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  • View in gallery

    Geographical distribution of the population of total CFs and percentage of CFs with different flashes: (a) total CFs, (b) with no flash, (c) with 1–3 flashes, (d) with 3–50 flashes, and (e) with >50 flashes. The distribution is created on a 2° × 2° grid from 16 years (1998–2013) of TRMM observations.

  • View in gallery

    Seasonal variation of CFs and intense CFs with >50 flashes. (a) Population of total CFs (b) Percentage of intense CFs with >50 flashes.

  • View in gallery

    Environmental composites for intense CFs with >50 flashes during AMJ over SCUS. (a) Locations of intense CFs (black crosses) and topography (color fill). The red box represents the region of interest. (b) Composite 10-m wind vectors, 2-m temperature (contours), and 2-m specific humidity (color fill). (c)–(e) Composite wind vectors, temperature (contours), and relative humidity (color fill) at (c) 850, (d) 700, and (e) 500 hPa. (f) Composite CAPE (color fill) and CIN (contours). The bold black cross in (b)–(f) marks the centroid location of the intense CFs with >50 flashes. Composites are made using ERA-Interim data starting at the surface pressure. The area with high terrain is left blank if the terrain reaches that level in all analyses.

  • View in gallery

    (a) Composite vertical cross section of wind and equivalent potential temperature (color fill) of intense CFs with >50 flashes along the latitude where CFs occur during AMJ over SCUS. (b) Difference cross section of wind, equivalent potential temperature, and RH. The difference is the condition of weak CFs subtracted from the condition of intense CFs. The color fill represents equivalent potential temperature and the contours lines show the difference in RH. Positive values are in solid contours and negative values are in dashed contours. (c) As in (a), but along the longitude that CFs occur. (d) As in (b), but along the latitude that CFs occur. The thick black lines present the mean near-surface pressure.

  • View in gallery

    As in Fig. 3, but for HIMA during MAM.

  • View in gallery

    As in Fig. 4, but for HIMA during MAM.

  • View in gallery

    Composite profiles of (left) RH and SH and (right) potential and equivalent potential temperatures for CFs with different flashes in different seasons over HIMA. (a) RH (solid) and SH (dashed) in spring (MAM). (b) Potential and equivalent potential temperatures in MAM. (c) RH in JJA. (d) Potential and equivalent potential temperatures in JJA. (e) RH in SON. (f) Potential and equivalent potential temperatures in SON.

  • View in gallery

    As in Fig. 3, but for ARGEN during DJF.

  • View in gallery

    As in Fig. 4, but for ARGEN during DJF.

  • View in gallery

    As in Fig. 3, but for COLOM during JJA.

  • View in gallery

    As in Fig. 4, but for COLOM during JJA.

  • View in gallery

    As in Fig. 3, but for SAHEL during JJA.

  • View in gallery

    As in Fig. 4, but for SAHEL during JJA.

  • View in gallery

    As in Fig. 3, but for CONGO during MAM.

  • View in gallery

    As in Fig. 4, but for CONGO during MAM.

  • View in gallery

    As in Fig. 3, but for NWM during JJA.

  • View in gallery

    As in Fig. 4, but for NWM during JJA.

  • View in gallery

    Composite soundings for CFs with 1–3 flashes (dashes lines) and >50 flashes (solid lines) over HIMA during (a) MAM, (b) JJA, and (c) SON. The wind barbs are for CFs with >50 flashes. Temperature is shown in black lines, dewpoint temperature in green lines, and temperature of lifted most unstable parcels from near the surface in the red lines.

  • View in gallery

    Composite soundings for CFs with 1–3 flashes (dashes lines) and >50 flashes (solid lines) during the warm season over different regions: (a) ARGEN during DJF, (b) SAHEL during JJA, (c) COLOM during JJA, (d) SCUS during AMJ, (e) NWM during JJA, and (f) CONGO during MAM. The wind barbs are for CFs with >50 flashes. Temperature is shown in black lines, dewpoint temperature in green lines, and temperature of lifted most unstable parcels in the red lines.

  • View in gallery

    Box-and-whisker plots of (a) CAPE, (b) CIN, (c) wind shear from 1 to 6 km above the surface (SHEAR1–6km), and (d) storm relative helicity from 1 to 3 km above the surface (SRH1–3km) for weak (light blue) and intense (pink) CFs over different regions. The thick line denotes the median, and the box outlines the 25th and 75th percentiles. The small circles represent outliers.

