Investigation of the Weather Conditions During the Collapse of the Morandi Bridge in Genoa on 14 August 2018 Using Field Observations and WRF Model

atmosphere

Article Investigation of the Weather Conditions During the Collapse of the Morandi Bridge in Genoa on 14 August 2018 Using Field Observations and WRF Model

Massimiliano Burlando 1,* , Djordje Romanic 1,2,3 , Giorgio Boni 1 , Martina Lagasio 4 and Antonio Parodi 4 1 Department of Civil, Chemical and Environmental Engineering, University of Genoa, 16100 Genoa, Italy; [email protected] (D.R.); [email protected] (G.B.) 2 Wind Engineering, Energy and Environment (WindEEE) Research Institute, Western University, London, ON N6M 0E2, Canada 3 Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, QC H3A 0G4, Canada 4 CIMA Research Foundation, 17100 Savona, Italy; [email protected] (M.L.); [email protected] (A.P.) * Correspondence: [email protected]

Received: 1 June 2020; Accepted: 3 July 2020; Published: 7 July 2020

Abstract: On 14 August 2018, Morandi Bridge in Genoa, Italy, collapsed to the ground that was 40 m below. This tragedy killed 43 people. Preliminary investigations indicated poor design, questionable building practices, and insuﬃcient maintenance—or a combination of these factors—as a possible cause of the collapse. However, around the collapse time, a thunderstorm associated with strong winds, lightning, and rain also developed over the city. While it is unclear if this thunderstorm played a role in the collapse, the present study examines the weather conditions before and during the bridge collapse. The study particularly focuses on the analysis of a downburst that was observed around the collapse time and a few kilometers away from the bridge. Direct and remote sensing measurements are used to describe the evolution of the thunderstorm during its approached from the sea to the city. The Doppler lidar measurements allowed the reconstruction of the gust front 1 shape and the evaluation of its displacement velocity of 6.6 m s− towards the lidar. The Weather Research and Forecasting simulations highlighted that it is still challenging to forecast localized thunderstorms with operational setups. The study has shown that assimilation of radar reﬂectivity improves the timing and reconstruction of the gust front observed by local measurements.

Keywords: Morandi bridge; weather conditions; ﬁeld measurements; thunderstorm; downburst; Gust front; lidar; WRF model; bridge collapse; severe weather

1. Introduction Morandi Bridge in Genoa, Italy—named after its designer Riccardo Morandi—was built during 1963–1967, and it collapsed on 14 August 2018 at 11:36 a.m. local Italian time (09:36 UTC). The collapse caused 43 fatalities. Preliminary investigations after the collapse indicated poor design, questionable building practices, and insuﬃcient maintenance, or a combination of them, as possible causes of the collapse. At the time of the collapse, a violent thunderstorm was striking the city, but it is still not clear whether it played a role in this disaster due to the lack of meteorological measurements in the vicinity of the bridge at the collapse time. Thunderstorms are severe weather phenomena associated with strong winds, sometimes hail, lightning, and heavy rain. On average, there are 20:00 thunderstorms around the world at any given

Atmosphere 2020, 11, 724; doi:10.3390/atmos11070724 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 724 2 of 33 moment in time [1]. In the Gulf of Genoa, thunderstorms are usually associated with low-pressure systems (e.g., the Alps orographic cyclogenesis) [2], and the region has previously been identified as the area with high frequency cyclones [3–9]. Similar to our study, Zolt et al. [10] used field observations and model simulations to analyze meteorological conditions of severe storm that occurred on 4 November 1966 over Italy and caused 118 casualties. Parodi et al. [11] simulated a macroburst that occurred over the Genoa region on 14 October 2016. Their study used an ensemble of kilometer-scale simulations and tested the performances of different microphysics and planetary boundary layer schemes at simulating this severe event. Downburst outflows are created when a cold downdraft that originates from a thunderstorm cloud (i.e., cumulonimbus cloud) impinges on the surface causing radially advancing winds that are characterized with high velocities in the near-surface layer. The negative buoyancy of the downdraft is caused by evaporation, melting and/or sublimation of precipitation particles, such as raindrops, hail, and graupel, and additionally amplified by the drag due to the falling precipitation. Fujita [12] 1 reported that the wind gusts in the strong downburst outflows can reach 75 m s− , which is equivalent to a wind gust in an EF3-rated tornado based on the Enhanced Fujita scale of tornadoes [13]. In addition, downburst outflows are highly transient and three-dimensional (3D), which makes them profoundly more difficult to analyze and accurately represent in space and time in comparison to the large-scale atmospheric boundary layer (ABL) winds. Downbursts are classified into microbursts (<4 km) and macrobursts (>4 km) based on the horizontal dimensions of the outflow, as well as wet and dry, depending on whether or not the precipitation reaches the surface. The leading edge of the cold outflow that advances radially once the downdraft in the precipitation zone hits the surface is known as the thunderstorm gust front. Over the last several years many studies focused on field measurements of downburst winds in the Ligurian and the northern Tyrrhenian Sea in the Mediterranean (e.g., Reference [2,14,15]). Indeed, a collaborative effort between meteorologists and wind engineers seems to be the most efficient way of conducting multidisciplinary and cross-disciplinary research on thunderstorm winds with the end goal of mitigating downburst-caused damages on structures and the environment [2,15,16]. This article is also a result of such collaboration. Burlando et al. [15] documented damages caused by thunderstorm downbursts in the Port of Genoa on 31 August 1994. The severe winds that occurred on that day overturned more than 80% of the big cranes used to load and unload containers from cargo ship. Shehata et al. [17] reported that the Manitoba Hydro Company in Canada lost approximately USD 10 million in a failure of 18 transmission line towers due to the thunderstorm winds. When it comes to bridges and other horizontal structures, it has been well documented that winds can produce different along-and cross-wind instabilities that ultimately might cause the structural collapse of the whole bridge. Solari [18] discussed a variety of examples of wind-induced bridge collapses and the physical processes that cause these structural instabilities. However, the structural analysis and bridge responses to thunderstorm winds are beyond the scope of this study. Instead, the goal of this paper is to examine the weather conditions and their dynamics before and during the collapse of the Morandi Bridge in Genoa on 14 August 2018. The current study makes use of a variety of meteorological measurements from different platforms, such as the standard meteorological measurements from weather stations, satellite, and radar observations, lightning counters, and high-resolution ultrasonic anemometer measurements from the Port of Genoa, as well as the Doppler wind data from a lidar scanner also installed at the Port of Genoa. In addition, the field observations are complemented with numerical simulations conducted using the Weather Research and Forecasting (WRF) model with different forcing and boundary conditions, including the assimilated radar data in some cases. At this point, the authors want to explicitly state that this article does not suggest that the collapse of the Morandi Bridge was directly or even indirectly caused by the thunderstorm downburst or any associated thunderstorm phenomena that occurred on 14 August 2018 in that region. However, since the exact cause and triggers to the bridge collapse are still under investigation and largely unknown, this paper intends to provide the needed information Atmosphere 2020, 11, 724 3 of 33 on the weather conditions and their dynamics at different spatiotemporal scales before and during the collapse. The rest of this paper is organized in the following manner. Section2 describes the data and numerical simulations that were used in the analyses of weather conditions on 14 August 2018 over Genoa and the broader region. Section3 analyzes the weather scenario before and during the bridge collapse with the emphasis on the thunderstorm that occurred over Genoa. This section analyzes the meteorological precursors for the observed thunderstorm, as well as the local scale observations of thunderstorm characteristics making use of weather station data (anemometer, thermometer, etc.), Doppler radar, and lidar measurements. Section4 describes the spatiotemporal evolution of the thunderstorm using the WRF simulations and presents several derived quantitative indices used to characterize the severity of observed thunderstorm winds. Section5 provides a concluding discussion of the most relevant findings presented in this study.

2. Data and Numerical Simulations

2.1. Meteorological Data Diﬀerent data sources were used to describe this event from the synoptic to the local scale. The synoptic meteorological conditions were analyzed utilizing the Global Forecast System (GFS) analyses of the National Centers for Environmental Prediction (NCEP) that are available on a 0.5 ◦ × 0.5◦ grid every 6 h. The development of the thunderstorm that approached Genoa from the southwest was monitored using the meteorological radar of the Liguria Region, which is operated by the Italian Meteo-radar Network. The radar range is approximately 100 km. The lightning strikes were recorded through the Blitzortung network (http://www.blitzortung.org/), as well as the LAMPINET (Lightning Network of the Italian Air Force Meteorological Service) system [19–21]. The local weather conditions were monitored using the meteorological stations of the Meteo-Hydrological Observatory of Liguria Region, operated by the Regional Agency for Environmental Protection (ARPAL), and the weather METAR station of the Genoa Airport. Lastly, the gust front evolution in time and space was analyzed employing the anemometric stations available in the Port of Genoa (managed by the Port Authority, Genoa, Italy) and the lidar scanner (property of the University of Genoa, Genoa, Italy). The lidar scanner was also used to partially reconstruct a gust front that occurred in the precipitation zone of the thunderstorm cloud. Table1 lists the available meteorological stations in the area that was a ﬀected by the investigated thunderstorm. Except for the GEPOA and GEPVA stations (see Table1 and Figure1), all other weather stations measured precipitation (PR), but only three of these stations measured wind speed and direction (W). All available measurements are either averaged or cumulated over the 1 h period around the collapse.

Table 1. Meteorological stations managed by the Regional Agency for Environmental Protection of Liguria Region available in the coastal area aﬀected by the thunderstorm on 14 August 2018.

Coordinates 1 Station ID (Lon ◦ E, Lat ◦ N, Altitude m Parameters ASL) GEBOL (8.89561, 44.45530, 47) T, PR RIGHI (8.93433, 44.42797, 360) T, PR, W CFUNZ (8.94591, 44.40035, 30) P, T, R, PR, RAD GEPCA (8.96109, 44.43439, 30) PR GEPEG (8.82460, 44.43227, 69) T, PR GEQUE (8.97260, 44.42367, 200) T, RH, PR MADGR (8.74299, 44.43344, 104) T, RH, PR Atmosphere 2020, 11, 724 4 of 33

Table 1. Cont.

Coordinates 1 Station ID (Lon ◦ E, Lat ◦ N, Altitude m Parameters ASL) MGAZZ (8.84485, 44.44247, 310) T, PR GEPOA (8.92317, 44.40816, 25) W GEPVA (8.95222, 44.39278, 10) W 1 P = pressure (hPa), T = temperature (◦C), RH = relative humidity (%), PR = cumulated precipitation, RAD = mean 2 Atmospheresolar 20 radiation20, 11, x (W FOR/m PEER), W = REVIEWwind speed (m/s) and direction (deg). 4 of 33

Figure 1. TopographicTopographic map of the Genoa region showing the the position position of of Morandi Morandi Bri Bridgedge (blue (blue line), line), Regional Agency for for Environmental Environmental Protection Protection ( (ARPAL)ARPAL) meteorological stations (green circles), Port Authority anemometric stations stations (red (red squares), airport airport METAR METAR station (cyan diamond), and and lidar scannerscanner of the University of Genoa (magenta tr triangle).iangle).

Anemometric stations stations available available within within the the port port area area are are shown shown in in Table Table 22.. Unfortunately, windwind 1 speed measurements were were available with a resolution of 1 m s −1 only,only, whereas wind direction was available with a 1° 1◦ resolution. The The anemometers’ anemometers’ sampling sampling rate rate of of about about 4 4 s s is is high high enough enough to to allow allow thethe evaluation evaluation of of gust gust front front movement movement in space in space and its and evolution its evolution in time. in However, time. However, the higher the sampling higher samplingfrequency frequency would be would needed be to needed report to the report actual the maximum actual maximum wind speed wind and speed turbulence and turbulence intensity intensitycharacteristics characteristics during the during collapse. the collapse. Data coverage Data coverage around the around collapse the timecollapse shows time the shows high recoverythe high recoverypercentage percentage of valid velocity of valid readingsvelocity readings in a period in ofa period 1 h centered of 1 h centered on the bridge on the collapse bridge coll time.apse time. The employed lidar that has been continuously in operation since April 2018 is a 3D scanning WindCubeTable 400S 2. Specification lidar, developed of the byanemometric Leosphere stations (Orsay, managed France). by Lidar’s the Port PPI Authority (Plan Position of Genoa. Indicator) mode of operation scans the azimuthal range of 100–250◦ and up to a maximum distance of 14 km in Coordinates Data Coverage Data Coverage the radial direction.Station ID The scanning elevations are between 2.5 and 10 from the horizontal with a 2.5 (Lon ° E, Lat ° N, Altitude m ASL) 14 August◦ 2018◦ 09:00–10:00 UTC ◦ increment. Each PPI scan requires approximately 50 s to complete. The position of the lidar (and its 01 (8.91838, 44.41119, 16) 66.6 78.8 scanning area) is02 shown in Figure(8.91939,1 (magenta 44.40780, triangle) 17) together with64.9 the location of81.6 anemometer 11 (red square) that is located03 next to(8.91501, the lidar. 44.40771, The lidar 15) was acquired72.4 in the context of79.8 the European Project THUNDERR [2207] (Detection, Simulation,(8.88499, 44.40220, Modeling 15) and Loading84.4 of Thunderstorm85.7 Outﬂows to Design Wind-Safer and08 Cost-E ﬃcient(8.83541, Structures) 44.41562, with the16) purpose of thunderstorm66.5 monitoring90.8 and detection. 09 (8.82859, 44.41862, 15) 65.9 87.9 11 (8.77698, 44.41754, 25) 62.7 88.0 16 (8.92797, 44.40157, 18) 72.4 78.0

The employed lidar that has been continuously in operation since April 2018 is a 3D scanning WindCube 400S lidar, developed by Leosphere (Orsay, France). Lidar’s PPI (Plan Position Indicator) mode of operation scans the azimuthal range of 100–250° and up to a maximum distance of 14 km in the radial direction. The scanning elevations are between 2.5° and 10° from the horizontal with a 2.5° increment. Each PPI scan requires approximately 50 s to complete. The position of the lidar (and its scanning area) is shown in Figure 1 (magenta triangle) together with the location of anemometer 11 (red square) that is located next to the lidar. The lidar was acquired in the context of the European Project THUNDERR [22] (Detection, Simulation, Modeling and Loading of Thunderstorm Outflows

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The instrument has been used to measure the ﬁne structure of gust fronts and downbursts associated with thunderstorm activity. This lidar measures the radial component (i.e., Doppler velocity) of the 3D wind speed in the range 30 m s 1, and with a gate resolution of 150 m. ± − Table 2. Speciﬁcation of the anemometric stations managed by the Port Authority of Genoa.

