Nocturnal Tornado Climatology - AMS Journals - American ...

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Nocturnal Tornado Climatology* AMANDA K. KIS AND JERRY M. STRAKA School of Meteorology, University of Oklahoma, Norman, Oklahoma (Manuscript received 13 April 2009, in final form 23 September 2009) ABSTRACT Very few studies on nocturnal tornadoes have been performed, and operational forecasting of nocturnal tornadoes is often guided by the results of studies that are biased toward daytime tornadoes. However, it is likely that tornado environments vary significantly over the diurnal cycle. For example, the depth and nature of storm inflow may change as the daytime boundary layer transitions into a stable nighttime boundary layer, and a low-level jet (LLJ) may form above in the residual layer and free atmosphere. The study performed herein is used to investigate features unique to nocturnal boundary layers and the free atmosphere above that may affect nocturnal tornadoes. A climatology of significant (F2–F5) nocturnal tornadoes in the contiguous United States between 2004 and 2006 shows that environments deemed by previous climatologies as unfavorable for late afternoon/early evening tornadogenesis are in fact conducive to significant nocturnal tornadogenesis. These nocturnal environments may be characterized by marginal convective instability with shallow stable boundary layers. Substantial low-level shear, storm relative helicity (SREH), and exceptionally strong nocturnal low-level jets stand out as the most common features of significant nocturnal tornadoes and have utility in distinguishing environments of weak nocturnal tornadoes from environments of significant nocturnal tornadoes. Analysis of the data gathered in the climatology shows that the suggestions of existing tornado climatologies are inadequate and even misguiding for forecasting nocturnal tornadoes. Several recommendations for operational forecasting of nocturnal tornadoes are made based on the results of this climatology.

1. Introduction Nocturnal tornadoes are defined as occurring between sunset and sunrise. They compose only about a quarter of observed tornadoes, yet account for over 40% of tornado casualties (Ashley 2007). This dichotomy was recently made very apparent during the active year of 2008, throughout which nocturnal tornadoes accounted for many injuries and fatalities that included 23 deaths by a single enhanced Fujita scale 3 (EF3) tornado in Tennessee during the Super Tuesday outbreak on 5 February 2008. While the increased vulnerability of humans to nocturnal tornadoes relative to daytime tornadoes is partly due to a lack of public awareness at night

* Supplemental information related to this paper is available at the Journals Online Web site: http://dx.doi.org/10.1175/ 2010WAF2222294.s1.

Corresponding author address: Amanda K. Kis, School of Meteorology, University of Oklahoma, Ste. 5900, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: [email protected] DOI: 10.1175/2009WAF2222294.1 Ó 2010 American Meteorological Society

of impending hazardous weather conditions (Ashley et al. 2008), scientific knowledge of nocturnal tornadoes is also lacking. Despite a growing collection of tornado climatologies geared toward improving operational forecasting of severe weather, few of these studies discriminate between daytime and nocturnal tornadoes, with the exception of preliminary studies by Skaggs (1969) and Maddox (1993). In fact, many are biased toward late afternoon/early evening tornadoes (e.g., Johns et al. 1993; Rasmussen and Blanchard 1998; Hamill and Church 2000; Rasmussen 2003; Davies and Johns 1993). For these reasons, climatologies that focus specifically on nocturnal tornadoes are urgently needed. Useful nocturnal tornado climatologies should focus on aspects that uniquely distinguish nocturnal tornadoes and their environments from daytime and late afternoon/ early evening tornadoes. For example, the depth of the inflow into the storm and tornado may change as the classical daytime mixed boundary layer transitions into the shallow nocturnal stable layer, and convection may tend to be elevated rather than surface based. Stable boundary layers have been shown in modeling studies to

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FIG. 1. Diurnal evolution of an idealized boundary layer (from Stull 1988).

inhibit tornadogenesis (e.g., Leslie and Smith 1978), and their presence may preclude forecasts of nocturnal tornadoes. However, it is probable that some percentage of nocturnal tornadoes form within stable boundary layers, and the likelihood of this scenario must be examined. In addition, low-level jets (LLJ) are common features at night over the Great Plains, and the storm mode may shift from ‘‘classic’’ isolated supercells to quasi-linear convective systems (QLCSs) as LLJs strengthen their vertical wind shear and organize convection into more linear modes. Also, LLJs may impact tornadogenesis by increasing low-level vertical wind shear and stormrelative environmental helicity (SREH; Davies-Jones et al. 1990) as well as by enhancing moist inflow. In this study, a climatology of nocturnal tornadoes is constructed with the aim of understanding the mesoscale and storm-scale features that uniquely differentiate nocturnal tornadoes from daytime tornadoes. Attention particularly will be given to the influence of stable boundary layers, storm mode, LLJs, and vertical wind-shearrelated parameters. The next section of this paper provides a brief background on nocturnal boundary layer structures, addresses the current knowledge on nocturnal tornadoes, and is used to motivate the rest of the study. Section 3 describes the criteria used to construct the climatology, and section 4 presents the results of the climatology. Section 5 discusses the implications of the results and is followed by conclusions in section 6.

