–TB10.8 difference at 13:00 UTC, 17 June 2011 with the lightning observed during the following hour. The selected threshold value is used to delineate the areas that are expected to present lightning activity during the following hour. The image is relatively similar to that of the TB10.8 channel with sufficient correct estimations and a small fraction of wrong positive estimations or non-expected lightning activity. The false alarm cases (Case 2 in the figure) are primarily the result of deep, opaque clouds such as towering cumulus or cumulonimbus at the dissipating stage that do not present significant electrical phenomena or to the existence of areas and periods of just cloud-to-cloud activity. The occasional misses (Case 1 in the figure) may be attributed to the fact that growing cloud masses may reach the required depth later within the hour of the nowcasting estimation range. 3.1.3. The cloud growth rate IF, TB10.8trend The cloud growth rate is an important feature of a developing cumulus cloud. Every cumulus cloud in the stage of development exhibits a gradually lower top temperature due to continuous vertical expansion. For example a TB10.8 value of −4 °C means that the cloud top temperature is 4° lower than it was 15 min ago, which indicates that the cloud top is located several hundred meters higher than it was 15 min ago. This significant growth can be associated to high possibility for electrification and lightning activity. Table 5 and Fig. 6 summarize the estimation statistics for a series of different threshold values. As can be seen, POD, FAR and BIAS are increasing with the increase of the threshold. The CSI presents its peak value (0.44) at −4 and −6 °C. POD at − 4 °C is 0.81 but it reduces significantly at − 6 °C, when is it

only 0.67. FAR and BIAS are better for − 6 °C (0.43 and 1.2) than for − 4 °C (0.51 and 1.71). It is obvious that − 4 and − 6 °C are the best threshold values, but they present different characteristics. The first one tends to estimate correctly most of the upcoming activity but with slightly increased overestimation which leads to false alarms. The second presents lower false alarm rate and overestimation but also misses a lot of cases of upcoming activity. Since we believe that it is very important to be prepared for the upcoming activity despite the occasional false alarms, the −4 °C threshold was selected as the optimum one. The spatial distribution of the 15 minute trend of TB10.8 at 13:00UTC, 17 June 2011 and the recorded lightning of the following hour are illustrated in Fig. 7. The −4 °C threshold value is used. As expected from the preceding analysis, the activity in the following hour is estimated with increased efficiency despite the slightly high rate of false alarms (Case 2 in Fig. 7). The frequent false alarms are related primarily to the fact that not all the growing cumuli will reach the cumulonimbus state in the following hour. It should be mentioned that the misses are almost absent (e.g. the Case 1 strokes cluster) indicating that usually at least one IF succeeds in estimating the upcoming activity. Based on the analysis presented in this section, Table 6 summarizes the IFs' characteristics and thresholds that are used by the nowcasting tool. It is clear that these thresholds are different by those proposed by Mecikalski and Bedka (2006), Siewert et al. (2010) and Matthee and Mecikalski (2013), mainly due to the different geographical regions and conditions in which the thresholds were applied.

Fig. 6. (a) POD and FAR, (b) CSI and (c) BIAS for 6 TB10.8trend threshold values.

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

65

Fig. 7. The spatial distribution of the TB10.8trend along with the actual lightning strokes of the next hour. The optimum threshold value isoline is also shown.

3.2. Stage 2 — the MSG combined estimation As pointed out in the previous section, any of the 3 IFs can be used as standalone estimators of upcoming lightning activity. However, every single IF is indicative of a different, equally important cloud feature. In an effort to improve the performance of the developed tool, the possibility of creating a combined estimation which includes all three individual IFs is examined (MSG combined estimation). For each of the 7 areas, a separate estimation of CL is produced by the combination of the 3 IFs. The set of threshold values that was used was: (i) TB10.8 b − 16 °C, (ii) TB10.8trend b −4 °C and (iii) TB6.2–TB10.8 N −25 °C. Table 7 summarizes the averaged statistics for CL1, CL2 and CL3, while Fig. 8 illustrates its contents. The higher POD value (0.88) is found when just one of the three IFs is producing a positive estimation (CL1). However, in this case FAR (0.55) and BIAS (1.97) are relatively high, indicating significant overestimation

