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  • View in gallery

    (a) Location of Nanjing radar is represented by black triangle. Blue dots show the locations of the lightning detection system sensors. The Yangtze River Delta (YRD) defined in the study is marked by green circle. Red lines indicate the urban areas. Orography is given by grayscale. (b) Case example identified by SCIT at 1.5° elevation angle at 1756 LST 20 Aug 2014. The black triangles indicate storm positions in temporal series, and the circles represent current storm positions.

  • View in gallery

    Composite environment fields at (left) 500 and (right) 850 hPa for the (a),(d) pre-mei-yu, (b),(e) mei-yu, and (c),(f) post-mei-yu periods over 2010–14. The scale of the wind vector is marked in the solid box. The radar site is indicated by the blue triangle. The magenta line indicates the Yangtze River. Contours indicate the mean pressure (at left) and color-filled contours represents the relative humidity (at right).

  • View in gallery

    The subseasonal variations of the storm environment characteristics over the study region (green circle in Fig. 1) during the pre-mei-yu, mei-yu, and post-mei-yu periods: (a) most unstable CAPE (MUCAPE), (b) LNB, (c) total precipitable water, and (d) wind shear between 200 and 850 hPa. Squares represent the mean values, and whiskers indicate 25% and 75% quartiles, respectively.

  • View in gallery

    Variations in the monthly-normalized (a) numbers of storms and (b) CG lightning regionally summed over the study area (green circle in Fig. 1) during the pre-mei-yu, mei-yu, and post-mei-yu periods (number per month).

  • View in gallery

    The spatial distribution of monthly storms, showing the (a) total, (b) pre-mei-yu, (c) mei-yu, and (d) post-mei-yu periods; the gray triangle indicates the radar location. A black line represents the Hongze Lake and a magenta line indicates the Yangtze River.

  • View in gallery

    As in Fig. 5, but for the lightning distribution.

  • View in gallery

    The cumulative distribution frequencies (CDFs) of storm properties over the study area (green circle in Fig. 1) during the pre-mei-yu, mei-yu, and post-mei-yu periods: (a) maximum reflectivity, (b) duration, (c) top, (d) VIL, (e) speed, and (f) size.

  • View in gallery

    The diurnal variations of storm numbers during the pre-mei-yu, mei-yu, and post-mei-yu periods regionally summed over the study area (green circle in Fig. 1).

  • View in gallery

    The diurnal variations of storm properties during the (left) pre-mei-yu, (middle) mei-yu, and (right) post-mei-yu periods over the study area (green circle in Fig. 1): (a)–(c) maximum reflectivity, (d)–(f) duration, (g)–(i) top, (j)–(l) VIL, (m)–(o) speed, and (p)–(r) size.

  • View in gallery

    Diurnal variations of lightning numbers (regionally summed) and mean value of peak current (regionally averaged) for the negative CG lightning (red line with asterisk) during the pre-mei-yu, mei-yu, and post-mei-yu periods over the study area (green circle in Fig. 1).

  • View in gallery

    Spatial distributions of the storm occurrence frequency between 0000 and 0300, 0300 and 0600, 0600 and 0900, 0900 and 1200, 1200 and 1500, 1500 and 1800, 1800 and 2100, and 2100 and 2400 LST during the mei-yu period. The white line indicates the Yangtze River; the black line represents Hongze Lake.

  • View in gallery

    As in Fig. 11, but for storms during the post-mei-yu period.

  • View in gallery

    As in Fig. 11, but for lightning during the mei-yu period.

  • View in gallery

    As in Fig. 11, but for lightning during the post-mei-yu period.

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Subseasonal and Diurnal Variability in Lightning and Storm Activity over the Yangtze River Delta, China, during Mei-yu Season

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  • 1 Key Laboratory for Mesoscale Severe Weather/Ministry of Education and School of Atmospheric Science, Nanjing University, Nanjing, China
  • | 2 State Key Laboratory of Severe Weather and Joint Center for Atmospheric Radar Research of the China Meteorological Administration and Nanjing University, Beijing, China
  • | 3 Jiangsu Research Institute of Meteorological Sciences, Nanjing, China
  • | 4 Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania
  • | 5 Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, Pennsylvania
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Abstract

Using 5 years of operational Doppler radar, cloud-to-ground (CG) lightning observations, and National Centers for Environmental Prediction reanalysis data, this study examined the spatial and temporal characteristics of and correlations between summer storm and lightning activity over the Yangtze River Delta (YRD), with a focus on subseasonal variability and diurnal cycles. The spatiotemporal features of storm top, duration, maximum reflectivity, size, and cell-based vertical integrated liquid water were investigated using the Storm Cell Identification and Tracking algorithm. Our results revealed that there was high storm activity over the YRD, with weak diurnal variations during the mei-yu period. Specifically, storms were strongly associated with mei-yu fronts and exhibited a moderate size, duration, and intensity. On average, afternoon storms exhibited stronger reflectivity and higher storm tops than morning storms, as evidenced by the afternoon peak in CG lightning. The storm intensity strengthened in the post-mei-yu period, due to an increase in atmospheric instability; this was accompanied by a higher frequency of CG lighting. The diurnal cycles of storm frequency and CG lightning in the post-mei-yu period followed a unimodal pattern with an afternoon peak. This is attributable to increased thermodynamic instability in the afternoon, as little diurnal variation was observed for wind shear and moisture. An inverse correlation between lightning occurrence and mean peak current (MPC) for negative CG lightning was evident during the pre-mei-yu and mei-yu periods. The diurnal variation in MPC for negative CG lightning agreed well with that for storm intensity.

