Features of MCSs in the Central United States Using Simulations of ERA5-Forced Convection-Permitting Climate Models

Yunsung Hwang aSchool of Environment and Sustainability, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
bGlobal Institute for Water Security, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

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Zhenhua Li bGlobal Institute for Water Security, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

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Yanping Li aSchool of Environment and Sustainability, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
bGlobal Institute for Water Security, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

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Abstract

In this work, we characterized the occurrences and conditions before the initiations of mesoscale convective systems (MCSs) in the central United States, using 15 years of observations and convection-permitting climate model simulations. The variabilities of MCSs in summer were obtained using high-resolution (4 km) observation data [Stage-IV (stIV)] and ECMWF Re-Analysis v5 (ERA5)-forced Weather Research and Forecasting (WRF) Model simulations (E5RUN). MCSs were identified using the object tracking algorithm MODE-time domain (MTD). MTD-determined MCSs were divided into daytime short-lived MCSs (SLM12), daytime long-lived MCSs (LLM12), nighttime short-lived MCSs (SLM00), and nighttime long-lived MCSs (LLM00). E5RUN showed skill to simulate MCSs by obtaining similar statistics in occurrences, areal coverages, and propagation speeds compared to those of stIV. We calculated the 15 parameters using sounding data from E5RUN before an MCS was initiated (−1, −3, −6, and −9 h) at each location of an MCS. The parameters were tested to figure out the significance of predicting the longevities of MCSs. The key findings are 1) LLM12 showed favorable thermodynamic variables compared to that of SLM12 and 2) LLM00 showed significant conditions of vertically rotating winds and sheared environments that affect the longevity of MCSs. Moreover, storm-relative helicity of 0–3 km, precipitable water, and vertical wind shear of 0–6 km are the most significant parameters to determine the longevities of MCSs (both daytime and nighttime MCSs).

Significance Statement

The purpose of this study is to understand the features of mesoscale convective systems (MCSs) in observational data and convection-permitting climate model simulations. We tested long-term simulations using new forcing data (ERA5) to see the benefits and limitations. We designed a novel approach to obtain the distributions of meteorological parameters (instead of obtaining one value for one event of MCS) before initiations of MCSs to understand preconvective conditions (times from −9 to −1 h from initiation). We also divided MCSs into daytime/nighttime and short-/long-lived MCSs to help predict MCSs longevity considering the initiation times. Our results provide hints for the forecasters to predict MCS longevity based on preconvective conditions from parameters discussed in this work.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher's Note: This article was revised on 30 September 2022 to correct a typographical error in the second set of equations in section 2b when originally published.

Corresponding author: Yanping Li, yanping.li@usask.ca

Abstract

In this work, we characterized the occurrences and conditions before the initiations of mesoscale convective systems (MCSs) in the central United States, using 15 years of observations and convection-permitting climate model simulations. The variabilities of MCSs in summer were obtained using high-resolution (4 km) observation data [Stage-IV (stIV)] and ECMWF Re-Analysis v5 (ERA5)-forced Weather Research and Forecasting (WRF) Model simulations (E5RUN). MCSs were identified using the object tracking algorithm MODE-time domain (MTD). MTD-determined MCSs were divided into daytime short-lived MCSs (SLM12), daytime long-lived MCSs (LLM12), nighttime short-lived MCSs (SLM00), and nighttime long-lived MCSs (LLM00). E5RUN showed skill to simulate MCSs by obtaining similar statistics in occurrences, areal coverages, and propagation speeds compared to those of stIV. We calculated the 15 parameters using sounding data from E5RUN before an MCS was initiated (−1, −3, −6, and −9 h) at each location of an MCS. The parameters were tested to figure out the significance of predicting the longevities of MCSs. The key findings are 1) LLM12 showed favorable thermodynamic variables compared to that of SLM12 and 2) LLM00 showed significant conditions of vertically rotating winds and sheared environments that affect the longevity of MCSs. Moreover, storm-relative helicity of 0–3 km, precipitable water, and vertical wind shear of 0–6 km are the most significant parameters to determine the longevities of MCSs (both daytime and nighttime MCSs).

Significance Statement

The purpose of this study is to understand the features of mesoscale convective systems (MCSs) in observational data and convection-permitting climate model simulations. We tested long-term simulations using new forcing data (ERA5) to see the benefits and limitations. We designed a novel approach to obtain the distributions of meteorological parameters (instead of obtaining one value for one event of MCS) before initiations of MCSs to understand preconvective conditions (times from −9 to −1 h from initiation). We also divided MCSs into daytime/nighttime and short-/long-lived MCSs to help predict MCSs longevity considering the initiation times. Our results provide hints for the forecasters to predict MCS longevity based on preconvective conditions from parameters discussed in this work.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher's Note: This article was revised on 30 September 2022 to correct a typographical error in the second set of equations in section 2b when originally published.

Corresponding author: Yanping Li, yanping.li@usask.ca

1. Introduction

Deep moist convection is important for its role in energy transfer in global circulation (Glickman 2000). Convective storms can generate severe weather by offering vertically developed columns of free convection. Mesoscale convective systems (MCSs) are defined as organized convections associated with precipitation systems with sizes greater than 100 km in at least one horizontal direction (Houze 1993). Known for their persistence, MCSs can linger from several hours to a few days. MCSs can be useful to farmers because they can provide rainfall (Fritsch et al. 1986; Jirak et al. 2003; Ashley et al. 2003). However, they can also be hazardous as they can be accompanied by severe localized winds, damaging hail, flash flooding, and tornadoes (Maddox 1980; Houze et al. 1990; Trapp and Weisman 2003; Jirak and Cotton 2007). Regardless of whether their impacts are beneficial or harmful, knowledge of MCSs’ development in the central United States is required for several reasons: First, MCSs contribute substantial precipitation to the hydrological cycle in the warm season in the contiguous United States (Fritsch et al. 1986; Carbone et al. 2002; Trier et al. 2010; Feng et al. 2019). MCSs account for 30% to 70% of the total warm-season precipitation in the central United States (Feng et al. 2016; Haberlie and Ashley 2019; Feng et al. 2019). Second, in the central United States, MCS-contributed precipitation has increased both in amount and frequency in the last 35 years (Feng et al. 2019).

MCSs are commonly initiated by frontal boundaries that provide a kinematic lift of the air parcel in the central United States during the warm season. However, long-lived MCSs require additional features that support their development and organization. Evans and Doswell (2001) indicated that one of these features is the strength of the vertical mean wind (0–6 km) interacting with cold pool development, which maintains MCSs (Rotunno et al. 1988; Weisman and Rotunno 2004; Schumacher 2009). They also showed that instabilities [i.e., convective available potential energy (CAPE)] and wind shear can provide additional energy for long-lived MCSs. The authors used vertical mean wind and the existence of a cold pool to predict MCS propagation. Other authors have presented key parameters that enable MCSs to develop upscale growth to become MCSs: 1) low-level warm air advection, 2) low-level vertical wind shear, and 3) convective instability (Corfidi 2003; Jirak and Cotton 2007). Another study (Coniglio and Corfidi 2006) focuses on the propagation speed and longevity of MCSs. They used 348 warm-season MCS proximity soundings from various MCS types to develop parameters (probabilities of MCS maintenance and speed) that showed promising results when tested in the summer of 2005.