Table 6 The utilized IFs along with their description and the warm period threshold values. Interest Field

Description

Threshold value

10.8 μm TB 10.8 μm TB 15 min trend 6.2–10.8-μm TB difference

Cloud-top degree of glaciation Cloud growth rate Cloud depth

b−18 °C b−4 °C N−25 °C

and frequent false alarms. When 2 IFs are required to give a positive estimation (CL2) POD is still in satisfactory levels (0.72) while FAR is reduced below 0.5 (0.46) and BIAS acquires a value close to 1 (1.35). In the case that all 3 IFs agree to issue a warning (CL3), the POD is relatively small (0.58), while the FAR is not significantly reduced compared to CL2. The CSI is just over 0.4 for all CL and reaches its higher value (0.45) for CL2. Overall, the MSG combined estimation, although satisfactory, cannot be considered a significant improvement compared to the single IF estimations. It should be noted here that none of the three possible permutations of CL2 estimations (TB10.8 plus TB6.2–TB10.8, TB10.8 plus TB10.8trend and finally TB6.2–TB10.8 plus TB10.8trend) seems to present substantially better performance than the others. Utilizing the pre-described verification procedure, it was found the PODs range from 0.59 to 0.67, the FARs range from 0.40 to 0.46, the CSIs range from 0.42 to 0.45 and the BIAS range from 1.01 to 1.22.

Table 7 POD, FAR, CSI and BIAS for Certainty Levels 1, 2 and 3 of the MSG combined estimation.

POD FAR CSI BIAS

CL1

CL2

CL3

0.88 0.55 0.43 1.97

0.72 0.46 0.45 1.35

0.58 0.40 0.42 0.96

66

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

Fig. 8. (a) POD and FAR, (b) CSI and (c) BIAS for certainty levels 1, 2 and 3 of the MSG combined estimation.

Fig. 9. (a) POD and FAR, (b) CSI and (c) BIAS for certainty levels 1, 2 and 3 of the MSG-ZEUS combined estimation.

3.3. Stage 3 — the MSG-ZEUS combined estimation The existence of electrification in a cloud is a requirement for the manifestation of lightning activity. The absence of activity 15 min prior to the estimation can be regarded as a negative factor for the existence of electrification inside the cloud masses. To quantify that notion the Certainty Level (CL) in the MSG-ZEUS combined estimation tool is reduced by one level, when no lightning was observed in the previous 15 min over each area. The results of the verification of this approach are presented in Table 8 and Fig. 9. Compared to the MSG combined estimation, the POD statistic is slightly lower in every case but the FAR presents a higher reduction. BIAS is clearly reduced while CSI is increased compared to the MSG tool.

Table 8 POD, FAR, CSI and BIAS for certainty levels 1, 2 and 3 of the MSG-ZEUS combined estimation.

POD FAR CSI BIAS

CL1

CL2

CL3

0.81 0.44 0.50 1.44

0.70 0.37 0.50 1.11

0.50 0.08 0.47 0.54

For the CL1, the POD of the MSG-ZEUS combined estimation tool is quite high (0.81) and the FAR is below 0.5 (0.44). The CSI acquires its higher value (0.81) for CL1 while the BIAS is close to 1 (1.44). CL2 statistics are also within acceptable limits but the fact that the POD is reduced more than the FAR is a negative point. CL3 presents remarkably low FAR (0.08) but the POD is quite low (0.5) and below acceptable limits. Fig. 10a and b presents two examples of the final product of our nowcasting tool. These maps are referring to the 13:00 UTC, June 17th of 2011 and 11:45 UTC, July 2nd of 2010. Each of the 17 individual areas of the Greek mainland is highlighted with a different color whenever a warning is issued while they remain transparent when CL0 is valid. These maps and the associated warnings are renewed every 15 min and are considered valid for the following hour. The strokes that were actually recorded by the ZEUS system during the following hour are superimposed to offer a visual inspection of our tool's performance. The daily variation of the CL in comparison with the actual lightning activity for two different areas is illustrated in Fig. 11. The delimited regions represent the part of the day that lightning activity was recorded by ZEUS network over two areas during 17 June 2011. For the first area (Fig. 11a) the estimation is very efficient. The activity starts at 10:15 UTC and ends at 17:15 UTC. Most of this activity is correctly estimated with CL3 and in some cases with CL2 or CL1. Only a small portion of the activity was not nowcasted (10:15–11:00UTC) while the false alarms were