Coordinates Data Coverage Data Coverage Station ID (Lon E, Lat N, ◦ ◦ 14 August 2018 09:00–10:00 UTC Altitude m ASL) 01 (8.91838, 44.41119, 16) 66.6 78.8 02 (8.91939, 44.40780, 17) 64.9 81.6 03 (8.91501, 44.40771, 15) 72.4 79.8 07 (8.88499, 44.40220, 15) 84.4 85.7 08 (8.83541, 44.41562, 16) 66.5 90.8 09 (8.82859, 44.41862, 15) 65.9 87.9 11 (8.77698, 44.41754, 25) 62.7 88.0 16 (8.92797, 44.40157, 18) 72.4 78.0

Figure1 shows the position of all meteorological and anemometric stations included in Tables1 and2, as well as the METAR station and the lidar scanner location. The lidar scanner is side by side with anemometer 11, thus the two symbols overlapping each other. The position of Morandi Bridge is also shown in blue. The bridge was in a suburban and industrial area along the Valley of Polcevera, which stretches for approximately 10 km in the south-north direction. The riversides are reaching 600 m ASL (above sea level) in the northernmost part of the valley and around 200 m ASL close to the bridge. Anemometer 07—the closest anemometer to the bridge—was 2.7 km southward from the center of the bridge. The weather station GEBOL was 3.2 km northward from the bridge.

2.2. WRF Model Setup The Weather Research and Forecasting (WRF) model results are used here to investigate the possibility of predicting this kind of events with numerical weather prediction (NWP) models in operational configuration. The WRF model [23] is a compressible non-hydrostatic model with mass-based terrain-following coordinates developed at the National Center for Atmospheric Research (NCAR) in collaboration with several institutes and universities for operational weather forecasting and atmospheric science research. This work adopted the WRF version 3.8.1 (Advanced Research WRF dynamic core) and the data assimilation package (WRFDA) version 3.9.1. The two different domain configurations used in the current study are both operationally used since 2018 at the CIMA (Centro Internazionale in Monitoraggio Ambientale) Research Foundation on behalf of ARPAL (Liguria Region Environment Protection Agency). The first configuration is without data assimilation and uses three nested domains with the horizontal resolutions of 13.5 (250 250 grid points), 4.5 (451 450) and 1.5 km (943 883) × × × and 50 vertical levels (Figure2a; top of the domains at 50 hPa). This setup was used to produce two different forecasts: (1) the first using the initial and lateral boundary conditions from the NCEP-GFS (National Centers for Environmental Prediction—Global Forecast System) with the horizontal resolution of 0.25 0.25 and a 3-h time resolution (hereafter WRF-GFS); and (2) the second using ECMWF-IFS ◦ × ◦ (European Center for Medium-Range Weather Forecasts—Integrated Forecasting System) forcing with the horizontal resolution of 0.125 0.125 and a 3-h time resolution (hereafter WRF-IFS). ◦ × ◦ AtmosphereAtmosphere2020 2020,,11 11,, 724x FOR PEER REVIEW 66 ofof 3333

(a)

(b)

FigureFigure 2.2. TheThe Weather ResearchResearch andand ForecastingForecasting (WRF)(WRF) domainsdomains usedused inin thethe simulationssimulations ((aa)) withoutwithout andand ((bb)) withwith datadata assimilation.assimilation.

The second domain setup is composed out of three nested domains with the horizontal resolution of 22.5 (76 × 73 grid points), 7.5 (172 × 163), and 2.5 km (346 × 316) and 50 vertical levels (Figure 2b). The two numerical simulations performed using this setup, as in the previous case, relied

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The second domain setup is composed out of three nested domains with the horizontal resolution of 22.5 (76 73 grid points), 7.5 (172 163), and 2.5 km (346 316) and 50 vertical levels × × × (Figure2b). The two numerical simulations performed using this setup, as in the previous case, relied on the NCEP-GFS and ECMWF-IFS initial and boundary conditions, but with the additional implementation of a Three-Dimensional Variational(3DVAR)data assimilation. In this study,the 3DVAR method is employed with a 3-h cycling update technique. The basic goal of this technique is to provide an optimal estimate of the true state of the atmosphere through the minimization of a proper cost function [24]. To distinguish them from the first setup, these two simulations are hereafter referred to as the WRF-GFS-DA and WRF-IFS-DA, respectively. The WRF-GFS-DA and WRF-IFS-DA runs include a first 3DVAR assimilation at 00:00 UTC. The model then produces a 3-h forecast followed by the second assimilation cycle at 03:00 UTC and later the third assimilation cycle at 06:00 UTC; afterwards, the model generated a 24-h forecast. The 3DVAR method was employed here consistently with the WRF operational configuration to assimilate radar reflectivity into the WRF model. The data assimilation was performed following the procedure in Lagasio et al. [25] by using the modified direct operator described in Equation (11) in Reference [25]. All simulations in this study were initialized at 00:00 UTC on 14 August 2018. All simulations were performed with the same set of physical parameterizations that have been successfully tested in several studies of similar events [25–27]. The surface layer was modeled using the Mesoscale Model 5 (MM5) scheme that uses the stability functions from Reference [28–30] to compute surface exchange coefficients for heat, moisture, and momentum. Convective velocity calculations from Reference [31] were used to enhance surface fluxes of heat and moisture. The Rapid Update Cycle (RUC) scheme with 6 soil levels (0, 5, 20, 40, 160, 300 cm) was utilized for the parameterization of land surface processes. The soil model solves the heat diffusion and Richards moisture transfer equations with the layered discretization [32,33]. The planetary boundary layer (PBL) dynamics was parameterized with the diagnostic non-local Yonsei University PBL scheme [34], which accounts for counter gradient terms to represent fluxes due to non-local gradients and has explicit treatment of the entrainment layer at the PBL top. The entrainment is made proportional to the surface buoyancy flux in line with results from studies using large eddy simulation models [35]. This study used the WRF-single-moment 6 (WSM6) microphysics scheme with six classes that include the prediction of graupel and other microphysics processes [36]. Finally, the radiative processes were parameterized using the longwave and shortwave Rapid Radiative Transfer Model for General Circulation Models (RRTMG) schemes [37]. The results of Parodi et al. [11] are used to derive the deterministic model setup used here. The model setup chosen is the best-performing one among the ensemble previously studied in Reference [11] for similar conditions over the same area.

3. Results and Discussion: Observations

3.1. Weather Conditions at Large Scales Between 16 and 19 August 2018, two low-pressure systems, Querida and Roswitha (following the naming convention used by the Institute of Meteorology of the Freie Universität Berlin, Berlin, Germany), initially located at the latitudes approximately between 55–65◦ N, traveled eastward following the main sub-arctic zonal ﬂow along the storm track through the Atlantic Ocean to Northern Europe. On 18 August, at 00:00 UTC, these two depressions were situated over the northwestern Scandinavia (Querida I and II) and to the east of Iceland (Roswitha) (Figure3a). Along with these depressions, a trough that extended in the meridional direction down to the Northern Mediterranean caused instability along its path as it moved from the Bay of Biscay to the north of Italy. On the morning of 18 August, a secondary pressure minimum developed over the Gulf of Lion and moved eastward over the Ligurian Sea bringing an intensive thunderstorm activity. While this severe weather was particularly pronounced in the coastal areas of northwestern and central Italy, it rapidly disappeared in the afternoon. The tropopause height contour at 11,000 m (Figure3b; green contour) indicates that Atmosphere 2020, 11, 724 8 of 33 the position of the tropopause cutoﬀ was associated with the secondary cyclogenesis that occurred in the lee of the Alps. The similar triggering mechanism of severe weather in the Gulf of Genoa was also reported in Reference [2]. Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 33

FigureFigure 3. Mean 3. Mean sea levelsea level pressure pressure (contours (contours in in hPa) hPa and) and tropopause tropopause height (shaded (shaded contours contours inin m m)) over Europeover Europe from from the Globalthe Global Forecast Forecast System System (GFS) (GFS) analysis analysis ((aa)) atat 00:0000:00 UTC and and (b (b) )at at 12 12:00:00 UTC. UTC. The greenThe green contour contour shown shown in the in the panel panel below below corresponds corresponds to to the the cutocutoffﬀ thatthat occurred over over the the Ligurian Ligurian Sea inSea the in morning the morning on 14 on August 14 August 2018. 2018.

SlowSlow time time evolution evolution of deep of deep convective convective clouds clouds above above the the Gulf Gulf ofof Genoa can can be be observed observed between between 09:1509 and:15 10:00and 10 UTC:00 UTC (Figure (Figure4) from 4) from satellite satellite images. images. Although Although the the four four instancesinstances of cloud cloud top top heights heights shown in Figure 4 look similar, the noticeable feature in these images is the convection that started to shown in Figure4 look similar, the noticeable feature in these images is the convection that started to develop between two previously disjoint cloud clusters. We observe that the clouds above the Genoa develop between two previously disjoint cloud clusters. We observe that the clouds above the Genoa region at 09:15 UTC were not connected to the cloud system that developed south from Genoa and region at 09:15 UTC were not connected to the cloud system that developed south from Genoa and west from Corsica. Over the next 45 min, the two regions merged to form a long convective line that weststretched from Corsica. in the south Over-north the next direction. 45 min, Around the two09:45 regions UTC, the merged tops of convective to form a longclouds convective above Genoa line that stretchedwere still in at the 12,000 south-north m, thereby direction. indicating Around the strong 09:45 and UTC, well the-developed tops of convective convection. clouds These deep above cumuliform clouds were responsible for the observed gust front that was detected before the bridge collapse and are discussed later in Section 3.3.

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Genoa were still at 12,000 m, thereby indicating the strong and well-developed convection. These deep cumuliform clouds were responsible for the observed gust front that was detected before the bridge collapse and are discussed later in Section 3.3. Atmosphere 2020, 11, x FOR PEER REVIEW 9 of 33

FigureFigure 4. Cloud 4. Cloud top top heights heights above above ItalyItaly andand surrounding regions regions from from the the MSG MSG (Meteosat (Meteosat Second Second Generation) satellite data. The retrieval time of images, every 15-min from 0915 UTC (a) to 1000 UTC Generation) satellite data. The retrieval time of images, every 15-min from 0915 UTC (a) to 1000 UTC (d), is indicated in each plot and the red (dashed) circle represents a wider region around Genoa. (d), is indicated in each plot and the red (dashed) circle represents a wider region around Genoa. The radar images in Figure 5 show the existence of two dominant precipitation cells The radar images in Figure5 show the existence of two dominant precipitation cells characterized characterized by a strong radar reflectivity that exceeded 55 dBZ. The north cell was initially situated by a strong radar reflectivity that exceeded 55 dBZ. The north cell was initially situated southwest of southwest of Genoa (Figure 5a), but, in the next 20 min it moved in the northeast direction, and finally Genoabetween (Figure 09:530a), and but, 09: in40 theUTC next, it was 20 situated min it moved above the in city. the northeastThe south cell, direction, on the other and hand, finally stayed between 09:30sta andtionary 09:40 throughout UTC, it this was process. situated One above of the theinteresting city. The features south of cell,the northern on the precipitation other hand, cell stayed stationaryis its deformation throughout that this is manifested process. Oneas deeper of the propagation interesting of featuresthe central of precipitation the northern zone precipitation in the cell isnorth its deformationdirection. Between that 09 is:20 manifested and 09:40 UTC, as deeper the isoec propagationho of 40 dBZ ofpropagated the central approximately precipitation 6800 zone m in the northinland direction. from the coastline Between, where 09:20 it and was 09:40at 09:20 UTC, UTC. the Therefore, isoecho the of 40northward dBZ propagated propagation approximately velocity 6800of m precipitation inland from cell the that coastline, traversed where Genoa it around was at 09:20the bridge UTC. collapse Therefore, time thewas northwardabout 5.7 m propagation s−1. The northward movement of the precipitation cell is likely to be influenced by the orographic channeling 1 velocity of precipitation cell that traversed Genoa around the bridge collapse time was about 5.7 m s− . Thethat northward can be inferred movement from ofFigure the 5 precipitation. While the bottom cell isof likelythe two to valleys be influenced is below 100 by m the ASL, orographic the mountains on both sides of the valleys that run across Genoa rise to almost 10:00 m ASL. The location channeling that can be inferred from Figure5. While the bottom of the two valleys is below 100 m of Morandi Bridge in the west valley of Genoa is shown in Figure 5, with respect to the precipitation ASL, the mountains on both sides of the valleys that run across Genoa rise to almost 10:00 m ASL. cell. The influence of orography and river valley on thunderstorm propagation was investigated in Reference [38]. The authors concluded that thunderstorms influenced by orographic effects are more compact in comparison to thunderstorms over flat terrain. This difference is caused by the smaller

Atmosphere 2020, 11, 724 10 of 33

The location of Morandi Bridge in the west valley of Genoa is shown in Figure5, with respect to the precipitation cell. The influence of orography and river valley on thunderstorm propagation was investigated in Reference [38]. The authors concluded that thunderstorms influenced by orographic effectsAtmosphere are more 2020 compact, 11, x FOR inPEER comparison REVIEW to thunderstorms over flat terrain. This difference is caused10 of 33 by the smaller and unidirectional (only along the direction of the valley) supply of low-level moisture in the orographicand unidirectional case. However, (only along the numericalthe direction study of the of Curićet valley)´ supply al. [38 ] of was low carried-level moisture out for a in di thefferent orographic case. However, the numerical study of Ćurić et al. [38] was carried out for a different geographic region (the Balkan Peninsula), and more research is needed on the influence of orography geographic region (the Balkan Peninsula), and more research is needed on the influence of orography on gust fronts and thunderstorm propagation, particularly in coastal regions. on gust fronts and thunderstorm propagation, particularly in coastal regions.