© 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: Kun Zhao, zhaokun@nju.edu.cn

Abstract

Using 5 years of operational Doppler radar, cloud-to-ground (CG) lightning observations, and National Centers for Environmental Prediction reanalysis data, this study examined the spatial and temporal characteristics of and correlations between summer storm and lightning activity over the Yangtze River Delta (YRD), with a focus on subseasonal variability and diurnal cycles. The spatiotemporal features of storm top, duration, maximum reflectivity, size, and cell-based vertical integrated liquid water were investigated using the Storm Cell Identification and Tracking algorithm. Our results revealed that there was high storm activity over the YRD, with weak diurnal variations during the mei-yu period. Specifically, storms were strongly associated with mei-yu fronts and exhibited a moderate size, duration, and intensity. On average, afternoon storms exhibited stronger reflectivity and higher storm tops than morning storms, as evidenced by the afternoon peak in CG lightning. The storm intensity strengthened in the post-mei-yu period, due to an increase in atmospheric instability; this was accompanied by a higher frequency of CG lighting. The diurnal cycles of storm frequency and CG lightning in the post-mei-yu period followed a unimodal pattern with an afternoon peak. This is attributable to increased thermodynamic instability in the afternoon, as little diurnal variation was observed for wind shear and moisture. An inverse correlation between lightning occurrence and mean peak current (MPC) for negative CG lightning was evident during the pre-mei-yu and mei-yu periods. The diurnal variation in MPC for negative CG lightning agreed well with that for storm intensity.

© 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: Kun Zhao, zhaokun@nju.edu.cn

1. Introduction

The spatial distribution and diurnal cycles of storms and precipitation over East Asia during the monsoon have been investigated extensively in recent years (Lin et al. 2011; M. Chen et al. 2014; Chen et al. 2015, 2017, 2018). However, few studies have focused on the properties of warm-season storms or their diurnal cycles over the Yangtze River Delta (YRD) in China, which is located in one of three maximum rainfall centers collectively referred to as the Yangtze–Huaihe River basin (YHRB) (Ding and Chan 2005). An understanding of these storms and their properties is important for weather and climate predictions. For example, the temperature and humidity in the upper troposphere can be affected by the height and intensity of storms (Folkins 2002). In addition, storm properties can be used as evaluation benchmarks for numerical models and parameterization schemes (Davis et al. 2003; Arakawa 2004; Liang et al. 2004; Starzec et al. 2018).

The mei-yu (baiu in Japan) season over the YRD is associated with a northward shift of the East Asian summer monsoon (Sampe and Xie 2010; Luo et al. 2013). During the mei-yu period (mid-June to mid-July), a zonal rainband with heavy rainfall is frequently observed. This rainband, referred to as the mei-yu front, is controlled by frontal structures in the lower troposphere and characterized by a sea level pressure trough, horizontal shear, sharp moisture gradients, and relatively weak temperature gradients (Ding and Chan 2005; Sampe and Xie 2010). Previous studies have shown that the associated convection that becomes organized in the midnight-to-morning hours during the mei-yu period is closely related to ascending moisture flow over convectively generated cold pools (Luo et al. 2014; He et al. 2018). Another possible mechanism driving the mei-yu front is the synoptic-scale frontal convergence created by accelerated southerly moisture flows resulting from clockwise rotation of low-level winds (Chen et al. 2010; Chen et al. 2017). Xue et al. (2018) used convection-permitting simulations to investigate the key mechanism controlling the mei-yu precipitation diurnal cycle. They found that the convergence forced by low-level ageostrophic winds results in an early morning peak and an evening minimum in precipitation, which can be explained by the boundary layer inertial oscillation theory (Blackadar 1957). Diurnal variations in the precipitation amount, frequency, and intensity have received more attention in recent years (Chen et al. 2010; Bao et al. 2011; Xu and Zipser 2011; M. Chen et al. 2012; G. Chen et al. 2012; G. Chen et al. 2014; Chen et al. 2017; Xu 2013). During the mei-yu period, frequent nocturnal long-duration rainfall events with shallow echo tops contribute to the early-morning rainfall maximum over the YRD, whereas deep convection produces a secondary afternoon peak in precipitation (Chen et al. 2010; Xu and Zipser 2011). During the post-mei-yu period, the diurnal cycle of deep convection, precipitation, and lightning exhibits a single peak (Xu and Zipser 2011). Luo et al. (2013) compared the summer storm properties over southern China and the YHRB based on satellite observations. They found that the contribution of heavy rainfall to total rainfall amount is greater in the YHRB than in southern China. They also showed that convective intensity strengthens progressively from the premonsoon period to the monsoon period and further to the postmonsoon period over the YHRB and southern China. Furthermore, in most of the Asian monsoon regions, convection is stronger during the monsoon break and postmonsoon periods than during monsoon activity (Kodama et al. 2005; Yuan and Qie 2008; Xu et al. 2009; Xu and Zipser 2011; Luo et al. 2013). The abovementioned studies focused mainly on the spatial distribution of storms and cloud tops across different regions and/or periods based on satellite data such as data from the Tropical Rainfall Measuring Mission that recorded rainfall from a particular location twice a day. Temporal resolution limitations and attenuation (Xu 2013) have limited the ability to investigate the properties of storms, including storm lifetime, movement, vertical structure, and diurnal cycle. Observations from the Chinese operational radar network provide a new opportunity to gain insight into the three-dimensional (3D) structure and evolution of storms during different phases of the monsoon period for characterizing diurnal variations in storm properties.