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

even fewer (17:15 UTC). For the second area (Fig. 11b) the estimation is relatively poorer. The first period of activity that starts on 10:15 and ends on 11:00 UTC is not nowcasted at all. The second period starts at

67

12:15 and ends at 15:00 UTC and is estimated with CL3 for most of the time. Three periods of false alarms are also evident (15:00, 15:15 and 17:00 UTC).

Fig. 10. Example of the final product of the MSG-ZEUS combined estimation tool for (a) 10:30 UTC of 17/6/2010 and (b) 11:45 UTC of 02/07/2010.

68

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

4. Conclusions and discussion The paper presents the development of a lightning activity nowcasting tool for the Greek mainland and the results of its verification procedure. The application of the tool is confined within the warm period of the year (May to September) because during this period the lightning activity is localized mostly over land where most of the population, the social and the economical activity are concentrated. The nowcasting tool uses MSG IR imagery and ZEUS lightning data as input. The brightness temperatures of two channels of the SEVIRI instrument are utilized to create 3 IFs: the brightness temperature of the channel 9 (TB10.8), the temperature difference between channels 5 and 9 (TB6.2–TB10.8) and the 15 minute time trend of channel 9 (TB10.8trend). The final product of the tool is a combined estimation which uses the 3 separate IFs and the observed lightning of the 15 min that precede the nowcasting period. The development of the tool was based on the idea that when the IF value meets a predefined threshold value, the pixel is considered to be expecting lightning activity within the next hour. A wide range of threshold values for every IF were proposed in the literature. However these values could not be used, because the implementation area is different and also the aim of the tool is to estimate the lightning activity regardless of the stage of the development of the thunderstorms or the existence of other cloud forms in their immediate vicinity. The estimation efficiency of different threshold values was assessed through a verification procedure. The continental Greek area was divided in 17 areas, according to their climatology (Galanaki et al., 2015). Seven areas that accumulate most of the lightning activity were selected as verification test areas. The procedure was based on the comparison of the estimations against the actual lightning activity of each area during the next hour, as it was detected by the ZEUS lightning detection network. Twenty days with lightning activity were used. A series of estimation efficiency statistics was computed for each of the 7 test areas and an average value for each statistical score was calculated. It was shown that the optimum threshold value for the brightness temperature of the 10.8 μm channel (TB10.8) was − 16 °C. Regarding the TB6.2–TB10.8 multispectral

Fig. 11. The daily variation of the certainty level (blue bar) in comparison to the actual lightning activity (delineated region) for two different areas. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

