1. Introduction
A quasi-linear convective system (QLCS) is a type of mesoscale convective system (MCS; Houze 2004, 2018) that is characterized by a linear organization of convective cells. This type of storm is often responsible for large socioeconomic impacts related to its high potential of producing convective hazards, particularly damaging wind gusts (Ashley and Mote 2005; Smith et al. 2008; Schoen and Ashley 2011; Smith et al. 2013; Mathias et al. 2017; Taszarek et al. 2019; Pacey et al. 2021), tornadoes (Trapp et al. 2005; Clark 2011; Smith et al. 2012; Bech et al. 2015; Mulder and Schultz 2015; Clark and Smart 2016; Buckingham and Schultz 2020; McDonald and Weiss 2021), excessive precipitation (Schumacher and Johnson 2005, 2008, 2009; Ashley and Ashley 2008; Mateo et al. 2009; Peters and Schumacher 2015; Hu et al. 2021), and, to a lesser extent, large hail (Hocker and Basara 2008; Ashley et al. 2019). QLCSs are difficult to study because they feature complex life cycles and morphologies. Their duration can exceed several hours during which they can travel more than a thousand kilometers. Their proper identification is complex and requires access to proximity radar data, which is available only in selected parts of the world. This is one of the reasons why QLCS climatologies have been rarely developed outside the United States and have been mainly limited to case studies for selected countries.
Since QLCSs can feature a variety of complex morphologies, there have been numerous attempts to classify them using different assumptions. Some studies based their classifications on morphological archetypes (Bluestein and Jain 1985; Blanchard 1990; Loehrer and Johnson 1995; Klimowski et al. 2004), while others included stratiform precipitation modes corresponding to propagation of the system and position of the rainfall area (Parker and Johnson 2000; Parker 2007; Zheng et al. 2013; Surowiecki and Taszarek 2020). Evaluation of QLCS morphological features is important because some types are more likely to produce specific convective weather hazards than others. For example, it was found that bow echoes and associated mesovortices demonstrate a high potential of producing damaging winds (Trapp et al. 2005; Atkins and St. Laurent 2009; Celiński-Mysław and Palarz 2017; Ashley et al. 2019). The most damaging winds are typically found on the edge of the bow echo where a strong low-level mesovortex can be found (Weisman and Trapp 2003; Davis et al. 2004; Wakimoto et al. 2006; Davis and Trier 2007; Atkins and St. Laurent 2009; French and Parker 2014; Xu et al. 2015; Taszarek et al. 2019). Heavy rain and flooding events are associated with systems characterized by slow movement and/or a large area of high rainfall rates, which was primarily found for leading stratiform precipitation modes (Doswell et al. 1996; Parker and Johnson 2000; Pettet and Johnson 2003; Hu et al. 2021). Rain accumulation is also enhanced by a process of back-building, where cells repetitively form and pass over the same location for several hours, which is often called a training storm (Schumacher and Johnson 2005, 2006, 2009; Schumacher 2009). Precipitation resulting from such storms is important as large MCSs are responsible for a significant share of warm-season rainfall in midlatitudes, such as the United States, China, and Europe (Punkka and Bister 2005; Schumacher and Johnson 2008; He et al. 2016; Haberlie and Ashley 2019).
Climatologies focusing on MCS occurrence have been widely created for the United States (Geerts 1998; Haberlie and Ashley 2018a,b, 2019; Cheeks et al. 2020) and in lesser extent for parts of Mexico (Ramos-Pérez et al. 2022), subtropical South America (Salio et al. 2007), China (Yang et al. 2015), and Europe (Morel and Senesi 2002a; Rigo and Llasat 2007; Kolios and Feidas 2010; Lewis and Gray 2010; Surowiecki and Taszarek 2020). Some parts of literature also addressed climatologies of derechos, which are a particularly dangerous type of QLCS (Johns and Hirt 1987; Bentley and Mote 1998; Bentley and Sparks 2003; Celiński-Mysław and Matuszko 2014; Guastini and Bosart 2016; Gatzen et al. 2020; Fery and Faranda 2024; Squitieri et al. 2023a,b). European studies that can be linked to QLCS occurrence focused primarily on identification of bow echo storms (Púčik et al. 2011; Celiński-Mysław and Palarz 2017; Celiński-Mysław et al. 2019, 2020) and derecho case studies (Gatzen 2004; Punkka et al. 2006; Hamid 2012; Gospodinov et al. 2015; Toll et al. 2015; Mathias et al. 2017, 2019; Taszarek et al. 2019; Sipos et al. 2021; Chernokulsky et al. 2022; González-Alemán et al. 2023). Globally, there have been only a few radar-derived studies explicitly addressing QLCS climatologies that included automated detection algorithms for the United States (Ashley et al. 2019; Cui et al. 2021) and a manual QLCS identification performed for southern Brazil by Ribeiro and Seluchi (2019).