  • View in gallery

    Composite hodograph of intense CFs with >50 flashes during the warm season over different regions.

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What Are the Favorable Large-Scale Environments for the Highest-Flash-Rate Thunderstorms on Earth?

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  • 1 Department of Physical and Environmental Sciences, Texas A&M University–Corpus Christi, Corpus Christi, Texas
  • | 2 Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah
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Abstract

A 16-yr Tropical Rainfall Measuring Mission (TRMM) convective feature (CF) dataset and ERA-Interim data are used to understand the favorable thermodynamic and kinematic environments for high-flash-rate thunderstorms globally as well as regionally. We find that intense thunderstorms, defined as having more than 50 lightning flashes within a CF during the ~90-s TRMM overpassing time share a few common thermodynamic features over various regions. These include large convective available potential energy (>1000 J kg−1), small to moderate convection inhibition (CIN), and abundant moisture convergence associated with low-level warm advection. However, each region has its own specific features. Generally, thunderstorms with high lightning flash rates have greater CAPE and wind shear than those with low flash rates, but the differences are much smaller in tropical regions than in subtropical regions. The magnitude of the low- to midtropospheric wind shear is greater over the subtropical regions, including the south-central United States, Argentina, and southwest of the Himalayas, than tropical regions, including central Africa, Colombia, and northwest Mexico, with the exception of Sahel region. Relatively, favorable environments of high-flash-rate thunderstorms in the tropical regions are characterized by higher CAPE, lower CIN, and weaker wind shear compared to the high-flash-rate thunderstorms in the subtropical regions, which have a moderate CAPE and CIN, and stronger low to midtropospheric wind shear.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Nana Liu, nliu@islander.tamucc.edu

Abstract

A 16-yr Tropical Rainfall Measuring Mission (TRMM) convective feature (CF) dataset and ERA-Interim data are used to understand the favorable thermodynamic and kinematic environments for high-flash-rate thunderstorms globally as well as regionally. We find that intense thunderstorms, defined as having more than 50 lightning flashes within a CF during the ~90-s TRMM overpassing time share a few common thermodynamic features over various regions. These include large convective available potential energy (>1000 J kg−1), small to moderate convection inhibition (CIN), and abundant moisture convergence associated with low-level warm advection. However, each region has its own specific features. Generally, thunderstorms with high lightning flash rates have greater CAPE and wind shear than those with low flash rates, but the differences are much smaller in tropical regions than in subtropical regions. The magnitude of the low- to midtropospheric wind shear is greater over the subtropical regions, including the south-central United States, Argentina, and southwest of the Himalayas, than tropical regions, including central Africa, Colombia, and northwest Mexico, with the exception of Sahel region. Relatively, favorable environments of high-flash-rate thunderstorms in the tropical regions are characterized by higher CAPE, lower CIN, and weaker wind shear compared to the high-flash-rate thunderstorms in the subtropical regions, which have a moderate CAPE and CIN, and stronger low to midtropospheric wind shear.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Nana Liu, nliu@islander.tamucc.edu

1. Introduction

Even in their rarity, intense convective phenomena often present a significant threat to life and property. Knowing where there is a high potential of intense thunderstorms could improve weather forecasting and help the public to be better prepared for such events (Brooks et al. 2003). Favorable conditions for convection are proposed in past studies, such as conditional instability, abundant moisture in low-level troposphere, dynamical lifting, and strong wind shear between lower and upper levels (e.g., Fulks 1951; Miller 1972). Later case studies have revealed that both mesoscale and/or synoptic-scale processes contribute to initial lifting prior to thunderstorms, and are usually critical in storm initiation (e.g., Carlson et al. 1983; Cotton et al. 1983; Doswell 1984; Rockwood and Maddox 1988). Once storms are initiated, wind shear is believed to be particularly important in influencing storm severity and longevity (e.g., Weisman and Klemp 1982; Schoenberg Ferrier et al. 1996; Takemi 2007).