During the day, diabatic heating from the surface can overcome the inversion layer formed before sunrise. Radiative heating can warm the air parcel near the ground, causing the air parcel to ascend freely even when there is an inversion layer (Grabowski et al. 2006). Nighttime convection has different mechanisms because no heat sources exist after sunset. A series of studies have suggested the central United States as a source region, which has feedback without diabatic heating (i.e., lack of surface-based CAPE) (Heideman and Fritsch 1988; Tripoli and Cotton 1989a,b). The authors suggest that advected warm air (during the day) from the west can act as an elevated heat source to maintain the convection (Tripoli and Cotton 1989a,b). At least one study indicates that eastward propagating gravity waves developed over the elevated terrain (the Rocky Mountains) can trigger convections at night over the plains (Geerts et al. 2017). When convection is triggered, the system maintains itself by obtaining energy sources from latent heat released in emerging MCSs (Tuttle and Davis 2006; Trier et al. 2010). Other studies have discovered that a southerly low-level jet (LLJ) impacts nighttime MCS development and maintenance (Arritt et al. 1997; Pu and Dickinson 2014).

The occurrences and initiation of MCSs in the central United States have been studied by many researchers due to their contribution to precipitation. Warm-season extreme precipitation events, many associated with MCSs, have been studied for several decades all over the world (Laing and Fritsch 2000), including east of the Rocky Mountains (Heideman and Fritsch 1988; Tripoli and Cotton 1989a,b; Li and Smith 2010; Feng et al. 2019). Researchers have improved the understanding of synoptic conditions of convection initiation using observational data (Jirak and Cotton 2007; Coniglio and Corfidi 2006; Geerts et al. 2017), and numerical weather prediction (NWP) model simulations (Pu and Dickinson 2014; Feng et al. 2019). Moreover, the longevity of MCSs was studied and discussed to understand the features of initiating and maintaining MCSs. Coniglio et al. (2010) emphasized that rapidly developing MCSs included strong LLJ, larger CAPE, smaller 3–10-km wind shear, and smaller geostrophic potential vorticity on isentropic surfaces (compared to slowly developing MCSs). Moreover, long-lived MCSs (mature stage period ≥ 8 h) showed strong (and broad) LLJ, frontal zones, and sheared environments (i.e., deep layer, from 0.5 to 6 km) among 94 MCSs (2005–08) identified from RUC (Coniglio et al. 2010). A recent study found that long-lived MCSs (≥9 h) indicated midlevel cyclonic circulation (i.e., synoptic-scale trough) and upper-level divergences (i.e., 300 hPa) to maintain favorable convective conditions (Yang et al. 2017). Yang et al. (2017) showed the benefits of studying MCSs using convection-permitting model simulations (i.e., 4-km grid spacing) in the warm seasons of 2011 and 2012. Moreover, a recent study shows improved performances of ECMWF Re-Analysis v5 (ERA5)-forced simulations [in relative humidity (RH)] in a case study of an MCS in the United Arab Emirates when evaluated using ground station data (Francis et al. 2021).

The ERA5 dataset (Hersbach et al. 2020) was released with high spatial and vertical resolutions (a 30-km grid spacing and 137 vertical layers up to 80 km) by the Copernicus Climate Change Service (C3S) from European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 dataset showed improvements as a reference dataset for hydrological modeling in North America over the 1979–2018 period (compared to observed precipitation from ground stations in the United States and Canada) (Tarek et al. 2020). Moreover, the ERA5 dataset showed good agreements with satellite-gauge precipitation data from the Global Precipitation Climatology Project, GPCP-SG for central Europe, and the South Asian Monsoon regions compared to daily rain rates from 1983 to 2016 (Hassler and Lauer 2021). At least one study emphasized the benefits of ERA5 forced simulations (resolution: TCo319L91, i.e., 36-km grid spacing, and 91 vertical levels) by showing better skill scores in continuous ranked probability skill scores (CRPSSs; compared to ERA5 itself) based on atmospheric variables from 2000 to 2016 (Vitart et al. 2019).

Recent studies showed the benefits of high-resolution convection-permitting model simulations forced by ERA5. The 1-km grid-spacing ERA5 [and the North American Mesoscale Analyses (NAM-ANL)] forced simulations showed better performance over a coastal valley in northern British Columbia, Canada, than the simulations forced by North American Regional Reanalysis (NARR) when compared to observed precipitation from four ground stations for 2017 (Onwukwe et al. 2022). Case studies of 3-km grid-spacing convection-permitting model simulations showed better performances of ERA5 forced simulations in precipitation systems and near-surface wind than those forced by the Global Data Assimilation System Final analysis (FNL) and the Global Forecast Systems (GFS) at Yerevan, Armenia (Gevorgyan 2018a,b). Despite the benefits of previous studies, they were focused on a few cases (Gevorgyan 2018a,b; Onwukwe et al. 2022) and tested in coarser resolutions (Vitart et al. 2019; Tarek et al. 2020; Hassler and Lauer 2021). It is necessary to obtain high spatiotemporal resolution convection-permitting model simulations and to evaluate results using long-term observational data with similar resolutions. Mesoscale features in high-resolution NWP model simulations can be used to better understand MCSs that were partially obtained but limited in observations (unevenly distributed ground stations or 12-hourly sounding data) or reanalysis data (hourly ERA5 data has a 30-km grid spacing).

The purpose of this study is to utilize the improved high-resolution ERA5 to force convection-permitting model simulations to study the occurrences and initiations of MCSs in the central United States. Our goal is to improve our understanding of 1) MCS distributions and characteristics in time and space and 2) parameters important to MCS initiation by utilizing convection-permitting climatological simulations. The findings will help researchers understand the characteristics and potential longevities of MCSs by providing detailed information as represented in meteorological parameters. The observation and modeling datasets and techniques to identify MCSs and to analyze MCSs statistically are described in section 2. The characteristics of MCSs in the analysis region are discussed and explained using statistical tests in section 3. Finally, a summary and discussion are provided in section 4.

2. Data and methodology

a. Data

In this study, observed and simulated precipitation systems were used to characterize MCSs in the central United States. We used 15 years of hourly data (from 2004 to 2018) from radar networks and an NWP model simulation using the Weather Forecasting and Research (WRF) Model. The analysis domain was chosen to include the complex terrain and initiation of MCSs east of the Rocky Mountains, as shown in Fig. 1.

Fig. 1.
Fig. 1.

The coverage of Stage-IV (superimposed as a black contour), the selected region from Stage-IV (a dotted black contour), the simulation domain (a red dotted contour), and the analysis region of this study (a red rectangle) are represented.

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

1) Stage- IV data

Stage-IV is a merged radar-estimated and rain gauge precipitation dataset and has a 4-km grid-size mosaic in hourly, 6-hourly, and daily accumulation and is maintained by the National Centers for Environmental Prediction. Historically, Stage-IV data were incorporated into data assimilation. Moreover, the advantages of high temporal and spatial resolutions enabled their further use to accurately measure precipitation and to compare these measurements to the ground station-, radar-, and satellite-based algorithms (Nelson et al. 2016). Some studies have compared the precipitation systems from observational data and regional climate model simulations (Prein et al. 2017; Scaff et al. 2019).

2) ERA-5 forced simulation: E5RUN

In this study, convection-permitting 4-km NWP simulations using WRF were conducted to test the high resolution spatiotemporal meteorological parameters in MCSs using ERA5 forcing data (Hersbach et al. 2020). ERA5 replaced the ERA-Interim reanalysis (ERAI, from 31 August 2019) by providing higher resolution at a 30-km grid (80 km for ERAI) and resolving the atmosphere using 137 levels (60 levels for ERAI) from the surface up to a height of 80 km. Based on an understanding of the benefits of ERA5, an experimental simulation was set up in the central United States. The analysis region is represented in Fig. 1 as a red contour. Note that the eastern and western edges are excluded for eliminating boundary effects (the southern part of the domain is excluded to be consistent with the Stage-IV analysis domain, i.e., a black contour in Fig. 1).