2. Background Only a few studies on nocturnal tornadoes have been presented in the literature. However, atmospheric regime

changes that occur as solar heating decreases have been the focus of much study. Such work suggests that nocturnal tornadoes may form in and encounter very different environments than daytime tornadoes, both in the boundary layer and the free atmosphere above. The archetypal daytime convective boundary layer transitions into a shallow, stable nocturnal boundary layer as sunset commences and solar heating decreases (Fig. 1). As the stable nocturnal boundary layer decouples from the free atmosphere, an LLJ forms just above the boundary layer inversion (Bonner 1968), with the majority of LLJs in the central United States reaching maximum speeds at or below 1 km above ground level (AGL) (Whiteman et al. 1997). The Great Plains nocturnal LLJ is a common warm season phenomenon observed in the south-central and central plains of the United States, with a strong southerly component that transports warm, moist air from the Gulf of Mexico northward into the plains regions (Bonner 1968; Helfand and Schubert 1995). Low-level jets in other geographic regions and with other orientations can also be dynamically forced by synoptic systems, or form as the Great Plains LLJ becomes embedded in synoptic systems. Since the era of the first tornado forecast, the presence of an LLJ has been recognized as being important in forecasting severe weather outbreaks (Fawbush et al. 1951). Low-level jets with certain orientations increase warm, moist inflow to mesoscale regions that can destabilize the lower troposphere and help initiate convection (Beebe and Bates 1955). Strong low-level wind shear created by an LLJ also can organize existing convection into linear modes (e.g., Maddox 1983; Limaitre and Brovelli 1990). Not surprisingly, long-lived mesoscale convective systems that require significant dynamic

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forcing—including quasi-linear convection such as squall lines—are commonly observed with the presence of an LLJ and share its diurnal maximum (e.g., Porter et al. 1955; Maddox 1983). While the Great Plains LLJ is generally interpreted as a warm season phenomenon, it is possible that synoptically driven nocturnal LLJs play a role in organizing the QLCSs that account for considerably more tornadoes during the cool season than do isolated cells (Trapp et al. 2005). While attributing significant tornadoes to linear convection may seem contrary to the majority of studies on tornadoes, individual or multiple supercells can form within squall lines in environments of very strong vertical wind shear and can persist if they are oriented such that they do not interfere with one another (Weisman et al. 1988). A useful climatology of nocturnal tornadoes must investigate the presence and consequences of shallow stable boundary layers and LLJs in order to begin to understand the dynamics that distinguish nocturnal tornadoes from daytime tornadoes and assist forecasters in identifying environments conducive to nocturnal tornadoes. It also must address implications of the diurnally forced shift from buoyancy to vertical shear of the horizontal wind as a dominant factor in convection via production of strong vertical pressure gradients in the low levels. The following section describes how the climatology that will be analyzed herein is constructed to specifically address these issues.

3. Methodology a. Climatology construction The official National Climate Data Center (NCDC) Storm Data reports were filtered with the SVRPLOT software (Hart 1993) and used to construct a sample of nocturnal tornadoes occurring in the contiguous United States between 1 January 2004 and 31 December 2006. These dates were chosen because the Rapid Update Cycle-2 (RUC-2) mesoscale model data that were used extensively in this study were archived by the Storm Prediction Center (SPC) only for this period. Nocturnal tornadoes were defined as those occurring between 2100 and 0700 local solar time (LST). The sample was further restricted to significant (F2–F5) nocturnal tornadoes in an effort to eliminate most nonsupercellular tornadoes, as was done in Rasmussen and Blanchard (1998, hereafter RB98). Tropical events were also removed. These criteria produced a sample of 70 tornadoes (Table S1, available as supplemental material at the Journals Online Web site: http://dx.doi.org/10.1175/2010WAF2222294.s1; Fig. 2). Archived Weather Surveillance Radar-1988 Doppler (WSR-88D) Next-Generation Doppler Radar (NEXRAD)

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FIG. 2. Composite of sampled significant nocturnal tornadoes tracks. [Plotted using SVRPLOT software (Hart 1993).]

level-II reflectivity data were available for 69 of the 70 sampled tornadoes. Storm mode was subjectively categorized by the authors for each of the 69 cases into five categories: circulation embedded in a continuous QLCS (Fig. 3a), broken line of supercells (Fig. 3b), circulation on the southern end of a QLCS (Fig. 3c), isolated supercell (Fig. 3d), and circulation embedded in the leading edge of a mesoscale convective system (MCS) (Fig. 3e). Storm mode data are recorded in Table S1. ‘‘Circulation’’ may refer to either a deep mesocyclone of a supercell or a shallow mesovortex embedded within a QLCS. Mesovortices are difficult to detect on radar but may produce strong tornadoes. The authors did not attempt to distinguish between these types of circulations because they wished to focus on tornadic nocturnal environments rather than the origins of nocturnal tornadogenesis, as environmental data can more immediately impact the public by aiding operational forecasters in overnight severe weather events. It should be noted that the results in the following section show that all of the storms in the sample herein featured sufficient shear for supercellular structures to form within linear modes of convection (Weisman et al. 1988). The sparse spatial and temporal coverage of the observed soundings prevented observed soundings with close spatial and/or temporal proximity to the sampled tornadoes from being found for all but 1 of the 70 cases. To gather sounding data on significant nocturnal tornado proximity environments (defined below) and boundary layer structures, RUC-2 0-h analysis gridpoint soundings were used instead to find close approximate environmental conditions in the storm inflow regions of the sampled tornadoes. The RUC-2 is a mesoscale model with 20-km horizontal resolution with hourly analyses. RUC-2 0-h analyses incorporate the standard 0000 and 1200 UTC NWS rawinsonde observations, as well as

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FIG. 3. Examples of storm modes from NCDC-archived WSR88D NEXRAD level-II reflectivity: (a) supercell embedded in a continuous QLCS, (b) broken line of supercells, (c) supercell on the southern end of a QLCS, (d) isolated supercell, and (e) supercell embedded in the leading edge of an MCS. Black lines indicate the tornadic regions of the storms.