Despite a significant amount of research to date on the mechanisms of intense convection, the majority of data collected on those events are from the United States (e.g., Cotton et al. 1983; Carey and Rutledge 1996; Gallo et al. 2012) and some case studies over specific regions, such as the Himalayas (Barros and Lang 2003; Rasmussen and Houze 2012), Brazil (Chaves and Cavalcanti 2001; Pinto et al. 2004), west Africa (Taylor et al. 2010; Cetrone and Houze 2011), and Australia (Bringi et al. 1996; May 1996). Comprehensive knowledge of intense thunderstorms over underdeveloped regions is still inadequate. In many sparsely populated and remote regions, records or reporting of intense thunderstorms are limited and only a few countries have official systems for these reports. Besides limited availability of reports, changes through time and a lack of uniformity in standards for data collection among different countries make comparisons over different regions difficult.

Data from satellites offer one possible approach to solving the problem of different data standards, owing to their large spatial–temporal and near-uniform coverage. The Tropical Rainfall Measuring Mission (TRMM; Kummerow et al. 1998) was a satellite that become a useful tool in the effort to explore intense convection across the tropics and subtropics since its launch (Liu and Zipser 2005; Zipser et al. 2006; Houze et al. 2015). Recently, the Global Precipitation Measurement (GPM) Core Observatory satellite (Hou et al. 2014) and the International Space Station Lightning Imaging Sensor (ISS/LIS; Blakeslee and Koshak 2016) have extended the potential for studies of precipitation and convection to the middle and high latitudes (e.g., Liu and Zipser 2015; Liu and Liu 2016).

With the help of satellite missions, we get a more realistic picture of the worldwide distribution of thunderstorms. The wealth of satellite observations provides opportunities to explore intense thunderstorms globally. A number of studies have revealed that continental convective storms often have more intense convection and exhibit a strong preference over specific regions, such as in the plains area downstream of major mountains (Spencer and Santek 1985; Orville and Henderson 1986; Alcala and Dessler 2002; Liu et al. 2012). Therefore, these favorable regions are of great interest to both educators and researchers, such as South Asia (Houze et al. 2007; Romatschke et al. 2010; Romatschke and Houze 2011), South America (Seluchi and Marengo 2000; Xie et al. 2006; Romatschke and Houze 2010; Rasmussen and Houze 2011; Rasmussen et al. 2014), central Africa, and West Africa (Nicholls and Mohr 2010; Zuluaga and Houze 2015), as well as northeast China (Liu and Liu 2018). These studies have advanced our knowledge and understanding of the frequency and characteristics of intense convection.

The available information is still insufficient to specify the processes that lead to intense thunderstorms over many remote regions of the world. A number of processes may be involved that favor formation of these systems. Detailed analysis of the large-scale fields of temperature, moisture, and wind at standard levels would permit the identification of regions that are preferable for intense thunderstorms. This has inspired the present paper in which we select seven hotspots for high-lightning-flash-rate storms, then investigate the environments that accompany or precede them over the zone covered by TRMM. These include the hotspots of the most intense convection on Earth demonstrated by prior studies (Mohr and Zipser 1996; Liu and Zipser 2005; Zipser et al. 2006; Cecil and Blankenship 2012; Houze et al. 2015; Albrecht et al. 2016; Liu and Liu 2016).

There are different ways to describe the intensity of a convective systems. Lightning is one of the most dramatic aspects of intense thunderstorms, and cloud-to-ground lightning flashes have been among the leading causes of weather-related fatalities (Curran et al. 2000). There has been growing public concern in recent years in the forecasting and assessment of the intensity of convective storms around the world based on lightning (e.g., MacGorman and Burgess 1994; Lang and Rutledge 2002; Carey et al. 2003). Previous researchers have developed several empirical relationships between lightning and convective parameters, such as cloud-top height (Price and Rind 1992; Boccippio et al. 2002; Barthe et al. 2010; Dahl et al. 2011; Basarab et al. 2015), upward ice mass flux (Allen and Pickering 2002; Deierling et al. 2008; Finney et al. 2014), convective precipitation rate (Meijer et al. 2001), and updraft characteristics (Wiens 2005; Deierling and Petersen 2008). For example, Deierling and Petersen (2008) found that the total flash rate is highly related to the updraft volume with vertical velocity greater than 5 m s−1 as well as greater than 10 m s−1. Moreover, flash rate is related to other proxies that are used to define intense convection, as confirmed by using ground- and satellite-based observations (Price and Rind 1992; Carey and Rutledge 1996; Mohr et al. 1996; Petersen et al. 1996; Ushio et al. 2001; Liu et al. 2012). This also implies that intense storms can correspond to various convective variables, which might not necessarily be related to each other. In this study, convective intensity is inferred from the lightning flash rate observed by the TRMM LIS.