While MCS climatologies can be constructed using satellite data, which offers good spatiotemporal homogeneity (Morel and Senesi 2002a; Kolios and Feidas 2010; Lewis and Gray 2010), the same does not apply to QLCS events, which require radar data for their proper identification (Jirak et al. 2003). Considerable improvements in terms of coverage and quality of radar data, observed in recent years across Europe, have opened an opportunity to use such data in studying convective hazards. Thus, in this work, we use pan-European Operational Programme for the Exchange of Weather Radar Information (OPERA) radar data in 15-min intervals to construct a climatology of QLCSs for 2014–21. OPERA, severe weather reports, and lightning detection data were combined to identify spatiotemporal variability of QLCSs, their life cycle, severity, morphological features, accompanying hazards, and social impacts. To account for the spatial inhomogeneity of OPERA data (which are a blend from several countries with differing radar technologies and quality), QLCS identification in this paper is performed manually, similar to the methodology applied by Surowiecki and Taszarek (2020) for Poland.
2. Data and methods
a. Radar data.
Meteorological radar data allows for a detailed assessment of storm development, severity, morphology, as well as convective mode and evolution with high spatial and temporal resolution. In this work, we use the pan-European weather radar composite product of the OPERA (Huuskonen et al. 2014; Saltikoff et al. 2019) operated by the European Meteorological Network (EUMETNET). Data used in this work were collected from the Odyssey, the OPERA data center, which generates and archives composite products using raw data from individual radar sites via compositing algorithms. Data postprocessing from formats HDF5 and BUFR was provided using the Geospatial Data Abstraction Library (GDAL; GDAL/OGR Contributors 2022). From the OPERA database, we used composite maximum reflectivity, where each pixel contains the maximum of all polar cell decibels of reflectivity values of the contributing radars at a given location. Data are available for the period starting from 2012; however, there were frequent data gaps in the first 2 years. For this reason, we use the OPERA data for the period starting from 2014 up to the end of 2021 when we began our QLCS identification work. The temporal resolution of the database is 15 min with a horizontal grid spacing of 2 km on a Lambert azimuthal equal-area projection. The area for which OPERA data are available is limited to radar networks and countries which are members of the project. In this research, we considered only areas where data availability was higher than 80% over the 8 years of analysis (Fig. 1a). Since the OPERA network is a blend of data from many countries using differing radar technologies and quality, it features large spatial inhomogeneities, as discussed by Scovell et al. (2013) and Saltikoff et al. (2019). Mean reflectivity and frequency of >30 dBZ time steps across the period evaluated in this work show a bias toward the radar sites (Figs. 1b,c). The frequency of situations >50 dBZ (Fig. 1d) indicates which radar sites feature interference spikes, biases toward larger reflectivity values, and situations with potential overcompensation effects of the central processing software (e.g., Latvia). OPERA data also feature temporal inhomogeneities, as a result of the increasing number of radars in the OPERA network over the years. The data quality issues made the application of automated QLCS detection algorithms like those in Haberlie and Ashley (2018a,b) nearly impossible for OPERA and required a manual investigation of each case.
Characteristics of OPERA dataset: (a) availability (%), (b) mean reflectivity (dBZ), and frequency of situations with maximum reflectivity (c) >30 and (d) >50 dBZ for the study period. (a) also presents regions analyzed in the study.
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
b. Lightning data and severe weather reports.
For the detection of lightning activity within QLCS systems, we used data from the arrival time difference (ATD) lightning detection network (ATDnet; Anderson and Klugmann 2014; Enno et al. 2020) operated by the Met Office. ATDnet employs a system that locates lightning discharges using the ATD method (Lee 1986). During the study period, ATDnet consisted of 10 sensors. According to Enno et al. (2016, 2020), ATDnet contains up to 90% of cloud-to-ground flashes and around 25% of intracloud flashes. ATDnet is characterized by higher lightning location bias compared to short-range systems such as EUCLID (Poelman et al. 2016). However, ATDnet, as a long-range lightning location system, is able to locate flashes over remote areas or large bodies of water, which makes this dataset capable of representing spatial and temporal patterns of lightning flashes over broader areas with smaller spatial biases in contrast to short-range systems, which typically cover smaller areas.
As an auxiliary database supporting the determination of QLCS features and their intensity, we used severe weather reports from the European Severe Weather Database (ESWD; Dotzek et al. 2009), which is operated by the European Severe Storms Laboratory (ESSL). We used reports of severe wind gusts, large hail, tornadoes, and excessive rain. Definitions of these hazards can be found in the reporting criteria available on the ESWD website. For this analysis, we used reports with a quality control status of at least QC0+, which means reports passed a plausibility check by the ESSL staff. ESWD data feature strong spatiotemporal biases with more efficient severe weather reporting observed over central and western Europe with an overall increasing number of reports over the years (Groenemeijer et al. 2017; Taszarek et al. 2020a). While the biases could have affected some of our results, ESWD reports were considered only as supportive information for QLCS intensity determination.
c. Determination of QLCS cases.