Lightning activity is an effective indicator of deep convection and climate change, and has been widely investigated over different regions (Hondl and Eilts 1994; Zipser and Lutz 1994; Watson et al. 1995; Gremillion and Orville 1999; Zipser et al. 2006; Yuan and Qie 2008; Mosier et al. 2011; Seroka et al. 2012; Metzger and Nuss 2013; Qie et al. 2014; Zheng et al. 2016). Laboratory studies show that riming electrification is the main charge separation mechanism associated with thunderstorms, occurring mainly during graupel–ice crystal collisions with supercooled water (Takahashi 1978; Saunders 1993). Many previous studies concentrated on the lightning rate, spatial distribution, and diurnal cycle (Zajac and Rutledge 2001; Qie et al. 2003; Yuan and Qie 2008; Xia et al. 2015, 2018; Yang et al. 2016). Observational studies have indicated that the peak current of negative lightning is closely related to thunderstorm intensity. This putative relationship is associated with the small eddies created by strong turbulence within afternoon storms; under these conditions, the distances between electrostatic thundercloud charges become smaller, resulting in frequent lightning occurrences but with less charge available to flow into the return stroke, resulting in a smaller peak current and flash size (Bruning and MacGorman 2013; Chronis et al. 2015a,b). However, there have been relatively few studies investigating the relationship between various storm properties and the peak amount of lightning (Shafer et al. 2000; Carey and Rutledge 2003; Zheng et al. 2016).

In this study, the summer storm properties and lightning activity over the YRD during the pre-mei-yu, mei-yu, and post-mei-yu periods from 2010 to 2014 were investigated. In contrast to previous studies based on satellite imagery, high-resolution ground-based radar and cloud-to-ground (CG) lightning observations were used here for the first time to reveal the spatiotemporal structure of storms and lightning activity with fine resolution. Relevant atmospheric conditions were also examined using reanalysis data. Based on statistical characterization, the environmental conditions influencing storm activity and CG lightning over the YRD are discussed with respect to the different monsoon phases.

The rest of the paper is organized as follows. The data and methodology are introduced in section 2. Section 3 describes the large-scale flow and environmental conditions during the pre-mei-yu, mei-yu, and post-mei-yu periods. Section 4 presents the spatial distribution of storms, as well as lightning and storm properties. Section 5 compares the diurnal cycle of storm properties and lightning. A brief summary and conclusions are given in section 6.

2. Data and methodology

a. Radar data

The radar data used in this study were collected by the China Next Generation Weather Radar in Nanjing (NJRD) during the warm season (1 May to 31 August) from 2010 through 2014. The wavelength of the NJRD system is 10 cm (S band), with a 1° beam width. The radar scan mode was volume coverage pattern 21; scans were acquired at ~6-min intervals.

NJRD is located near the Yangtze River (YR) at an altitude of 187 m (Fig. 1a). The terrain of the study area (green circle in Fig. 1a) is lower than 300 m. Therefore, there is no obvious blocking or ground clutter in the NJRD data; however, anomalous propagation tends to contaminate radar reflectivity. Based on the horizontal and vertical reflectivity structures of nonprecipitation echoes, automatic quality control was implemented, following the procedure described by Zhang et al. (2004).

Fig. 1.
Fig. 1.

(a) Location of Nanjing radar is represented by black triangle. Blue dots show the locations of the lightning detection system sensors. The Yangtze River Delta (YRD) defined in the study is marked by green circle. Red lines indicate the urban areas. Orography is given by grayscale. (b) Case example identified by SCIT at 1.5° elevation angle at 1756 LST 20 Aug 2014. The black triangles indicate storm positions in temporal series, and the circles represent current storm positions.

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

b. Retrieval of storm properties

Storm properties were retrieved via the Storm Cell Identification and Tracking (SCIT) algorithm (Johnson et al. 1998), which is a 3D centroid-based storm cell algorithm. The SCIT algorithm first identifies the contiguous range gates and azimuths with radar reflectivity above a specified threshold as two-dimensional (2D) components in an elevation scan. In this study, this step was repeated over a reflectivity threshold of 30–60 dBZ in 5-dBZ increments, to improve storm cell identification performance. Additionally, 2D storm components with an area of less than 10 km2 were discarded. The vertical association of 2D storm components in all elevation scans was created to form 3D storm cells. Finally, the identified storm cells were tracked in space using a discrete time interval (every 6 min in this study), if the distance between the centroid of storm cells and the first-guess location determined by the storm cell’s previous motion vector or default motion vector was less than the threshold value (30 m s−1 times the time interval between two contiguous volume scans). The storm properties—including the storm top, maximum storm reflectivity, cell-based vertical integrated liquid (VIL), storm duration, storm size, and moving speed—were determined. The storm top was defined as the height of the storm center of the highest storm component with a reflectivity threshold of 40 dBZ.

To keep only the convective storm cells, two supplemental selections were applied, based on the maximum storm reflectivity and the cell-based VIL. Previous studies indicate that a reflectivity threshold greater than 40 dBZ is a reasonable criterion for separating convective and stratiform rain (Reap and MacGorman 1989; Livingston et al. 1996; Lin et al. 2011; M. Chen et al. 2012). In this study, if the maximum storm reflectivity of a given storm cell during its lifetime was less than 40 dBZ, the storm cell was discarded. Considering that the reflectivity of the bright band of the stratiform can be larger than 40 dBZ, a VIL threshold of 6.5 kg m−2 was then used to remove the storm cell with the brightest band, as proposed by Zhang and Qi (2010). Figure 1b shows an example of a SCIT algorithm application.