FigureFigure 5. Radar 5. Radar reﬂectivity reflectivity (dBZ) (dBZ) measured measured byby thethe LigurianLigurian Doppler Doppler radar. radar. The The panels panels (a–c ()a show–c) show three consecutive VMI (Vertical Maximum Intensity) images at 09:20, 09:30, and 09:40 UTC, three consecutive VMI (Vertical Maximum Intensity) images at 09:20, 09:30, and 09:40 UTC, respectively. respectively. The position of Morandi bridge is indicated by the black cross. The position of Morandi bridge is indicated by the black cross.

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The 5-min Surface Rainfall Intensity (SRI) estimates from the national mosaic of the Italian The 5-min Surface Rainfall Intensity (SRI) estimates from the national mosaic of the Italian Meteo-Radar Network is here used to evaluate the storm cell motion towards the coast in the period Meteo-Radar Network is here used to evaluate the storm cell motion towards the coast in the period between 09:00 and 10:00 UTC. The application of the region growth algorithm to the binary image between 09:00 and 10:00 UTC. The application of the region growth algorithm to the binary image rain/no rain [39] shows that the cell was isolated from the rest of the precipitation field observed at rain/no rain [39] shows that the cell was isolated from the rest of the precipitation ﬁeld observed larger scales. The center of mass of the precipitating cell was identified, and the cell centroid at larger scales. The center of mass of the precipitating cell was identiﬁed, and the cell centroid displacement between subsequent images was used to evaluate speed and direction of the cell. displacement between subsequent images was used to evaluate speed and direction of the cell. The results in Figure 6 demonstrate that the precipitation cell was moving north-eastward until The results in Figure6 demonstrate that the precipitation cell was moving north-eastward until 09:25 UTC, when the cell reached the coast. Once landed, the cell intensity decayed, its shape from 09:25 UTC, when the cell reached the coast. Once landed, the cell intensity decayed, its shape approximately elliptical changed to an arrowhead-like shape pointing northward as it wedged along from approximately elliptical changed to an arrowhead-like shape pointing northward as it wedged the Polcevera Valley. At the same time, the translation speed reduced to almost 2 m s−1, and the along the Polcevera Valley. At the same time, the translation speed reduced to almost 2 m s 1, and propagation direction backed almost 90° from 09:25 to 09:45 UTC. The translational speed −of the cell the propagation direction backed almost 90 from 09:25 to 09:45 UTC. The translational speed of the cell approaching the coast was around 4 m s◦−1, with a highest speed being exceeding 6 m s−1 between 09:15 approaching the coast was around 4 m s 1, with a highest speed being exceeding 6 m s 1 between and 09:25 UTC. − − 09:15 and 09:25 UTC.

FigureFigure 6. Rain6. Rain cell cell observed observed oﬀ shoreoffshore the the Port Port of Genovaof Genova on on 4 August 4 August 2018 2018 between between 09:00 09:00 UTC UTC (panel (panel (a))(a and)) and 09:50 09:50 UTC UTC (panel (panel (f))), (f))), every every 10-min. 10-min. The The arrow arrow is placedis placed in thein the cell’s cell’s center center of massof mass and and its its lengthlength is proportional is proportional to cell’s to cell’s translational translational velocity. velocity. Observed Observed 5-min 5- meanmin m intensitiesean intensities were were as high as ashigh 1 100as mm 100 h mm− . h−1.

ThisThis pronounced pronounced convective convective activity activity above above Genoa Genoa and and south south from from the the city city was was also also detected detected by by thethe Blitzortung Blitzortung network network for lightningfor lightning strikes strikes detection detection (Figure (7Figurea). We 7 observea). We thatobserve the lightningthat the strikeslightning werestrikes organized were organiz along theed south-northalong the south line- thatnorth stretched line that from stretched the north from part the ofnorth Corsica part toof theCorsica north to of the Genoa.north These of Genoa. results These show resultsthat the show lighting that was the predominantly lighting was concentrated predominantly over concentrated the convective over line the situatedconvective above line the situated sea, and above the number the sea of, lightningand the number strikes aboveof lightning Genoa wasstrikes less abov signiﬁcant.e Genoa was less significant.

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Figure 7. (a) Lightning strikes recorded between 09:30 and 10:00 UTC on 14 August 2018 over Italy Figure 7. (a) Lightning strikes recorded between 09:30 and 10:00 UTC on 14 August 2018 over Italy and and the surrounding regions (source: Blitzortung.org). (b),(c) Lightning Network of the Italian Air the surrounding regions (source: Blitzortung.org). (b),(c) Lightning Network of the Italian Air Force MeteorologicalForce Meteorological Service Service (LAMPINET) (LAMPINET counts) counts of lightning of lightning strikes strikes above above Genoa Genoa in two indi twoﬀerent different time intervalstime intervals (background (background maps maps source source in (b),( inc): (b “©),(cGoogle): “© Google Earth”). Earth”). The thick The black thickline black in (lineb),( cin) shows(b),(c) theshows location the location of Morandi of Morandi Bridge. Bridge.

TheThe position position of of lightning lightning strikes strikes measur measureded through through LAMPINET LAMPINET has an has accuracy an accuracy of 500 m of ( 500Figure m (Figure7b,c). In7 b,c).the bridge In the collapse bridge hour, collapse lightning hour, strikes lightning were strikes mostly were recorded mostly over recorded the sea (Figure over the 7b) seaand (Figurelater over7b) the and coast later and over east the part coast of andGenoa east (Figure part of 7c). Genoa The largest (Figure number7c). The of largest strikes number occurred of between strikes occurred09:35 and between 09:40 UTC 09:35 at 4 and–5 km 09:40 from UTC the at bridge, 4–5 km in fromthe area the around bridge, inthe the Old area Port around of Genoa. the OldThe Portspatial of Genoa.extent of The the spatial cumuliform extent clouds of the cumuliformin Figure 4 was clouds predominantly in Figure4 was in the predominantly west-east direction, in the west-east which is direction,not necessarily which the is dominant not necessarily direction the of dominant the zone directioncharacterized of the by zone the highest characterized frequency by of the lightning highest frequencystrikes around of lightning Genoa ( strikesFigure around7b,c). We Genoa also report (Figure that7b,c). the We lightning also report strikes that were the lightning not observed strikes by wereLAMPINET not observed in the byvicinity LAMPINET of the Morandi in the vicinity Bridge of (thick the Morandi black line). Bridge (thick black line). DueDue toto thethe highhigh lightninglightning activity, thethe initialinitial speculationspeculation thatthat appearedappeared inin newspapersnewspapers waswas thatthat lightinglighting could could have have struck struck the the stays stays or rain or could rain could have destabilized have destabilized the base, the thereby base, causingthereby a causing landslide. a Whilelandslide. these While hypotheses these hypotheses were abandoned were abandoned soon after, soon the after, possibility the possibility that strong that strong winds winds could co haveuld playedhave played a role ina role the bridge in the collapse bridge collapse was considered was considered an unlikely an option unlikely by option experts by at expertsthe beginning at the ofbeginning their investigations. of their investigations. This study This also study demonstrated also demonstrated that the possibility that the possibility a lightning a strikelightning triggered strike thetriggered bridge the collapse bridge is collapse minimal. is minimal. Despite this Despite lack of this wind lack investigation, of wind investigation, the weather the is weather oﬃcially is ruledofficially out ruled as a contributingout as a contributing factor for factor the bridge for the collapse, bridge collapse, at least at least the time at the of time writing of writing this study. this Nevertheless,study. Nevertheless, the current the currentmeteorological meteorological analysis analysis of weather of conditionsweather conditions that occurred that inoccurred Genoa atin theGenoa time at of the the time bridge of collapsethe bridge is a collapse relevant is study a relevant to support study the to weather support hypothesis the weather in bridge hypothesis collapse in orbridge possibly collapse reconsider or possibly the role reconsider of weather the inrole this of disaster.weather in this disaster.

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3.2. Local Observations Even if quantitative measurements of weather conditions in the immediate bridge vicinity are not available, it is evident based only on the video available very close to the accident (released by the ItalianAtmosphere Finance 2020, 11, x Guard FOR PEER on REVIEW 1 July 2019 and taken from a security camera placed about 15013 of m33 to the west of the bridge), as well as many eyewitnesses, that severe weather conditions were present at the Italian Finance Guard on 1 July 2019 and taken from a security camera placed about 150 m to the the bridge around the time of the collapse. Figure8 shows two frames extracted from the video at west of the bridge), as well as many eyewitnesses, that severe weather conditions were present at the the timebridge of around collapse the (Figure time of8a) the and collapse. few seconds Figure 8 immediately shows two frames after the extracted bridge from had fallenthe video (Figure at the8b). Thetime arrow of collapse in Figure (Figure8a shows 8a) and the few lighting seconds that immediately occurred to after the the east-southeast bridge had fallen of the (Figure bridge 8b). during The the collapse,arrow in Figure while Figure8a shows8b depictsthe lighting the cloudthat occurred of dust to and the debris east-southeast at the ground of the thatbridge was during caused the by the collapsedcollapse, while bridge Figure (see 8 Arrowb depicts 1). the The cloud ﬁnest of particlesdust and debris in the at dust the cloudground were that suspendedwas caused inby thethe air andcollapsed advected bridge to the (see left Arrow of the photo1). The by finest the particles wind that in wasthe dust blowing cloud fromwere southsuspended to north. in the Arrow air and 2 in Figureadvected8b shows to the the left strong of the inclination photo by the of wind a tree that to the was left, blowing therefore from conﬁrming south to north. that Arrow a strong 2 in wind Figure was likely8b blowingshows the at strong the time inclination of the collapse.of a tree to The the left full, therefore event recorded confirm ining the that video, a strong which wind is was considered likely relevantblowing to this at the study, time isof availablethe collapse. at: Thehttp: full//www.gdf.gov.it event recorded in/stampa the video,/ultime-notizie which is considered/anno-2019 relevant/luglio / video-del-crollo-del-viadotto-polcevera-ponte-morandi-del-14-agosto-2018#nullto this study, is available at: http://www.gdf.gov.it/stampa/ultime-notizie/anno-2019/luglio/video. - del-crollo-del-viadotto-polcevera-ponte-morandi-del-14-agosto-2018#null.

Figure 8. Two snapshots of the video that was recorded by a security camera located nearby and to Figure 8. Two snapshots of the video that was recorded by a security camera located nearby and the west of the bridge. The video was taken (a) during the collapse of Morandi Bridge and (b) to the west of the bridge. The video was taken (a) during the collapse of Morandi Bridge and (b) immediately after the collapse. Arrow 1 in (a) indicates the lightning captured by the camera during immediately after the collapse. Arrow 1 in (a) indicates the lightning captured by the camera during the collapse. Arrow 1 and 2 in (b) show qualitatively the strength of wind that affects, respectively, the collapse. Arrow 1 and 2 in (b) show qualitatively the strength of wind that aﬀects, respectively, the advection of dust and tilts the tree. the advection of dust and tilts the tree. The standard METAR meteorological measurements from the Air Force weather station of the Genoa Airport are shown in Figure 9. The temporal resolution of data is not consistent throughout the records, but, on average, the wind velocity and air temperature data are available every 20–30

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The standard METAR meteorological measurements from the Air Force weather station of the Genoa Airport are shown in Figure9. The temporal resolution of data is not consistent throughout the records, but, on average, the wind velocity and air temperature data are available every 20–30 min. TheAtmosphere temporal 2020 resolution, 11, x FOR PEER of pressure REVIEW data is at every approximately 2.5 h. Figure9a shows that the14 of wind 33 direction at the time of bridge collapse abruptly changed for almost full circle (i.e., from 350 to min. The temporal resolution of pressure data is at every approximately 2.5 h. Figure 9a shows that◦ approximately 10 from 08:50 to 10:20 UTC) in the counterclockwise direction. At the exact time of the wind direction◦ at the time of bridge collapse abruptly changed for almost full circle (i.e., from the350° bridge to approximately collapse, the wind10° from was 08 blowing:50 to 10 from:20 UTC) 140◦ in(southeast). the counterclockwise Around the direction. same period, At the the exact mean 1 1 windtime speed of the increased bridge collapse, fromapproximately the wind was blowing 2 m s− fromto 7.7 140° m s(southeast).− at the time Around of collapse the same (Figure period,9a). 1 Afterwards,the mean wind the wind speed speed increased declined fromto approximately about 4.5 m s2− mat s−1 1000 to 7.7 UTC. m s−1 Bearing at the time in mindof collapse that the (Figure values 1 are9a). 10 Afterwards, min averages, the thewind mean speed velocity declined of ~8to about m s− 4.5demonstrates m s−1 at 1000 that UTC. the Bearing event wasin mind characterized that the withvalues relatively are 10 low min wind averages, speeds. the For mean example, velocity this of value ~8 m corresponds s−1 demonstrates to a wind that speed the event with returnwas periodscharacterized lower than with two relatively years in low Liguria wind [ 40speeds.]. This For is furtherexample, conﬁrmed this value by corresponds observing theto a recorded wind speed wind 1 gustwith of return 16 m s periods− at 09:50 lower UTC than (14 two min years after in the Liguria collapse), [40]. This which is further is the overallconfirmed maximum by observing value the that wasrecorded measured wind from gust 09:40 of 16 tom 09:50s−1 at 09 UTC.:50 UTC Note (14 that, min in after the the time collapse), interval which between is the 09:40 overall and maximum 09:50 UTC, thevalue mean that (maximum) was measured wind from speeds 09: measured40 to 09:50 atUTC. the ARPALNote that weather, in the time stations interval GEPOA between and GEPVA09:40 and (see 1 1 Figure09:502 )UTC, were the 7.9 mean (16.1) (maximum) m s − and wind 5.9 (15.5) speeds m measured s− , respectively. at the ARPAL The prevailing weather stations wind directions GEPOA and were fromGEPVA south (see and Figure west-southwest, 2) were 7.9 respectively.(16.1) m s−1 and 5.9 (15.5) m s−1, respectively. The prevailing wind directions were from south and west-southwest, respectively.