For the purposes of QLCS identification, we created animations with 15-min temporal steps covering a period of 2014–21. Each animation frame consisted of 1) OPERA radar reflectivity, 2) ESWD severe weather reports, and 3) ATDnet lightning data. Based on these animations, a person doing a manual investigation with GIS software derived polygons indicating QLCS areal extent (Fig. 2). Manual evaluation of each case made it possible to define various QLCS features, including recognition of morphological and precipitation archetypes while accounting for spatiotemporal biases and differing quality of OPERA radar data (Fig. 1). To ensure QLCSs were manually identified with consistency, polygons spatially delineating the bounds of QLCS evolution were created by a single researcher (lead author of this work), as in Surowiecki and Taszarek (2020). QLCS polygons identified in this work are consistent with the MCS definition (Parker and Johnson 2000), which means that the system should develop as a result of a deep moist convection and has a long-axis length of 100 km. QLCSs also needed to last for at least 3 h, which converts to 12 consecutive 15-min OPERA radar scans. QLCSs were first identified when a linear storm structure with reflectivity of at least 40 dBZ was observed. The end of a QLCS’s life cycle was indicated by the disintegration of linear radar reflectivity structure, significant weakening of the entire storm system, and/or disappearance of lightning activity. An example of QLCS identification is included in the online supplemental material.
Examples of QLCS polygons collected in this study.
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
It is important to mention the limitations of manual QLCS identification. In some cases (e.g., Fig. 2d), certain sections of the convective line were weak (<35 dBZ or barely any lightning activity), and it was a subjective decision of the person performing QLCS identification to not include those sections as a part of the QLCS. Manual polygonization was also burdened with minor spatial shifts of QLCS polygon borders (±50 km). Thus, along with spatial variability of radar quality (Fig. 1), lightning, and severe weather report inhomogeneities, it is evident that any methodology employed will have drawbacks and the resulting database will be imperfect but still should be useful.
In total, the manual investigation of databases allowed us to identify 1475 QLCS events over the 8 years studied. QLCSs were binned into intensity categories of marginal (1151 cases, 78.0%), moderate (272 cases, 18.5%), and those reaching a class of a derecho (52 cases, 3.5%). Determination of intensity was based on radar-derived morphology of the QLCS and subjective evaluation of socioeconomic impacts based on ESWD reports. In some regions, where severe weather reporting was notably less efficient (e.g., the Balkans), more weight was placed on intensity estimation based on radar signatures. For example, it is well known that bow echoes are typically associated with enhanced potential of severe weather, particularly damaging winds (Przybylinski 1995). Further details on assumptions related to QLCS intensity ratings used in this study are provided in Table 1.
Definition of QLCS intensity classes included in the database.
Manual evaluation of each case made it possible to identify morphological features accompanying QLCS, such as bow echo (BE), a bow echo complex (BEC), a squall-line bow echo (SLBE), a mesoscale convective vortex (MCV), and a back-building (BB) mode (Table 2, Fig. 3). These features were based on a modified classification proposed by Bluestein and Jain (1985), Klimowski et al. (2004), and Surowiecki and Taszarek (2020). We also considered following QLCS stratiform precipitation modes: trailing stratiform (TS), embedded stratiform (EMB), leading stratiform (LS), parallel stratiform (PS), training line/adjoining stratiform (TL/AS), and no stratiform (NS), further explained in Table 2 and Fig. 3. Precipitation archetypes used in this work were based on Parker and Johnson (2000), Gallus et al. (2008), Zheng et al. (2013), and Surowiecki and Taszarek (2020). It is important to note that multiple features could have been assigned to one QLCS, especially for long-lived systems that evolve over time (Parker and Johnson 2000; Dalal et al. 2012).
Mean annual number and fraction of QLCS precipitation modes.
Schematic representation of QLCS types identified in this study.
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
d. QLCS socioeconomic impacts.
The polygons and collected metadata allowed us to define QLCS areal extent, system duration, speed, forward motion, width, length, accompanying hazards, and the resulting number of injuries and fatalities. For each case, we calculated coverage and fraction of QLCS area associated with severe wind, tornado, large hail, excessive precipitation, and lightning, assuming 40-km circles around each event (consistent with Storm Prediction Center and European Storm Forecast Experiment forecast verification assumptions). The aforementioned collected QLCS attributes contributed to a better understanding of what QLCS storm modes were most efficient at generating severe weather. In addition, for the purposes of this study, we used an equation, which includes QLCS area and number of injuries and fatalities (QLCS Impact Index—it appears in supplemental material), to rank QLCS events in terms of their socioeconomic impacts. Five of the most impactful events based on that index are described in section 3e.
