ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

Lightning detection and prediction evaluation by microwave ground based radar and infrared

space-born integrated approach

Mario Montopoli1,2, Domenico Cimini3,2, Errico Picciotti4,2, Saverio Di Fabio5,2 and Frank S. Marzano5,2

1Dept. of Geography, Univ. of Cambridge, Downing Place, CB2 3EN Cambridge, UK, mm911@cam.ac.uk

2 Center of Excellence CETEMPS, University of L’Aquila, via vetoio 67040 L’Aquila Italy 3 Istituto di Metodologie per l’analisi Ambientale, CNR, Tito scalo, Potenza, Italy

domenico.cimini@imaa.cnr.it 4 HIMET, Strada Statale 17 Ovest, 36 , 67100, L'Aquila, Italy

errico.picciotti@himet.it 5 Department of Information Engineering, Electronics and Telecommunications, Sapienza

University of Rome, via Eudossiana 18, 00184 Rome, Italy saverio.difabio@alice.it, marzano@die.uniroma1.it

(Dated: 30 May 2012)

Mario Montopoli

1. Introduction As is commonly experienced in everyday life, lightings represent a feared threat for a plenty of human activities (e.g.

power interruptions for industrial purpose, flight safety etc.). Thus, the detection and the temporal prediction of lightings is of fundamental importance for the citizens safety and for preventing economic losses. In this light, this work analyses the potential use of a synergistic multi-sensor approach to detect and predict the lighting occurrence. This is done considering as input information: the ground weather radar reflectivity and the regional instability indexes retrieved from the brightness temperatures given by the SEVIRI radiometer on the Meteosat Second Generation (MSG) satellite. Data from ground based lighting sensors are also used as validation tool. They are part of the Blitzortung [3] and SIRF network [2] which includes VLF sensors able to detect the cloud-to-ground flashes [4]-[11]. Both satellite and radar data are pre-processed to track the temporal evolution of atmospheric instability and convective precipitation, respectively. The combination of this two information is accomplished through a basic three schemes which output is given in terms Probability of Lightning (PoL). A regression model to convert the radar and SEVIRI radiometer information into PoL is previously set up using the lighting sensors data as “ground” truth.

The correlations between the lightning activity and the estimated PoL are shown for nine selected case studies occurred in central Italy during 2010-2011. They are chosen to test the prediction capability of the proposed PoL-based approach on heterogeneous conditions, which include both intense, low and none lightning activity. The evaluation of the lightning predictability is dealt with in terms of probability of detection, false alarm rate, critical success index and the early warning time. The score index analysis reveal that in some cases the retrieved PoL can reach 100%, even though this result is obtained at the expenses of high false alarms that vary from 3% to 85% across the considered heterogeneous case studies. With respect to other papers on the same topic, this work put in evidence the role that different source of information (i.e. microwave radars and infrared radiometers) can play in for a synergistic prediction of lightnings.

2. Available measurements The source of available data for this study includes single polarization radar data, satellite infrared observations and lightning information (numbers, time and position of the flashes) retrieval from network sensor.

The radar data comes from the C-band Doppler system which operates at 5.6 GHz with a half-power beamwidth of 1.5° and a magnetron transmitter 250-kW power peak. It is located at 1710 m above-the-sea-level (ASL) on the top of Monte Midia (MID) in the Abruzzo region in Italy. It acquires 4 elevations angles (namely, at 0°, 1° 2°, 3°) and 360 azimuth angles with a 1° angular resolution, each scan covering an area of 120 km in range with 250-m range resolution every 15 min. Only the co-polar reflectivity is used in this study.

The geostationary satellite observations are provided by the SEVIRI (Spin enhanced visible infrared imager) aboard the geo-synchronous MSG satellite. SEVIRI is a 12-channel radiometers with tree detectors in the visible and near-infrared spectrum at 0.5, 0.8 and 1.6 µm, one in the mid-infrared at 3.9 µm, two in the water-vapor band at 6.2 and 7.3 µm, five detectors in the infrared at 8.7, 9.7, 10.8, 12.0 and 13.4 µm and one in the high-resolution visible window. SEVIRI has a nadir footprint of about 1 km at visible and 3 km at infrared channels. These lead to a decreasing spatial resolution going from the equator to high latitudes, resulting in roughly 3 and 5 km at mid-latitudes, respectively for visible and infrared channels. SEVIRI provide images every 15 minutes (every 5 minutes in rapid scan mode). In this study we only consider the

ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

estimate of the potential temperature and the related instability indexes [1], as discussed later. The number of lightnings and their position is recorded by two ground-based networks of lightning sensors, which are the

SIRF network or the Blitzortung network depending on their availability. The SIRF [2] private network exploits combined VLF/EHF sensors with Time Of Arrival (TOA) and Direction Finder (DF) techniques, whereas Blitzortung (BLITZ) [3] is a voluntary network employing VLF sensors with TOA central processing. Data of both SIRF and Blitzortung lightning networks are released every minute and the expected accuracy of lightning localization is about few kilometers in central Italy depending on the network density and sensor capability. The received information of lightnings from ground networks is used here for validation purpose only.

The analyzed domain of central Italy, where the above-mentioned sources of information were available, is shown in figure 1. In this figure the radar coverage is color coded whereas the target area is indicated by a white circle. Colors give information of the complex orography of the area of interest.

Fig. 1 Left table: analyzed case studies together with the source of information used. Right panel: the radar coverage and the digital elevation model are shown by means of the color bar while the target area is indicated by white the circle. The

asterisks in the table refer to case studies that span between two days.

3. Integration methodology for the probability of lightning estimation.

3.1 Input data and processing

The input data considered for the computation of the PoL are the maximum of the radar reflectivity (Zmax), within the target area (D) and the combined instability indexes from MSG in the same area. The choice of consider Zmax instead some information derived from the vertical profile of reflectivity is due to the lack of vertical information which can be extracted from the MID radar. Zmax is defined as follows:

     𝑍!"#  (𝑡) = max(!,!)∈!

𝑍(𝑥, 𝑦, ℎ, 𝑡) (1)

In (1), Z is the volumetric instantaneous radar reflectivity, the coordinates (x,y,h) identify the radar resolution volume in Cartesian coordinates and t represents the time at which a radar volume is acquired.

On the other hand, the instability indexes describe the atmosphere instability, which is considered as a proxy for thunderstorm and rainfall potential [1]. They are estimated from the vertical profiles of temperature and humidity. The geostationary satellite observations, such as those from SEVIRI aboard the MSG satellite, offer the opportunity to estimate these indexes on wide areas every 15 min. We considered the K-Index (KI), the Maximum Buoyancy index (MB) and the Lifted Index (LI). To make their use uniform, the instability indexes Ij(x,y,t) have been normalized with respect to their own warning thresholds as follows, obtaining a Combined Instability Index CII(t):

𝐶𝐼𝐼(𝑥, 𝑦, 𝑡) =

!!!

!!!!

!! !,!,! !!!,!!!,!!!!,!

(2)

where wj are weights such that W= w1+ w2+w3, I1=KI, I2=MB and I3=LI (expressed in K), whereas Ij,m and Ij,M are, respectively, the moderate and high potential thresholds for Ij. These thresholds delimit, for the respective index, the range of low, moderate and high probability of thunderstorm occurrence. Ij,m , for j=1,2,3, has been set up to 25 K, 20 K and -3 K whereas Ij,M is 35 K, 25 K and 5 K, respectively. Eventually the maximum and the average of CII , over the target area, D, is considered as input for the PoL estimation:

       𝐶!"# = max(!,!)∈!

𝐶𝐼𝐼(𝑥, 𝑦, 𝑡)   , 𝐶!"# = mean(!,!)∈!

𝐶𝐼𝐼(𝑥, 𝑦, 𝑡) (3)

ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

Note that after instability index normalization, the moderate and high potential critical thresholds are 0 and 1, respectively, for both Cmax and Cavg.

3.2 Probability of lightnings

After some experimental tests we found that the following expression well suite for describing the PoL, [12]:

𝑃𝑜𝐿!(𝑡) =!