measurements from velocity–azimuth display (VAD) wind profilers, aircrafts, ships, WSR-88Ds, satellite retrievals, mesonets, and other instruments that make measurements more frequently (Benjamin et al. 2009). Especially small pressure spacing of the RUC-2 at and just above the ground makes RUC-2 gridpoint soundings particularly useful in examining boundary layer structures. For grid points with surface elevations near sea level, the minimum pressure thicknesses between pressure levels are 2.5, 5.0, 7.5, and 10 hPa for the bottom four layers and 15 hPa for all layers above. (These minimum pressure thicknesses are reduced over higher terrain to

avoid ‘‘bulges’’ of sigma layers protruding upward in these regions.) RUC-2 analysis gridpoint soundings were generated on the hours immediately preceding and succeeding each tornado in the climatology and were viewed using the national version of the Skew-T Hodograph Analysis and Research Program (NSHARP) software (Hart and Korotky 1991). Spatial proximity of 80 km from the sounding site to the tornado was required in an area assumed to characterize the storm inflow (i.e., between 908 and 1808 relative to each sampled tornado). These criteria identified at least one proximity sounding site for 29 of the 70 cases (Table S2, available as

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supplemental material at the Journals Online Web site: http://dx.doi.org/10.1175/2010WAF2222294.s1). For those tornadoes occurring within 15 min of a 0-h analysis, proximity soundings were computed only for that analysis. In all other cases, soundings from the preceding and succeeding 0-h analyses were computed, and measurements were averaged. RUC-2 0-h analyses were also used to generate 850-hPa maps to analyze low-level flow characteristics, and such maps were available for 57 of the 70 cases (Table S1).

b. Comments on the use of RUC-2 gridpoint soundings Unlike standard rawinsonde observations, RUC-2 0-h analyses are available hourly, have greater spatial coverage over the continental United States, and incorporate measurements from many data sources in the troposphere. However, soundings derived from these analyses are innately different from the real atmosphere, and their representations of proximity environments and boundary layer structures must be examined before use in such a study as the one presented in this paper. Examination of boundary layer structures in this study using RUC-2 model soundings is potentially complicated by near-surface thermodynamic and dynamic biases. Thompson et al. (2003, hereafter T03) gathered those gridpoint soundings from RUC-2 0-h analyses considered by the authors to be ‘‘least accurate,’’ and compared them to observed soundings with spatial and temporal proximities. Thompson et al. found the largest temperature and mixing ratio errors near the surface, with an average of 0.58C underestimation and 0.1–0.2 g kg21 overestimation, respectively. Temperature errors were smallest at the 0-h analysis and increased with subsequent forecast hours. However, temperature errors were similar to the measurement accuracy of NWS rawinsonde observations (NOAA 2008). The wind speed was generally 0.5–1 m s21 too large from the surface to 600 hPa. Again, these errors were within the measurement accuracy of rawinsonde observations (NOAA 2008). The error was near zero for both 0–1- and 0–6-km shears. Thus, T03 suggested that RUC-2 soundings were representative of atmospheric environments and suitable for describing proximity storm environments. Of the RUC-2 0-h analysis proximity gridpoint soundings gathered in the study herein, only one had an observed sounding that shared spatial and temporal proximities. This prevented error analysis of the soundings in this study, as it has in other model proximity sounding studies (e.g., Markowski et al. 2003). In lieu of an error analysis for the soundings gathered herein, the authors of this study propose that RUC-2 0-h analysis gridpoint soundings will provide good representations of

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relevant boundary layer and free-atmosphere features in significant nocturnal tornado environments, and the errors estimated by T03 should be kept in mind as results are presented and considered in the following section. Forecasters using RUC-2 soundings are advised to crosscheck the RUC-2 data with other data sources to determine representativeness in real-time situations with potential for tornadoes.

4. Results a. Spatial and temporal distribution All of the cases identified in the climatology occurred east of the Rocky Mountains, and all but one case occurred south of the line marked by Michigan’s southern border (Fig. 2). None occurred in the Florida Peninsula. A corridor of highest activity extended from Mississippi and Alabama northwestward into Missouri. Maxima in Mississippi–Alabama and Missouri are apparent in Fig. 2 as denser clusters of tracks within the corridor of most intense activity. Thirty-eight of the 70 sampled significant nocturnal tornadoes occurred during cool season months (defined for this study as 16 October–15 February; see Table S1). In agreement with previous tornado climatologies (e.g., Fike 1993; Guyer et al. 2006), the majority of these tracked through the Southeast, and especially through coastal regions. Twenty-four of the 70 significant nocturnal tornadoes occurred during the spring (defined for this study as 16 February–15 May), and the majority of these were located in the south-central and central plains. This seasonal geographic shift in activity somewhat corresponds to the climatological seasonal shift in severe weather from the southeastern United States to the central United States as the cool season progresses into the warm season (Brooks et al. 2003). Only 8 of the 70 significant nocturnal tornadoes occurred during summer (defined for this study as 16 May–15 October). Three distinct maxima in tornado activity resulted when cases were binned into 1-h categories (Fig. 4). The first occurred between 2100 and 2300 LST, and activity decreased by about 60% in the following bin. A second maximum, half the size of the first, occurred in the 0100– 0159 LST bin. A similar maximum has been noted in overnight convection (e.g., Wallace 1975; Maddox 1980; Jirak et al. 2003), and roughly corresponds to the predawn maximum in LLJ speed according to Stull (1988). Tornado activity then decreased in consecutive bins until the 0600–0659 LST bin. The time of night did not discriminate well among tornado strength, and F2–F4 tornadoes occurred across the spectrum of nighttime hours with no apparent time preference.