3. Results and discussion
a. Temporal variability.
Over the period 2014–21, the mean annual number of QLCSs was 184, including 144 rated as marginal, 34 rated as moderate, and 6 rated as a derecho. The highest number of 169 marginal events took place in 2020, the highest of 52 moderate events took place in 2019, and the highest of 13 derechos took place in 2017 (Fig. 4a). At least 152 QLCS cases were observed each year, with a maximum of 209 reported in 2017 (Fig. 4b). Since previous climatologies indicated markedly different annual cycles of lightning over various parts of Europe (Galanaki et al. 2018; Enno et al. 2020; Taszarek et al. 2020a), we evaluate monthly patterns in QLCSs in the division for two regions. First, the “south region” covers an area west of 18°E longitude and south of 46°N latitude, while the “north and central region” covers the remaining part of our domain (Fig. 1a). QLCS cases are assigned to these regions based on their polygon centroid coordinates.
Temporal variability of QLCS occurrence: (a) annual distribution of QLCS events; (b) daily cumulative frequency diagram of QLCS counts in each year; (c),(d) monthly distribution of events for regions; and (e) start and end UTC time of QLCSs in the database.
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
For the north and central region, we observed 1031 QLCSs, which comprise 69.9% of cases in our database. In that region, a period of higher QLCS frequency lasts from May (119 cases) to September (91 cases) with the highest frequency observed in June and July (237 each; Fig. 4c). However, June is the month with the largest frequency of moderate (62) and derecho (10) events. During winter, QLCSs across the north and central region are rare (especially in December) and are primarily limited to narrow cold-frontal rainbands (NCFR; Gatzen et al. 2011; Ludwig et al. 2015; Surowiecki and Taszarek 2020).
Across the south region, the annual pattern is different with much smaller differences observed between seasons (Fig. 4d). While the lowest activity is still observed in winter (February), the peak is in September and October (up to 70 cases in each month). Only three derecho cases were documented across this region, most likely due to the majority of this area being covered by water, which limits the chance to meet the derecho criteria. On the other hand, the warm waters of the Mediterranean Sea are the main driver of the deep moist convection activity during autumn. In this context, QLCS activity over this area is consistent with European lightning climatologies (Galanaki et al. 2018; Enno et al. 2020; Taszarek et al. 2020a). As shown by González-Alemán et al. (2023), an extremely severe Mediterranean derecho on 17 August 2022 (that year was not included in our work) was driven by record-high positive anomalies in sea surface temperatures that significantly contributed to QLCS intensity.
In both regions, most QLCSs developed in the afternoon between 1200 and 1500 UTC and dissipated between 1500 and 2100 UTC (Fig. 4e). However, the division into intensity categories indicated that stronger QLCSs tend to last longer and dissipate in the late evening hours, which is especially clear for derecho events. The time of initiation for derechos also typically occurs earlier, even in the early morning, between 0600 and 1500 UTC. Gatzen et al. (2020) indicated that derecho events across Germany developed most frequently between 1200 and 1700 UTC, while Surowiecki and Taszarek (2020) estimated a peak squall-line/bow-echo development hour for 1400 UTC with dissipation around 0000 UTC for Poland. MCSs across the Mediterranean basin evaluated by Kolios and Feidas (2010) most frequently initiated between 1500 and 1700 UTC and dissipated between 1800 and 2100 UTC, which is broadly consistent with our results.
b. Spatial variability.
To fairly evaluate QLCS spatial variability, QLCS cases were divided between warm (April–September) and cold (October–March) seasons to reflect the occurrence of QLCSs and their movement based on common supporting synoptic environments, as in Surowiecki and Taszarek (2020).
The most frequent events in the database were marginal warm-season cases (933), which occurred throughout the domain (Fig. 5a). Warm-season moderate (230; Fig. 5c) and derecho events (32; Fig. 5e) moved from the south and southwest and were most frequent in central and western parts of Europe. These events were most commonly supported by overlapping strong instability and sufficient vertical wind shear associated with the passage of the trough. The strong instability typically materializes when rich low-level moisture originating from the Mediterranean or strong evapotranspiration is overspread by an elevated mixed layer emanating from northwest Africa (Carlson and Ludlam 1968; Hamid 2012; Mathias et al. 2017). During the cold season, the majority of southwestern Europe QLCS cases moved from southwest to northeast, while northern and central Europe QLCSs approached from the west-northwest (Figs. 5b,d,f). Such distributions are associated with the passage of extratropical cyclones with surface cold fronts that are the main cause of cold-season QLCSs in this part of Europe (Ludwig et al. 2015; Mathias et al. 2019; Celiński-Mysław et al. 2020; Gatzen et al. 2020; Pacey et al. 2021). In the cold season, the warm waters of the Mediterranean Sea are also supportive of the development of marginal and moderate QLCS events moving from the south, which are observed most frequently over the Ligurian Sea and north Mediterranean (Figs. 5b,d). Our results obtained for Central Europe are in agreement with Surowiecki and Taszarek (2020), where warm-season MCSs in Poland originated from the south and cool-season MCSs approached from the northwest.