The simulation used the Advanced Research version of the WRF Model (Skamarock et al. 2008) version 4.2.1 with 4-km grid spacing and 37 vertical levels and the following WRF physics options: 1) RRTMG (Iacono et al. 2008), 2) Noah-MP (Niu et al. 2011), 3) the Yonsei University (YSU) planetary boundary layer scheme (Hong et al. 2006), and 4) Thompson aerosol-aware microphysics (Thompson and Eidhammer 2014). We ran simulations from 0000 UTC 23 May to 0000 UTC 1 September each year and used data for analysis from 0000 UTC 1 June to 2300 UTC 31 August from 2004 to 2018. The spinup time to stabilize the simulations was 9 days, which was discarded for the analysis of the study. The choice of spinup time is consistent with the fact that ERA5-forced simulations showed better results after a week (Vitart et al. 2019). Additionally, spectral nudging is applied to synoptic scales (i.e., scales around 2000 km and above) to reduce the warm-season warm and dry biases in the central United States as discussed in (Liu et al. 2017). The spectral nudging was applied to 1) geopotential height, 2) horizontal wind, and 3) temperature. The nudging starts from the approximate planetary boundary layer (PBL) top and linearly increases to the full strength at the fifth level above the PBL (i.e., lower level: nudging from 200 to 300 m from the ground).

b. Methodology

1) Performances of E5RUN in simulating precipitation systems

The precipitation systems simulated in E5RUN are evaluated compared to those in observational data [i.e., Stage-IV (stIV)]. Average rain rates (RR) and occurrences of RR ≥ 5.0 mm h−1 (FREQ5R) are obtained in two-dimensional maps and Hovmöller diagrams using the following equations:
2D_RR(i,j)=t=0t=pRR(t,i,j)15×(30+31+31)×24,2D_FREQ5R(i,j)=t=0t=pFreq[RR(t,i,j)5.0 mm h1]15×3,HOV_RR(i,t2)=t=0t=pj=1j=nRR(t,i,j)15×(30+31+31)×n,HOV_FREQ5R(i,t2)=t=0t=pj=1j=nFreq[RR(t,i,j)5.0 mm h1]15×3,t=0,,p,indexoftimedimension(anentireperiodofanalysistime),t2=0,,23,index of time in UTC(24h),i=1,,m, index of longitude (i.e.,west to east),j=1,,n, index of latitude (i.e.,south to north).
Every RR in the pixel is accumulated and averaged throughout the analysis period, i.e., hourly RR in summer (3 months) for 15 years. The 2D_RR(i, j) and HOV_RR(i, t2) are in millimeters per hour (mm h−1) and 2D_FREQ5R(i, j) and HOV_FREQ5R (i, t2) are in per month (month−1) to represent average RR and frequencies from stIV and E5RUN. Mean absolute errors (MAEs) are obtained in between stIV and E5RUN using the following equations:
MAE[2D_RR(i,j)]=i=1i=mj=1j=n|E5RUN[2D_RR(i,j)]stIV[2D_RR(i,j)]|m×n,MAE[2D_FREQ5R(i,j)]=i=1i=mj=1j=n|E5RUN[FREQ5R(i,j)]|stIV[FREQ5R(i,j)]m×n,MAE[HOV_RR(i,t2)]=i=1i=mt2=0t2=23|E5RUN[HOV_RR(i,t2)]stIV[HOV_RR(i,t2)]|m×24,MAE[HOV_FREQ5R(i,t2)]=i=1i=mt2=0t2=23|E5RUN[HOV_FREQ5R(i,t2)]stIV[HOV_FREQ5R(i,t2)]|m×n,t2=0,23,indexoftimeinUTC(24h),i=1,,m, indexoflongitude (i.e.,westtoeast),j=1,,n, indexoflatitude (i.e.,southtonorth).

We obtained two-dimensional Pearson correlation coefficients (Pearson R values) 1) between E5RUN(2D_RR) and stIV(2D_RR), 2) between E5RUN(HOV_RR) and stIV(HOV_RR), 3) between E5RUN(FREQ5R_RR) and stIV(FREQ5R_RR), and 4) between E5RUN(HOV_FREQ5R) and stIV(HOV_FREQ5R). The statistical scores (MAE and Pearson R) between stIV and E5RUN are obtained to evaluate the performances of simulating precipitation systems in E5RUN. FREQ5R is calculated to see how RR of 5 mm h−1 correlates in stIV and E5RUN in two-dimensional maps and Hovmöller diagrams for 15 years.

2) Detecting and tracking MCSs

We defined an object as an MCS considering spatial coverages and temporal continuities. Previous studies discussed the spatial continuity with 100 km in at least one dimension (Houze 2004) and longevities as long as 5 h and more (Coniglio et al. 2010; Prein et al. 2017). In specific, Coniglio et al. (2010) used a threshold of an MCS as: 1) a region of reflectivity ≥ 35 dBZ (5.6 mm h−1), 2) the region includes reflectivity ≥ 50 dBZ (48.6 mm h−1), and 3) the region lasted at least 5 h. Model Evaluation Tools (MET, developed by the Developmental Testbed Center, https://dtcenter.org) was chosen to detect MCSs considering criteria in previous studies. MET includes the Method for Object-Based Diagnostic Evaluation (MODE) used in the evaluation of model results with observations (Cai and Dumais 2015). Moreover, MODE-time domain (MTD) was used to further define and track the objects based on spatial and temporal continuities of objects. The procedures of MTD are as follows:

  1. convolving (dilating/smoothing) objects with user-defined smoothing length (conv_radius = 8 pixels, 32 km).

  2. defining objects with a user-defined threshold (conv_thresh = 5.0 mm h−1).

  3. identifying and numbering contiguous objects in the time domain, considering the minimum three-dimensional object size (i.e., latitude, longitude, and time), while the minimum size is predefined by a user (min_volume = 2000).

When the smoothing radius and threshold are set to larger values (i.e., steps 1 and 2), only a few objects have remained (Prein et al. 2017). We set “min_volume = 2000” to define the minimum volume of an object in the space and time domain. For example, if an object has a uniform size of 25 × 8 pixels (100 km × 32 km) and lasts for 10 h, this object is determined as an MCS (i.e., min_volume = 25 × 8 × 10 = 2000). We tested and found the design of the MTD can detect MCSs that agree to previous criteria (i.e., sizes and longevities).

The MCSs were further divided into subsets based on quantiles of longevities in observation and simulation separately. We defined short-lived MCSs as those with longevities < 75th percentile and long-lived MCSs as those with longevities ≥ 75th percentile. We determined MCSs as a daytime MCS if an MCS is initiated (determined as an MCS in MTD) from 1200 to 2300 UTC and a nighttime MCS if it is initiated from 0000 to 1100 UTC. Note that LST can be calculated roughly by deducting 6 h from UTC in the analysis region to have 1800–0500 LST for nighttime MCSs and 0600–1700 LST for daytime MCSs. The 75th percentile was selected to represent long-lived MCSs as the results of tests conducted over thresholds from 25th to 95th percentiles by comparing spatial coverages between short- and long-lived MCSs.

3) Characteristics of MCS environments

The features of summertime MCSs are derived from hourly E5RUN based on the initiations of MCSs. The initiation time is identified as the first time step when an object is observed (obtaining at least one grid cell of 5 mm h−1) and the object overlaps with other objects in the future continuously (checking objects in the next hour and repeating until no objects are found at t + 1 h).