field, the most efficient threshold was −25 °C. Finally, when the threshold of −4 °C was used, the TB10.8trend showed the best performance. For all these fields, the POD value was over 0.7, the FAR was in the vicinity of 0.5 and the BIAS was close to 1. In an attempt to improve the estimation efficiency of the tool, the MSG combined estimation tool was developed. For each area an upcoming lightning activity Certainty Level (CL) was issued every 15 min. The CL took the value 0, 1, 2 or 3 when none, 1, 2 or all 3 individual IFs suggested upcoming lightning activity. A CL0 means that lightning activity is not expected in the area during the next hour, while CL1, CL2 and CL3 indicate increasing certainty of activity. The verification procedure that was used for the separate IFs was also used for the assessment of the efficiency of the MSG combined estimation tool. The statistics suggested that the tool is able to nowcast successfully the upcoming activity. A disadvantage of the MSG tool is the relatively high overestimation and the frequent false alarms. Finally a so-called MSG-ZEUS combined estimation tool was developed. The observed lightning strokes of the preceding 15 min were employed. In the case that the area lacked activity in the previous 15 min, electrification inside the cloud masses was considered possibly absent and the manifestation of lightning activity in the following hour was less likely. To represent that, the CL of the MSG-ZEUS tool was reduced by one level. The verification of the results showed that an overall improvement of the estimation was achieved in that manner, mainly regarding the reduction of false alarms and overestimation. A very satisfactory POD (0.81) combined with a relatively low FAR (0.44) and a week overestimation (BIAS equal to 1.44) is produced when only one of the three IFs is required to issue a warning (CL1). The final product of the described work is the MSG-ZEUS combined estimation tool. The tool will provide every 15 min a Certainty Level estimation (from 0 to 3) for possible upcoming lightning activity for the seventeen areas of the Greek mainland. This estimation, that will be valid for the following hour, ranges from 0 (lightning activity is not expected) to 3 (very high certainty of lightning occurrence). The nowcasts are available to the public in the form of a map of the Greek mainland showing the CL of each area through the webpage of the project TALOS (http://www.meteo.gr/talos/en). The end-user of the product can choose the Certainty Level of estimation based on his specific needs. When the need for preparation against the upcoming activity is prevailing over the discomfort caused by frequent false alarms, CL1 estimations should be used. On the contrary, when increased estimation certainty is required in order to avoid costly and disturbing false alarms, CL3 should be preferred. A series of problems that surfaced during the development of the tool and specific points of interest require a little more elaboration. An important issue of the developed nowcasting system is that it has to be able to estimate the lightning activity of the next hour regardless of the life cycle stage of the thundercloud or the preexisting convection or the presence of cirrus or other cloud forms in the immediate vicinity of the area of interest. All these features can definitely lead to increased false alarms, because it is very difficult to discriminate between those kinds of clouds and actual convective clouds exhibiting lightning activity. A way to circumvent the problem might be to select and examine cases where the convective clouds are in their development stage with no pre-existing convection in the area. However, since our aim is to develop a nowcasting tool that will be operational 24/7, we do not have the option to exclude any case from the analysis. The ZEUS lightning detection system is designed to detect mainly cloud-to-ground strokes. In cases of sole intra-cloud strokes, the activity can be completely missed by the system. Keeping that in mind, we strongly believe that some of the false alarms of the developed nowcasting system are not actually false alarms, because the manifested lightning activity is comprised by cloud-to-cloud strokes only. Up to now the tool has only been tested during the warm season. The implementation during the cold season will definitely require readjustment of the tool and maybe the use of additional sources of data.