Forward motion vectors of marginal, moderate, and derecho events divided into (a),(c),(e) warm and (b),(d),(f) cold seasons. The location of the arrows represents the centroid of each QLCS polygon.
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
The QLCS polygon density distribution for 2014–21 indicates that around 40 QLCSs (5 yr−1), regardless of intensity, occurred in the corridor stretching from southeastern France, up to northern Germany, Hungary, and portions of neighboring countries (Fig. 6a). The spatial distribution of marginal and moderate QLCSs (Figs. 6b,c) was similar to all QLCSs (albeit with lower frequencies), with a spatial frequency maximum in southern France, Romania, and parts of the Mediterranean coastline, consistent with Kolios and Feidas (2010) and Morel and Senesi (2002a). A derecho corridor (one to two cases per year) appears across portions of France, eastward to Czechia and Poland, with a derecho frequency maximum noted in Germany (Fig. 6d), similarly found by Celiński-Mysław and Matuszko (2014), Gatzen et al. (2020), and Surowiecki and Taszarek (2020).
(a) Spatial distribution of all QLCSs included in the database and (b)–(d) intensity classes in the period 2014–21.
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
During the spring, a QLCS frequency maximum was identified over France, with about one to two cases per year, with incidental QLCS occurrences also noted across the remainder of Europe (Fig. 7a). Summer is characterized by the highest frequency of QLCSs, with more than two cases per year in the western, central, and eastern parts of Europe. The largest summertime frequency is in Hungary and Slovakia with more than four cases per year (Fig. 7b). By autumn, the QLCS frequency decreases across most of Europe though a relative peak in QLCS frequency was noted across southeastern France (around three cases per year), as well as Slovenia into Croatia (Fig. 7c). During winter, QLCSs are rare and limited mainly to northwestern Europe (around one case per year; Fig. 7d). Consistent with previous MCS and lightning climatologies for Europe, we hypothesize that an increased QLCS frequency also occurs over the central parts of the Mediterranean and the Balkan Peninsula, but radar data from these areas were not available within the OPERA network. Further information on the spatiotemporal variability of specific QLCS precipitation and morphological features is provided in the supplemental material.
Spatial distribution of all QLCS events among seasons in the period 2014–21.
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
c. QLCS characteristics.
QLCS duration, width, length, areal coverage, and speed were also evaluated as a function of QLCS intensity (Fig. 8). As in Surowiecki and Taszarek (2020), QLCS duration was dependent on storm mode, with bow echoes having the longest duration of 8–10 h. In satellite-based studies, duration (>3 h) and areal coverage of the convective system (>10 000 km2) were also the main criteria used to classify MCSs (Morel and Senesi 2002b; Jirak et al. 2003; Cheeks et al. 2020; Feng et al. 2023). However, due to cloud anvils being much larger than radar-derived features, these thresholds are difficult to compare with our study. In the present study, marginal events were generally characterized by a life cycle of 3–4 h, width of 200–250 km, length of 150–200 km, covered an area of typically less than 40 000 km2, and moved with a speed of 40–50 km h−1 (Fig. 8). Cases classified as moderate featured a typical duration of 4–7 h, width and length of around 300 km, covered around 60 000 km2, and moved with a speed of around 50–65 km h−1. The most extreme QLCS characteristics came with derecho cases that lasted as long as 27 h, had a width and/or length exceeding 1500 km, covered an area of up to 885 000 km2, and moved with a speed exceeding 120 km h−1. All analyzed intensity categories were characterized by a very similar dominant direction of QLCS movement, which was from the southwestern and western direction (Fig. 8). However, this parameter seems to depend somewhat on geographical location (Fig. 5). For derechos, a notably higher fraction of cases were found with western and northwestern forward motion vectors, which are mostly related to cold-season events (Fig. 5f).
Probability density function of selected QLCS characteristics as a function of QLCS intensity.
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
The longest QLCS track in our database occurred on 9 August 2018 and had a length of 2200 km (Fig. 9a). This storm was associated with the surface low pressure system and accompanying cold front passing through western and central Europe. It produced intense lightning and numerous excessive precipitation and damaging wind gusts across the Netherlands and Germany. The QLCS with a maximum width of 1635 km was registered on 10 January 2015 in the form of an NCFR (Fig. 9b). While this QLCS did not produce much lightning, widespread damaging gusts did occur across much of central Europe. Both cases were classified as derechos.