For example, if a three-dimensional object (in longitude × longitude × time) is identified (i.e., the stacked up two-dimensional dilated objects of 5 mm h−1), the earliest hour (in the time domain) is set to the initiation time. In the processes of developing and dissipating, an object can have precipitation less than 5 mm h−1. The method can lose some of the information at the early and late stages by having shorter lifetimes compared to an algorithm discussed in Lakshmanan et al. (2009). Lakshmanan et al. (2009) defined an object based on the saliencies (similar to detecting cone shapes in two-dimensional maps) in the storm cells and assumed an object started from a single point. It should be noted that the initiation time in this study is the earliest hour when the precipitation system is fully developed. The modification of the threshold can extend (or shorten) the MCS lifetime a few hours more (or less). When the object at the initiation is determined, the 15 variables from every pixel inside the object are collected as lists at −1, −3, −6, and −9 h from the initiation. Additional information was obtained on these variables such as the spatial distributions of each variable to investigate their variabilities instead of having one spatially averaged value. A set of candidate variables was determined to show environments as 1) most unstable convective available potential energy (MUCAPE; J kg−1), 2) most unstable convective inhibition (MUCIN; J kg−1), 3) lifting condensation level (LCL; m), 4) difference in the level of free convection (LFC) and LCL (LFC − LCL; m), 5) precipitable water (PW; kg m−2), 6) storm-relative helicity of 0–1 and 0–3 km (SRH1km and SRH3km; m2 s−2), 7) vertical wind shear of 3–10, 0–3, and 0–6 km (WSHR3–10km, WSHR3km, and WSHR6km; m s−1), and 8) isentropic potential vorticity at 335–355 K (IPV335K, IPV340K, IPV345K, IPV350K, and IPV355K; potential vorticity units; 1 PVU = 10−6 m2 s−1 K kg−1).

MUCAPE and MCUCIN capture the features of potential maximum convective energy and inhibition associated with the most unstable parcel by lifting air parcels at the lower level (from the surface to lower 300 hPa in this study). MUCAPE is typically used for overnight/elevated convection because the surface-based CAPE can be misleading if the elevated heat sources at night are not captured. Large CAPE and small CIN (large MUCAPE and small MUCIN; Song et al. 2019; Schumacher and Johnson 2009) for convection initiations were observed in the previous study in the Appalachian Mountains (Letkewicz and Parker 2010). A recent study showed rising of warm dry air (less mixing for moist air) allows mixing with conditionally unstable air to result in stronger convection in high LCL height using idealized large-eddy simulations (LESs) (Mulholland et al. 2021). Another study using LESs showed deep moist convection was favorable when LFC − LCL is small (Kang and Bryan 2011) (i.e., LFC − LCL = 0 means CIN = 0). SRH was calculated using storm motions and used to determine supercell thunderstorms (Bunkers et al. 2000; Thompson et al. 2003). Forecasting using SRH and WSHR6km showed fewer errors compared to that of CAPE using soundings from Rapid Update Cycle (RUC) (Thompson et al. 2003). Another study used RUC analyses to figure out environments that determine rapidly and slowly developing MCSs including vertical wind shear above 3 km (wind shear of 3–10 km) (Coniglio et al. 2010). Coniglio et al. (2010) emphasized the importance of IPV to determine inertial instabilities by considering static stabilities (below the tropopause). In the study, rapidly developing MCSs showed lower values of IPV at 345 K than that of slowly developing MCSs (Coniglio et al. 2010).

4) Determination of statistical significance

The two-sample Kolmogorov–Smirnov (KS) test (Wilks 2011) was used to test how distributions of variables from long and short-lived MCSs are different. The tests were done for daytime and nighttime MCSs separately. We obtained D values defined as
Dij=supx[Fij(x)Gij(x)],i=1,,15,indexofcandidatevariables,j=indexofdaytimeornighttimeMCSs,
while supx is the supremum function, Fij(x) is an empirical cumulative distribution function (CDF) of long-lived MCSs, Gij(x) is an empirical CDF of short-lived MCSs. We obtained P values calculated using Kolmogorov D statistic tables (Smirnov 1948). The KS test was chosen to compare two distributions with different sample sizes (short-lived MCSs have about 3 times more samples than long-lived MCSs). After obtaining values from the KS test, larger D values and P values < 0.01 (99% confidence) or <0.05 (95% confidence) meant that there were significant differences between the variables in short- and long-lived MCSs. Additionally, Pearson R values of averaged fields were obtained comparing short- and long-lived MCSs. The purpose of obtaining Pearson R values was to compare the difference among variables to figure out which variables are discriminant to distinguish the longevities of MCSs considering average two-dimensional fields. We repeated the KS test and obtained Pearson R values at −1, −3, −6, and −9 h before the initiation of MCS to obtain the changes of these variables in time.

3. Results

a. Features of MCSs

The average rain rates (i.e., RR) and occurrences of RR ≥ 5 mm h−1 (i.e., FREQ5R) from observational data (stIV) and ERA5 forced WRF simulations (E5RUN) in the central United States exhibited similarities in distribution. Note that there were uncertainties in precipitation data along the Gulf of Mexico (Figs. 2a,g) where the region is far away from the radar network (radar coverages) and lacks rain gauge data to be used to correct biases in precipitation as discussed in Nelson et al. (2016). The MAEs of E5RUN are 0.05 mm h−1 for RR (Figs. 2c,f), 1.73 month−1 for FREQ5R in two-dimensional maps (Fig. 2c), and 1.63 month−1 for FREQ5R in Hovmöller diagrams (Fig. 2l). The Pearson Rs of E5RUN are 1) RR in two-dimensional maps: 0.65, 2) RR in Hovmöller diagrams: 0.79, 3) FREQ5R in two-dimensional maps: 0.66, and 4) FREQ5R in Hovmöller diagrams: 0.80. The quantitative scores showed great performances of E5RUN compared to those of stIV.

Fig. 2.
Fig. 2.

Average rain rates (RR) in summer [June–July–August (JJA)] (mm h−1) of (a) Stage-IV (stIV), (b) ERA5-forced simulation (E5RUN), and (c) differences between stIV and E5RUN, Hovmöller diagrams of (d) stIV, (e) E5RUN, and (f) E5RUN-stIV. Occurrences of (g)–(i) RR ≥ 5 mm h−1 and (j)–(l) shown in Hovmöller diagrams. The mean absolute error (MAE) is represented as numbers in titles in (c), (f), (i), and (l).

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

MCSs propagated east from the Rocky Mountains to the Great Plains, resulting in average RR ≥ 0.15 mm h−1 (yellow to red shades) over the Great Plains (Figs. 2a,b) in stIV and E5RUN. The propagations are shown in Hovmöller diagrams by obtaining diagonal strips of high average RR values (i.e., 0.15 mm h−1) from the east during early hours (i.e., 0000–0300 UTC) to the west during later hours (i.e., 1200–1500 UTC), as seen in Figs. 2d and 2e. The results of stIV also showed higher FREQ5R in the Hovmöller diagram (Fig. 2j), which showed negative difference values (E5RUN-stIV ≤ 2 month−1) around 102°–93°W at 0000–1500 UTC (Fig. 2l).

E5RUN showed significant improvements in simulating propagating precipitation systems in the central United States (Figs. 2e,k) while previous ERAI-forced simulations showed dry biases in the region (Liu et al. 2017). We found the discontinued features of propagating precipitation systems in Hovmöller diagrams (i.e., could not be maintained as systems) from 101°W to the east both from future and historical simulations (10 years) using the data provided in Rasmussen and Liu (2017) (not shown). Note that the forcing data were not the only differences (i.e., the domain size and details in physical schemes) to obtain different results but E5RUN simulated continuous features of precipitation systems in the central United States. However, E5RUN also revealed limitations compared to that of stIV in some regions. E5RUN is underestimated in RR and FREQ5R over the central United States (southeastern Oklahoma and northern Missouri) in Figs. 2b and 2h. Moreover, E5RUN showed underestimation of RR and FREQ5R in Hovmöller diagrams from 99° to 93°W at 0300–1500 UTC by showing negative values (E5RUN-stIV) in Figs. 2f and 2l. E5RUN showed promising results; however, dry biases in the central United States were not fully solved in ERA5 forcing data (see blue shades in Figs. 2f,l).