Although SCIT has been successfully used to investigate long-term storm activity over various regions (MacKeen et al. 1999; Mohee and Miller 2010; Mosier et al. 2011; Lin et al. 2011; Seroka et al. 2012) and has a proven storm cell detection accuracy exceeding 90%, the algorithm still has some limitations. For example, the algorithm does not account for storm cell merging and splitting. Further, the algorithm has a poor cell detection rate due to the cone of silence near the radar site and low vertical resolution at greater distances from the radar site. To reduce the poor cell detection rate, only storm cells located within 20–150 km from the radar site were used in this study.

The storm location was used to reveal the storm distribution with a horizontal resolution of approximately 10 km. The largest values of reflectivity, top, VIL, size, and average speed over the storm’s lifetime were used to resolve the subseasonal variation in and diurnal cycle of storms over the YRD.

c. CG lightning data

CG lightning was detected by China Lightning Detection Network sensors located within the study area, as shown in Fig. 1a. The mean detection radius of a sensor is approximately 300 km, and the location error is approximately 300 m. The lightning flash detection efficiency is approximately 90% (Xia et al. 2015). The lightning statistics calculated in this study were based on a single lightning flash, which may consist of a series of separate return strokes (commonly referred to as a multiplicity). Data quality control was performed in accordance with methods described by Cummins and Murphy (2009). Positive CG lightning with an intensity <15 kA was removed, to prevent its mischaracterization as cloud lightning. The stroke-to-flash grouping algorithm (Cummins and Murphy 2009) was adopted to collect associated strokes into a single flash within a clustering radius of 10 km, centered by the first stroke with a time interval of less than 0.5 s.

d. Additional data

Global gridded analysis data from the National Centers for Environmental Prediction Final Operational Model Global Tropospheric Analyses dataset (FNL; https://rda.ucar.edu/datasets/ds083.2/) were used to investigate large-scale conditions over the study area. The horizontal resolution of the reanalysis data is 0.5°, and data were acquired at 0000, 0600, 1200, and 1800 UTC [0800, 1400, 2000, and 0200 local standard time (LST), respectively].

The version 4 Defense Meteorological Satellite Program-Operational Line Scanner gridded Stable Lights product (https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html#AVSLCFC) developed by the Earth Observation Group, National Oceanic and Atmospheric Administration/National Geophysical Data Center (Peterson 2003; Lindén et al. 2015) was used to identify rural and urban areas; data are in the form of composite images, each with 30-arc-s resolution that provides an average annual brightness level ranging from 0 to 63 (the larger number corresponds to a higher brightness level). This product is widely used to identify urban areas in different cities (e.g., Small et al. 2005, 2011). In this study, the gridded Stable Lights product was applied to identify urban areas, using a brightness level >40 (2013), as shown in Fig. 1a.

e. Determination of the mei-yu period

The spatial and temporal characteristics of and correlation between summer storm activity and lightning over the YRD were investigated, with a focus on subseasonal variability and diurnal cycles. The warm season (1 May to 31 August) from 2010 to 2014 was divided into three distinct synoptic regimes, as follows: 1) the pre-mei-yu period, characterized as dry and stable with mainly westerly wind flow; 2) the mei-yu period, characterized as an unstable and very moist period with large-scale weather systems (mei-yu fronts) associated with horizontal wind shear and sharp gradients in relative humidity in the lower troposphere (Sampe and Xie 2010); and 3) the post-mei-yu period, characterized as a very unstable and moist regime without large-scale weather system involvement. The pre-mei-yu period is defined as the period from 1 May until the onset of mei-yu events. The post-mei-yu period is from the end of the mei-yu events until 31 August. The definition of the mei-yu period over the study region was based on the operational forecast from the China Meteorological Administration (Table 1). The mei-yu period varied from one year to the next over the study period from 2010 through 2014, with the longest and shortest mei-yu periods of 37 and 15 days recorded in 2011 and 2013, respectively.

Table 1.

Pre-mei-yu, mei-yu, and post-mei-yu days of each year in 2010–15 over YRD. Numbers of days during each period are in parentheses.

Table 1.

3. Environmental conditions

Figure 2 presents the average subseasonal transitions in wind, pressure, and relative humidity at 500 and 850 hPa based on 5 years of FNL data. During the pre-mei-yu period, strong westerlies at 500 hPa dominated over East China (Fig. 2a). Moist air mainly prevailed over southern China and the South China Sea (Fig. 2d). Figure 3 presents the environmental characteristics averaged over the study area (green circle in Fig. 1a) with respect to the three periods considered (pre-mei-yu, mei-yu, and post-mei-yu). The pre-mei-yu period exhibited the smallest and lowest amount of most unstable convective available potential energy (MUCAPE), the lowest level of neutral buoyancy (LNB), the least total precipitable water (TPW), and the weakest vertical wind shear (200–850 hPa) over the YRD, among the three periods (Fig. 3).

Fig. 2.
Fig. 2.

Composite environment fields at (left) 500 and (right) 850 hPa for the (a),(d) pre-mei-yu, (b),(e) mei-yu, and (c),(f) post-mei-yu periods over 2010–14. The scale of the wind vector is marked in the solid box. The radar site is indicated by the blue triangle. The magenta line indicates the Yangtze River. Contours indicate the mean pressure (at left) and color-filled contours represents the relative humidity (at right).

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

Fig. 3.
Fig. 3.

The subseasonal variations of the storm environment characteristics over the study region (green circle in Fig. 1) during the pre-mei-yu, mei-yu, and post-mei-yu periods: (a) most unstable CAPE (MUCAPE), (b) LNB, (c) total precipitable water, and (d) wind shear between 200 and 850 hPa. Squares represent the mean values, and whiskers indicate 25% and 75% quartiles, respectively.