The objective in this study is to examine the dynamic and thermodynamic conditions that accompany or precede the most intense thunderstorms, over different regions, and compare their differences and similarities. First, we present the geographical distribution and seasonal variations of high-flash-rate thunderstorms across the tropics and subtropics observed by TRMM. Then the favorable meteorological environments of these events over selected regions are explored, using the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim; Dee et al. 2011). Finally, a discussion of the similarities and differences in the favorable environments associated with these events is provided. With this objective, this paper is arranged in the following order: data and methodology in section 2, results in section 3, followed by summary and discussion in section 4.

2. Data and methods

a. TRMM convective feature datasets for identifying intense thunderstorms

TRMM carried multiple instruments (Kummerow et al. 1998), including the Precipitation Radar (PR), TRMM Microwave Imager (TMI), Visible and Infrared Scanner (VIRS), and LIS. LIS has provided valuable observations in studying lightning (Cecil et al. 2005; Petersen et al. 2005; Pessi and Businger 2009; Xu et al. 2010). With ~70%–90% detection efficiency (Boccippio et al. 2002) and ~90-s sampling duration, the total lightning rate, composed of both cloud-to-ground and intracloud lightning, can be derived for each thunderstorm that TRMM samples.

In this study, 16-yr (1998–2013) TRMM, version 7, products from PR and LIS are used. First, convective features (CFs) are defined by grouping the contiguous area with convective precipitation derived with PR after Liu and Zipser (2013), using the precipitation feature approach described in Liu et al. (2008). The LIS lightning flash counts are summarized within each CF as an indicator of the convective intensity. Then four categories of storms are classified using the flash counts with ~90-s sampling duration: no flash, weak (1–3 flashes), moderate (3–50 flashes), and the most intense (>50 flashes). This classification is arbitrary. Note that the CF as defined here can range from a single cell storm to a large, organized convective complex. The highest flash rates generally result from CFs with a precipitating area greater than 1000 km2 (Table 1). Over the 16-yr period, a total of ~25 million CFs was observed by TRMM. This study only includes CFs with at least four contiguous pixels (with size greater than ~75 km2). There are more samples in the subtropical latitudes due to the ±35° orbit (Fig. 1a).

Table 1.

Mean of selected intensity proxies for intense CFs (more than 50 lightning flashes) and weak CFs (with 1–3 lightning flashes) over seven selected regions. The values in the parentheses are for weak CFs. The last two columns represent the 25th and 75th percentiles of convective precipitation area.

Table 1.
Fig. 1.
Fig. 1.

Geographical distribution of the population of total CFs and percentage of CFs with different flashes: (a) total CFs, (b) with no flash, (c) with 1–3 flashes, (d) with 3–50 flashes, and (e) with >50 flashes. The distribution is created on a 2° × 2° grid from 16 years (1998–2013) of TRMM observations.

Citation: Journal of the Atmospheric Sciences 77, 5; 10.1175/JAS-D-19-0235.1

To demonstrate how representative 50 or more lightning flashes per CF is of intense convection, maximum 40-dBZ echo-top height and minimum brightness temperature at 85- and 37-GHz channels are listed for CFs with more than 50 flashes in Table 1. The mean convective precipitation area of intense CFs with more than 50 flashes over those selected regions are greater than 2000 km2. This indicates that these high-flash-rate thunderstorms generally originate from organized convection with large convective precipitation area. The mean maximum 40-dBZ echo-top heights (MAXHT40) of high-flash-rate CFs are generally greater than 10 km, corresponding to the top 0.1% most intense precipitation systems (Zipser et al. 2006), and deep convective cores (Houze et al. 2015). Wu et al. (2016) also showed that the 1000 most intense convective systems over the southern Himalayas front, with 40-dBZ echo-top heights greater than 10 km, correspond to those systems having the highest flash rate (≥32 flashes per minute). According to Zipser et al. (2006), the minimum brightness temperature at 85 GHz (MIN85PCT) colder than 160 K is consistent with the top 1% most intense systems while the minimum brightness temperature at 37 GHz (MIN37PCT) colder than 220 K corresponds to the top 0.1% most intense systems. Therefore, though only a high lightning flash rate is used, the low microwave brightness temperatures and the high 40-dBZ echo tops confirm that CFs with more than 50 flashes are generally the most extreme convective storms, hereafter using this as a synonym for “intense thunderstorms,” as in Zipser et al. (2006).