1) An example of an MTD-determined MCS

An example case was selected to demonstrate how MTD works and what the results of MTD look like. A case on 19 August 2016 was chosen considering synoptic and mesoscale conditions. Strong wind and hail reports were issued by the Storm Prediction Center (SPC) in the central United States (especially in Kansas and Oklahoma, https://www.spc.noaa.gov/exper/archive/event.php?date=20160817). MTD-determined objects from stIV are represented as yellow contours in Fig. 3. The objects propagated from the border of Kansas and Nebraska to the east of Kansas. E5RUN successfully simulated RR ≥ 5.0 mm h−1 and indicated as red contours in Fig. 3. Note that the purpose of showing the example is to demonstrate how MTD determined objects in stIV and E5RUN. The convection-permitting regional climate simulation of precipitation systems in this study was conducted not to predict weather phenomena at exact times and locations. The simulations were designed to provide insight into how MCSs develop during daytime and nighttime.

Fig. 3.
Fig. 3.

Examples of hourly MTD-determined MCSs from 1900 to 0600 UTC 20 Aug 2016. Shades are showing RR from observation (i.e., stIV), superimposed with yellow contours (MTD-determined objects in stIV) and red contours (MTD-determined objects in E5RUN).

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

2) Features of MTD-determined MCSs

The statistics of MTD-determined MCSs (longevity, areal coverages, propagation speeds, and east–west and north–south components) from stIV and E5RUN are shown in Fig. 4. The distribution of longevities was divided into nighttime and daytime MCSs. Long-lived MCSs were defined as having longevities greater or equal to the 75th percentile of longevities in stIV (11 h for both daytime and nighttime MCSs in gray) and E5RUN (12 h for daytime and 13 h for nighttime MCSs in cyan) as shown as dotted lines in Figs. 4a and 4b. Note that there were 1217 cases of nighttime short-lived MCSs (SLM00), 432 cases of nighttime long-lived MCSs (LLM00), 844 cases of daytime short-lived MCSs (SLM12), and 308 cases of daytime long-lived MCSs (LLM12), from stIV. E5RUN indicated 893 cases of SLM00, 336 cases of LLM00, 1285 cases of SLM12, and 510 cases of LLM12. E5RUN overestimated the number of daytime MCSs (i.e., SLM12 and LLM12) while underestimating the cases of nighttime MCSs (i.e., SLM00 and LLM00). LLM00 and LLM12 are represented as solid lines (SLM00 and SLM12 as dotted lines) in Figs. 4c4j. The mean coverages of LLM00 and LLM12 in E5RUN showed underestimation compared to those in stIV (Figs. 4c,d). Similar biases in E5RUN were shown in SLM00 and SLM12 (stIV ≥ E5RUN). Propagation speeds were similar in SLM00 and LLM00; however, LLM12 in E5RUN revealed propagation speeds about 1 m s−1 higher than those in stIV (Fig. 4f). There were no clear differences between east–west and north–south propagation speeds, while SLM12 in stIV showed values about 2 m s−1 higher than those of E5RUN (Fig. 4h). Note that MTD-determined MCSs showed mean propagation directions to the east (Figs. 4g,h) and the south (Figs. 4i,j) for both stIV and E5RUN. We concluded that the E5RUN showed the skills to simulate MCSs by obtaining similar statistics to those of stIV.

Fig. 4.
Fig. 4.

Statistics of longevities of (a) nighttime MCSs (MCS00) and (b) daytime MCSs (MCS12). Dotted vertical lines show the 75th percentile of longevities in stIV (11 h for both daytime and nighttime MCSs in gray) and E5RUN (12 h for daytime and 13 h for nighttime in cyan). The average values during life cycles of MCS as areal coverages (c) MCS00 and (d) MCS12, propagation speeds (e) MCS00 and (f) MCS12, east–west component of propagation speeds of (g) MCS00 and (h) MCS12, and north–south component of propagation speeds (i) MCS00 and (j) MCS12.

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

The occurrences of SLM00, LLM00, SLM12, and LLM12 are shown in two-dimensional maps as in Fig. 5 and Hovmöller diagrams in Fig. 6. LLM00 and LLM12 in both stIV and E5RUN contributed to FREQ5R more than that of SLM00 and SLM12, considering that the number of short-lived MCSs was about 3 times more than that of long-lived MCSs (See Figs. 4a,b). SLM00 and LLM00 in E5RUN showed similar distributions compared to those of stIV. LLM00 in E5RUN showed high occurrences (≥2 month−1) over the coastal areas (i.e., south of 30°N) and in the central United States in Figs. 5d and 5e (overestimation in Iowa). SLM00 in E5RUN indicated similar significant contributions (≥1.5 month−1) in the north-central United States while obtaining lower values in Oklahoma, Kansas, Nebraska, and Missouri with about 0.3 month−1 less than SLM00 in stIV.

Fig. 5.
Fig. 5.

The occurrences of MTD-determined MCSs of short-lived nighttime MCSs (SLM00) in (a) E5RUN, (b) stIV, and (c) differences between (a) and (b). Occurrences of (d)–(f) long-lived nighttime MCSs (LLM00), (g)–(i) short-lived daytime MCSs (SLM12), and (j)–(l) long-lived daytime MCSs (LLM12).

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

Fig. 6.
Fig. 6.

As in Fig. 5, but represented in Hovmöller diagrams. Note that the y axes of (a)–(f) start from 0000 UTC and (g)–(l) start from 1200 UTC.

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

Both SLM12 and LLM12 in E5RUN overestimated significantly over the northern part of the analysis region (Fig. 5k). Moreover, SLM12 and LLM12 showed positive values in differences from those of stIV as shown in Fig. 5l. LLM12 in E5RUN showed a broad distribution of high occurrences (≥2.7 month−1), in the analysis region north of 40°N while LLM12 in stIV showed high occurrences (≥1.5 month−1) in Oklahoma, Kansas, Nebraska, and Missouri. It should be noted that LLM12 showed the overestimation of MCSs may be from unsolved (improved when compared to ERAI-forced simulations) dry biases (discussed in Liu et al. 2017) in the central United States (also in RR and FREQ5R in Figs. 2f,l). We acknowledge that the impact of dry biases on simulating MCSs was the largest in LLM12 and it is advised to be cautious about the existence of dry biases when simulating MCSs using ERA5 as forcing data.

The features of propagating MCSs are shown in Fig. 6. E5RUN captured the features of propagation of MCSs for SLM00 and LLM00 in space and time by obtaining envelopes of high occurrences in Figs. 6b,e (similar to that of stIV in Figs. 6a,d). Both SLM00 and LLM00 in E5RUN showed less strong features than those in stIV from 99° to 93°W from 0000 to 1500 UTC for SLM00 (0600–1500 UTC for LLM00) in Figs. 6c and 6f. The features of SLM12 and LLM12 in stIV and E5RUN showed multiple small envelopes of high occurrence rather than obtaining a few huge envelopes as in SLM00 and LLM00 (Figs. 6g,h,j,k). Note that the locations of envelopes of high occurrences of SLM12 and LLM12 in stIV and E5RUN were around 2100–0300 UTC and 102°W. SLM12 and LLM12 in stIV and E5RUN might have enough diabatic heating to initiate convections in the afternoon (from 1200 LST). Notably, SLM12 and LLM12 in E5RUN may obtain additional energy (overestimation) to enable upscale growth and to propagate farther to the east (SLM12) and northeast (LLM12) individually (Figs. 6k and 5k). The overestimated occurrences are more distinguishable in SLM12 in Fig. 6h than LLM12 in Fig. 6k. We think the results of E5RUN successfully captured the features of occurrences of MCSs in SLM00, SLM12, LLM00, and LLM12 considering the differences in two-dimensional maps and Hovmöller diagrams.

b. Features of parameters before the initiations of MCSs in E5RUN

In this section, we obtained distributions of 15 parameters in the areas of MCSs’ initiations. The features of parameters from MCSs (i.e., SLM00, LLM00, SLM12, and LLM12) are divided into −9, −6, −3, and −1 h before the initiations (initiations of −9, −6, −3, and −1 h). Potential key parameters that can be utilized as guidelines to MCS longevities would be determined and discussed based on the statistical significance of the parameters. Note that it is possible to include pixels from already initiated MCSs in previous times when it is considered from −9 to −1 h from the initiations of MCSs. The ratios of contaminated pixels (already determined as MCSs initiation previously) compared to the current MCS initiations are shown in Table 1. The largest ratio is 2.78% in LLM00 at initiation −6 h, thus the contamination pixels in this study are regarded as negligible.