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

During the mei-yu period, the westerlies weakened and shifted northward at 500 hPa (Fig. 2b). The ridge of the west Pacific subtropical high was located at 23°N. With the enhancement in low-level southwesterly winds, warm moist air was transported to the YRD (Fig. 2e). The MUCAPE (Fig. 3a), LNB height (Fig. 3b), TPW (Fig. 3c), and vertical wind shear (Fig. 3d) increased dramatically from the pre-mei-yu period to the mei-yu period. During the post-mei-yu period, the westerlies moved farther northward, and the ridge of the west Pacific subtropical high was located at 29°N (Fig. 2c). Warm and moist air from the South China Sea extended to the YRD (Fig. 2c) during the post-mei-yu period. TPW (Fig. 3c) over the YRD decreased slightly from the mei-yu period to the post-mei-yu period. The regional mean MUCAPE (Fig. 3a) over the study area (Fig. 1a) increased from 328 J kg−1 during the mei-yu period to 1115 J kg−1 during the post-mei-yu period. In addition, the MUCAPE (Fig. 3a) at 1400 LST exceeded 1700 J kg−1. At the same time, the LNB height (Fig. 3b) and wind shear (Fig. 3d) reached peak values during the post-mei-yu period. Thus, atmospheric conditions during the post-mei-yu period were more favorable for organized deep convection than those of the previous (pre-mei-yu and mei-yu) periods.

4. Spatial distributions and storm properties

Figure 4 presents the monthly normalized (over 30 days) number of storms and CG lightning during each period over the study area (green circle in Fig. 1a). The mei-yu period had the highest frequency of storms among the three periods (Fig. 4a) but not the highest frequency of CG lightning (Fig. 4b). The higher frequency of storms is attributable to continuous convective initiation driven by the mei-yu front; however, due to lower instability during the mei-yu period, the storms were not as intense, and the frequency of CG lightning was lower (Fig. 3a). The monthly normalized number of storms (Fig. 4a) was slightly less in the post-mei-yu period than in the mei-yu period; however, CG lightning occurrence in the post-mei-yu period was nearly double that of the mei-yu period (Fig. 4b). Notably, the MUCAPE increased from the mei-yu period to the post-mei-yu period, especially in the afternoon, when strong storm formation and lightning production are favored. The increased wind shear also facilitates storm organization, especially multicellular storms and broad updrafts (Palucki et al. 2011; Fuchs et al. 2015), leading to the production of more lightning. The monthly normalized CG lightning occurrence (Fig. 4b) increased from the pre-mei-yu period to the mei-yu period and further to the post-mei-yu period. Most CG lightning was negative (Fig. 4b) during the warm season. The percentages of positive CG lightning events (the ratio of positive CG lightning to total CG lightning) during the pre-mei-yu, mei-yu, and post-mei-yu periods were 17%, 7%, and 4%, respectively (Fig. 4b). The exceptionally moist air during the mei-yu and post-mei-yu periods provided favorable conditions for negative CG lightning formation (Carey and Buffalo 2007; Fuchs et al. 2015).

Fig. 4.
Fig. 4.

Variations in the monthly-normalized (a) numbers of storms and (b) CG lightning regionally summed over the study area (green circle in Fig. 1) during the pre-mei-yu, mei-yu, and post-mei-yu periods (number per month).

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

Figures 5 and 6 present the spatial distributions of monthly normalized storms and CG lightning (the number of storms and CG lightning events over individual grids during each period was normalized with respect to 30 days), respectively. The warm-season storm and CG lightning maximum regions were located upstream and downstream, respectively, of the Nanjing radar site (gray triangle in Fig. 5) over the urban areas along the YR (Figs. 5a and 6a). During the pre-mei-yu period, the storm frequency relative to the other two periods was much lower (Fig. 5b). During the mei-yu period, there was a clear storm maximum region (Fig. 5c) with a southwest-to-northeast long axis of over 150 km near the YR, which is associated with storms embedded in the mei-yu rainband. The storms within the mei-yu rainband only produced several CG lightning events (Fig. 6c). During the post-mei-yu period, the spatial distribution of storms was rather homogeneous. However, two local maximum regions were located near Hongze Lake and in an area downstream of the Nanjing radar site along the YR; the latter corresponds to the maximum region of CG lightning shown in Fig. 6d. TPW and wind shear values were comparable during the mei-yu and post-mei-yu periods. One notable difference was observed for the MUCAPE, which increased from 328 J kg−1 during the mei-yu period to 1115 J kg−1 during the post-mei-yu period. Similarly, the MUCAPE at 1400 LST increased from 465 to 1729 J kg−1 between the two periods. Previous studies have shown that thermodynamic instability is closely related to convective updrafts, which could lift supercooled water to the mixed-phase region for electrification (Williams and Stanfill 2002; Williams et al. 2005). The thermodynamic instability factor appears to play more of a role than wind shear or TPW with respect to the differences in lighting frequency between the mei-yu and post-mei-yu periods over the YRD.

Fig. 5.
Fig. 5.

The spatial distribution of monthly storms, showing the (a) total, (b) pre-mei-yu, (c) mei-yu, and (d) post-mei-yu periods; the gray triangle indicates the radar location. A black line represents the Hongze Lake and a magenta line indicates the Yangtze River.

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

Fig. 6.
Fig. 6.

As in Fig. 5, but for the lightning distribution.