Figure 9. Meteorological measurements from the Genoa Airport weather station on 14 August 2018: (a)Figure wind speed9. Meteorological (black line; measurements primary y-axis) from and the wind Genoa direction Airport (greyweather line; station secondary on 14 Augusty-axis); 2018: (b) air (a) wind speed (black line; primary 푦-axis) and wind direction (grey line; secondary 푦-axis); (b) air temperature at 2 m above ground; and (c) sea level pressure at 2 m above ground. In (a), the asterisk temperature at 2 m above ground; and (c) sea level pressure at 2 m above ground. In (a), the asterisk symbol shows the velocity peak. The vertical red (dotted) lines indicate the bridge collapse time. symbol shows the velocity peak. The vertical red (dotted) lines indicate the bridge collapse time.

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An important validation that the event was a thunderstorm downburst comes from the analysis of wind velocity records in conjunction with air temperature and pressure data (Figure9b,c). In our case,Atmosphere the air2020 temperature, 11, x FOR PEER is aREVIEW more useful quantity due to the higher temporal resolution of these15 data. of 33 Figure9b shows that a decrease in temperature occurred simultaneously with the increase of surface data. Figure 9b shows that a decrease in temperature occurred simultaneously with the increase of wind speed and abrupt shifts in wind direction (Figure9a). The decrease of temperature at 08:50 UTC surface wind speed and abrupt shifts in wind direction (Figure 9a). The decrease of temperature at coincides precisely with both the increase of wind speed and abrupt change of wind direction. In 08:50 UTC coincides precisely with both the increase of wind speed and abrupt change of wind the next hour (until 09:50 UTC), the temperature dropped for 4 ◦C. The subsequent increase of surface direction. In the next hour (until 09:50 UTC), the temperature dropped for 4 °C. The subsequent air pressure (Figure6c) also follows the kinematics and dynamics theory of gust fronts, but the low increase of surface air pressure (Figure 6c) also follows the kinematics and dynamics theory of gust temporal resolution of data prevents a more comprehensive analysis. fronts, but the low temporal resolution of data prevents a more comprehensive analysis. The accumulated surface precipitation in the collapse hour increased in the eastward direction The accumulated surface precipitation in the collapse hour increased in the eastward direction (Figure 10). While precipitation was not observed in the western parts of the port, the east side received (Figure 10).1 While precipitation was not observed in the western parts of the port, the east side 33.8 mm h− of rain between 09:00 and 10:00 UTC. The weather station situated in the same valley as received 33.8 mm h−1 of rain between 09:00 and 10:00 UTC. The weather station situated in the same1 Morandi Bridge (GEBOL; Figure 10) and approximately 3.2 km north of the bridge, received 12 mm h− valley as Morandi Bridge (GEBOL; Figure 10) and approximately 3.2 km north of the bridge, received of rain. Therefore, the zone of the strongest precipitation coincided with the area characterized by −1 12 mm h of rain. Therefore, the zone of the strongest precipitation coincided with the area1 the highest activity of lightning strikes (Figure8). We also observe that the heavy rain (i.e., >10 mm h− ) characterized by the highest activity of lightning strikes (Figure 8). We also observe that the heavy was localized at the GEBOL, RIGHI, and CFUNZ stations (Figure 10), thereby indicating that Morandi −1 rain (i.e., >10 mm h ) was localized at the GEBOL,1 RIGHI, and CFUNZ stations (Figure 10), thereby Bridge probably received around 10 mm h− of rain in the collapse hour. However, since the weather indicating that Morandi Bridge probably received around 10 mm h−1 of rain in the collapse hour. stations cover approximately two times longer area in the zonal direction than in the meridional However, since the weather stations cover approximately two times longer area in the zonal direction direction, the observation that the variability of precipitation in the west-east direction is larger than than in the meridional direction, the observation that the variability of precipitation in the west-east in the south-north direction might be skewed. No hail nor other ice particles were observed in this direction is larger than in the south-north direction might be skewed. No hail nor other ice particles period at any weather station in the Genoa region. were observed in this period at any weather station in the Genoa region.

Figure 10. 10.Accumulated Accumulated surface surface precipitation precipitation between between 09:00 and 09:00 10:00 and UTC 10 in:00 Genoa. UTC The in accumulated Genoa. The accumulated precipitation at the CFUNZ station shown1 in mm h−1 and the size of other droplets scaled precipitation at the CFUNZ station shown in mm h− and the size of other droplets scaled accordingly. Backgroundaccordingly. mapBackground source: “map© Google source Earth”.: “© Google Earth”.

Further, we discuss discuss the the high high-frequency-frequency velocity velocity records records from from the the anemometers anemometers located located along along the thePort Port of Genoa. of Genoa. The Theposition position of these of these stations stations is reported is reported in Table in Table 2 and2 and is shown is shown in Figure in Figure 2. 2In. Inthe thehour hour centered centered around around the the collapse collapse time time (09 (09:36:36 UTC), UTC), for for the the majority majority of of stations stations,, the the wind wind direction was continuouslycontinuously changing in the counterclockwise direction starting around the north direction at the beginningbeginning ofof the the hour hour (about (about 09:00 09:00 UTC) UTC) and and returning returning to theto the north north direction direction at the at endthe ofend the of hour the (abouthour (about 10:00 UTC) 10:00 (Figure UTC) ( Figure11). Notice 11). thatNotice the that same the trend same was trend also wasobserved also observedin Figure 9 in. Although Figure 9. Although the counterclockwise change of wind direction is observed at all eight anemometers, the shift is less pronounced at the anemometers 08, 09, and 11. These three anemometers are in the west part of the port area and approximately 5–10 km away from the other five stations. The wind direction at the time of the bridge collapse was approximately 180° at the anemometer station 07. At the same time, in the east stations (03, 01, 02, 16), the wind was blowing from the third quadrant (between 180° and 270°), whereas the west stations (08, 09, 11) recorded the wind direction from the second

Atmosphere 2020, 11, 724 16 of 33 the counterclockwise change of wind direction is observed at all eight anemometers, the shift is less pronounced at the anemometers 08, 09, and 11. These three anemometers are in the west part of the port area and approximately 5–10 km away from the other ﬁve stations. The wind direction at the time of the bridge collapse was approximately 180◦ at the anemometer station 07. At the same Atmosphere 2020, 11, x FOR PEER REVIEW 16 of 33 time, in the east stations (03, 01, 02, 16), the wind was blowing from the third quadrant (between 180◦ and 270 ), whereas the west stations (08, 09, 11) recorded the wind direction from the second quadrant quadrant◦ (between 90° and 180°). This spatial distribution of wind directions resembles the cloud- (between 90 and 180 ). This spatial distribution of wind directions resembles the cloud-scale (~10 km) scale (~10 km)◦ radial◦ outflow produced by the gust front, which was spreading from underneath the radial outﬂow produced by the gust front, which was spreading from underneath the cloud base. cloud base.

FigureFigure 11.11. Wind speed (black dots; primary y풚-axis)-axis) and wind direction (grey dots; secondary y풚-axis)-axis) fromfrom 88 anemometersanemometers inin thethe PortPort ofof Genoa.Genoa. TheThe verticalvertical redred (dotted)(dotted) lineslines indicateindicate thethe bridgebridge collapsecollapse time.time. Panels Panels ( (aa))–(–(dd)) represent represent the the stations stations west west and and south south from from the the Morandi Morandi Bridge, Bridge, while while panels panels (e)– ((eh)–() areh) arethe thestations stations southeast southeast from from the thebridge bridge (see (see Figure Figure 2 for2 for details). details).

Similarly,Similarly, thethe windwind speedspeed recordsrecords alsoalso exhibitexhibit noticeablenoticeable didifferencesﬀerences betweenbetween thethe westwest andand easteast partsparts ofof thethe portport (Figure(Figure 1111).). TheThe didifferencesﬀerences betweenbetween thethe peakpeak windwind speedspeed closeclose thethe bridgebridge collapsecollapse timetime andand thethe windwind speed speed at at the the beginning beginning of of that that hour hour are are higher higher at at the the anemometer anemometer stations stations located located in thein the east east part part of theof the port port (Figure (Figure 11e–h). 11e– Theh). The velocity velocity records records in the in westthe west and centraland central parts parts of the of port the port (Figure 11a–d), on the other hand, show pronounced nonstationary signature before the velocity peak that occurred around the collapse time. Figure 11 shows clearly that the maximum wind speed—which occurred before 09:36 UTC for stations 07, 08, 09, 11, and after the collapse time for stations 01, 02, 03, 16—had a slight time shift from the westernmost to the easternmost anemometer. This can be interpreted as the signature of the gust front passage that follows the cloud translation from south-west to north-east over approximately 15 km of the coast. This result is in accordance with the estimated movement of precipitation zone in Figures 7 and 8. All anemometer

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(Figure 11a–d), on the other hand, show pronounced nonstationary signature before the velocity peak that occurred around the collapse time. Figure 11 shows clearly that the maximum wind speed—which occurred before 09:36 UTC for stations 07, 08, 09, 11, and after the collapse time for stations 01, 02, 03, 16—had a slight time shift from the westernmost to the easternmost anemometer. This can be interpreted as the signature of the gust front passage that follows the cloud translation from south-west to north-east over approximately 15 km of the coast. This result is in accordance with the estimated Atmospheremovement 2020 of, 1 precipitation1, x FOR PEER REVIEW zone in Figures7 and8. All anemometer measurements (Figures9 and17 11of )33 show that the near-surface wind velocities around the collapse time were approximately two times measurements (Figures 9 and 11) show that the near-surface wind 1velocities around the collapse time smaller than a velocity in the weakest tornado (EF0-rated; 29 m s− ). −1 were Overall,approximately these results two times demonstrate smaller than that a the velocity thunderstorm in the weakest produced tornado a gust (EF0 front-rated; that 29 originated m s ). overOverall, the sea andthese approached results demonstrate the anemometers, that the thunderstorm as well as the produced bridge, from a gust the front prevailingly that originated south overdirection. the sea This and is approac shown inhed Figure the anemometers, 12, which is a as representation well as the bridge, of the windfrom velocitythe prevailingly vectors (1south min direction.averaged) This obtained is shown from in the Figure measurements 12, which ofis a the representation 8 anemometers of the in thewind Port velocity of Genoa: vectors Figure (1 min 12a averaged)depicts the obtained situation from prior the to themeasurements thunderstorm of occurrence,the 8 anemometers at 08:50 UTCin the when Port theof Genoa: wind was Figure blowing 12a depictsfrom the the mountains; situation prior Figure to the12b thunderstorm shows the gust occurrence, front that, at at08 09:25:50 UTC UTC, when had the reached wind was the blowing western fromstations; the afterwards,mountains; theFigure outflow 12b shows intensifies the andgust afrontffects that all stations, at 09:25 (Figure UTC, 12hadc), reached which, at the 09:35 western UTC, stations;showed aafterwards radial outflow-like, the outflow diverging intensifies pattern and affects (notice all the stations southerly (Figure flow 12c) at station, which 07, at that 09: possibly35 UTC, showproduceded a aradial flow outflow channeling-like in diverging the Polcevera pattern Valley (notice towards the southerly the bridge flow location, at station indicated 07 that by possibly the blue producedline); finally, a f atlow 09:40, channeling the outflow in the center Polcevera had moved Valley to towards the east ofthe the bridge stations, location as all, i thendicated wind vectorsby the bluepointed line westward.); finally, at 09:40, the outflow center had moved to the east of the stations, as all the wind vectors pointed westward.

Figure 12. Vectors of thethe windwind velocityvelocity measuredmeasured by by the the 8 8 anemometers anemometers in in the the Port Port of of Genoa Genoa before before (a) (anda) and during during (b) –((bd)–)( thed) the thunderstorm. thunderstorm.

Recently, Burlando et al. [[15]15] demonstrated that the majority of thunderstorm downbursts in in thisthis region region are are generated over the sea and then advance towards the shore. A A similar similar trend trend in in terms terms of downburst formation and and movement movement was was also also reported reported by by Burlando Burlando et et al. al. [2] [2] for a downburst event in the Port Port of of Livorno, Livorno, Italy Italy,, on 1 October 2012. As As already already discussed discussed in in relation relation to to the the METAR METAR measurements presented presented in in Figure Figure 99,, thethe gustgust frontfront waswas relativelyrelatively low-intensitylow-intensity asas thethe maximummaximum −11 velocity recorded at all anemometer stations was in the order ofof 1515 mm ss− ..