(a) The longest and (b) the widest QLCS track included in the database. The QLCS on (a) 9 Aug 2018 developed across southern France and moved northeast up to central Sweden, while the QLCS on (b) 10 Jan 2015 developed over southeastern Great Britain and the North Sea and moved southeast up to Poland and Czech Republic.
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
The slowest moving QLCSs (<20 km h−1) were evenly distributed across Europe, with localized frequency maxima noted over southern France and parts of southern Europe (Fig. 10a), roughly coinciding with the marginal QLCS frequency maxima (Fig. 6b). For the fastest-moving QLCSs (>80 km h−1), a frequency corridor was clearly noted across northwestern Europe, with a frequency maximum located over northeastern France to western Germany (Fig. 10b). The fast-moving QLCS frequency corridor can be linked to extratropical cyclones frequently passing through this area with strong mean flow resulting from large spatial pressure gradients, especially during winter. Climatological studies of MCSs in the United States (Feng et al. 2019; Cheeks et al. 2020) indicated that the 10th–90th percentiles of MCS movement speeds ranged from 6 to 30 m s−1 (22 to 108 km h−1). In our database, the corresponding range of percentiles was between 6.1 and 20.4 m s−1 (22 and 73 km h−1). Lower values of the upper percentile range obtained in our research may be related to less favorable severe thunderstorm environments in Europe, which are characterized by both lower CAPE and mean wind as compared to the United States (Taszarek et al. 2021).
Spatial variability of (a) slow- and (b) fast-moving QLCSs in the database.
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
d. Accompanying hazards.
Lightning is the most frequent hazard associated with QLCSs. On average, lightning covered 94.4% of the QLCS path, with warm-season cases featuring a mean of 96.5% compared to 85.6% for cold-season cases (Fig. 11). However, a notable fraction of cold-season QLCS demonstrated limited lightning activity (Fig. 12). For example, half of the cold-season derechos had less than 70% of the track covered by lightning. This can be justified by much smaller thermodynamic instability accompanying such events. Cold-season derechos are often driven by strong vertical wind shear and synoptic-scale lift associated with extratropical cyclones. While a considerable share of severe wind reports within such systems is driven by convection (thus lighting activity), it is possible that some fractions originate from isallobaric flow (Gatzen et al. 2020; Surowiecki and Taszarek 2020). This introduces some uncertainties and makes studying cold-season cases more challenging than their warm-season counterparts. On the contrary, the highest lightning coverage of 99.6% was found for QLCSs producing an MCV feature.
Mean percentage of QLCS area covered with convective hazards (a single lightning detection or severe weather report covers a 40-km circle).
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
Box-and-whisker plots of (a),(b) storm reports and (c),(d) lightning and barplots of (e),(f) injuries and (g),(h) fatalities based on QLCS intensity categories and seasons. The median on the box-and-whisker plots is represented as a horizontal line inside the box, the edges of the box represent the 25th and 75th percentiles, and the whiskers represent the 10th and 90th percentiles.
Citation: Bulletin of the American Meteorological Society 105, 8; 10.1175/BAMS-D-23-0257.1
In terms of severe weather reports, severe winds were the most frequent and covered on average 7.9% of the QLCS area (Fig. 11). The second most frequent hazard was excessive precipitation with a mean coverage of 6.1%, then large hail (2.9%), and tornadoes (0.5%) as the least common. This is in line with Klimowski et al. (2003), Duda and Gallus (2010), and Smith et al. (2012, 2013), who showed that QLCSs produce a considerably higher fraction of severe wind gusts compared to other convective modes across the United States. Ashley et al. (2019) also highlighted that 28% of severe winds, 21% of tornadoes, and 10% of severe hail reports were attributed to QLCSs across the central and eastern United States and that these proportions were even higher among cold-season and nocturnal cases.
The presence of a bow echo (BEC and SLBE) and/or MCV often doubled the mean coverage of severe weather hazards compared to an average QLCS (Fig. 11). While still relatively small, coverage of large hail reports exceeding 6% in BEC and MCV cases was the largest among all QLCS features evaluated in this work. Among intensity categories, marginal events had a mean severe weather coverage of each hazard (excluding lightning) between 0.2% and 3.9% compared to the range of 1.2%–18.9% for moderate cases (Fig. 11) with very similar fractional distributions for both warm and cold seasons (Figs. 12a,b). In terms of absolute coverage of severe weather reports (and lightning), cold-season events featured typically larger numbers due to the overall bigger area affected by these systems (Figs. 12a,c). QLCSs classified as derechos (both warm and cold) had more than half of their tracks covered with severe weather reports, with the vast majority by severe wind reports (Fig. 11). Derechos also had the highest coverage of tornado reports (1.5%) in comparison to other QLCS types.