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TABLE 1. Mean, median, minimum, and maximum vertical wind velocity differences for 0–1, 0–3, 0–6, and 0–8 km, and the EIL. Vertical velocity difference (m s21)

FIG. 4. Sampled significant nocturnal tornadoes distributed into 1-h bins (69 cases total).

b. Storm mode Linear convection dominated storm mode for significant nocturnal tornadoes. Sixty-one of the 69 significant nocturnal tornadoes with available radar data occurred in QLCSs. Twenty-seven of these 69 linearly convective cases occurred in circulations embedded in continuous QLCSs, 29 cases in broken lines of supercells, and 5 cases in circulations on the southern ends of QLCSs (Table 1). Thirty-seven of the 69 cases in QLCSs occurred during the cool season, and 31 of these occurred in Gulf coast or Atlantic coastal states. Only 7 of the 69 cases with radar data occurred in isolated supercells (Table S1), and all but 1 of these occurred in a plains state. As the time of night increases, the data show a transition from individual supercell structures toward circulations embedded within continuous QLCSs. In the first half of the night, isolated supercells, broken lines of supercells, and supercells on the southern ends of squall lines dominate the significant tornadic storm modes, and circulations embedded within continuous QLCSs account for less than a quarter of the cases. After about 0100 LST, however, 63% of the significant nocturnal tornadoes are associated with an embedded circulation, and the remaining cases are associated with broken lines of supercells.

c. Low-level jet characteristics Maps of 850-hPa flow were generated with RUC-2 zero-hour analyses for 57 of the 70 sampled significant nocturnal tornadoes, and flow characteristics are detailed in Table S3 (available as supplemental material at the Journals Online Web site: http://dx.doi.org/10.1175/ 2010WAF2222294.s1). An LLJ was loosely defined as a relatively narrow band of wind stronger than the background geostrophic flow at 850 hPa, with a lower threshold of 15 m s21 (estimated error of 62 m s21). These criteria indicated an LLJ over the tornado location in all

Height interval (km)

Mean

Median

Min

Max

0–1 0–3 0–6 0–8 EFF

20 22 30 34 20

20 22 30 32 20

10 15 22 25 5

28 32 34 45 27

but 1 of the 57 cases. The majority of LLJs appeared to be embedded in and/or forced by synoptic systems (Table S3). More than three-quarters of these LLJs had a strong southerly component of wind as they curved cyclonically around a positively tilted 850-hPa trough or low, with their associated tornadoes occurring on the eastern side of the trough or low in southerly or southwesterly flow. Only 7 of the 56 LLJs did not appear to be significantly affected by synoptic forcing. Low-level jet speed over the sampled significant tornadoes was between 15 and 25 m s21 for 35 cases, and greater than 25 m s21 for the remaining 21 cases. Two cases were located beneath regions of the LLJ that reached speeds of 35 m s21. Similar to the inertial oscillation theory of the evolution of LLJ speed described by Stull (1988), the data suggest that LLJ speed in general had an increasing trend during the beginning of the night, reached maximum speeds between 0500 and 0800 UTC, and decreased afterward. While tornado strength spanned the spectrum of LLJ speed, tornado intensity in general trended upward as LLJ strength increased. Of the 57 sampled significant nocturnal tornadoes with 850-hPa flow data, 56 cases had corresponding available radar data. Isolated cells seemed to prefer LLJs on the weaker end of the spectrum (Table S3); however, only three cases with isolated cells had corresponding LLJ data, and the small sample size may inhibit meaningful conclusions on their relationship with the LLJ. Significant tornadic linear modes of convection spanned the spectrum of LLJ strength. As LLJ strength increased, the occurrence of circulations embedded in continuous QLCSs decreased, and the occurrence of broken lines of supercells increased. Eighty-five percent of the significant tornadic circulations embedded within continuous QLCSs occurred with LLJ speeds of less than or equal to 25 m s21, and more than 60% of the significant tornadic broken lines of supercells occurred with LLJ speeds greater than or equal to 25 m s21. The sample sizes of these modes with corresponding LLJ data (20 and 27, respectively) are more robust, and thus this change in storm morphology with increasing LLJ

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strength may be more seriously considered. Additionally, the evolution of LLJ speed during the course of the night as described in the preceding paragraph was followed by a corresponding change in storm mode according to the trends in storm morphology described in section 4b.

d. RUC-2 zero-hour analysis proximity soundings RUC-2 0-h analysis gridpoint proximity sounding sites were identified for 29 of the 70 sampled significant nocturnal tornadoes (Table S2), and data were available for all but the tornado occurring at 0913 LST 30 May 2004. Convective available potential energy (CAPE), convective inhibition (CIN), and lifting condensation level (LCL) heights were computed for each proximity sounding using surface-based (SB), mean-layer (ML), and effective parcel (EFF) considerations [see Craven et al. (2002) for definitions of SB and ML considerations of CAPE, CIN, and LCL heights]. Effective parcel measurements are computed for the effective inflow layer (EIL), described by Thompson et al. (2007). Measurements of CAPE and CIN begin at the ground and proceed upward at successive levels. The first level with CAPE greater than 100 J kg21 and CIN greater than 2250 J kg21 is defined as the EFFLCL, and the layer with CAPE greater than 100 J kg21 and CIN greater than 2250 J kg21 at each level is defined as the EIL. The effective parcel technique may be especially relevant to nocturnal convection because it accounts for inflow that is potentially elevated above stable nocturnal boundary layers. Convective available potential energy was also measured between 0 and 3 km. Low-level stability was evaluated for each sounding, and a stable boundary layer was defined as a layer adjacent to the ground in which potential temperature (u) increased with height. The depth of a stable boundary layer extends from the ground to the highest measured height where u increases relative to the previous measurement height. Twenty-one of the 28 significant nocturnal tornadoes with proximity sounding data had proximity soundings that featured shallow stable boundary layers (Table S2). The average stable layer depth was 242 m, and ranged from 28 to 482 m. The majority of the sampled significant nocturnal tornadoes occurred in environments of large CIN, and measures of CIN were only slightly larger in the mean for cases with stable boundary layers relative to cases without stable boundary layers. Twenty-six of the 28 cases featured large SBCIN that exceeded RB98’s optimal value to identify significant tornadic soundings of 16 J kg21 of CIN. (CIN was computed by RB98 for a uniformly mixed layer in the lowest 1000 m; RB98 calculated optimal values for individual parameters to