!!!"# !! ! ! !! (4)

eq. (4) describes a sigmoidal function that increases from zero to k as a function of positive increments of the variable X. X can be equals to Cavg or Cmax or Zmax as defined in (1) and (3). As shown in figure 2, where temporal series of radar maps are shown together with collocated lightings, there exists a good correlation between the radar reflectivity (X=Zmax) and the lighting activity (see black crosses) especially in the areas where the reflectivity is particularly high. In general, the parameters l and m vary following a three scheme depending on the availability of the input information. The values of k, l and m in (4), are listed in table 1 for the various combinations of the radar and satellite availability. In this table note as, when the Zmax and Cavg are both available and then usable, the parameters l and m of PoLZmax depend from the values of Cavg through the s-shaped (f1) and z-shaped (f2) sigmoidal functions not made explicit here for brevity. Figure 3 shows the behavior of PoLX when X=Zmax (left panel) and X =Cavg or Cmax (right panel). The dependence of PoLZmax from CII values is also shown. Note as the maximum PoL derived from Cavg or Cmax has been limited to 55%. This is done because CIIs provide an indication of the potential convective triggering and development in clear air well in advance with respect to the time when the thunderstorm will occur. Thus even thought CIIs at instant t are high, the PoL, at the same instant, can be conservatively maintained low. This choice tends to avoid false alarms. The overall PoL on the domain D is then calculated weighting the various PoLX:

𝑃𝑜𝐿! = 𝛾!𝑃𝑜𝐿!"#$ + 𝛾!𝑃𝑜𝐿!"#$+𝛾!𝑃𝑜𝐿!"#$ (5)

The values adopted for the weights γi are listed in table 1.

Sources PoLZmax parameters PoLCavg or PoLCmax parameters PoLD weights RAD SAT k l m k l m γ1 γ2 γ3 Yes Yes 100 f1(Cavg) f2(Cavg) 55 10 0.5 0.5 0.3 0.2 Yes No 100 0.2 48 0 - - 1 0 0 No Yes 0 - - 55 10 0.5 0 0.2 0.8 No No 0 - - 0 - - 0 0 0

Tab. 1: Parameters used in eq. (4) for the PoLX. f1 and f2 are the s-shaped and z-shaped sigmoidal functions, respectively.

Fig. 2 An example from August 21 st, 2010 of the temporal evolution of C-band Monte Midia Vertical Maximum Intensity of

reflectivity. Registered lightnings are marked by black crosses.

ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

Fig. 3: Probability of lightnings of the radar reflectivity (left) and that of the combined instability index from MSG satellite

(right).

4. Results The results are discussed in terms of time series of source information (Zmax ,Cavg and Cmax), validation information (number of lightnings: Nlig) and the derived product (areal PoLD) for the case studies, listed in figure 1 (left panel). The spatial domain D used in eq.s (1) and (3) and here adopted to explain results, coincide with the target area in figure 1. Figure 4 shows the time series of Zmax, Cavg, Cmax and Nlig as indicated in the upper left legend. The considered case studies are reported in the title of each panel. At the glance, it is quite evident that Zmax(t) follows quite well the lightning activity. A deep inspection of the figure 4 shows as in panel a), Zmax(t) and Nlig(t) start to assume significant values at the same instant. Differently, in panel g) Zmax(t) is quite large (>30dBZ) but Nlig(t)=0. Additionally, in panel c), Zmax(t) fits well with Nlig(t) and it starts increasing before the lightning activity begins. For what concerns satellite instability indexes, there are cases where a hefty increase of Cmax and Cavg over the unity well matches with the start of the lightning activity (panels a, b, c, e, f) as opposed to the case where there is not a clear trend of Cmax and Cavg (panel d). Additionally, cases where no lightning occurred seem to be well caught by the combined instability index, at least in terms of its average (panels from g to i). As discussed in previous sections, the multi-sensor approach tends to merge available data, i.e. Zmax(t), Cavg(t) and Cmax(t) shown in 4, into a PoLD(t), defined in (5) and shown in figure 5 by dashed green lines. In most of cases we have a sort of “Mexican hat” shape for PoLD(t) and its left tail gives an indication of the increasing threat from the incoming lightning activity. To test the lightning prediction capabilities, we have considered the skill scores as the Probability of Detection (POD), False Alarm Rate (FAR) and Critical success Index (CSI) and the Early Warning Time (EWT). The EWT is the difference between the instants when the first lightning of the active period occurs and the instant when PoLD>Pth where Pth is a prescribed threshold. The optimal values of the contingency statistical scores POD, FAR and CSI are, respectively, 100%, 0% and 100%, whereas EWT should be adequately long, in hours since it represents the temporal warning capabilities. Results are listed in table 2. In this table, PoLD and PoLZmax are used as proxy of the lighting activity. The probability threshold, Pth has been fixed to 50%. When PoLZmax is considered alone, PoLD and Pth, are substituted by Zmax and Zth, respectively. In this case the threshold Zth has been fixed to 30 dBZ. In table 2, the cases 8)-10), which did not show any lightning activity, are well predicted by both PoLD or PoLZmax based approaches. On the contrary, case 2) still shows absence of lightnings, but the use of PoLD allows to perform a correct prediction (POD=0% and FAR=0%) as opposed to the use of Zmax only which shows a FAR=100%. The remaining case studies from 1), 3) to 6) show lighting activity and the performances of PoLD shows always a POD=100% and a FAR quite similar to that obtained using PoLZmax. For these events, it should be noted that the average EWT is quite similar when PoLD and PoLZmax are used (average EWT respectively of 2.25 and 2.30 hours). However, when PoLD is used its temporal history is easier to be tracked due to its nature of long-term precursor with respect to PoLZmax, as clearly evident from 0 in terms of Cavg and Cmax.