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3.3. Lidar Gust Front Detection and Analysis Approximately 15 min prior to the bridge collapse (i.e., between 09:20 and 09:35 UTC), the thunderstorm gust front was also captured by the Doppler lidar installed in the Port of Genoa (Figure 13). Note that the timing of the gust front in Figure 13 is coherent with the maximum wind speeds recorded by anemometer 11 (Figure 11). The lidar scanned four different elevation angles (ϕ) between 2.5◦ and 10◦ above the horizontal plane with a 2.5◦ increment. These results are particularly interesting because Figure 13 is among the few sets of published lidar velocity measurements of a thunderstorm gust front [41]. Unfortunately, the heavy rain that occurred behind the gust front affected the lidar measurements by reducing the range of data acquisition from about 5 km at 09:15 UTC to 1–2 km at 09:30 UTC. 1 This thunderstorm gust front was characterized with the velocities exceeding 20 m s− (Figure 13). The highest positive velocities (i.e., towards lidar) are observed at the lowest ϕ and between 09:15 and 09:23 UTC. Besides, Figure 13 shows the propagation of the region characterized by the maximum velocity towards lidar (observed for all ϕ angles). The direction of the maximum velocity is from 150◦ until approximately 09:23 UTC (Figure 13i), and then it starts shifting in the clockwise direction eventually reaching ~170◦ (Figure 13k). The width of this region is at least 3.5 km. However, the width of this region could have been larger but not detectable by the lidar due to the limited azimuthal scans. The azimuthal orientation (θ) of the zone with the maximum velocity seems to be independent of height, which indicates that the advancing gust front is not (significantly) inclined in the ϕ θ plane. − However, it is particularly important to note that, with increasing ϕ, this region of the maximum velocity is inclined towards lidar. For example, when benchmarked against Figure 13a (ϕ = 2.5◦), this region is closer to lidar in Figure 13d (ϕ = 10◦) than it is in Figure 13e (ϕ = 2.5◦), although the velocity slice in Figure 13e is later than the one in Figure 13d. The continuous advancements of this front between the elevations of ϕ = 2.5◦ and ϕ = 10◦ are nicely observed in Figure 13b,c for ϕ = 5◦ and ϕ = 7.5◦, respectively. Moreover, the same trend is found in the next set of four velocity slices, shown in Figure 13e–h. That is, at ϕ = 2.5◦ (Figure 13e), the front is approximately 2000 m away from lidar, while, at ϕ = 10◦ (Figure 13h), the front is already above lidar (or within 300 m away from the instrument). The distances between these two are found at the other two intermediate elevations. This displacement of the region of the maximum velocity with height is a characteristic of the radially advancing gust front of cold air in front of the thunderstorm cloud [42–46]. Further, Figure 13 enables the estimates of the displacement velocity of the gust front towards the lidar. Figure 14 shows the height ASL of the gust front (black dashed line in panels a–g) at different radial distances from the instrument along θ = 151◦. For the height corresponding to 120 m ASL (i.e., 115 m above lidar level plus 5 m ASL, which is the height of the lidar ASL), we estimate the displacement velocity 1 (Vd) of the gust front to be Vd1 = 6.3 m s− towards the instrument. A similar displacement velocity, 1 Vd2 = 6.9 m s− , is obtained at 70 m ASL (i.e., 65 m above lidar). Thus, an average displacement 1 velocity component towards the lidar is evaluated to be Vd = 6.6 m s− . Note that this velocity is about 1 1–2 m s− larger than the estimated velocity of the north-eastward propagation of the precipitation cell in the radar images (Figure6). The displacement velocities calculated above are very similar to the one estimated by Mueller and Carbone [44] by tracking radar reflectivity and Doppler velocity of a gust front associated with a thunderstorm outflow measured in Denver, Colorado, USA, in 1984. 1 They obtained a value of 6.9 m s− . AtmosphereAtmosphere2020 2020, 11, 1,1 724, x FOR PEER REVIEW 1918 of of 33 33

FigureFigure 13. 13.Doppler Doppler velocity velocity measured measured by by the the WindCube WindCube Scanning Scanning Lidar Lidar 400S 400S lidar lidar located located in the in Port the of Port Genoa. of Genoa. The beam The elevation beam elevation (ϕ) and ( the휑) scanningand the scanning time interval time ininterval MM:SS in (blue MM:SS text; (bl theue hourtext; inthe all hour plots in isall 09:00 plots UTC) is 09 provided:00 UTC) provided in each ﬁgure. in each The figure. black The dashed black line dashed in (a)–( lineg) in shows (a)–(g the) shows leading the edge leading of the edge maximum of the maximum velocity region.velocity (h region.)–(p) panels (h)–(p show) panels the show evolution the evolution of the thunderstorm of the thunderstorm outﬂow from outflow 21:04 from UTC 21:04 to 28:39 UTC UTC. to 28:39 UTC.

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Figure 14. 14. PostPost-processed-processed lidar lidar data data shown the height of gust front at didifferentﬀerent elevation elevation angles angles (symbols)(symbols) andand didifferentﬀerent scanningscanning timestimes (colored(colored lines).lines). Two blackblack arrowsarrows showshow thethe estimatedestimated valuevalue

of displacement velocity at at 115 115 m m ( (푉V푑1d1) )and and 65 65 m m (푉 (푑V2d)2 above) above lidar. lidar. The The black black dotted dotted line line depicts depicts the theinclination inclination of the of thegust gust front front head. head.

When combined with the meteorological measurements fromfrom thethe Genoa Airport weather station (Figure(Figure9 )9) and and precipitation precipitation measurements measurements in the in areathe area (Figure (Figure 10), this 10), displacement this displacement velocity velocity can further can befurther used tobe estimate used to the estimate mean height the mean of the height cold inflow of the [42 cold]. We inflow used [42] the. Genoa We used Airport the weather Genoa Airport station inweather this analysis station duein this to analysis its proximity due to to its the proximity coastline to andthe coastline effectively and the effectively same elevation the same as elevation the lidar. Theas the other lidar. weather The other stations weather listed stations in Table listed1 are locatedin Table at 1 higherare located elevations; at higher therefore, elevations the; gust therefore front, structurethe gust front and atmosphericstructure and conditions atmospheric diff conditionser from those differ observed from those at the observed sea level. at the First, sea we level. estimate First, thewe meanestimate air densitiesthe mean of air the densities ambient of and the gust ambient front airand masses. gust front The air air masses. density (Theρ) is air calculated density using(휌) is thecalculated equation using of state the equation for wet air: of state for wet air: pd e ρ = + , (1) 푝푑 RdT푒 RwT 휌 = + , 푅 푇 푅 푇 (1) where pd and e are the pressure of dry air and푑 water푤 vapor (in Pa), respectively, T is the air temperature 1 1 1 1 (in K), Rd = 287.058 J kg− K− is the gas constant of dry air, and Rw = 461.495 J kg− K− is the gas where 푝 and 푒 are the pressure of dry air and water vapor (in Pa), respectively, 푇 is the air constant푑 of water vapor. Since the relative humidity during the investigated period was ~100%, e is −1 −1 temperature (in K), 푅푑 = 287.058 J kg K is the gas constant of dry air, and 푅푤 = equal to the saturation water vapor pressure (es) because the dew point is equal to air temperature. 461.495 J kg−1 K−1 is the gas constant of water vapor. Since the relative humidity during the The relationship between es and air temperature can be expressed through the August-Roche-Magnus investigated period was ~100%, 푒 is equal to the saturation water vapor pressure (푒 ) because the formula as [47]: 푠 dew point is equal to air temperature. The relationshipA1t between 푒푠 and air temperature can be es = es0 exp , (2) expressed through the August-Roche-Magnus formulaA2 +as t[47]:

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where es0 = 610.94 Pa, A1 = 7.625, A2 = 243.04°C [48], and t is the air temperature (in ◦C). Further, the pressure of dry air is calculated using Dalton’s law of partial pressures as:

p = p e = p es, (3) d − − because of e = es (i.e., the relative humidity of ~100%). Here, p is the measured (total) atmospheric pressure of wet air (in Pa). Plugging in the temperature drop of 4 K (from 296.15 K, i.e. 23.2 ◦C, to 292.15 K, i.e. 19.0 ◦C) and the pressure rise of ~130 Pa (~1.3 mb) into the above Equations results in the air 3 3 densities of cold (gust front) and ambient air being ~1.2024 kg m− and ~1.1833 kg m− , respectively. Then, we further use the equation for displacement velocity (Vd) of gravity currents (e.g., Reference [42,49–51]) to estimate the mean depth of the cold inﬂow: r ρ2 ρ1 Vd = k gd − , (4) ρ1

3 where ρ1 and ρ2 are the densities of warm (ambient) and cold (gust front) air masses (in kg m− ), 1 g = 9.8053 m s− is the gravitational acceleration at 44◦ N and sea level, and k = 0.77 [45,52] is the constant that represents the ratio of internal to gravitational forces [42]. Lastly, d is the unknown mean depth of the cold inflow (in m) that will be obtained. The value of k is uncertain and other numbers were proposed, too [50,51]. Solving Equation (4) for d yields the value of d = 464 m. A value of 478 m is obtained if the air is assumed to be dry. For instance, Charba [42] used Equation (4) to 1 estimate the displacement velocity of 22.7 m s− , but the measured value of d in his study was 1350 m. The surface air pressure rise (~677 Pa) and temperature drop (~5 ◦C) in his work were larger than in our case. In addition, our velocity estimate is only the component towards the lidar of the overall propagation velocity. However, smaller values of Vd and d than in Charba [42] were also reported by Goff [53] and Mueller and Carbone [44]. Besides the value of k, another uncertainty associated with the above analysis is related to the usage of surface air densities in Equation (4), instead of the mean air density along the height of the gust front inflow. Unfortunately, temperature and pressure profiles at this location are not available for this event. Since the air density difference ρ ρ is usually constant with height [42] and ρ (z > 0) < ρ (z = 0), 2 − 1 1 1 the obtained value likely represents the upper limit of d. The higher wind speeds associated with the gust front passage (Figures 11 and 13) in comparison to displacement velocity of the gust front (Figure 14) are expected and reported elsewhere, too [42]. According to Goff [53], the slowly moving gust fronts, such as the one reported in this paper, are usually associated either with intensifying storms and accelerating outflow or with dissipating storms and decelerating outflows. The gust front leading edge was inclined in the direction of propagation (i.e., towards lidar) due to the increase of wind speed with height in the outflow (dashed line in Figure 14). The angle of inclination depends on density differences between ambient and gust front air masses, and wind speed, among other factors. In the analyzed case, the inclination angle is 10.1 from the horizontal plane, − ◦ with the minus sign indicating the negative inclination concerning the measurement location. That is, 1 the gust front surge line increases for 177 m km− , which is very similar to the result of Charba [42], 1 who obtained the value of 150 m km− for a gust front that occurred in central Oklahoma, USA. This comparative analysis indicates that the surface roughness might not be the dominant factor that governs the slope of the gust front leading edge (i.e., gust front surge line) because similar values are obtained for two profoundly different surface roughness characteristics. However, this subject deserves more research and a larger sample of data to confirm this assumption. Further partial reconstruction of this event is possible by applying various relationships between the height of cold inflow (d) and other features of the gust front. According to several studies that investigated gust fronts at full scale or using physical experiments and numerical simulations (e.g., Reference [42,43,45,53]), the height of the leading edge (H; called gust front head) is usually H 2d. This relationship enables us to further sketch the likely shape of the gust front that occurred over the sea Atmosphere 2020, 11, x FOR PEER REVIEW 22 of 33

Further partial reconstruction of this event is possible by applying various relationships between the height of cold inflow (푑) and other features of the gust front. According to several studies that investigatedAtmosphere 2020 ,gust11, 724 fronts at full scale or using physical experiments and numerical simulations22 (e.g., of 33 Reference [42,43,45,53]), the height of the leading edge (퐻; called gust front head) is usually 퐻 ≅ 2푑. This relationship enables us to further sketch the likely shape of the gust front that occurred over the approximately 15 min before the bridge collapse (Figure 15). The height of the gust front nose (265 m) sea approximately 15 min before the bridge collapse (Figure 15). The height of the gust front nose is estimated from Reference [42], where it was reported this height is approximately d/1.8. However, (265 m) is estimated from Reference [42], where it was reported this height is approximately 푑/1.8. other relationships were proposed in Reference [53], too; hence, there is an uncertainty associated However, other relationships were proposed in Reference [53], too; hence, there is an uncertainty with this estimate. Recall that the height of the gust front nose is not the height of the maximum associated with this estimate. Recall that the height of the gust front nose is not the height of the horizontal wind speed in the outflow. The maximum wind speed in downburst outflows is usually maximum horizontal wind speed in the outflow. The maximum wind speed in downburst outflows observed in the layer between 80 and 120 m above ground [54]. This is in accordance with the findings is usually observed in the layer between 80 and 120 m above ground [54]. This is in accordance with in Figure 13, which shows that the highest Doppler velocities are measured at the elevation ϕ = 2.5 . the findings in Figure 13, which shows that the highest Doppler velocities are measured at the◦ The slope of ~70 of the gust front head outline in the region above the gust front nose is simply elevation 휑 = 2.5°◦ . The slope of ~70° of the gust front head outline in the region above the gust front adapted from Goff [53], while the gust front head height (928 m) is estimated through the relationship nose is simply adapted from Goff [53], while the gust front head height (928 m) is estimated through H 2d. The region behind the gust front head is characterized by pronounced turbulence due to the relationship 퐻 ≅ 2푑. The region behind the gust front head is characterized by pronounced the Kelvin-Helmholtz instability [55]; therefore, there is no clear outline of the gust front edge in that turbulence due to the Kelvin-Helmholtz instability [55]; therefore, there is no clear outline of the gust part (notice that the blue dotted lines are not connected behind the gust front head in Figure 15). In front edge in that part (notice that the blue dotted lines are not connected behind the gust front head accordance with the gust front and downburst outflow theories [42,44–46], Figure 13c–h demonstrate in Figure 15). In accordance with the gust front and downburst outflow theories [42,44–46], Figure that the gust front was associated with two high-speed regions that were separate with the zone of 13c–h demonstrate that the gust front was associated with two high-speed regions that were separate smaller wind speeds. The above-referenced studies demonstrated that the cold inflow typically comes with the zone of smaller wind speeds. The above-referenced studies demonstrated that the cold in periodic surges, which is further schematically depicted in Figure 15. inflow typically comes in periodic surges, which is further schematically depicted in Figure 15.