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maximize identification of soundings associated with significant tornadoes in their sample.) Twelve of these cases had MLCIN values greater than 50 J kg21 in a range suggested by Davies (2004) to decrease the likelihood of tornadogenesis by reducing stretching, and in several cases SBCIN exceeded MLCIN by more than 50 J kg21. However, SBCAPE was measured for each tornado occurring in the presence of a stable boundary layer and/or large CIN, and these measurements suggest that the presence of a shallow stable boundary layer and/ or large CIN do not necessarily imply that convection is elevated. Cool, moist parcels from near the surface in a stable boundary layer may still be dynamically forced upward into a storm as long as the parcels remain positively buoyant within the storm. Effective parcel CIN values were in general nearly equal to or slightly less than corresponding MLCIN values, and fail to provide further clarification on the ‘‘elevated’’ nature of the environments of significant tornadic nocturnal convection. Thus, no conclusions on the occurrence and frequency of elevated significant tornadic nocturnal convection can be drawn from combinations of CAPE data with stable boundary layer and/or CIN data. Comparison of the thermodynamic parameters calculated for the nocturnal environments sampled herein with those calculated for the environments sampled by RB98 and Rasmussen (2003, hereafter R03) highlights their bias toward late afternoon/early evening values of CAPE caused by the use of 0000 UTC soundings in their samples. (CAPE was computed by RB98 for a uniformly mixed layer in the lowest 1000 m.) The median value of CAPE was 1314 J kg21 in RB98’s sample of significant tornadic supercells, and many cases exceeded extreme values of 1820 J kg21. An optimal value of 2100 J kg21 of CAPE maximized the identification of soundings associated with significant tornadoes in RB98’s sample. Twenty-three of the 28 cases herein with proximity sounding data featured MLCAPE value less than RB98’s median value of CAPE, with MLCAPE less than 1000 J kg21 for 19 of these cases, less than 500 J kg21 for 11 of these cases, and less than 250 J kg21 for 3 of these cases. All but 1 of the 28 cases had MLCAPE less than RB98’s optimal value of 2100 J kg21. In addition, R03 found an optimal value for 0–3-km CAPE of 69 J kg21. Twenty-two of the 28 cases had 0–3-km CAPE less than R03’s optimal value, 12 cases were less than 30 J kg21, 7 cases were less than 10 J kg21, and 3 cases had no measurable 0–3 km CAPE. These comparisons, as well as the CIN data discussed in the previous paragraph, highlight the occurrence of significant nocturnal tornadoes in ‘‘marginal’’ environments of both lower CAPE and larger CIN than is considered sufficient for daytime and late afternoon/early evening significant

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tornadoes, and suggest that the application to significant nocturnal tornadoes of thermodynamic parameter values derived from existing tornado climatologies may misguide forecasts of nocturnal tornadoes. Surface-based LCL heights ranged from 143 to 1263 m AGL, with a mean height of 627 m AGL. In addition, MLLCL heights ranged from 461 to 1495 m AGL, with a mean height of 880 m AGL. Also, EFFLCL heights ranged from 210 to 1645 m AGL, with a mean height of 1120 m AGL. Rasmussen and Blanchard (1998) found an optimal value for LCL height of 800 m AGL and suggested an upper threshold of LCL height of about 1200 m AGL to indicate environments conducive to significant tornadic supercells. Sixteen cases (25 cases) of the 28 had SBLCL heights less than 800 m AGL (1200 m AGL), and 14 cases (22 cases) of the 28 had MLLCL heights less than 800 m AGL (1200 m AGL). However, 24 cases (16 cases) of the 28 had EFFLCL heights that exceeded 800 m AGL (1200 m AGL). The high EFFLCL heights may reflect the EIL’s tendency to be elevated rather than surface based in nocturnal environments and suggest that the values from RB98’s database may not be applicable for effective parcel considerations during the nighttime. Vertical wind shear was calculated for the 28 cases from 0–1, 0–3, 0–6, and 0–8 km AGL, and in the EIL (denoted ‘‘EFF’’ for ‘‘effective parcel’’). Mean, median, minimum, and maximum values of velocity difference over the layers measured for the 27 cases are shown in Table 1. On average, nearly 60% of the 0–8-km AGL shear occurred in the lowest kilometer. Low-level jet strength was highly correlated with 0–1-km AGL shear (0.85) and moderately correlated with EFF shear (0.58) but exhibited low correlation with 0–3-km AGL and deeper-layer shears. These correlations suggest that the LLJ likely contributes largely to very shallow, nearsurface shear. The samples of isolated supercells and circulations embedded in the leading edges of MCSs with corresponding RUC-2 0-h analysis proximity soundings are too small to make any meaningful connections with 0–1-km AGL shear. Twenty-three of the significant nocturnal tornadoes with both LLJ data and radar data were associated with circulations embedded in continuous QLCSs, broken lines of supercells, and circulations on the southern end of continuous QLCSs. The data suggest that significant tornadic linear modes of convection often prefer strong LLJs with speeds greater than 20 m s21. Storm-relative environmental helicity based on storm motion using Bunker’s internal dynamics technique (Bunkers et al. 2000) for the 28 cases from 0–1 and 0–3 km AGL, and in the EIL (again denoted EFF). Measurements of SREH were exceptionally high at all