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

The cumulative distribution frequencies of the storm properties are presented in Fig. 7. A Student’s two-sample t test (Student 1908) revealed that the differences in storm properties among the three periods were statistically significant (p < 0.05). Storm reflectivity during the post-mei-yu period differed within 40–55 dBZ from those in the pre-mei-yu and mei-yu periods; these differences may be related to graupel and small hail (Straka et al. 2000; Park et al. 2009). For the post-mei-yu period, this suggests that there was a strong riming process favoring lightning (Zipser and Lutz 1994; Carey and Rutledge 1996, 2000; Zipser et al. 2006; Deierling and Petersen 2008). The VIL was also quantified, in an attempt to resolve its relationship to lightning, as previous studies had indicated that the two were well correlated (Watson et al. 1995; Shafer et al. 2000). Riming ice hydrometers are crucial for thunderstorm electrification (Takahashi 1978; Saunders 1993; Carey and Rutledge 1996, 2000; MacGorman et al. 2008; Xu 2013; Kumjian and Deierling 2015; Takahashi et al. 2017), with VIL providing a partial contribution. Although VIL includes warm rain processes at low levels that are not necessarily related to storm electrification, it can be a useful proxy for storm intensity and mixed-phase precipitation (ice) processes necessary for lightning production. Approximately 80% of the storms had a VIL of less than 15 kg m−2 during the pre-mei-yu and mei-yu periods, whereas the percentage during the post-mei-yu period was approximately 60% (Fig. 7d). Strong convective updrafts bring hydrometers to high levels to form high storm tops (Matthee et al. 2014); therefore, a higher storm top during the mei-yu and post-mei-yu periods suggests the presence of stronger updrafts.

Fig. 7.
Fig. 7.

The cumulative distribution frequencies (CDFs) of storm properties over the study area (green circle in Fig. 1) during the pre-mei-yu, mei-yu, and post-mei-yu periods: (a) maximum reflectivity, (b) duration, (c) top, (d) VIL, (e) speed, and (f) size.

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

Wind shear (Fig. 3d), which is also larger during the mei-yu (~19 m s−1) and post-mei-yu (~21 m s−1) periods than during the pre-mei-yu period (~9 m s−1), along with the CAPE, was at a level favorable for the development of organized deep storms with intense lightning activity (Rotunno et al. 1988). In addition, storms during the mei-yu and post-mei-yu periods occurred over longer durations (Fig. 7b) and larger areas (Fig. 7f) on average, compared with those of the pre-mei-yu period. These results indicate that longer lasting and broader convective updrafts can be expected during the mei-yu and post-mei-yu periods, which may also contribute to stronger lightning activity during these two periods (Williams et al. 1991; Zipser 2003; Palucki et al. 2011). An unstable and moist environment (Fig. 3c) also favors strong and long-lasting storms during the mei-yu and post-mei-yu periods (Cetrone and Houze 2006; May and Ballinger 2007; Peter et al. 2015). The mean storm motion speed (Fig. 7e) decreased from the pre-mei-yu period to the mei-yu period and further decreased during the post-mei-yu period, with the decrease closely related to the subseasonal variability in westerly steering wind speeds (Fig. 2).

Luo et al. (2013) reported that the storms during the mei-yu and post-mei-yu periods are strongly controlled by large-scale weather systems and local instability due to solar heating, respectively. This conclusion is confirmed by our results, which indicated that storm properties and lightning activity are mainly controlled by environmental conditions, especially thermodynamic conditions.

5. Diurnal cycles

a. Storm frequency and properties

The diurnal evolution of convection is a fundamental mode underlying regional climatic variation. Figure 8 presents the diurnal variation in regional storm number summed over the study area. Figure 9 presents the diurnal variations in storm properties, which were normalized to their maximum values for comparison. Figure 10 shows the diurnal variation in CG lightning occurrence. During the mei-yu period, diurnal storm number (Fig. 8) varied bimodally, with two comparable peaks at 1300 and 0500 LST. In a numerical study, Xue et al. (2018) showed that the morning peak is mainly associated with convergence forcing produced by low-level ageostrophic winds. The afternoon peak is related to solar heating (Luo et al. 2013). With regard to storm properties, two maximum regions were observed in Figs. 9b, 9e, 9h, and 9k. The predominant one occurred during the midday and afternoon hours (1000–1600 LST), and the secondary one appeared mostly during predawn and the early morning hours (0300–0800 LST). When the normalized frequency was larger than 0.2, the predominant maximum regions of duration (Fig. 9e), storm top (Fig. 9h), VIL (Fig. 9k), and size (Fig. 9q) roughly approximated the secondary ones. When the normalized frequency was smaller than 0.2, the maximum reflectivity (Fig. 9b), duration (Fig. 9e), and storm top (Fig. 9h) increased markedly at around 1600 LST. The 75% quartile of MUCAPE (Fig. 3a) at 1400 LST was indicative of moderate instability (~1500 J kg−1) and moderate wind shear (~16 m s−1) (Fig. 3d). Thus, under the influence of a large-scale mei-yu front, the diurnal variations in mei-yu storm number and properties were relatively weak. However, during the mei-yu period, a small proportion of the storms in the afternoon remained strong and tended to be more organized, resulting in an afternoon peak in CG lightning occurrence (1400–1600 LST), as shown in Fig. 10c.

Fig. 8.
Fig. 8.

The diurnal variations of storm numbers during the pre-mei-yu, mei-yu, and post-mei-yu periods regionally summed over the study area (green circle in Fig. 1).

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

Fig. 9.
Fig. 9.