FigureFigure 15. ConceptualConceptual model model of of the the gus gustt front front that that occurred occurred prior prior to to the the collapse collapse of of the Morandi BridgeBridge in in Genoa on on 14 August 2018. The The blue blue dashed dashed line line represents represents the the estimated estimated shape shape of of the the gust gust front. The blue and red colors indicate the regions of cold and warm air, while the black 푉 arrow front. The blue and red colors indicate the regions of cold and warm air, while the black V푑d arrow showsshows the the direction direction of of gust gust front front propagation. propagation. The The black black arrows arrows within within the the gust gust front front portray portray the the air air movementmovement relative relative to the front. See See text text for for full full interpretation interpretation and underlying assumptions.

ToTo conclude the discussion on lidar data, Figure Figure 1166 showsshows the the lidar lidar velocities velocities around around the the collapse collapse time.time. The The gust gust front front that that was was observed observed in in Figure Figure 1133 movedmoved away away from from the the lidar lidar,, and and only only small small radial radial −11 velocities (below 10 m s −) )were were measured measured around around the the bridge bridge collapse collapse time. time. A A positive positive (towards (towards lidar) lidar) andand negative negative (away (away from from lidar) lidar) velocity velocity regions regions were detected were detected for θ < 150 for ◦ 휃and< 150°θ > 200 and◦, respectively,휃 > 200° , respectively,at all four elevation at all four angles. elevation Wind angles. speed Wind was speed slightly was increasing slightly increasing with height. with Around height. theAround collapse the collapsetime (09:36 time UTC), (09:36 the UTC), core ofthe precipitation core of precipitation cell in Figure cell 7in had Figure already 7 had passed already over passed the lidar; over therefore,the lidar; thereforethe negative, the Doppler negative velocities Doppler for velocitiesθ > 200 for◦ are 휃 > observed.200° are After observed. 09:40 After UTC, 09:40 Figure UTC,7 shows Figure that 7 showsthe precipitation that the precipitation zone was locatedzone was east located of the east lidar, of whichthe lidar, results which in results the observed in the observed easterly winds easterly at windsthe location at the oflocation the lidar of (Figurethe lidar 16 (Figure). 16).

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Figure 16. DopplerDoppler velocity velocity measured measured during the time of bridge collapse by the WSL400s WSL400s lidar located in the the Port Port of of Genoa. Genoa. The The beam beam elevation elevation (휑 ()ϕ and) and the the scanning scanning time time interval interval in MM:SS in MM:SS (blue (blue text; text; the hourthe hour in all in plots all plots is 09 is:00 09:00 UTC) UTC) provided provided in each in each figure ﬁgure (a)– ((ad)–(). d).

4. Results Results and and D Discussion:iscussion: WRF WRF N Numericalumerical S Simulationsimulations The WRF WRF numerical numerical simulations simulations results results have have been firstly been analyzed firstly analyzed by computing by computing 5-min maximum 5-min windmaximum speeds wind at 10 speeds m above at 10 ground m above and ground the location and the of location the eight of anemometers the eight anemometers in the Port in of the Genoa. Port Theof Genoa. considered The considered period is 06:00–15:00 period is 06 UTC.:00– The15:00 WRF-IFS, UTC. The WRF-GFS, WRF-IFS, WRF-GFS-DA, WRF-GFS, WRF and-GFS WRF-IFS-DA-DA, and predictionsWRF-IFS-DA have predictions been interpolated have been interpolated at the anemometer at the anemometer locations employing locations employing the nearest the neighbor nearest neighborinterpolation interpolation method. method. The results The results in Figure in Figure 17 show 17 show that thethat WRF-IFS the WRF- experimentIFS experiment is capable is capable of 1 predictingof predicting a maximum a maximum wind wind speed speed up toup 11 to m 11 s− min s− the1 in periodthe period between between 08:00 08 and:00 09:00and 09 UTC.:00 UTC The same. The samemodel model predicts predicts even highereven higher maximum maximum wind speedwind betweenspeed between 12:00 and 12:00 13:00 and UTC. 13:00 The UTC. WRF-IFS-DA The WRF- 1 IFSforecasts-DA forecasts a maximum a maximum wind speed wind up speed to about up to 10 about m s− 10in m the s−1 in period the period between between 08:00 and08:00 09:00 and UTC,09:00 1 whereasUTC, whereas the maximum the maximum wind speedswind speeds around around 13 m s13− mare s− predicted1 are predicted for the for period the period 11:00–12:00 11:00–12 UTC:00 (FigureUTC (Figur 17d).e 1 Conversely,7d). Conversely, the WRF-GFS the WRF and-GFS WRF-GFS-DA and WRF-GFS simulations-DA simulations produce produce significantly significantly lower valueslower values of the 5-minof the 5 maximum-min maximum wind speedswind speeds at all eight at all locations. eight locations. To gain a deeper understanding of this thunderstorm system, the Vertical Maximum Intensity (VMI) maps of radar reflectivity from the four WRF simulations were plotted at the time of the wind speed maxima indicated with dashed vertical lines in Figure 17. The first two peaks in all time-series are assumed to occur roughly at the same time in all WRF configurations, i.e., the velocity peaks at 08:30 and 09:55 UTC (Figure 17). The last velocity peak, which is also the most intense event, occurs at 1240 UTC in the WRF-IFS simulation, at 13:25 UTC in the WRF-GFS, at 13:10 UTC in the WRF-GFS-DA, and at 11:30 UTC in the WRF-IFS-DA. For further comparison between the mode and observations, Figure 18a–c show the VMI measurements at 08:30, 09:55, and 13:25 UTC and their modeled counterparts. The VMI observed at 12:40 and 13:10 UTC are not reported in Figure 18 because reflectivity patterns were analogous to the ones at 13:25 UTC (Figure 18c). In general, one can observe that there is no correspondence between the concurrent reflectivity patterns of numerical simulations and observations. This finding is not surprising because numerical weather prediction models often introduce a time and space shift between forecast and measurements. This bias is usually more pronounced for small-scale phenomena, such as a single-cell thunderstorm. Some deep convective spots with the VMI values above 40 dBZ occur in all the four WRF simulations at 08:30 and 09:55 UTC (Figure 18d,g,j,m,e,h,k,n), but these structures are rather small, organized along narrow stripes from southwest to northeast, and they do not resemble the VMI patterns in Figure 18a,b.

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Instead, the observations at 08:30 and 09:55 UTC are more similar to the convection developed by WRF-IFS in the early afternoon at 1325 UTC (Figure 18c) and WRF-IFS-DA at 1130 UTC (Figure 18i). In particular, the near-surface wind speeds in the WRF-IFS-DA simulation reach approximately 1 20 m s− at 10 m above ground level (AGL) between 11:00 and 11:55 UTC in the central-eastern Liguria coastline. This value is close to the maximum velocities observed in anemometric measurements around the collapse time (Figure 11). Furthermore, the model shows the development of a gust front that originates from underneath the thunderstorm cloud. We also notice that the assimilation of the radar data into the GFS seems to initiate a convective motion between 11:00 and 12:00 UTC (Figure 17c), but the velocities do not reach the values observed in the WRF-IFS-DA (Figure 17d). Because the WRF-IFS-DA run provided the most accurate simulations of this thunderstorm event, Atmospherethe focus 20 of20 the, 11, followingx FOR PEER discussion REVIEW is mainly on these results. 24 of 33

Figure 17. Maximum 5-min wind speeds at 10 m above ground at the eight anemometers location in Figurethe Genoa 17. Maximum port (see Figures 5-min 2wind and speeds11 for anemometer at 10 m above locations ground andat the measurements, eight anemometers respectively): location (ina) theWRF-IFS; Genoa (portb) WRF-GFS; (see Figure (c)s WRF-GFS-DA;2 and 11 for anemometer (d) WRF-IFS-DA. locations The and vertical measurements, dashed lines respectively) show the time: (a) WRFinstances-IFS; used(b) WRF for- theGFS; analysis (c) WRF of-GFS radar-DA; reﬂectivity (d) WRF and-IFS thermodynamics-DA. The vertical diagrams.dashed lines show the time instances used for the analysis of radar reflectivity and thermodynamics diagrams.

To gain a deeper understanding of this thunderstorm system, the Vertical Maximum Intensity (VMI) maps of radar reflectivity from the four WRF simulations were plotted at the time of the wind speed maxima indicated with dashed vertical lines in Figure 17. The first two peaks in all time-series are assumed to occur roughly at the same time in all WRF configurations, i.e., the velocity peaks at 08:30 and 09:55 UTC (Figure 17). The last velocity peak, which is also the most intense event, occurs at 1240 UTC in the WRF-IFS simulation, at 13:25 UTC in the WRF-GFS, at 13:10 UTC in the WRF-GFS- DA, and at 11:30 UTC in the WRF-IFS-DA. For further comparison between the mode and observations, Figure 18a–c show the VMI measurements at 08:30, 09:55, and 13:25 UTC and their modeled counterparts. The VMI observed at 12:40 and 13:10 UTC are not reported in Figure 18 because reflectivity patterns were analogous to the ones at 13:25 UTC (Figure 18c). In general, one can observe that there is no correspondence between the concurrent reflectivity patterns of numerical simulations and observations. This finding is not surprising because numerical weather prediction models often introduce a time and space shift between forecast and measurements. This bias is usually more pronounced for small-scale phenomena, such as a single-cell thunderstorm. Some deep convective spots with the VMI values above 40 dBZ occur in all the four WRF simulations at 08:30 and 09:55 UTC (Figure 18d,g,j,m,e,h,k,n), but these structures are rather small, organized along narrow stripes from southwest to northeast, and they do not resemble the VMI patterns in Figure 18a,b.

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Figure 18. RadarRadar reflectivity reﬂectivity (dBZ) (dBZ) measured by the Ligurian Doppler radar. Vertical Maximum Intensity ( (VMI)VMI) maps from observations (a)a)–(–(c) and WRF simulations (d)d)–(–(l). The The position position of of Morandi Morandi bridge is indicated by the black cross.

Instead,The thermodynamic the observations conditions at 08:30 from and the 09 WRF-IFS-DA:55 UTC are runmore at similar 11:30 UTC to the are convection favorable for develop the onseted byof strongWRF-IFS single-cell in the early thunderstorms afternoon at (Figure 1325 UTC 19), such(Figure as the18c one) and observed WRF-IFS during-DA at the 1130 bridge UTC collapse. (Figure 18i)The. skewIn particular,T-logp diagram the near has-surface been extracted wind speeds for the in centralthe WRF region-IFS-DA of the simulation convective reach system approximatel (Figure 19b;y −1 20Lon m 8.97s at◦ 10E, Latm above 44.422 ground◦ N). The level diagram (AGL) between shows the 11: Most00 and Unstable 11:55 UTC Convective in the central Available-eastern Potential Liguria 1 coastline.Energy (MUCAPE) This value equal is close to 1245 to the J kg maximum− , which is velocities a value associated observed in with anemometric the likely development measurements of aroundthunderstorms the collapse in the time area. (Figure The precipitable 11). Furthermore, water ofthe 49 model mm indicatesshows the potentially development high of precipitation a gust front thatrates originates at the ground. from Thisunderneath value is the comparable thunderstorm to the cloud. accumulated We also surface notice precipitationthat the assimilation measurements of the radarat the data CFUNZ into stationthe GFS in seems the east to initiate part of a Genoa convective (Figure motion 10). Further,between the 11:00 high and values 12:00 ofUTC the ( SevereFigure Weather17c), but Threatthe velocities (SWEAT) do andnot Showalterreach the values Index (SWI)observed (391 in and the WRF4.6 C,-IFS respectively)-DA (Figure indicate 17d). Because a high − ◦ theprobability WRF-IFS of-DA a severe run provided thunderstorm the most in the accurate area. Similar simulations conclusions of this arethunderstorm drawn from event, the analysis the focus of ofthe the Total following Totals, discussion K-Index, and is mainly Lifted Indexon these (Figure results 19. ). The relatively low values of the wind shear and theThe Storm-Relativethermodynamic Helicity conditions (SREH) from depict the WRF the-IFS atmospheric-DA run at conditions 11:30 UTC that are arefavorable not favorable for the onsetfor the of development strong single of-cell supercell thunderstorms thunderstorms. (Figure 1 Finally,9), such it as is worththe one highlighting observed during that the the value bridge of collapse.the Microburst The skew WindspeedT-logp diagram Potential has Index been (MWPI), extracted which for the ranges central between region 0 andof the 5, isconvective equal to 2.2. system This (valueFigure is 1 indicative9b; Lon 8.97° of the E, Lat thermodynamic 44.422° N). The conditions diagram proneshows to the the Most occurrence Unstable of Convective downbursts Available [56]. Potential Energy (MUCAPE) equal to 1245 J kg−1, which is a value associated with the likely development of thunderstorms in the area. The precipitable water of 49 mm indicates potentially high precipitation rates at the ground. This value is comparable to the accumulated surface precipitation measurements at the CFUNZ station in the east part of Genoa (Figure 10). Further, the high values of the Severe Weather Threat (SWEAT) and Showalter Index (SWI) (391 and −4.6 °C, respectively) indicate a high probability of a severe thunderstorm in the area. Similar conclusions are drawn from the analysis of the Total Totals, K-Index, and Lifted Index (Figure 19). The relatively low values of the wind shear and the Storm-Relative Helicity (SREH) depict the atmospheric conditions that are not favorable for the development of supercell thunderstorms. Finally, it is worth

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highlighting that the value of the Microburst Windspeed Potential Index (MWPI), which ranges Atmospherebetween 20200 and, 11 ,5, 724 is equal to 2.2. This value is indicative of the thermodynamic conditions prone26 of 33to the occurrence of downbursts [56].

Figure 19. The WRF-IFS-DA thermodynamic diagram at 11:30 UTC in the middle of the convective Figure 19. The WRF-IFS-DA thermodynamic diagram at 11:30 UTC in the middle of the convective structure shown in Figure 18i (b) compared with the radiosounding taken at Milan Linate at 12:00 structure shown in Figure 18i (b) compared with the radiosounding taken at Milan Linate at 12:00 UTC UTC (a). IFS = Integrated Forecasting System; DA = data assimilation. (a). IFS = Integrated Forecasting System; DA = data assimilation.