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TABLE 2. Mean, median, minimum, and maximum SREH for 0–1 and 0–3 km, and the EIL. SREH (m2 s22) Height interval (km)

Mean

Median

Min

Max

0–1 0–3 EFF

414 443 403

431 477 403

168 196 80

677 746 626

depths measured (Table 2). Low-level jet strength showed a marked correlation with 0–1-km AGL SREH (0.75), 0–3-km AGL SREH (0.70), and EFF SREH (0.74), which suggests that the LLJ likely contributes strongly to low-level SREH as well as low-level shear.

e. Comparison with a weak nocturnal tornado climatology To further analyze the uniqueness of certain stability, low-level flow, vertical wind shear, and SREH features to significant nocturnal tornadoes, the sample of significant nocturnal tornadoes was compared to a similar sample of weak (F0–F1) nocturnal tornadoes. The weak nocturnal tornado sample was constructed as described in section 2 and was additionally restricted to weak tornadoes on days where there were no significant tornadoes. This additional constraint attempts to separate weak nocturnal tornado environments from significant nocturnal tornado environments. These criteria identified 171 weak nocturnal tornadoes cases (Table S4, available as supplemental material at the Journals Online Web site: http://dx.doi.org/10.1175/2010WAF2222294.s1). (Note that it is likely that many of the sampled weak nocturnal tornadoes were nonsupercellular.) Weak nocturnal tornado activity extended across most of the United States east of the Rocky Mountains, with a southern geographic maximum that roughly echoed the southern maximum of the significant nocturnal tornado activity (cf. Figs. 2 and 5). Relative to the significant nocturnal tornado activity, weak nocturnal tornadoes increased the total nocturnal tornado activity most strikingly along the Gulf coast and in the northern plains. There was, however, noticeably less weak nocturnal tornado activity relative to significant nocturnal tornado activity in Missouri. In contrast to significant nocturnal tornadoes, weak nocturnal tornadoes were maximized during the warm season. Thirty-nine of the 171 weak nocturnal tornado cases occurred during the cool season, while 45 cases occurred during spring and 87 cases during summer (Table S4). This contrast is made further apparent when the summer frequencies of weak and significant nocturnal tornadoes are compared. Weak nocturnal tornadoes roughly followed the same temporal patterns as their significant counterparts with

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FIG. 5. As in Fig. 2 but for sampled weak nocturnal tornadoes.

a notable exception in the middle of the night. While significant nocturnal tornado activity reached a secondary maximum in the 0100–0159 LST bin, weak nocturnal tornado activity was minimized in that bin (Fig. 6). Fifty-seven weak nocturnal tornadoes were randomly chosen from the 171 total sampled cases, and 850-hPa maps and RUC-2 gridpoint soundings were generated from 0-h analyses for each (Table S5, available as supplemental material at the Journals Online Web site: http://dx.doi.org/10.1175/2010WAF2222294.s1). RUC-2 0-h analysis proximity gridpoint sounding sites were defined as in section 2 and were identified for each of these 57 cases, and sounding data were available for all but the 30 April 2005 case. Of these 56 tornadoes with sounding data, 39 had proximity soundings that featured stable boundary layers, with a mean depth of 354 m, a minimum depth of 22 m, and a maximum depth of 1002 m. Comparison of these stable boundary layer depths with the stable boundary layer depths in significant nocturnal tornado environments measured herein suggests that weak nocturnal tornadoes form more often in environments with deeper stable layers than do significant nocturnal tornadoes. The same thermodynamic parameters computed in section 4 for the significant nocturnal tornadoes were computed for the 56 weak nocturnal tornadoes with proximity sounding data, and the results (Fig. 7) further emphasize that nocturnal tornadoes often occur in environments of marginal CAPE and/or large negative buoyancy that may inhibit stretching. All but five (two) of the weak nocturnal tornadoes with proximity sounding data had SBCAPE (MLCAPE) less than 2100 J kg21. Forty-four of the 57 weak nocturnal tornadoes with proximity sounding data had 0–3 km AGL CAPE less than 60 J kg21, and 19 of those cases had 0–3 km AGL CAPE less than 10 J kg21. While EFFCAPE was generally

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FIG. 6. Normalized sampled weak (black) and significant (gray) nocturnal tornadoes distributed into 1-h bins. Normalization achieved by dividing by the total number of tornadoes (69 significant nocturnal tornadoes; 172 weak nocturnal tornadoes).