The diurnal variations of storm properties during the (left) pre-mei-yu, (middle) mei-yu, and (right) post-mei-yu periods over the study area (green circle in Fig. 1): (a)–(c) maximum reflectivity, (d)–(f) duration, (g)–(i) top, (j)–(l) VIL, (m)–(o) speed, and (p)–(r) size.

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

Fig. 10.
Fig. 10.

Diurnal variations of lightning numbers (regionally summed) and mean value of peak current (regionally averaged) for the negative CG lightning (red line with asterisk) during the pre-mei-yu, mei-yu, and post-mei-yu periods over the study area (green circle in Fig. 1).

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

Storm activity during the post-mei-yu period exhibited a pronounced diurnal cycle with a peak in the early afternoon (1400 LST) with a larger amplitude than those of the pre-mei-yu and mei-yu periods (Fig. 8). The normalized frequencies of storm properties, including reflectivity (Fig. 9c), duration (Fig. 9f), storm top (Fig. 9i), VIL (Fig. 9l), and size (Fig. 9r), peaked in the afternoon (1200–1800 LST) during the post-mei-yu period. At 1400, the atmosphere was characterized as having a moderate to large MUCAPE (Fig. 3a), high moisture levels (Fig. 3c), and moderate wind shear (Fig. 3d), thus providing a favorable environment for strong and broad convective updrafts (Williams et al. 1991; Williams and Stanfill 2002; Zipser 2003; Kirkpatrick et al. 2011; Palucki et al. 2011). These strong, organized afternoon storms also produced more lightning (Fig. 10d) than did those of prior periods. Moisture and wind shear values were comparable between 0200 and 1400, whereas the MUCAPE differed greatly (~500 J kg−1 at 0200 LST and ~1600 J kg−1 at 1400 LST). This instability potentially plays an important role in controlling storm properties and lightning in the afternoon during the post-mei-yu period.

In the early morning (0300–0800 LST), fast mei-yu storms (Fig. 9n) potentially signify more strongly forced storms (Xue et al. 2018) or cold pool–forced mesoscale convective systems (MCSs) (Luo et al. 2014) that occur in the overnight hours with growing diurnally driven, isolated deep convection. This phenomenon is further strengthened by the longer lifetime (Fig. 9e) and larger maximum reflectivity (Fig. 9b) of these storms during the mei-yu period. By contrast, post-mei-yu storms are almost exclusively due to diurnally driven convection in the afternoon (Fig. 8). Figure 10 shows a clear afternoon peak in CG lightning correlated with the afternoon peak of thermally driven convection in both the mei-yu and post-mei-yu periods. However, the diurnal peaks in the number of storms in the mei-yu period were nearly the same in the afternoon and early morning hours (Fig. 8). These results indicate that thermally forced storms may be more electrically active (or more efficient at generating lightning) than nocturnal MCS-type systems in this region. The results are consistent with those presented in Fig. 9, which shows that afternoon thermally forced storms have deeper storm tops than nocturnal organized storms during the mei-yu period.

b. Lightning

The peak current of CG lightning is closely related to thunderstorm intensity. The diurnal cycle of the mean peak current (MPC) for negative CG lightning is shown in Fig. 10. The variability in MPC (Fig. 10) decreased from the pre-mei-yu (35–53 kA) to the mei-yu (33.4–39.6 kA) and post-mei-yu (32.6–34 kA) periods; this is coincident with increases in storm reflectivity, top, VIL, duration, and size (Fig. 7) driven by atmospheric instability and wind shear (Fig. 3). Taken together, these findings suggest that the relatively stronger turbulence within strong storms during the post-mei-yu period may create shorter distances among electrostatic thundercloud charges, resulting in frequent lightning with a small peak current (Bruning and MacGorman 2013; Chronis et al. 2015a,b).

During the warm season, the MPC increased from late evening to early morning, reaching a maximum at 0600 LST, and then decreased from 0700 to 1300 LST, reaching a minimum at 1300 LST (Fig. 10a). The inverse correlations between the MPC and lightning numbers are consistent with similar measurements acquired over the continental United States. However, the range of variation in the MPC revealed by the current study is obviously much larger than that for the United States, except for the oceanic subregion described by Chronis et al. (2015a).

The morning mei-yu storms with their frequent occurrences and moderate intensity produced some lightning, but with a high MPC (Fig. 10c). The morning MPC (0400–0900 LST) was relatively uniform with a large mean value, which is consistent with the morning peak observed for moderate mei-yu storms (Figs. 8 and 9); these storms are controlled by a sustained weather system characterized by weak instability, moderate wind shear, and moist air. From 1000 to 1800 LST, the MPC (Fig. 10c) decreased to less than 38 kA, reaching a minimum value around 33.4 kA at 1900 LST, which is close to the minimum storm number at 2000 LST shown in Fig. 8. This may be due to lightning from a relatively few slow-moving evening storms with strong reflectivity and high tops (Fig. 9).

The MPC (Fig. 10d) during the post-mei-yu period exhibited three peaks and seemed to have no relationship with respect to lightning occurrence; it should be noted that the MPC varied within a small range (32.6–34 kA).

c. Diurnal variation in spatial distribution

The storms and lightning mainly occurred during the mei-yu and post-mei-yu periods. Figures 11 and 12 present the spatial distributions of the 3-hourly accumulated storm frequency during the mei-yu and post-mei-yu periods, respectively, and Figs. 13 and 14 show the respective distributions of CG lightning during the same two periods. From 0000 to 0300 LST, mei-yu storms formed upstream of the Nanjing radar site along the YR. The maximum region of the storm continued to strengthen and expand from 0300 to 0600 LST. From 0900 to 1200 LST, the storm center shifted northeastward. The spatial distributions of the 3-hourly accumulated lightning occurrence revealed that most lightning was not related to this maximum region of storms (Figs. 13a–d). This phenomenon also appeared over the following time periods: 1500–1800, 1800–2100, and 2100–2400 LST. From 1200 to 1500 LST, a small maximum region of lightning (Fig. 13) was located along the margin of the maximum region of storms. These results indicate that lightning was produced by a minority of strong storms, rather than the frequent moderate storms that occurred during the mei-yu period.