b. Selected regions for the highest-flash-rate thunderstorms

Regions with frequent intense convection, identified by space radar, passive microwave, and lightning sensors, have been demonstrated for more than a few decades (e.g., Spencer and Santek 1985; Zipser et al. 2006; Said et al. 2013; Cecil et al. 2015; Albrecht et al. 2016). However, it is still fruitful to reexamine the climatological frequency of high-flash-rate thunderstorms on the basis of 16-yr TRMM–LIS comprehensive datasets. The percentage of the storms in different categories are identified by the CFs with no flashes, 1–3, 3–50, and >50 flashes divided by the total number of CFs in each 2° × 2° box shown in Fig. 1. The ocean has a much higher percentage of CFs with no flashes than land. Central Africa, mountains, and a few desert regions have much lower fraction of CFs with no flashes. The geographical distribution of percentage of weak, moderate and intense thunderstorms are consistent with prior studies (e.g., Orville and Henderson 1986; Zipser and Lutz 1994; Christian et al. 2003; Liu et al. 2012). Thunderstorms are found far more often over land than over the ocean. Over land, the most intense thunderstorms with more than 50 flashes, corresponding to about 33 flashes per minute, tend to occur in a few hotspot regions (Fig. 1e). This inspires the examination of the nature of the most intense thunderstorms over these hotspots. In this study, we choose regions (boxes as shown in Fig. 1e) having CFs with the highest flash rates. These regions include the south-central United States (SCUS; 32°–36°N, 95°–100°W), northwest Mexico (NWM; 27°–29°N, 108°–110°W), Argentina (ARGEN; 33°–36°S, 63°–67°W), the Sahel (SAHEL; 10°–14°N, 17°–21°E), Congo (CONGO; 3°S–2°N, 18°–24°E), Colombia (COLOM; 7°–9°N, 74°–76°W), and the Himalayas (HIMA; 33°–35°N, 72°–74°E).

The seasonal variations of total CFs and intense CFs with more than 50 flashes are shown in Fig. 2. It is not surprising that intense thunderstorms are found more frequently in the warm season. There is a smaller seasonal variation of both the total number and the percentage of intense CFs over CONGO than other regions. Two peaks of intense thunderstorms are found over HIMA in June and September, which is consistent with prior studies (Qie et al. 2014). It is well known that thunderstorms are typical spring and summer phenomena (e.g., Johns 1982; Uyeda et al. 2001; Qie et al. 2003; Schulz et al. 2005; Taszarek et al. 2015) due to more intense low-level atmosphere heating and higher equivalent potential temperature (Miller and Fritsch 1991). Therefore, the environments of intense CFs in the most active months are discussed for each region, respectively.

Fig. 2.
Fig. 2.

Seasonal variation of CFs and intense CFs with >50 flashes. (a) Population of total CFs (b) Percentage of intense CFs with >50 flashes.

Citation: Journal of the Atmospheric Sciences 77, 5; 10.1175/JAS-D-19-0235.1

c. ERA-Interim data for large-scale conditions

To derive large-scale thermodynamic and kinematic environments for intense thunderstorms, the ERA-Interim data (Dee et al. 2011) are used. With 37 vertical levels and 0.75° × 0.75° horizontal resolution, the ERA-Interim dataset is available every 6 h. In addition to the base state variables, for example, temperature, geopotential height, relative humidity, and horizontal wind components, additional parameters, that is, convective available potential energy (CAPE), convective inhibition (CIN), and the lifting condensation level (LCL), are derived from these basic variables. For each CF, those basic variables and additional parameters at the closet point to the centroid of the CF are derived. To focus on the preconvective conditions, the 6-hourly variables prior to the CF time are used. In this study, we use the most unstable CAPE (hereinafter referred to as CAPE), which is calculated by lifting parcels from multiple pressure levels between the surface and 700 hPa and finding the highest value. We do not account for the latent heating due to freezing. The low-level wind shear and storm relative helicity between 1–3 and 1–6 km for each CF are also calculated. For CFs selected over complex terrain, the wind vector at 1 km is used to derive the shear and helicity to avoid potential contamination of topography to the near-surface wind values on 0.75° grids. TRMM only provides snapshots of CFs, so we do not attempt to estimate storm motion in calculating helicity values. Because similar conclusions can be drawn from 1–3- and 1–6-km shear and helicity, only the results of 1–6-km shear and 1–3-km helicity are presented here.