larger than its SBCAPE and MLCAPE counterparts, nearly 60% of the cases had EFFCAPE less than 800 J kg21. Comparison of Fig. 7b to Fig. 6 of T03 underscores the tendency of nocturnal tornadoes to occur in environments of low CAPE relative to daytime and late afternoon/early evening tornadoes. (The majority of the proximity soundings in T03’s sample were taken between 1800 and 0600 UTC.) The larger ranges of SBCIN and MLCIN for weak nocturnal tornadoes relative to significant nocturnal tornadoes (Figs. 8a and 8b) suggest that weak nocturnal tornadoes often form in environments of greater negative low-level buoyancy than do significant nocturnal tornadoes. Thirty-four weak nocturnal tornado cases featured MLCIN that exceeded 50 J kg21. Of these cases, 24 cases had MLCIN that exceeded 100 J kg21, 15 cases exceeded 150 J kg21, and 10 cases exceeded 200 J kg21, which is well above Davies’s (2004) suggested range for large negative buoyancy that may begin to inhibit tornadogenesis. Effective parcel CIN with weak nocturnal tornadoes overlapped greatly with EFFCIN measured with significant nocturnal tornadoes (Fig. 8c) and was, in general, similar to or smaller than the corresponding measurements of SBCIN and MLCIN. A Student’s t test was performed on each thermodynamic parameter, and differences between weak and significant nocturnal tornadoes were found to be statistically insignificant for each measure of CAPE. Substantial overlap between the middle 50% of the data between weak and significant nocturnal tornadoes for each CAPE parameter in Fig. 7 shows that measurements of CAPE discriminated poorly between weak nocturnal tornado environments and significant nocturnal tornado environments, and the authors found no value ranges that might discriminate well between weak and significant nocturnal tornadoes. Differences in

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FIG. 7. Box plots of (a) SBCAPE, (b) MLCAPE, (c) EFFCAPE, and (d) 0–3-km AGL CAPE (J kg21) for sampled (left) weak and (right) significant nocturnal tornadoes. The blue box extends from the first quartile (25th percentile) to the third quartile (75th percentile) (interquartile range), and the red line is the median value. The lower error bar extends to the smallest data value that is greater than or equal to 1.5 3 (interquartile range) below the first quartile, and the upper error bar extends to the largest data value that is less than or equal to 1.5 3 (interquartile range) above the third quartile. Red plus signs are outliers.

measurements of CIN between weak and significant nocturnal tornadoes were, however, statistically significant. The middle 50% of the cases contained in the boxes for each CIN parameter show little overlap between the weak and significant nocturnal tornado boxes for SBCIN (Fig. 8a), MLCIN (Fig. 8b), and EFFCIN (Fig. 8c), respectively. Weak nocturnal tornadoes occurred within a much larger range of convective inhibition than did significant nocturnal tornadoes, and significant nocturnal tornadoes showed a preference for lower convective inhibition. Forecasters can use Fig. 8 as a guideline to estimate CIN values and ranges that might inhibit significant nocturnal tornadoes. Though thresholds are not ‘‘magic numbers,’’ the authors felt that some approximate subjective discriminators based on the analyzed data between the weak and significant nocturnal tornadoes should be suggested and could be used with

caution by forecasters. Thresholds for CIN parameters in this study are estimated for the samples of weak and significant nocturnal tornadoes, and are chosen so that at least 75% of the significant nocturnal tornadoes occurred with values of the parameter less than the threshold. Estimates from Fig. 8 suggest that 150 J kg21 of SBCIN, 75 J kg21 of MLCIN, and 50 J kg21 of EFFCIN discriminate well between the sampled weak nocturnal tornadoes and significant nocturnal tornadoes, and values higher than these thresholds indicate a decreasing possibility of significant nocturnal tornadoes. Differences in MLLCL and EFFLCL between weak and significant nocturnal tornadoes were not statistically significant, but differences in SBLCL were statistically significant. Comparison of the parameters shown in Fig. 9 shows that the largest differences between weak and significant nocturnal tornado LCL heights were with the

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FIG. 8. Box plots of (a) SBCIN, (b) MLCIN, and (c) EFFCIN [(21) 3 J kg21] for sampled weak and significant nocturnal tornadoes. Box plots are formatted as in Fig. 7.

surface-based consideration (Fig. 9a), with much greater overlap between weak and significant nocturnal tornadoes for the mixed-layer (Fig. 9b) and effective parcel (Fig. 9c) considerations. Significant nocturnal tornadoes occurred within a much larger range of SBLCL heights and the median value of SBLCL height for the significant nocturnal tornadoes was more than 300 m higher than the median value for the weak nocturnal tornadoes. Comparison of Fig. 9b and Fig. 7 of T03 suggests that nocturnal tornadoes may generally occur in environments of lower MLLCL heights than do daytime and late afternoon/early evening tornadoes. The 10th percentiles of MLLCL heights for weak and significant nocturnal tornadoes were 408 and 534 m lower, respectively, than the corresponding 10th percentile in T03’s sample; the 90th percentiles of MLLCL heights for weak and significant nocturnal tornadoes were 408 and 534 m lower, respectively, than the corresponding 90th percentile in T03’s sample; and the median values of MLLCL heights for weak and significant nocturnal tornadoes were 445 and 219 m lower, respectively, than the corresponding median values in T03’s sample. Additionally,

the relatively high EFFLCL heights (Fig. 9c) may again reflect the EIL’s tendency to be elevated in nocturnal environments. Maps of 850-hPa isotachs were available to be generated for 54 of the 56 randomly chosen weak nocturnal tornado cases with proximity sounding data. Of these 56 cases, 44 cases had LLJs as defined by the same criteria as for the significant tornado cases. The LLJs were weaker compared to the significant tornado cases, with only 10 cases exceeding 20 m s21. The majority appeared to be synoptically embedded and/or forced. In particular, many instances in the plains appeared to be interactions between the Great Plains LLJ and synoptic low-pressure systems and troughs. This scenario seems especially likely in the northern plains during the warm season, and may account for the increased number of tornado reports in this region as shown in Fig. 5. Unlike the thermodynamic parameters, shear-based parameters show much potential for discriminating between weak and significant nocturnal tornado environments. A Student’s t test was performed on vertical wind shear parameters, and differences between weak and

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FIG. 9. Box plots of (a) SBLCL, (b) MLLCL, and (c) EFF LCL (m AGL) for sampled weak and significant nocturnal tornadoes. Box plots are formatted as in Fig. 7.