Table 1

The ratio of contaminated pixels (the number of pixels from preexisting objects at initiation at −1, −3, −6, and −9 h divided by a size of a present object) divided into short-lived nighttime MCSs (SLM00), long-lived nighttime MCSs (LLM00), short-lived daytime MCSs (SLM12), and long-lived daytime MCSs (LLM12). The total numbers of pixels of initiations are represented in parentheses.

Table 1

The maps of initiations of MCSs are shown in Fig. 7 as Hovmöller diagrams and two-dimensional maps (superimposed with white contours of 90th percentiles of events, 2 for LLM00 and LLM12 and 3 for SLM00 and SLM12 in E5RUN and 2 for LLM00, LLM12, SLM00 and SLM12 in stIV. SLM12 and LLM12 in E5RUN indicated clustering at 1800–2100 UTC (1200–1500 LST in the central United States) in Figs. 7c and 7d where stIV showed similar features but lower accumulated values in Figs. 7k and 7l. SLM12 in E5RUN showed high occurrences of initiation around 90°–85°W, which agrees with higher occurrences near the coastal areas in Fig. 7g (not shown in SLM12 in stIV in Fig. 7o). On the contrary, SLM00 and LLM00 in E5RUN showed evenly distributed from 0000 to 1100 UTC where higher occurrences happened in the central United States (around 102°–96°W) in Figs. 7a and 7b (also shown in stIV in Figs. 7i,j). SLM12 in E5RUN showed more initiations of MCSs near the coastal areas than SLM00, LLM00, and LLM12 (Fig. 7g). Moreover, SLM12 and LLM12 in E5RUN showed larger numbers of initiations at the northern edge of the analysis region (Figs. 7g,h) compare to those of SLM00 and LLM00 in E5RUN (Figs. 7e,f).

Fig. 7.
Fig. 7.

The initiations of MCSs in Hovmöller diagrams (a) SLM00s (b) LLM00, (c) SLM12, and (d) LLM12 and (e)–(h) two-dimensional maps as from E5RUN. The 90th percentiles of events (2 for LLM00 and LLM12 and 3 for SLM00 and SLM12) are superimposed as white contours in (e)–(h). The initiations of MCSs in stIV (i)–(l) in Hovmöller diagrams and (m)–(p) two-dimensional maps. (m)–(p) The 90th percentiles of events in stIV (2 for LLM00, LLM12, SLM00, and SLM12) are superimposed as white contours in (m)–(p).

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

1) Average meteorological fields before the initiations of MCSs in E5RUN

In this section, average fields of parameters were obtained separately for SLM00, LLM00, SLM12, and LLM12 at initiation at −9, −6, −3 and −1 h. We showed representative parameters as MUCAPE, MUCIN, SRH3km, and WHSR6km in Figs. 811.

Fig. 8.
Fig. 8.

Average fields of MUCAPE (J kg−1) are shown for (a)–(d) SLM00, (e)–(h) LLM00, (i)–(l) SLM12, and (m)–(p) LLM12. Each column shows −1, −3, −6, and −9 h from MCS initiations. The 90th percentiles (3 for SLM00 and SLM12, 2 for LLM00 and LLM12) of MCS initiations are shown as white contours.

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

MUCAPE showed large MUCAPE values (≥1350 J kg−1) near the coastal areas in Fig. 8. SLM12 and LLM12 indicated increasing MUCAPE from initiation at −9 to −1 h in the central United States (See the third and fourth rows in Fig. 8). Moreover, SLM12 and LLM12 showed broader areas of large MUCAPE at initiation at −9, −6, and −3 h (Figs. 8i,m,j,n,k,o). However, SLM00 and LLM00 showed opposite results. MUCAPE decreased from initiation at −9 to −1 h (See the first and second rows in Fig. 8).

MUCIN showed large values in the central United States as shown in Fig. 9. SLM12 and LLM12 decreased dramatically from initiation at −9 to −1 h east of the Rocky Mountains (See the third and fourth rows in Fig. 9). LLM12 showed larger values of MUCNIN than that of SLM12 throughout the time. It should be noted that LLM12 may need a triggering perturbation/energy to overcome the inhibition (formed before the sunrise). SLM00 and LLM00 showed increasing values of MUCIN from initiation at −9 to −1 h (the first and second rows in Fig. 9).

Fig. 9.
Fig. 9.

As in Fig. 8, but average fields of MUCIN in (J kg−1) are represented.

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

SRH3km showed large values (≥135 m2 s−2) near the Rocky Mountains (to the north of 40°N) in Fig. 10. SLM12 and LLM12 indicated decreasing trends from initiation at −9 to −1 h in the central United States (See the third and fourth rows in Fig. 10). Moreover, LLM12 showed broader areas of large SRH3km throughout the time (See Figs. 10k,o,l,p). SLM00 and LLM00 showed gradual increases in values from initiation at −9 to −1 h. Moreover, LLM00 showed larger SRH3km than that of SLM00 throughout the time (see the first and second rows in Fig. 10).

Fig. 10.
Fig. 10.

As in Fig. 8, but average fields of SRH3km (m2 s−2) are represented.

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

WSHR3km showed large values (≥9 m2 s−1) in Nebraska and Iowa (to the north of 40°N and near 95°W) in Fig. 11. SLM12 and LLM12 indicated decreasing trends (initiation at −9 to −1 h) throughout the time (the same trends of SRH3km). LLM12 showed broader areal coverages of large WSHR3km than that of SLM12 at initiation at −9 and −6 h (See Figs. 11k,o and 8l,p). SLM00 and LLM00 showed increasing trends of values from initiation at −9 to −1 h. SLM00 showed a larger WSHR3km than that of LLM00 throughout the time (see the first and second rows in Fig. 11).

SLM12 and LLM12 showed typical preconvective conditions such as large MUCAPE, small MUCIN (Letkewicz and Parker 2010; Feng et al. 2019). Moreover, LLM12 indicated favorable conditions of higher LCL and smaller LFC-LCL than those of SLM12 which is supported by previous studies (not shown) (Kang and Bryan 2011; Mulholland et al. 2021). Note that LLM12 presented larger MUCIN than that of SLM12, where LLM12 might have another source of the lifting mechanism to overcome the inhibition such as diabatic heating during daytime. On the contrary, SLM00 and LLM00 showed less favorable conditions in the fields of MUCAPE, MUCIN, LCL, and LFC-LCL. SLM00 and LLM00 showed opposite trends to those of SLM12 and LLM12. SLM00 and LLM00 needed to have additional sources for overcoming the stable conditions (compared to those of SLM12 and LLM12) to start convections. Rotating upward winds and sheared environments could be the sources to overcome the lacking of instabilities. SRH1km, SRH3km, WSHR3–10km, WSHR3km, and WSHR6km of SLM00 and LLM00 showed significant increasing trends from initiation at −9 to −1 h where SLM12 and LLM12 did not show strong signals (i.e., decreasing trends from initiation at −9 to −1 h). We think convections in SLM12 and LLM12 were triggered by instabilities related to thermal energies while dynamic energies aided convections in SLM00 and LLM12. PW and IPVs did not show clear trends or features compared to the parameters discussed in this section. We focused more on detailed information on actual values in the regions of initiations in the next section.