Case Studies Combined (PoLD>50%) Only Radar (Z>30dBZ)

POD [%]

FAR [%]

CSI [%]

EWT [h]

POD [%]

FAR [%]

CSI [%]

EWT [h]

1) 28 Apr. 2011 100 0 100 1.00 100 28.57 71.42 1.50

2) 11 May 2010 0 0 - - 0 100 0 - 3) 18 Jul. 2010 100 69.23 30.76 3.25 100 60.00 40.00 2.50 4) 19 Jul. 2010 100 85.71 14.28 2.50 100 88.88 11.11 3.25

5) 21 Aug. 2010 100 6.89 93.10 1.50 88.89 0 88.88 0.25 6) 13 Sep. 2010 100 33.33 66.66 1.75 100 33.33 66.66 1.75

7) 05 Oct. 2010 100 3.12 96.87 1.25 100 13.88 86.11 2.25 8) 10 May 2010 0 0 - - 0 0 0 - 9) 26 Aug. 2010 0 0 - - 0 0 0 -

10) 08 Jul. 2010 0 0 - - 0 0 0 -

Tab. 2 Temporal evolution of the Probability of Lightning (PoL) as green curvs from the combined approach (radar+MSG) and number of lightning (in red).

ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

4. Conclusions An algorithm has been implemented to predict the lightning activity in the center Italy in terms of areal Probability of Lightning (PoL). The combination of the radar reflectifity and the MSG satellite instability indexes is accomplished through a basic three scheme where multi-sensor data merging is accomplished to obtain the PoL which is the output of the proposed scheme. Results have been analyzed using the available case studies, using radar-only PoLZmax and combined PoLD data to predict PoL. Even thought both radar data and MSG instability indexes are not directly associated to the physical mechanisms of the lightning formation, a good correlation between PoLD and the lightning activity has been noted on this basis of the analyzed case studies. In particular, when lightning activity is not present, the scenario is usually better predicted by PoLD than by PoLZmax based approaches. For moderate to intense areal lighting activity, the performances of PoLD show always a high probability of detection and a low false alarm. Worse results are obtained using PoLZmax approach. The major limitation of the available dataset is that radar data scans were not sufficiently detailed to extract a vertical profile of the reflectivity; a quantity that is more directly related to the lightning formation since it is useful to detect the glacial level with respect to freezing level. This information may easily ingested into the PolCast as a further information to be weighted into the areal probability of lightning. When integrating over a large area, such as the one analyzed here, the impact of more detailed radar information can be of minor impact. But we expect a more relevant influence of the parameters extracted from the whole radar volume in cases where the target area becomes smaller in size. A further improvement might come from the inclusion of ground-based lightning sensor data in to the PoLCast even though in this case we would loose the independent source for testing and validation.

Fig. 4 Temporal evolution of the radar reflectivity, average and maximun combined instability index from the MSG satellite

and number of lightning within the target area, respectively indicated in blue, bold and dotted green and red colors.Each panel has two scale axes: the left axes for the reflectivity and the number of lightnins; the right axes for the combined

instability index.

ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

Fig. 5 Temporal evolution of the Probability of Lightning (PoL) as green curvs from the combined approach (radar+MSG)

and number of lightning (in red)

Acknowledgment The Authors want to thanks the EU FP7 program since this work has been partially funded by the Marie Curie Fellowship

within the call FP7-POPLE-2010-IEF, Grant number: 273666 and also the Centro Funzionale di Protezione Civile of the Abruzzo Region (CFA), Italy for its support.

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