In terms of QLCS precipitation archetypes, the highest coverage with severe wind gusts was observed in EMB (9.8%), while the lowest was for NS (3.7%). The differences in severe reports coverage among the various precipitation modes were small, especially for large hail and tornadoes. Thus, the distinction among these QLCS classes may have little value for nowcasting severe convective hazards. In the case of heavy rain, the PS mode resulted in the largest coverage of such reports (11.8%). BB systems had an even higher coverage of 13.5%, which is broadly consistent with previous literature indicating their precipitation potential (Schumacher and Johnson 2005, 2006, 2008, 2009; Lee et al. 2016; Lagasio et al. 2017). Interestingly, the risk of severe wind gusts with such systems was still present (coverage of 5.5%).
A comparison of seasons showed that QLCSs occurring in the cold part of the year were characterized by higher coverage of severe wind gusts and tornado reports as compared to the warm season but overall lower coverage of large hail and excessive precipitation events (Fig. 11). The speed of the QLCS movement was found to be a much better discriminator of the coverage of severe wind gusts and excessive rainfall than stratiform precipitation modes. The coverage of severe wind gusts and excessive rainfall notably increased (decreased) from 3.0% (10.2%) to 24.9% (3.0%) between the slow-moving (<20 km h−1) and fast-moving (>80 km h−1) QLCS systems.
e. Socioeconomic impacts.
QLCSs in 2014–21 led to 104 fatalities (13 yr−1) and 886 injuries (110 yr−1; Figs. 12e,g). The majority of these were caused by severe wind gusts (73.6% of fatalities and 87.6% of injuries). Excessive rain events were associated with the rest of the fatalities (26.4%) but were responsible for only 1.4% of the injuries. The rest of the injuries were caused either by tornadoes (6.7%) or by large hail (4.3%). It is noteworthy that among 1475 QLCSs, only 10 were responsible for 47 fatalities and 486 injuries, which is around half of all QLCS-related injuries and fatalities. Among intensity categories, derechos had the highest injury and fatality rates (Fig. 12). While severe weather reports associated with cold-season QLCSs covered larger absolute areas, warm-season derechos had larger injury and fatality rates. This is likely due to the increased vulnerability in the warm season, partly stemming from the presence of leaves on deciduous trees, which makes them more prone to wind due to the larger area exposed to wind and drag (Vollsinger et al. 2005) and also due to more people being outdoors at risk. During our study period, falling trees or branches were responsible for 70.3% of wind-related fatalities in the warm part of the year (May–October) compared to 56.3% of wind-related fatalities in the cold part of the year (November–April).
To quantify socioeconomic impacts produced by QLCSs, we derived a list of five of the most impactful QLCS events (Table 3) based on the QLCS Impact Index (supplemental material). One of the events was a cold-season derecho that occurred on 9 February 2020. This system had a remarkable spatial coverage of severe weather reports spanning 613 000 km2, which accounted for nearly 80% of the total QLCS area. Among other cases, there are three that occurred in 2017 in the late summer (17 September 2017, 18 August 2017, and 11 August 2017). Each of these warm-season derechos had a track length of around 1000 km and width of 350–450 km, killed several, and injured at least 50 people. The derecho of 17 September 2017 affected several countries across the Balkan Peninsula, particularly Serbia and Romania. The system caused considerable damage, especially in the Timisoara area, with the highest measured wind gust of 144 km h−1 (Sipos et al. 2021). The QLCS of 18 August 2017 moved from southeastern France through Switzerland, south Germany, Austria, and the Czech Republic with a speed of around 100 km h−1, which is uncommon for warm-season events. The severe weather outbreak on 11 August 2017 was associated with a large bow echo with embedded MCV, with a peak wind gust of 151 km h−1 (Taszarek et al. 2019). This severe weather outbreak was also part of a family of four derechos spanning a 3-day period between 10 and 12 August that occurred in a belt from northern Italy through Austria, Czechia, Poland into northern Estonia. The oldest from the group of most intense QLCSs occurred on 9 June 2014 and had a shorter track compared to the other cases, but it moved across a densely populated conurbation of Düsseldorf and Essen. Large amounts of trees were brought down on the highways and catenary of the railways, severely impacting the traffic in the area over the following days and weeks. Düsseldorf airport measured a peak wind gust of 144 km h−1. Further details regarding this derecho are available in Mathias et al. (2017).
Five most impactful QLCSs in the period 2014–21.