2) Statistical analyses of variables initiating MCSs in E5RUN

In this section, we obtained distributions of 15 variables and presented them as boxplots to figure out the differences between short- and long-lived MCSs. We included values inside the region of MCS initiation to figure out the differences between SLM12 and LLM12 (SLM00 and LLM00) in each variable. The distributions were tested and scored using KS tests (i.e., SLM12 and LLM12, SLM00 and LLM00 separately), and Pearson R values were represented to distinguish differences in average fields of 15 variables (between short- and long-lived MCSs).

Box plots of 15 variables at initiation at −9, −6, −3, and −1 h are shown in Fig. 12. LLM12 showed larger increasing trends of MUCAPE than that of SLM12 from initiation at −9 to −1 h. LLM00 showed smaller spreads of MUCAPE than those of SLM00. Moreover, LLM00 and SLM00 showed smaller increments of MUCAPE ranges than those of SLM12 and LLM12 (Fig. 12a). LLM12 showed decreasing spreads of MUCIN from initiation at −9 to −1 h where SLM12 showed smaller spreads except at initiation at −1 h. In contrast, LLM00 showed smaller spreads of MUCIN compared to that of SLM00 throughout the time. LLM12 showed broader ranges of LCL than that of SLM12 and decreased from initiation at −9 to −1 h. The LCL distributions of SLM12 have not changed much in ranges. SLM00 and LLM00 showed larger distributions of LCL than those of SLM12 and LLM12. Moreover, SLM00 showed increasing trends of LCL from initiation at −9 to −3 h and decreased at initiation at −1 h (Fig. 12c). LFC-LCL showed decreasing trends (from initiation at −9 to −1 h) for SLM12, LLM12, SLM00, and LLM00 similar to those of MUCIN. LLM00 showed higher distributions of LFC-LCL than those of SLM00 from initiation at −9 to −3 h (different to those of MUCIN, compare Figs. 12b,d). The distributions of PW increased closer to the initiation for SLM12, LLM12, SLM00, and LLM00. LLM00 showed broader distributions of PW than those of SLM00 (See Fig. 12e). PW in LLM12 presented similar ranges compared to that of SLM12 and showed larger median values than SLM12. SRH1km and SRH3km showed broader distributions for LLM00 than those of SLM00 at initiation at −3 and −1 h. LLM00 indicated larger ranges of SRH3km at initiation at −3 and −1 h. Both distributions of SRH3km in SLM12 and LLM12 decreased from initiation at −9 to −1 h. WSHR3–10km showed broader ranges in LLM00 and LLM12 than those of SLM00 and SLM12 throughout the time (initiation at −9 to −1 h). Similar trends were observed in WSHR3km and WSHR6km. SLM12, LLM12, SLM00, and LLM00 showed increasing distributions of WSHR3km and WSHR6km from initiation at −9 to −1 h. IPV showed larger broader ranges for both LLM12 and LLM00 throughout the time at each isentropic level (i.e., 335 to 355K). LLM12 showed larger differences in ranges of IPVs from lower to higher isentropic levels than the differences between LLM00 and SLM00.

Fig. 11.
Fig. 11.

As in Fig. 8, but average fields of WSHR3km (m s−1) are represented.

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

Fig. 12.
Fig. 12.

Boxplots of 15 variables are shown for SLM12 as orange boxes, for LLM12 as red boxes, for SLM00 as cyan boxes, and for LLM00 as navy boxes. Previous hours from initiation (i.e., initiation at −1, −3, −6, and −9 h) are represented on the x axis. Numbers of pixels are 205 856: SLM12, 78 076: LLM12, 153 064: SLM00, and 48 762: LLM00.

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

The differences in distributions of 15 variables in MCS00 and MCS12 are shown in D values, P values (KS tests), and Pearson R values in Fig. 13. All P values showed less than 0.05 (and 0.01) to represent 95% (and 99%) confidences of two distributions are different considering the two-sided tests. MCS12 showed large D values of PW, SRH1km, SRH3km, and MUCAPE as key variables from initiation at −9 to −1 h in Figs. 13a–d. We found the features of key parameters considering all times before initiation (i.e., throughout the time from initiation at −9 to −1 h) as follows. MCS00 indicated WSHR3km, WSHR6km, IPV335K, and SRH3km as significant parameters in Fig. 13d. Both MCS00 and MCS12 obtained SRH3km and PW as key factors to determine short- and long-lived MCSs. MCS00 indicated more important variables from dynamic energies while MCS12 was determined more by thermodynamic energy-related variables. We obtained Pearson R from differences in average fields of MCS12 (between SLM12 and LLM12) and MCS00 (between SLM00 and LLM00) to compare MCS00 and MCS12 as shown on the third row in Fig. 13 from initiation at −9 to −1 h. All the variables showed positive correlation relationships with values larger than 0.6. MCS12 showed smaller values (i.e., statistically different) of Pearson R in SRH1km, SRH3km, MUCIN, IPV355K, and IPV340K. MCS00 indicated smaller values of Pearson R than those of MCS12, where SRH1km, SRH3km, MUCIN, IPV355K, and IPV340K showed the least values.

Fig. 13.
Fig. 13.

The (a)–(d) D values and (e)–(h) P values from KS tests, and (i)–(l) Pearson R values of 15 variables are shown as blue dots for MCS00 (comparing distributions of SLM00 and LLM000) and red dots MCS12 (SLM12 and LLM12) from initiation at −1 to −9 h. In (a)–(d), the five largest D values and smallest Pearson R values are superimposed as blue and red circles to emphasize the differences in the distributions of MCS00 and MCS12, respectively.

Citation: Weather and Forecasting 37, 9; 10.1175/WAF-D-22-0022.1

The summary of features representing differences between LLM00 and SLM00 (LLM12 and SLM12) is shown in Table 2. We compared the distributions of variables for long-lived MCSs versus short-lived MCSs (LLM12 versus SLM12 and LLM00 versus SLM00, respectively) and daytime versus nighttime long-lived MCSs qualitatively considering the three highest D values in Fig. 13 from initiation at −1 to −9 h. We hope the findings can be utilized as proof of a concept to determine the longevities of MCSs based on meteorological parameters discussed in this work.

Table 2

Key features that are favorable to long-lived MCSs (i.e., LLM00 and LLM12) compared to those short-lived MCSs (i.e., SLM00 and SLM12) before convective initiations in the left two columns. The right column shows the comparison of features between long-lived daytime (LLM12) and nighttime (LLM00).

Table 2

4. Summary and discussion

a. Summary

In this work, we characterized the variability of MCSs in the central United States using 15 years of observations (stIV) and simulations (E5RUN) in summer. We conducted convection-permitting model simulations forced by ERA5, which was opened to the public recently, replacing ERA-Interim with higher horizontal and vertical resolutions. The MCSs were identified and tracked by considering the areal coverages and temporal continuities using MTD. The MCSs were divided into categories of daytime [initiated from 1200 to 2300 UTC (MCS12)] and nighttime [formed between 0000 and 1100 UTC (MCS00)]. To determine the factors that contribute to their occurrences, the MCSs were further divided into short- and long-lived MCSs based on longevities (SLM00, LLM00, SLM12, and LLM12). We calculated 15 parameters using sounding data from E5RUN to figure out the key variables to distinguish short- and long-lived MCSs. The significance of each parameter was determined by considering D values (P values) from the KS test and Pearson R values of average fields comparing SLM00 and LLM00 (SLM12 and LLM12).