For each CF, a 30° × 30° grid of the large-scale conditions centered at the CF is obtained from the closest ERA-Interim product prior to the CF time. We composite the large-scale fields relative to the thunderstorm centers. The horizontal or vertical traditional and derived parameters centered at CFs are averaged to examine the common large-scale features of thunderstorms. The differences between intense and weak thunderstorms are examined by calculating anomalies of each large-scale field, comparing the composite fields of the intense CFs with more than 50 flashes to weak CFs with 1–3 flashes. Because sometimes weak and intense CFs may coexist in the same satellite overpasses, the orbits with intense CFs are removed from samples to calculate the large-scale condition background for weak CFs. After this, it is still possible that some weak CFs occur close to an intense CF that is just outside of a TRMM overpass. Therefore, some of the weak CFs may not be under pure “weak thunderstorm” environments. However, because there are many more weak thunderstorms than intense ones (Table 2), the composite of weak CFs should be dominated by weak thunderstorm conditions.

Table 2.

Mean thermodynamic and dynamic parameters of intense and weak CFs during the warm season over selected regions. The values in the parentheses are for intense CFs. The low-level shear (SHEAR1–6km) is calculated between 1 and 6 km; the storm-relative helicity (SRH1–3km) is calculated between 1 and 3 km assuming static storm motion. SHL donates the near-surface specific humidity. RHM represents the averaged midlevel (700–500 hPa) relative humidity (RH).

Table 2.

3. Results

The occurrence of intense convection in the United States has been highly correlated with the synoptic condition (e.g., Barnes and Newton 1986; van Delden 2001; Doswell 2001; Tuttle and Carbone 2004; Schumacher and Johnson 2005; Trier et al. 2006). Therefore, we select a similar methodology to explore the environments conducive to intense thunderstorms. This similar methodology then is applied to other regions to determine their preferred synoptic conditions for intense convection. Then the similarities and differences in the dynamic and thermodynamic factors for intense thunderstorms are compared among different regions.

a. SCUS (North Texas and Oklahoma; AMJ)

Thunderstorms over the southern plains of the United States have been well studied for more than half a century. As early as the 1950s (Beebe 1958; Fujita 1958), boundaries between moist air originating over the Gulf of Mexico and dry air originating over arid regions in northern Mexico, eastern New Mexico, and western Texas, or so-called drylines (e.g., Rhea 1966; Schaefer 1974; Thompson and Edwards 2000), have been identified as a major focus of intense thunderstorms over SCUS. More than 40% of thunderstorms in April–June (AMJ) have been found to be associated with the drylines (e.g., Rhea 1966; Schaefer 1974; Peterson 1983). Much effort has been made to explore the drylines and their association with convection initiation in the United States (e.g., Benjamin and Carlson 1986; Bluestein et al. 1988; Ziegler and Hane 1993; Shaw et al. 1997; Atkins et al. 1998; Ziegler and Rasmussen 1998; Hoch and Markowski 2005).

Benjamin and Carlson (1986) suggest that a favorable environment for the formation of intense thunderstorms is frequently a product of both surface-related processes and large-scale flow patterns. Surface processes are often related to the terrain configuration (McCarthy and Koch 1982; Carlson and Ludlam 1968; Steenburgh and Mass 1994). Therefore, the locations of intense CFs, as well as the topography, are shown (Fig. 3a) before investigating their large-scale field environments. Then Figs. 3b–f present the composite ERA-Interim dynamic and thermodynamic conditions centered at the location of each intense CF in AMJ over SCUS.

Fig. 3.
Fig. 3.