A. Karagiannidis et al. / Atmospheric Research 168 (2016) 57–69

During the cold part of the year, the average tropospheric temperature is clearly lower and therefore lower threshold values than those used for the warm part, are expected to be the optimum ones. Moreover, the winter convective cells over Greece are usually embedded in the extended could formations of mid-latitude depressions, a fact that hinders our effort to isolate them. Weather radar scans or numerical model outputs could be used as auxiliary data to help in the identification of these cells. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.atmosres.2015.08.011. Acknowledgments The authors acknowledge funding by the European Union (European Social Fund) and National Resources under the “ARISTEIA II” action of the Operational Programme “Education and Lifelong Learning” in Greece (TALOS Project; TALOS-3449). References Aminou, D.M.A., Jacquet, B., Pasternak, F., 1997. Characteristics of the Meteosat Second Generation (MSG) radiometer/imager: SEVIRI. Aerospace Remote Sensing'97. International Society for Optics and Photonics. Betz, H.D., Schmidt, K., Laroche, P., Blanchet, P., Oettinger, W.P., Defer, E., Dziewit, Z., Konarski, j., 2009. LINET — an international lightning detection network in Europe. Atmos. Res. 91, 564–573. Chronis, T.G., Anagnostou, E.N., Dinku, T., 2004. High-frequency estimation of rainfall from thunderstorms via satellite infrared and a long-range lightning network in Europe. Q. J. R. Meteorol. Soc. 130, 1555–1574. Defer, E., Pinty, J.P., Coquillat, S., Martin, J.M., Prieur, S., Soula, S., Richard, E., Rison, W., Krehbiel, P., Thomas, R., Rodeheffer, D., Vergeiner, C., Malaterre, F., Pedeboy, S., Schulz, W., Farges, T., Gallin, L.J., Ortéga, P., Ribaud, J.f., Anderson, G., Betz, H.D., Meneux, B., Kotroni, V., Lagouvardos, K., Roos, S., Ducrocq, V., Roussot, O., Labatut, L., Molinié, G., 2015. An overview of the lightning and atmospheric electricity observations collected in Southern France during the HYdrological cycle in Mediterranean EXperiment (HyMeX), Special Observation Period 1. Atmos. Meas. Tech. 8, 649–669. Deierling, W., Petersen, W.A., Latham, J., Ellis, S., Christian, H.J., 2008. The relationship between lightning activity and ice fluxes in thunderstorms. J. Geophys. Res. 113, D15210. http://dx.doi.org/10.1029/2007JD009700. Drobinski, P., Ducrocq, V., Allen, J.T., Alpert, P., Anagnostou, E., Béranger, K., Borga, M., Braud, I., Chanzy, A., Davolio, S., Delrieu, G., Dörnbrack, A., Estournel, C., Filali Boubrahmi, N., Font, J., Gacic, M., Grubisic, V., Grunfest, E., Gualdi, S., Hoff, H., Ivancan-Picek, B., Kottmeier, C., Kotroni, V., Lagouvardos, K., Lionello, P., Llasat, M.C., Ludwig, W., Lutoff, C., Mariotti, A., Montanari, A., Ozoy, E., Prigent, C., Richard, E., Romero, R., Rotunno, R., Ruin, I., Santaner, V.H., Sauri, D., Somot, S., Taupier-Letage, I., Therrien, R., Tintore, J., Uijlenhoet, R., Wernli, H., 2014. HyMeX, a 10-year multidisciplinary program on the Mediterranean water cycle. Bull. Am. Meteorol. Soc. 95, 1063–1082. Feidas, H., Cartalis, C., 2001. Monitoring mesoscale convective cloud systems associated with heavy storms using meteosat imagery. J. Appl. Meteorol. 40, 491–512. Feidas, H., Giannakos, A., 2011. Identifying precipitating clouds in Greece using multispectral infrared Meteosat Second Generation satellite data. Theor. Appl. Climatol. 104, 25–42. Gaffard, C., Nash, J., Atkinson, N., Bennett, A., Callaghan, G., Hibbett, E., Taylor, P., Turp, M., Schulz, W., 2008. Observing lightning around the globe from the surface. 20th International Lightning Detection Conference, Tucson, Arizona, pp. 21–23. Galanaki, E., Kotroni, V., Lagouvardos, K., Argiriou, A., 2015. A ten-year analysis of cloudto-ground lightning activity over the Eastern Mediterranean region. Atmos. Res. 166, 213–222. Giannaros, Th., Kotroni, V., Lagouvardos, K., 2015. Predicting lightning activity in Greece with the Weather Research and Forecasting (WRF) Model. Atmos. Res. 156, 1–13.