ForFor comparisoncomparison againstagainst WRFWRF simulations,simulations, FigureFigure 1919aa showsshows thethe radiosoundingradiosounding measurementsmeasurements atat 12:0012:00 UTC taken at at Milan Milan Linate Linate Airport, Airport, which which is is approximately approximately 120 120 km km to tothe the north north-northeast-northeast of ofGenoa. Genoa. In InMilan, Milan, the the airair very very close close to tothe the surface surface is drier is drier with with respect respect to tothe the conditions conditions simulated simulated in inWRF WRF.. This This difference difference is isexpected expected because because Genoa Genoa is is along along the the sea sea,, and and,, in in August August,, the sea surfacesurface temperaturetemperature isis atat thethe yearly yearly maximum. maximum. Moreover, Moreover, Figure Figure 19 19bb shows shows a profilea profile extracted extracted in in the the center center of theof the cloud cloud where where the the dew-point dew-point temperature temperature depression depression is is zero zero up up to to 500 500 hPa. hPa. Conversely,Conversely, inin MilanMilan onlyonly aa veryvery thinthin cloudcloud layerlayer existedexisted atat aboutabout 600600 hPa,hPa, whichwhich separatedseparated aa mid-levelmid-level layerlayer ofof drierdrier airair betweenbetween 700700 and and 800 800 hPa hPa from from a thickera thicker high-level high-level layer layer of dry of airdry between air between 550 and 550 300 and hPa. 300 This hPa upper. This layerupper is layer characterized is characterized by a nearly by a pseudoadiabatic nearly pseudoadiabatic lapse rate. lapse A very rate similar. A very thermodynamic similar thermodynamic behavior, bothbehavior, from both qualitative from qualitative and quantitative and quantitative points of point view,s of is view, observed is observed in the WRF in the simulation WRF simulation above 500above hPa. 500 The hPa radiosounding. The radiosounding profile recorded profile recorded in Milan depictsin Milan the depicts environment the environment prone to wet prone downburst to wet

Atmosphere 2020, 11, 724 27 of 33 Atmosphere 2020, 11, x FOR PEER REVIEW 27 of 33 formationdownburst [57 formation,58]. The higher [57,58] moisture. The higher at the moisture low levels at the due low to sea levels evaporation due to sea around evaporation Genoa actedaround to reduceGenoa convective acted to inhibitionreduce convective and played inhibition a role in and increasing played the a probability role in increasing of thunderstorm the probability formation. of thunderstormThe WRF-IFS-DA formation. simulation nicely depicts the existence of a strong near-surface outflow that showsThe diverging WRF-IFS pattern-DA simulation of winds in nicely the region depicts south the fromexistence Genoa of (Figurea strong 20 near). The-surface strongest outflow velocities that atshows 10 m abovediverging the groundpattern of are winds observed in the in region south south and southwest from Genoa part (Figure of the 20 outflow,). The strongest and the velocities intensity ofat the 10 windsm above increases the ground throughout are observed the hour. in south The and peak southwest wind speeds part of are the around outflow 20, and m s –1the. Theintensity WRF –1 simulationof the winds shows increases that the throughout highest winds the hour. occurred The peak along wind the south speeds part are of around the thunderstorm 20 m s . The system. WRF Thissimulation result is shows somewhat that counterintuitivethe highest winds because occurred the along thunderstorm the south waspart translatingof the thunderstorm towards the system. north andThis northeast. result is somewhat Assuming counterintuitiv the addition ofe thunderstorm because the thunderstorm translation to was the radially translating advancing towards downburst the north outflow,and northeast. the maximum Assuming winds the would addition be expected of thunderstorm in the northern transla regionstion to of the the radially outflow. advancing Figure 20 furtherdownburst shows outflow, that our the measuring maximum network, winds would as well be as expected the bridge in (purplethe northern cross regions in Figure of 20the), outflow. were not Figure 20 further shows that our measuring network, as well as the bridge (purple cross in Figure 20), exposed to the highest velocities in the outflow. were not exposed to the highest velocities in the outflow.

FigureFigure 20. 20.Ten-minute Ten-minute maximum maximum wind wind speed speed maps maps at at 5-min 5-min temporal temporal resolution resolution for for the the WRF-IFS-DA WRF-IFS- experimentDA experim (11:00–11:55ent (11:00– UTC).11:55 UTC). Wind Windvectors vectors are also are shown. also shown. The purple The purple cross indicates cross indicates the former the former position of the Morandi Bridge. position of the Morandi Bridge.

FigureFigure 21 2a1a shows shows an an updraft updraft and and downdraft downdraft (isosurfaces (isosurfaces equal equal to to ±11 mm ss−1)) in in combination combination with with ± − thethe maximum maximum 10 10 mm windwind speed (shaded (shaded colors colors and and vectors) vectors).. The The simulated simulated radar radar reflectivity reflectivity in the in thevertical vertical plan plan is isshown shown in in Figure Figure 2 121b.b. Similar Similar to to the the earlier earlier discussion, discussion, the the WRF WRF-IFS-DA-IFS-DA results results show show anan intense intense convective convective activity activity ahead ahead of theof the Liguria Liguria coastline coastline at 11:30 at 11 UTC.:30 UTC. The The larger larger scale scale cyclonic cyclonic flow patterflow nearpatter the near surface the surface (Figure ( 21Figurea) acts 21 asa) theacts main as the provider main provider of the inflow of the for inflow the updraft for the updraft in the forward in the flankforward of the flank thunderstorm. of the thunderstorm. We also observe We also that observe the cold that downdraft the cold downdraft that originated that originated inside the inside cloud reducesthe cloud the air reduces temperature the air at 2temperature m ASL upon at its 2 impingement m ASL upon (Figure its impingement 21b). The temperature (Figure 2 di1b)fference. The temperature difference between the cold pool and the ambient air around the thunderstorm is similar between the cold pool and the ambient air around the thunderstorm is similar to the temperature drop to the temperature drop of ~4 K (from 296 K (23.2 °C) to 292 K (19.0 °C)) that was observed at the of ~4 K (from 296 K (23.2 ◦C) to 292 K (19.0 ◦C)) that was observed at the Genoa airport station around Genoa airport station around the collapse time (Figure 9). This result also further validates the the collapse time (Figure9). This result also further validates the analytical procedure that was used in analytical procedure that was used in the derivation of Figure 15 in Section 3.3. the derivation of Figure 15 in Section 3.3.

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Figure 21. The WRF-IFS-DA results at 11:30 UTC above the Genoa region. (A) The updraft and Figure 21. The WRF-IFS-DA results at 11:30 UTC above the Genoa region. (A) The updraft and −1 downdraft system (red and green isosurfaces are ±1 m s 1) in combination with the 10 m maximum downdraft system (red and green isosurfaces are 1 m s− ) in combination with the 10 m maximum −1 wind speed (m s 1, vectors and colors). (B) A vertical± cross section of the reflectivity field (dBZ) and wind speed (m s− , vectors and colors). (B) A vertical cross section of the reﬂectivity ﬁeld (dBZ) and the 2 m temperature (K). the 2 m temperature (K).

TheThe WRF-IFS-DA WRF-IFS-DA numerical numerical simulation simulation demonstrates that the forecast of radar reﬂectivity,reflectivity, thermodynamicthermodynamic propertiesproperties ofof thethe atmosphereatmosphere atat largerlarger scales,scales, andand windwind ﬁeldfield atat thethe locallocal scalescale cancan potentiallypotentially bebe usedused asas aa usefuluseful tooltool forfor determiningdetermining thethe meteorologicalmeteorological precursorsprecursors andand contributingcontributing factors to the development of thunderstorms in this region. The most significant drawback of the current simulations is the delay of the forecasted thunderstorm with respect to the observed event. Similar results in terms of delayed precipitation were also reported in another case study of a severe

Atmosphere 2020, 11, 724 29 of 33 factors to the development of thunderstorms in this region. The most significant drawback of the current simulations is the delay of the forecasted thunderstorm with respect to the observed event. Similar results in terms of delayed precipitation were also reported in another case study of a severe storm prediction using the WRF model [59]. In addition, in perspective, the current simulations might be used to model gust fronts and downbursts at much finer scales by adopting the proper downscaling techniques or coupling the WRF with other higher resolution and small-scale models (e.g., Reference [60–62]). This is further supported by the results of Fiori et al. [26], which concluded the cloud-resolving model resolutions (~200 m) are needed to properly represent the triggering mechanisms of deep convection in this region. However, as mentioned earlier, the goal of this paper was not to test a research setup of the WRF model, but rather the operational configurations that are employed in the daily meteorological practice of Liguria, Italy.

5. Summary and Conclusions This article analyzed the weather conditions and their dynamics before and during the collapse of the Morandi Bridge on 14 August 2018. This disastrous event that occurred at 09:36 UTC caused 43 casualties. Since the forensic investigation of the collapsed bridge has not pinpointed the exact factors that caused the collapse, the goal of this paper was to provide the contributions in terms of observed and modeled weather conditions around the collapse time. Therefore, the analyses in this study were based on direct meteorological measurements from the weather stations in the area, wind velocity records from eight anemometers installed along the Port of Genoa, remote satellite and radar observations, and the Global Forecast System (GFS) analysis. The local weather station of 1 Genoa Airport recorded the wind gust of 16 m s− a few minutes after the collapse time. The time records of air temperature, pressure, and wind velocity from this weather station, as well as the higher frequency velocity measurements from the eight anemometers located along the coastline, showed the well-known signature of transient thunderstorm conditions during the collapse hour. This observation was confirmed using the satellite (i.e., cloud top heights) and radar (i.e., composite radar reflectivity) data. The radar data showed the existence of two cells close to Genoa with a strong radar reflectivity exceeding 55 dBZ. The satellite images, on the other hand, depicted a convective line that stretched in the south-north direction and extended from Corsica to northern Italy (thus, crossing Genoa). The event was also characterized by the high frequency of lightning strikes, but they were not recorded in the close vicinity of Morandi Bridge, but rather a few kilometers east of it. In addition„ this study presented the rare lidar measurements of downburst outflow in the form of a gust front that advanced ahead of the parent cumulonimbus cloud. Furthermore, we developed and applied a technique for gust front surge reconstruction using lidar measurements and theoretical relationships that analytically link the main parameters of this phenomenon. The displacement velocity of the gust front in the direction of the lidar approximately 20 min before the bridge collapse was 1 estimated to be 6.6 m s− , and the height of the cold pool behind the gust front head was calculated to be at 464 m. The front was advancing from the sea towards the land and the observed features were measured approximately 10–15 km south from the bridge. Besides, the lidar velocities showed multiple high-velocity regions behind the leading edge of the gust front. These results corroborate well with the estimated translation of the convective precipitation zone from the radar reflectivity observations. The numerical simulations of this event were carried out using the Weather Research and Forecasting (WRF) model with four different initial and boundary conditions: (1) WRF-IFS (from the European Center for Medium-Range Weather Forecast); (2) WRF-GFS (from the National Centers for Environmental Prediction); (3) WRF-GFS-DA; and (4) WRF-IFS-DA. The WRF-GFS-DA simulation uses the assimilated radar reflectivity data into the GFS analysis, whereas the WRF-IFS-DA run assimilated the radar reflectivity data into the WRF-IFS. While the first three numerical simulations under-estimated the maximum velocity during the bridge collapse hour, the WRF-IFS-DA simulation proved the good matching with the observations. However, it should be noted that all simulations have the tendency to delay the event for several hours (WRF-IFS-DA again provided the smallest delay). Atmosphere 2020, 11, 724 30 of 33

The simulated radar reflectivity, near-surface wind field, and thermodynamic indices demonstrated that these products can be used as a valuable tool in the analysis of weather precursors for severe thunderstorms in this region. In conclusion, this paper demonstrated the existence of a severe thunderstorm in the Genoa region during the collapse of the Morandi Bridge on 14 August 2018. While the strong and transient wind conditions in the form of wet downburst, the associated gust front, and lightning were all present in the minutes before and during the bridge collapse, this study does not firmly conclude that the severe weather was the major triggering factor for the collapse. However, we do acknowledge that the investigated weather conditions might have played some role in the failure of the Morandi Bridge. Future studies on the structural integrity of the bridge under these wind actions are needed. This article will be complemented in the future with a study that will resolve the entire thunderstorm flow field from the lidar and radar measurements. This activity is ongoing, and the authors are currently facing the difficulty to link together lidar and radar data, as well as to properly reconstruct the three-dimensional wind field inside the cloud from the radial velocity measurements of a single Doppler radar and single lidar. Furthermore, in perspective, the three-dimensional wind field could be assimilated in a cloud model in order to extrapolate the wind flow conditions at the bridge location considering the real thunderstorm evolution and its interaction with topography. Finally, WRF model is used here to investigate the predictive capability of this thunderstorm by the operational model setup, which assimilates radar reflectivity only. The assimilation of more observations, like radial wind from Doppler radar, could improve the model forecast and will be the subject of future investigations that are currently ongoing.

Author Contributions: M.B. supervised and designed the study, and he was responsible for data collection. M.B. and D.R. performed data analysis and manuscript preparation and editing. D.R. performed the literature review. M.L. and A.P. performed numerical simulations, analyzed numerical results and edited the corresponding sections in this paper. G.B. analyzed radar reflectivity and storm motion. All authors have read and agreed to the published version of the manuscript. Funding: The contribution of Massimiliano Burlando and Djordje Romanic to this research is funded by the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 741273) for the project THUNDERR—Detection, simulation, modeling and loading of thunderstorm outflows to design wind safer and cost-efficient structures—through an Advanced Grant 2016. Acknowledgments: The authors thankfully acknowledge the cooperation of the Port Authority of Genoa for providing the anemometric measurements in the port of Genoa. Satellite images are based on level 1 data recorded by SEVIRI instrument onboard Meteosat Second Generation satellites, operated by EUMETSAT. Thanks are due to LRZ Supercomputing Centre, Garching, Germany, 845 where the numerical simulations were performed on the SuperMUC Petascale System, Project-ID: pr62ve. Antonio Parodi is grateful to Stavros Davis (LMD/IPSL, CNRS) for his help in preparing Figure 19 through an NCL script. Conflicts of Interest: The authors declare no conflict of interest.