Environmental composites for intense CFs with >50 flashes during AMJ over SCUS. (a) Locations of intense CFs (black crosses) and topography (color fill). The red box represents the region of interest. (b) Composite 10-m wind vectors, 2-m temperature (contours), and 2-m specific humidity (color fill). (c)–(e) Composite wind vectors, temperature (contours), and relative humidity (color fill) at (c) 850, (d) 700, and (e) 500 hPa. (f) Composite CAPE (color fill) and CIN (contours). The bold black cross in (b)–(f) marks the centroid location of the intense CFs with >50 flashes. Composites are made using ERA-Interim data starting at the surface pressure. The area with high terrain is left blank if the terrain reaches that level in all analyses.

Citation: Journal of the Atmospheric Sciences 77, 5; 10.1175/JAS-D-19-0235.1

The centroid of the intense thunderstorms is located along the moist side of the large-gradient of specific humidity (SH) 2 m above the surface, which indicates the position of the dryline in Fig. 3b. Southeasterly flow brings moist air (SH > 12 g kg−1) from the Gulf of Mexico toward the storm center. The convergence between the warmmoist flow and the westerly dry flow behind the dryline is identifiable. The convergence at the near surface creates ascent (Hane et al. 1997; Richter and Bosart 2002; Murphey et al. 2006). Ziegler et al. (1997) confirmed that updrafts of up to 5 m s−1 are common along dryline convergence bands. The continued strong southerly wind (>10 m s−1) at 850 hPa indicates the low-level jet (Fig. 3c). At 700 hPa, south of the storm centroid, hot and dry continental air flows northeastward above moist low-level air providing large CIN (Carlson and Ludlam 1968). Farther aloft, the shortwave trough, ahead of a thermal trough, is identifiable based on the curvature in the wind field on the 500-hPa composite map (Fig. 3e). In accordance with past studies (e.g., Rhea 1966; Hane et al. 1997; Ziegler et al. 1997; Weiss et al. 2006), a 500-hPa trough is a frequent feature together with a dryline. Schultz et al. (2007) also noted that strong synoptic patterns contribute to the strength of the dryline. In the composite CAPE map, the storm centroid is within the modest CAPE values (~1200 J kg−1), with larger values farther south. Although there is an extensive area of large CAPE (>1500 J kg−1) to the south, intense storms occur at the northern edge of the large CAPE region, where CIN values are lower. It implies that CIN can both hinder and help the formation of intense thunderstorms. A large CIN can suppress convection even when CAPE favors intense convection to the south. At the same time, a moderate CIN also can help accumulate the energy allowing intense convection to form even when CAPE is relatively weaker. Note that the impact of intense thunderstorms on their environment is not considered.

Composite cross sections of equivalent potential temperature Θe related to the intense thunderstorms, as well as the differences in equivalent potential temperature and RH compared to the weak ones, are constructed at the storm-centroid latitude–longitude to provide further insight into the favorable properties of intense thunderstorm environments (Fig. 4). Characterized by a sharp gradient in Θe, the dryline profile is similar to the dryline structure suggested by prior studies (Rockwood and Maddox 1988). The high Θe from the surface to around 700 hPa indicates a deep southerly warm moist flow (Figs. 4a,c). Low-level warm moist advection, together with the southwesterly dry flow aloft (low Θe < 330 K), create a potentially unstable environment. The southerly wind at low levels (below 700 hPa) and the strong westerly flow above creates a strong wind shear from 1 to 6 km (about 14 m s−1). Increased strength of the southerly wind for intense thunderstorms implies a stronger warm moist advection for intense thunderstorms than for weak ones (Figs. 4b,d). Near the storm centroid, the positive difference in RH implies a moisture plume around 800 hPa. Compared to weak thunderstorms, intense thunderstorms are associated with warm moist advection at low levels and a drier airflow above 700 hPa (Figs. 4b,d). This indicates that the composite environment of intense thunderstorms has more instability than the composite environment of weak CFs.

Fig. 4.
Fig. 4.

(a) Composite vertical cross section of wind and equivalent potential temperature (color fill) of intense CFs with >50 flashes along the latitude where CFs occur during AMJ over SCUS. (b) Difference cross section of wind, equivalent potential temperature, and RH. The difference is the condition of weak CFs subtracted from the condition of intense CFs. The color fill represents equivalent potential temperature and the contours lines show the difference in RH. Positive values are in solid contours and negative values are in dashed contours. (c) As in (a), but along the longitude that CFs occur. (d) As in (b), but along the latitude that CFs occur. The thick black lines present the mean near-surface pressure.