69

Harris, R.J., Mecikalski, J.R., MacKenzie Jr., W.M., Durkee, P.A., Nielsen, K.E., 2010. The definition of GOES infrared lightning initiation interest fields. J. Appl. Meteorol. Climatol. 49, 2527–2543. Jirak, I.L., Cotton, W.R., McAnelly, R.L., 2003. Satellite and radar survey of mesoscale convective system development. Mon. Weather Rev. 131, 2428–2449. Kolios, S., Feidas, H., 2013. An automated nowcasting system of mesoscale convective systems for the Mediterranean basin using Meteosat imagery. Part I: system description. Meteorol. Appl. 20, 287–295. Kotroni, V., Lagouvardos, K., 2008. Lightning occurrence in relation with elevation, terrain slope and vegetation cover over the Mediterranean. J. Geophys. Res. 113, D21118. http://dx.doi.org/10.1029/2008JD010605. Kotroni, V., Lagouvardos, K., 2014. Climatology of lightning activity in the Mediterranean. Proceedings of the 12th International Conference on Meteorology, Climatology and Atmospheric Physics COMECAP 2014 2, pp. 28–32. Lagouvardos, K., Kotroni, V., Betz, H.D., Schmidt, K., 2009. A comparison of lightning data provided by ZEUS and LINET networks over Western Europe. Nat. Hazards Earth Syst. Sci. 9, 1713–1717. Latham, J., Petersen, W.A., Deierling, W., Christian, H.J., 2007. Field identification of a unique globally dominant mechanism of thunderstorm electrification. Q. J. R. Meteorol. Soc. 133, 1453–1457. Lensky, I.M., Rosenfeld, D., 2003. A night-rain delineation algorithm for infrared satellite data based on microphysical considerations. J. Appl. Meteorol. 42, 1218–1226. Levizzani, V., Schmetz, J., Lutz, H.J., Kerkmann, J., Alberoni, P.P., Cervino, M., 2001. Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation. Meteorol. Appl. 8, 23–41. Lynn, B.H., Yair, Y., Price, C., Kelman, G., Clark, A.J., 2012. Predicting cloud-to-ground and intracloud lightning in weather forecast models. Weather Forecast. 27, 1470–1488. Matthee, R., Mecikalski, J.R., 2013. Geostationary infrared methods for detecting lightning-producing cumulonimbus clouds. J. Geophys. Res. 118, 6580–6592. Mecikalski, J.R., Bedka, K.M., 2006. Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Mon. Weather Rev. 134, 49–78. Mecikalski, J.R., MacKenzie Jr., W.M., Koenig, M., Muller, S., 2010. Cloud-top properties of growing cumulus prior to convective initiation as measured by Meteosat Second Generation. Part I: infrared fields. Appl. Meteorol. Climatol. 49, 521–534. Mecikalski, J.R., Li, X., Carey, L.D., McCaul Jr., E.W., Coleman, T.A., 2013. Regional comparison of GOES cloud-top properties and radar characteristics in advance of first-flash lightning initiation. Mon. Weather Rev. 141, 55–74. Meyer, V.K., Höller, H., Betz, H.D., 2013. Automated thunderstorm tracking: utilization of three-dimensional and radar data. Atmos. Chem. Phys. 13, 5137–5150. Morales, C.A., Anagnostou, E.N., 2003. Extending the capabilities of high-frequency rainfall estimation from geostationary-based satellite infrared via a network of long-range lightning observations. J. Hydrometeorol. 4, 141–159. Papagiannaki, K., Lagouvardos, K., Kotroni, V., 2013. A database of high-impact weather events in Greece: a descriptive impact analysis for the period 2001–2011. Nat. Hazards Earth Syst. Sci. 13, 727–736. Ricciardelli, E., Romano, F., Cuomo, V., 2008. Physical and statistical approaches for cloud identification using Meteosat Second Generation-spinning enhanced visible and infrared imager data. Remote Sens. Environ. 112, 2741–2760. Roberts, R.D., Rutledge, S., 2003. Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Weather Forecast. 18, 562–584. Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., Ratier, A., 2002. An introduction to Meteosat Second Generation (MSG). Bull. Am. Meteorol. Soc. 83 (7), 977–992. Siewert, C.W., Koening, M., Mecikalski, J.R., 2010. Application of Meteosat second generation data towards improving the nowcasting of convective initiation. Meteorol. Appl. 17, 442–451. Wong, J., Barth, M.C., Noone, D., 2013. Evaluating a lighting parameterization based on cloud-top height for mesoscale numerical model simulations. Geosci. Model Dev. 6, 429–443. Yair, Y., Lynn, B., Price, C., Kotroni, V., Lagouvardos, K., Morin, E., Mugnai, A., Llasat, M.C., 2010. Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) model dynamic and microphysical fields. J. Geophys. Res. 115, D04205. http://dx.doi.org/10.1029/2008JD010868. Zinner, T., Mannstein, H., Tafferner, A., 2008. Cb-TRAM: Tracking and monitoring sever convection from onset over rapid development to mature phase using multichannel Meteosat-8 SEVIRI data. Meteorog. Atmos. Phys. 101 (3-4), 191–200.