References

1. NSSL. Thunderstorm Basics. Available online: http://www.nssl.noaa.gov/education/svrwx101/ thunderstorms/ (accessed on 27 June 2019). 2. Burlando, M.; Romanić,D.; Solari, G.; Hangan, H.; Zhang, S. Field data analysis and weather scenario of a downburst event in Livorno, Italy, on 1 October 2012. Mon. Weather Rev. 2017, 145, 3507–3527. [CrossRef] 3. Radinović,D. Mediterranean Cyclones and Their Influence on the Weather and Climate; WMO: Geneva, Switzerland, 1987. 4. Maheras, P.; Flocas, H.A.; Patrikas, I.; Anagnostopoulou, C. A 40 year objective climatology of surface cyclones in the Mediterranean region: Spatial and temporal distribution. Int. J. Clim. 2001, 21, 109–130. [CrossRef] 5. Trigo, I.F.; Bigg, G.R.; Davies, T.D. Climatology of Cyclogenesis Mechanisms in the Mediterranean. Mon. Weather Rev. 2002, 130, 549–569. [CrossRef] 6. Flocas, H.A.; Simmonds, I.; Kouroutzoglou, J.; Keay, K.; Hatzaki, M.; Bricolas, V.; Asimakopoulos, D. On Cyclonic Tracks over the Eastern Mediterranean. J. Clim. 2010, 23, 5243–5257. [CrossRef] Atmosphere 2020, 11, 724 31 of 33

7. Kouroutzoglou, J.; Flocas, H.A.; Keay, K.; Simmonds, I.; Hatzaki, M. Climatological aspects of explosive cyclones in the Mediterranean. Int. J. Clim. 2011, 31, 1785–1802. [CrossRef] 8. Drobinski, P.; Ducrocq, V.; Alpert, P.; Anagnostou, E.; Béranger, K.; Borga, M.; Braud, I.; Chanzy, A.; Davolio, S.; Delrieu, G.; et al. HyMeX: A 10-year multidisciplinary program on the Mediterranean water cycle. Bull. Am. Meteor. Soc. 2013, 95, 1063–1082. [CrossRef] 9. Romanić,D.; Curić,M.;´ Lompar, M.; Joviˇcić,I. Contributing factors to Koshava wind characteristics. Int. J. Clim. 2016, 36, 956–973. [CrossRef] 10. Zolt, S.D.; Lionello, P.; Nuhu, A.; Tomasin, A. The disastrous storm of 4 November 1966 on Italy. Nat. Hazards Earth Syst. Sci. 2006, 6, 861–879. [CrossRef] 11. Parodi, A.; Lagasio, M.; Maugeri, M.; Turato, B.; Gallus, W. Observational and Modelling Study of a Major Downburst Event in Liguria: The 14 October 2016 Case. Atmosphere 2019, 10, 788. [CrossRef] 12. Fujita, T.T. Downbursts: Meteorological features and wind field characteristics. J. Wind Eng. Ind. Aerodyn. 1990, 36, 75–86. [CrossRef] 13. McDonald, J.R.; Mehta, K.C.; Mani, S. A Recommendation for an Enhanced Fujita Scale (EF-Scale); Wind Science and Engineering Center, Texas Tech University: Lubbock, TX, USA, 2006; p. 111. 14. De Gaetano, P.; Repetto, M.P.; Repetto, T.; Solari, G. Separation and classification of extreme wind events from anemometric records. J. Wind Eng. Ind. Aerodyn. 2014, 126, 132–143. [CrossRef] 15. Burlando, M.; Zhang, S.; Solari, G. Monitoring, cataloguing, and weather scenarios of thunderstorm outflows in the northern Mediterranean. Nat. Hazards Earth Syst. Sci. 2018, 18, 2309–2330. [CrossRef] 16. Solari, G. Emerging issues and new frameworks for wind loading on structures in mixed climates. Wind Struct. 2014, 19, 295–320. [CrossRef] 17. Shehata, A.Y.; El Damatty, A.A.; Savory, E. Finite element modeling of transmission line under downburst wind loading. Finite Elem. Anal. Des. 2005, 42, 71–89. [CrossRef] 18. Solari, G. Wind Science and Engineering: Origins, Developments, Fundamentals and Advancements, 1st ed.; Springer International Publishing: Basel, Switzerland, 2019; ISBN 978-3-030-18814-6. 19. Biron, D.; De Leonibus, L.; Zauli, F. The lightning network LAMPINET of the Italian Air Force Meteorological Service. In Proceedings of the 19th International Lightning Detection Conference, Tucson, AZ, USA, 24–25 April 2006. 20. Biron, D. LAMPINET—Lightning Detection in Italy. In Lightning: Principles, Instruments and Applications: Review of Modern Lightning Research; Betz, H.D., Schumann, U., Laroche, P., Eds.; Springer: Dordrecht, The Netherlands, 2009; ISBN 978-1-4020-9079-0. 21. De Leonibus, L.; Biron, D.; Sist, M.; Labate, D.; Zauli, F.; Melfi, D. Wind intensity reconstruction over Italy through lampinet lightning data. In Proceedings of the 21st International Lighting Detection Conference, Orlando, FL, USA, 19–20 April 2010. 22. Solari, G.; Burlando, M.; Repetto, M.P. Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures. J. Wind Eng. Ind. Aerodyn. 2020, 200, 104142. [CrossRef] 23. Skamarock, W.C.; Klemp, J.B.; Dudhia, J.; Gill, D.O.; Barker, D.M.; Duda, M.; Huang, X.-Y.; Wang, W.; Powers, J.G. A Description of the Advanced Research WRF Version 3; National Center for Atmospheric Research: Boulder, CO, USA, 2008; p. 113. 24. Ide, K.; Courtier, P.; Ghil, M.; Lorenc, A.C. Unified notation for data assimilation: Operational, sequential and variational. J. Meteorol. Soc. Jpn. Ser. II 1997, 75, 181–189. [CrossRef] 25. Lagasio, M.; Silvestro, F.; Campo, L.; Parodi, A. Predictive capability of a high-resolution hydrometeorological forecasting framework coupling WRF cycling 3DVAR and continuum. J. Hydrometeor. 2019, 20, 1307–1337. [CrossRef] 26. Fiori, E.; Ferraris, L.; Molini, L.; Siccardi, F.; Kranzlmueller, D.; Parodi, A. Triggering and evolution of a deep convective system in the Mediterranean Sea: Modelling and observations at a very fine scale. Q. J. R. Meteorol. Soc. 2017, 143, 927–941. [CrossRef] 27. Lagasio, M.; Parodi, A.; Procopio, R.; Rachidi, F.; Fiori, E. Lightning Potential Index performances in multimicrophysical cloud-resolving simulations of a back-building mesoscale convective system: The Genoa 2014 event. J. Geophys. Res. Atmos. 2017, 122, 4238–4257. [CrossRef] 28. Paulson, C.A. The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J. Appl. Meteor. 1970, 9, 857–861. [CrossRef] Atmosphere 2020, 11, 724 32 of 33

29. Dyer, A.J.; Hicks, B.B. Flux-gradient relationships in the constant flux layer. Q. J. R. Meteorol. Soc. 1970, 96, 715–721. [CrossRef] 30. Webb, E.K. Profile relationships: The log-linear range, and extension to strong stability. Q. J. R. Meteorol. Soc. 1970, 96, 67–90. [CrossRef] 31. Beljaars, A.C.M. The parametrization of surface fluxes in large-scale models under free convection. Q. J. R. Meteorol. Soc. 1995, 121, 255–270. [CrossRef] 32. Smirnova, T.G.; Brown, J.M.; Benjamin, S.G. Performance off different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Weather Rev. 1997, 125, 1870–1884. [CrossRef] 33. Smirnova, T.G.; Brown, J.M.; Benjamin, S.G.; Kim, D. Parameterization of cold-season processes in the MAPS land-surface scheme. J. Geophys. Res. Atmos. 2000, 105, 4077–4086. [CrossRef] 34. Hong, S.-Y.; Noh, Y.; Dudhia, J. A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev. 2006, 134, 2318–2341. [CrossRef] 35. Noh, Y.; Cheon, W.G.; Hong, S.Y.; Raasch, S. Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteorol. 2003, 107, 401–427. [CrossRef] 36. Hong, S.-Y.; Lim, J.-O.J. The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteorol. Soc. 2006, 42, 129–151. 37. Iacono, M.J.; Delamere, J.S.; Mlawer, E.J.; Shephard, M.W.; Clough, S.A.; Collins, W.D. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res. 2008, 113, D13103. [CrossRef] 38. Curić,M.;´ Janc, D.; Vujović,D.; Vuˇcković,V. The effects of a river valley on an isolated cumulonimbus cloud development. Atmos. Res. 2003, 66, 123–139. [CrossRef] 39. Gonzalez, R.C.; Woods, R.E. Digital Image Processing, 2nd ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2002; ISBN 978-0-201-18075-6. 40. Zhang, S.; Solari, G.; De Gaetano, P.; Burlando, M.; Repetto, M.P. A refined analysis of thunderstorm outflow characteristics relevant to the wind loading of structures. Probabilistic Eng. Mech. 2018, 54, 9–24. [CrossRef] 41. Pantillon, F.; Adler, B.; Corsmeier, U.; Knippertz, P.; Wieser, A.; Hansen, A. Formation of wind gusts in an extratropical cyclone in light of Doppler lidar observations and large-eddy simulations. Mon. Weather Rev. 2019, 148, 353–375. [CrossRef] 42. Charba, J. Application of gravity current model to analysis of squall-line gust front. Mon. Weather Rev. 1974, 102, 140–156. [CrossRef] 43. Wakimoto, R.M. The life cycle of thunderstorm gust fronts as viewed with Doppler radar and rawinsonde data. Mon. Weather Rev. 1982, 110, 1060–1082. [CrossRef] 44. Mueller, C.K.; Carbone, R.E. Dynamics of a thunderstorm outflow. J. Atmos. Sci. 1987, 44, 1879–1898. [CrossRef] 45. Droegemeier, K.K.; Wilhelmson, R.B. Numerical simulation of thunderstorm outflow dynamics. Part I: Outflow sensitivity experiments and turbulence dynamics. J. Atmos. Sci. 1987, 44, 1180–1210. [CrossRef] 46. Lompar, M.; Curić,M.;´ Romanic, D. Implementation of a gust front head collapse scheme in the WRF numerical model. Atmos. Res. 2018, 203, 231–245. [CrossRef] 47. Lawrence, M.G. The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications. Bull. Am. Meteor. Soc. 2005, 86, 225–234. [CrossRef] 48. Alduchov, O.A.; Eskridge, R.E. Improved Magnus form approximation of saturation vapor pressure. J. Appl. Meteor. 1996, 35, 601–609. [CrossRef] 49. Von Kármán, T. The engineer grapples with nonlinear problems. Bull. Am. Math. Soc. 1940, 46, 615–683. [CrossRef] 50. Middleton, G.V. Experiments on density and turbidity currents: I. Motion of the head. Can. J. Earth Sci. 1966, 3, 523–546. [CrossRef] 51. Daly, B.J.; Pracht, W.E. Numerical study of density-current surges. Phys. Fluids 1968, 11, 15–30. [CrossRef] 52. Seitter, K.L. The effect of arc cloud generation on thunderstorm gust front motion. In Proceedings of the Preprints, 13th Conference on Severe Local Storms, Boston, MA, USA, 17–20 October 1983; pp. 249–252. 53. Goff, R.C. Vertical structure of thunderstorm outflows. Mon. Weather Rev. 1976, 104, 1429–1440. [CrossRef] 54. Hjelmfelt, M.R. Structure and life cycle of microburst outflows observed in Colorado. J. Appl. Meteor. 1988, 27, 900–927. [CrossRef] Atmosphere 2020, 11, 724 33 of 33

55. Britter, R.E.; Simpson, J.E. Experiments on the dynamics of a gravity current head. J. Fluid Mech. 1978, 88, 223–240. [CrossRef] 56. Pryor, K.L. Progress and Developments of Downburst Prediction Applications of GOES. Weather Forecast. 2015, 30, 1182–1200. [CrossRef] 57. Caracena, F.; Maier, M.W. Analysis of a microburst in the face meteorological mesonetwork in southern Florida. Mon. Weather Rev. 1987, 115, 969–985. [CrossRef] 58. Burlando, M.; Romanic, D. The ground-breaking contributions to downburst monitoring, modelling, and detection. In Oxford Handbook on Non-Synoptic Wind Storms; Oxford University Press: Oxford, UK, 2020. 59. Lompar, M.; Curić,M.;´ Romanic, D. Simulation of a severe convective storm using a numerical model with explicitly incorporated aerosols. Atmos. Res. 2017, 194, 164–177. [CrossRef] 60. Chen, F.; Kusaka, H.; Bornstein, R.; Ching, J.; Grimmond, C.S.B.; Grossman-Clarke, S.; Loridan, T.; Manning, K.W.; Martilli, A.; Miao, S.; et al. The integrated WRF/urban modelling system: Development, evaluation, and applications to urban environmental problems. Int. J. Clim. 2011, 31, 273–288. [CrossRef] 61. Solari, G.; Repetto, M.P.; Burlando, M.; De Gaetano, P.; Pizzo, M.; Tizzi, M.; Parodi, M. The wind forecast for safety management of port areas. J. Wind Eng. Ind. Aerodyn. 2012, 104–106, 266–277. [CrossRef] 62. Burlando, M.; Carassale, L.; Georgieva, E.; Ratto, C.F.; Solari, G. A simple and efficient procedure for the numerical simulation of wind fields in complex terrain. Bound.-Layer Meteorol. 2007, 125, 417–439. [CrossRef]

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