Fig. 11.
Fig. 11.

Spatial distributions of the storm occurrence frequency between 0000 and 0300, 0300 and 0600, 0600 and 0900, 0900 and 1200, 1200 and 1500, 1500 and 1800, 1800 and 2100, and 2100 and 2400 LST during the mei-yu period. The white line indicates the Yangtze River; the black line represents Hongze Lake.

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

Fig. 12.
Fig. 12.

As in Fig. 11, but for storms during the post-mei-yu period.

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

Fig. 13.
Fig. 13.

As in Fig. 11, but for lightning during the mei-yu period.

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

Fig. 14.
Fig. 14.

As in Fig. 11, but for lightning during the post-mei-yu period.

Citation: Journal of Climate 33, 12; 10.1175/JCLI-D-19-0453.1

At 0200 and 1400 LST, low and high MUCAPE, respectively, as well as moderate wind shear and high moisture were observed during the post-mei-yu period. The atmospheric instability did not favor storm initiation during the nighttime hours (Figs. 12a,b,h), but conditions became more favorable during the afternoon (Figs. 12e,f). There were two local maximum regions of storms in the afternoon—one near Hongze Lake (region A) and the other in an urban area along the YR, downstream of the Nanjing radar site (region B). Region A may be related to the lake breeze driven by the temperature gradient between the lake and the land surface. The lake breeze may converge with southerly monsoonal flows to enhance local convective activity (King 1996; King et al. 2003; Wang et al. 2019). Region B is collocated with the local maximum region of CG lighting (Figs. 14e,f) in the afternoon, which may be related to an effect in which a strong sensible heat flux enhances cloud formation over urban areas (Inoue and Kimura 2004). Notably, the prevailing wind direction influences the location of these events (Haberlie et al. 2015). In addition, the circulation caused by an urban heat island can interact with various processes; thus, this phenomenon may be another contributing factor for various precipitation patterns (Tang and Miao 1998; Chen et al. 2007; Zhang et al. 2011; Dou et al. 2015; Li et al. 2015).

6. Summary and conclusions

S-band weather radar data and CG lightning data from 2010 to 2014 were used to investigate the characteristics of convective storms over the YRD in China. The storm properties, including the storm duration, size, top, maximum reflectivity, and VIL, were derived using the SCIT algorithm. The subseasonal variability in storm characteristics and lighting intensity during different phases of the mei-yu season were investigated. The results indicated that storm and lighting characteristics exhibit strong subseasonal variability under different synoptic forcings. The environmental factors influencing storm and lighting activity during different mei-yu phases were further resolved using reanalysis data. The main findings of this study are given below.

  1. During the mei-yu period, storms occurred most frequently along the YR, with peak activity occurring in the early morning and afternoon. The mei-yu storms were strongly controlled by the synoptic-scale weather system called the mei-yu front. During this period, the average environmental conditions were characterized by weak instability, moderate wind shear, and high moisture levels, which resulted in the frequent occurrence of long-lived moderate storms. As such, there was relatively weak diurnal variation in mei-yu storm number and storm properties. On average, the afternoon storms were associated with higher reflectivity and higher storm tops than were morning storms. These afternoon storms may be associated with increasing atmospheric instability in the afternoon, accounting for the afternoon peak in CG lightning.

  2. The largest values of reflectivity, top, VIL, size, duration, and frequency of CG lightning among the three periods were observed for post-mei-yu storms, due to increased atmospheric instability during this period. The diurnal cycles of the post-mei-yu storms, their properties (storm reflectivity, top, VIL, size, and duration), and CG lightning occurrence followed a unimodal pattern with a maximum occurrence frequency in the afternoon. This is attributable mostly to greater thermodynamic instability in the afternoon, as the wind shear and moisture levels exhibited little diurnal variation.

  3. The peak current of negative CG lightning was closely related to storm intensity. An inverse correlation between the MPC for negative CG lightning and lightning occurrence was evident during the pre-mei-yu and mei-yu periods. The range of variation in the MPC for negative CG lightning decreased from the pre-mei-yu period to the mei-yu period and further to the post-mei-yu period, which corresponds well with patterns associated with storm intensity.

Storms and CG lightning located around urban areas during the post-mei-yu period may be related to urban effects. Future work will include the examination of mesoscale forcing induced by urban effects, the river valley, and other possible contributing factors related to the storm and lightning maximum regions during the post-mei-yu period, based on convection-permitting simulations.

Acknowledgments

This work was primarily supported by the National Key Research and Development Program of China (Grant 2017YFC1501703), the National Natural Science Foundation of China (Grants 41475015, 41275031, 41805025, 41322032), and the Open Research Program of the State Key Laboratory of Severe Weather. Xingchao Chen is supported by the Office of Science of DOE Biological and Environmental Research as part of the Regional and Global Modeling and Analysis program. We acknowledge the Jiangsu Meteorological Bureau for collecting and archiving the radar data. The data supporting the analysis and conclusions of this paper, including the processed radar observations and the synoptic data, can be requested by contacting the office at yang.zhengwei@nju.edu.cn.

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