E5RUN showed improved simulations of RR in the central United States. A clear propagating signal in the Hovmöller diagram was shown in RR (limited in the simulations using ERA-Interim forcing data). E5RUN showed the skills to simulate MCSs by obtaining similar statistics to those of stIV in areal coverages and propagation speeds. The features of occurrences of MCSs in stIV and E5RUN showed similar results in SLM00, SLM12, LLM00, and LLM12 considering the differences in two-dimensional maps and Hovmöller diagrams. Note that E5RUN showed overestimation in SLM12 (coastal areas near the Gulf of Mexico) and LLM12 (north of 40°N).

The key findings of average fields are as follows: 1) SLM12 and LLM12 showed typical preconvective conditions (i.e., larger MUCAPE, smaller MUCIN, higher LCL, and smaller LFC-LCL) and 2) SLM00 and LLM00 showed favorable conditions in the fields of SRH1km, SRH3km, WSHR3–10km, WSHR3km, and WSHR6km (i.e., larger SRH1km, SRH3km, WSHR3–10km, WSHR3km, and WSHR6km). We think convections in SLM12 and LLM12 were triggered by instabilities more and SLM00 and LLM12 were aided by dynamic energies.

We discovered characteristics of 15 variables before convection initiations (i.e., initiation from −9 to −1 h) considering the KS test and Pearson R values. MCS12 indicated large D values (i.e., significantly different) of PW, SRH1km, SRH3km, and MUCAPE. MCS00 represented important parameters as WSHR3km, WSHR6km, IPV335K, and SRH3km. We think SRH3km, PW, and WSHR6km would be the key factors to predict the longevity of MCSs. It can be advised to take a look at the summarized table if a specific time is considered (e.g., need to announce the prediction using sounding data from −3 to −6 h).

b. Discussion

MCSs in the central United States in observation and simulations were characterized based on their initiation time in the daytime (SLM12 and LLM12) or the nighttime (SLM00 and LLM00). We focused more on parameters that can determine the longevities of MCSs before the initiations. The results can be compared to the findings in previous studies on daytime or nighttime MCSs. The features of strongly sheared environments in LLM00 agreed with the findings in Coniglio et al. (2010). E5RUN showed limitations of SLM00 and LLM00 by representing overestimated occurrences of MCSs near the eastern (SLM00) and the northern (LLM00) borders. It should be noted that the overestimated MCSs can be from boundary effects (MCSs cannot flow out at the edges due to the boundaries). We advise removing more regions near the boundaries to consider the issues. We think the overestimation could be caused by excessive dynamic energies from simulated structures of wind fields from E5RUN (i.e., stronger shears or SRH). Further studies would be necessary to evaluate wind fields. Coniglio et al. (2010) pointed out that small IPVs observed in rapidly developing MCSs. In this study, large IPVs in LLM12 were presented where it can be understood as LLM12 were slowly developed and had more time to be maintained (i.e., long-lived MCSs). We could not compare the findings in Yang et al. (2017) directly but we could get hints from prominent dynamic processes in LLM00. If possible, we would recommend taking into consideration of nighttime MCSs to test midlevel trough and upper-level divergence discussed in Yang et al. (2017) in the future. The characteristics of MCSs in summer [compared to that in spring, Song et al. (2019)] are large CAPE and CIN, especially for LLM12. We could not explicitly study the roles of LLJ [discussed in Coniglio et al. (2010); Geerts et al. (2017); Yang et al. (2017); Feng et al. (2019)] in this study. However, positive SRH1km (and SRH3km) for long-lived MCSs in our study is consistent with LLJ’s effect on the rotating winds from the surface to 1 km, approximately 900 hPa (and 3 km, approximately 700 hPa). For direct comparisons with these results, we need to obtain the maps of LLJ, midlevel trough, and upper-level divergence for all the objectively analyzed events. Due to the time limitation, we focused on the main purpose of this study to provide potential key parameters before the initiations of MCSs directly and statistically instead of providing a qualitative understanding of weather charts from a subset of events.

This study’s findings provide information to help researchers understand the differences in MCSs’ longevity and initiation times, as well as a deeper understanding of features of short- and long-lived daytime and nighttime MCSs. The results can be used by forecasters and climate scientists to design experimental simulations for historical and future climates. A better understanding of MCS can be used as a proof of concept for experimental designs to develop and improve convection parameterization schemes. Finally, researchers will benefit from the study’s understanding of warm-season MCS climatology.

Acknowledgments.

This research was enabled in part by support provided by West Grid (https://www.westgrid.ca) and Compute Canada (www.computecanada.ca). The authors thank the support of the Global Water Futures Program by the Canada First Research Excellence and Global Institute for Water Security. Yanping Li acknowledges support from a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant. We thank Andreas F. Prein for his pivotal contribution to providing Stage-IV data. We thank two anonymous reviewers for their thoughtful and constructive suggestions to improve the manuscript.

Data availability statement.

Stage-IV data in this study are openly available from the Earth Observing Laboratory (EOL) data archive at https://data.eol.ucar.edu/dataset/21.093 (https://doi.org/10.5065/D6PG1QDD). The numerical model simulations in this study are too large to archive or transfer. Instead, we provided the information about schemes and WRF version in section 2. The ERA5 forcing data are openly available from the Research Data Archive (RDA) in the National Center for Atmospheric Research (NCAR), at https://rda.ucar.edu/datasets/ds630.0/.

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Save
  • Arritt, R. W., T. D. Rink, M. Segal, D. P. Todey, C. A. Clark, M. J. Mitchell, and K. M. Labas, 1997: The Great Plains low-level jet during the warm season of 1993. Mon. Wea. Rev., 125, 21762192, https://doi.org/10.1175/1520-0493(1997)125<2176:TGPLLJ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashley, W. S., T. L. Mote, P. G. Dixon, S. L. Trotter, E. J. Powell, J. D. Durkee, and A. J. Grundstein, 2003: Distribution of mesoscale convective complex rainfall in the United States. Mon. Wea. Rev., 131, 30033017, https://doi.org/10.1175/1520-0493(2003)131<3003:DOMCCR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bunkers, M. J., B. A. Klimowski, J. W. Zeitler, R. L. Thompson, and M. L. Weisman, 2000: Predicting supercell motion using a new hodograph technique. Wea. Forecasting, 15, 6179, https://doi.org/10.1175/1520-0434(2000)015<0061:PSMUAN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, H., and R. E. Dumais Jr., 2015: Object-based evaluation of a numerical weather prediction model’s performance through forecast storm characteristic analysis. Wea. Forecasting, 30, 14511468, https://doi.org/10.1175/WAF-D-15-0008.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carbone, R., J. Tuttle, D. Ahijevych, and S. Trier, 2002: Inferences of predictability associated with warm season precipitation episodes. J. Atmos. Sci., 59, 20332056, https://doi.org/10.1175/1520-0469(2002)059<2033:IOPAWW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coniglio, M. C., and S. Corfidi, 2006: Forecasting the speed and longevity of severe mesoscale convective systems. Symp. on the Challenges of Severe Convective Storms, Atlanta, GA, Amer. Meteor. Soc., P1.30, https://ams.confex.com/ams/Annual2006/webprogram/Paper104815.html.

    • Crossref
    • Export Citation
  • Coniglio, M. C., J. Y. Hwang, and D. J. Stensrud, 2010: Environmental factors in the upscale growth and longevity of MCSs derived from Rapid Update Cycle analyses. Mon. Wea. Rev., 138, 35143539, https://doi.org/10.1175/2010MWR3233.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Corfidi, S. F., 2003: Cold pools and MCS propagation: Forecasting the motion of downwind-developing MCSs. Wea. Forecasting, 18, 9971017, https://doi.org/10.1175/1520-0434(2003)018<0997:CPAMPF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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