Citation: Journal of the Atmospheric Sciences 77, 5; 10.1175/JAS-D-19-0235.1

The reexamination of the large-scale conditions of intense thunderstorms over the SCUS confirms previous studies about how large-scale patterns and the physical processes interact and create a favorable environment for the development of such events. This agreement with prior studies adds confidence for the approaches and data used here. Next, we apply similar analysis to other regions.

b. Southwest slope of HIMA (northern Pakistan)

Convection just southwest of the Himalayas, with high lightning rates (e.g., Christian et al. 2003; Zipser et al. 2006; Cecil et al. 2015) has been a frequent scientific topic for researchers. Asian monsoons play a critical role in the occurrence of intense convection over this region (Romatschke et al. 2010; Qie et al. 2014; Virts and Houze 2016). Most of the TRMM–LIS-identified intense CFs over the region are found in the warm season (May–September), as shown in Fig. 2a. More intense CFs are observed in summer than in spring. A higher percentage of the intense CFs is found in June (Fig. 2b). Previous studies have also revealed that convective systems in the premonsoon period(March–May) over this region are more intense than in other seasons, with a maximum in lightning activity (Lal and Pawar 2009; Ranalkar and Chaudhari 2009). We have therefore examined the favorable environments of intense thunderstorms in three seasons: spring [March–May (MAM)], summer (JJA), and fall [September–November (SON)]. Here, we only show the spring synoptic pattern of intense thunderstorms but include composite vertical profiles from three seasons for the comparison among different seasons.

The composites of large-scale conditions of intense storms, as well as the topography and locations of intense thunderstorms, are shown in Fig. 5. The storm centroid was found in a region with a strong near-surface humidity gradient along the Himalayan foothills (also at other levels, Fig. 5b). Weston (1972) and Wu et al. (2013) also suggested that severe convection tends to occur in regions with a sharp moisture gradient at the low layer. Another key feature found at the near surface is the two moisture pathways, which have been suggested by Wu et al. (2016). One is from the Bay of Bengal, along the base of the Himalayas, even though the averaged wind vectors do not favor this source. Another one is from the Arabian Sea (SW), which is consistent with the moisture advection in Fig. 6b. We speculate that winds from the Bay of Bengal occur less frequently than the ones from the SW and therefore do not obviously appear in the composite field. Southwesterly flow at low levels encounters the dry north-northwesterly air flowing from the Afghan highlands at midtroposphere over the region. The southwesterly low-level jet brings warm, moist air from the Arabian sea into the storm centroid area, which increases the convective instability. South of the storm centroid, the westerly flow from the Afghan highland is dry from 700 to 500 hPa. At 500 hPa, the storm centroid is ahead of a shortwave trough (Fig. 5e). The large-scale ascent ahead of the trough may play a role in triggering convection over HIMA, while other vertical lifting mechanisms (e.g., orography, surface heating) may be equally important over the area (Carlson et al. 1983; Houze et al. 2007; Romatschke et al. 2010). Representative of the boundary layer moisture and temperature stratification, CAPE values are consistent with the near-surface moisture distribution, as shown in Fig. 5b. The storm centroid located within the modest CIN also suggests that thunderstorms tend to form along the lid edge, created by the hot, dry air above the low-level moist air. In spite of a large CAPE, large CIN (>100 J kg−1) southwest of the storm centroid likely suppresses the development of convection as shown in Fig. 5a. It indicates that even when the right conditions are met at the surface to the southwest of the storm centroid, the capping inversion can prevent the release of convective instability until the CIN is relatively small at the foothills of the northwestern Himalayas (Medina et al. 2010). Figures 6a and 6c demonstrate the vertical wind shear over the intense thunderstorm region. Note that intense thunderstorms have lower relative humidity at low levels than weaker thunderstorms; however, they are associated with warmer low-level air, higher specific humidity and higher equivalent potential temperature (Figs. 6 and 7).

Fig. 5.
Fig. 5.

As in Fig. 3, but for HIMA during MAM.<