4. Summary and concluding remarks
The increase in coverage and quality of radar data across Europe over recent years allows for the derivation of a continental-scale QLCS climatology. Nearly a decade of OPERA, ESWD severe weather reports, and ATDnet lightning data were used to manually construct the QLCS climatology and categorize QLCSs by severity, morphology, severe hazards, and societal impacts. In total, 1475 QLCSs were identified, with 1151 events classified as marginal, 272 as moderate, and 52 as derechos. The most important findings are highlighted below:
The mean annual number of QLCSs was 184, including 144 rated as marginal (78.3%), 34 as moderate (18.5%), and 6 as a derecho (3.2%).
Spatially, the highest annual frequency of QLCSs was in France, Benelux, Germany, and Hungary (more than five cases per year).
Among seasons, QLCSs are most frequent during the warm season in western and central Europe (peak in June and July), with the QLCS frequency maximum shifting to southern Europe in late autumn (peak in September and October).
In winter, QLCSs are rare, and they appear most often in northwestern Europe in the form of NCFRs.
The duration, width, length, area, and speed of QLCSs increased with their severity. The longest classified QLCS track had a length of 2200 km, with the widest QLCS reaching 1635 km wide.
Warm-season QLCSs moved predominantly from the south/southwest (S/SW), while cold-season QLCSs moved from the west/northwest (W/NW).
QLCSs typically develop around 1200–1800 UTC and dissipate between 1500 and 2100 UTC. Stronger QLCSs (especially derechos) tend to last longer.
Fastest-moving QLCSs occurred over northwestern Europe (coinciding with derechos), while the slowest-moving QLCSs occurred over southern France (coinciding with marginal events).
Lightning covered almost 95% of the QLCS area, with warm season cases featuring typically larger coverage compared to cold season.
Among ESWD reports, severe winds were the most frequent (covering on average 7.9% of the track area), followed by excessive precipitation (6.1%), large hail (2.9%), and then tornadoes (0.5%).
Excessive precipitation events were the most frequent with the PS archetype (11.8%), especially when the BB feature was present (13.5%).
TS and EMB stratiform precipitation modes were the most frequent and occurred in around half of all QLCS cases.
Bow echo features (BE, SLBE, and BEC) were reported in around 29% of QLCSs. An MCV feature was rare and occurred in only 9% of QCLSs. The presence of a bow echo and/or MCV typically doubled the risk of convective hazards.
Derechos had the largest coverage of severe weather reports, mostly related to severe wind events (49.8%). They also had the largest coverage of tornado reports (1.5%).
Warm-season derechos had the largest relative frequency of injuries, fatalities, storm reports, and lightning and were typically associated with the largest societal impacts.
QLCSs caused 104 fatalities (13 yr−1) and 886 injuries (110 yr−1).
According to the WMO (2021), floods and storms were the leading cause of economic losses in Europe between 1970 and 2021, and strong winds are considered one of the most impactful and costly hazards across Europe (Ulbrich et al. 2013). As indicated by Pilorz et al. (2023), on average, 28 people die and 216 are injured per year in central Europe due to convective hazards (severe wind, hail, tornadoes, and excessive rain). Results presented in this study indicate that QLCSs occur regularly across Europe and they pose an enhanced risk of producing convective hazards.
Many researchers highlighted that along with a warming climate, the frequency of convective hazards and their conducive environments has already increased or/and will increase in the future (Mohr and Kunz 2013; Púčik et al. 2017; Rädler et al. 2019; Taszarek et al. 2020b; Lepore et al. 2021; Pilguj et al. 2022; Battaglioli et al. 2023). Thus, in our future work, we plan to use the developed QLCS database in studying their accompanying environments. The results of such work may lead to a better understanding of QLCS occurrence across Europe, potential improvements in their operational forecasting, and more reliable QLCS environmental estimates with reanalysis and climate projection data.
Acknowledgments.
Funding for this work was provided by the Grants of the Polish National Science Centre (2019/33/N/ST10/00403 and 2020/39/D/ST10/00768). Computational tasks were performed at the Wrocław Centre of Networking and Supercomputing (http://www.wcss.wroc.pl, Grant 170). We would like to thank all organizations providing us with the data used in this study, i.e., EUMETNET for the OPERA composite and Met Office for the ATDnet database. We also thank the employees of the Terrestrial Remote Sensing Department of the Institute of Meteorology and Water Management, National Research Institute, for their support in data access. We kindly thank three anonymous reviewers for their helpful suggestions. Part of the work on the publication was carried out during the stay (of the corresponding author) at NOAA/National Severe Storms Laboratory (Norman, Oklahoma).
Data availability statement.
OPERA dataset operated by EUMETNET was accessed by Odyssey, the OPERA data center (www.eumetnet.eu/activities/observations-programme/current-activities/opera/), based on the license agreement for research and educational use of this data. The ATDnet lightning dataset was provided by the Met Office but is not publicly available due to the contemporary nature of the data: contact debbie.osullivan@metoffice.gov.uk for usage information. European severe weather reports are available at the European Severe Weather Database (https://eswd.eu/).
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