significant nocturnal tornadoes were found to be highly statistically significant for each measured depth. Twotailed p values were on the order of 1025 or better, which indicates that the results are highly statistically significant. (A p value assumes that the null hypothesis is true and is the probability of obtaining a result at least as extreme as the one that was actually observed.) Both deeper-layer and low-level vertical wind shears were substantially larger in significant tornado cases than in weak tornado cases. The middle 50% of the cases contained in the boxes for each shear parameter in Fig. 10 shows very little overlap for 0–1- and 0–3-km AGL shear and no overlap for deeper-layer shears, and the median values for weak and significant nocturnal tornadoes for all measured layers are significantly offset. Low-level shear was especially strong, and 0–1-km AGL shear accounted for nearly 60% of the 0–8-km AGL shear in the average. Forty-seven of the 56 weak nocturnal tornado cases with sounding data had 0–1 km AGL shear that exceeded Craven and Brooks’s (2004) velocity difference threshold of 10 m s21 to discriminate significant tornadoes from weak tornadoes and null cases, and

26 cases exceeded 15 m s21. Thus, it appears that Craven and Brook’s criterion has diminished the usefulness in distinguishing between weak and significant nocturnal tornado environments. Forecasters can use Fig. 10 as a guideline to estimate shear values and ranges that might support significant nocturnal tornadoes. Thresholds for velocity difference parameters in this study are estimated for the samples of weak and significant nocturnal tornadoes, and are chosen so that at least 75% of the weak nocturnal tornadoes occurred with values of the parameter less than the threshold. Estimates from Fig. 10 suggest that 18 m s21 of the velocity difference over the 0–1-km AGL layer, 20 m s21 of velocity difference over the 0–3-km AGL layer, 25 m s21 of velocity difference over the 0–6-km AGL layer, 28 m s21 of velocity difference over the 0–8-km AGL layer, and 19 m s21 of velocity difference over the EIL discriminate well between the sampled weak nocturnal tornadoes and significant nocturnal tornadoes and indicate an increasing possibility of significant nocturnal tornadoes. A Student’s t test was performed on SREH parameters, and differences between weak and significant nocturnal

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FIG. 10. Box plots of (a) 0–1-, (b) 0–3-, (c) 0–6-, and (d) 0–8-km velocity differences (m s21 over the measured depth) and (e) the EFF velocity difference for sampled weak and significant nocturnal tornadoes. Box plots are formatted as in Fig. 7.

tornadoes were found to be highly statistically significant for each parameter, with two-tailed p values on the order of 1023 or better. While the overlap between the middle 50% of the cases was notable for both 0–1 km AGL (Fig. 11a) and 0–3 km AGL (Fig. 11b) SREH, the median values were highly offset, and SREH over the different depths measured was in general substantially

larger in significant tornado cases than in weak tornado cases. Of the 56 weak nocturnal tornadoes with sounding data, 48 cases exceeded R03’s optimal value of 120 m2 s21 of 0–1-km AGL SREH for identifying soundings associated with significant tornadoes, and 35 cases exceeded RB98’s optimal value of 200 m2 s21 of 0– 3 km AGL SREH. Thus, it appears that these optimal

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FIG. 11. Box plots of (a) 0–1- and (b) 0–3-km SREH, and (c) EFF SREH (m2 s22 over the measured depth) for sampled weak and significant nocturnal tornadoes. Box plots are formatted as in Fig. 7.

values calculated from RB98’s sample lose their utility when applied to nocturnal tornadoes. There was, however, very little overlap for EFF SREH (Fig. 11c), and this measure of SREH may be of greater use when forecasting nocturnal tornadoes. Thresholds for SREH parameters in this study are estimated for the samples of weak and significant nocturnal tornadoes, and are chosen so that at least 75% of the weak nocturnal tornadoes occurred with values of the parameter less than the threshold. Based on Fig. 11, estimates of 350 m2 s22 of 0–1-km AGL SREH, 425 m2 s22 of 0–3-km AGL SREH, and 350 m2 s22 of EFF SREH discriminate well between the sampled weak nocturnal tornado environments and significant nocturnal tornado environments and indicate an increasing possibility of significant nocturnal tornadoes.

5. Discussion The results of this climatology point to the presence of an LLJ as a defining characteristic of nocturnal tornado environments. The repeated incidence of LLJs with nocturnal tornadoes and the apparent connection between

LLJ speed and low-level shear as found in this study may explain the high frequency of nocturnal tornadoes in circulations embedded within quasi-linear convection, which require strong vertical wind shear. High correlation of LLJ speed with low-level shear and SREH suggests that the large values of low-level shear and SREH measured with significant nocturnal tornadoes are likely often a consequence of strong LLJs. The data in this study do not, however, lead to the conclusion that an LLJ is a necessity for nocturnal tornadoes. The connection between tornadoes and shallow-layer shear up to about 3 km above the surface and SREH may be partly due to supercells’ requirement of environmental horizontal streamwise vorticity, produced by vertical wind shear, that can be tilted vertically by the storm updrafts (Davies-Jones 1984). In addition, strong shallow-layer shear can produce large vertical pressure gradients within supercell updrafts when horizontal streamwise vorticity is tilted vertically. These large induced upward accelerations within the storm updrafts could help nocturnal convection to overcome low-level stability/large negative buoyancy and produce

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tornadogenesis in environments of large CIN and even stable boundary layers. Measurements herein of exceptionally strong low-level SREH suggest that LLJs may aid nocturnal tornadogenesis by dynamically producing near-surface horizontal vorticity in the form of quasi-horizontal boundary layer rolls. While boundary layer rolls are often associated with thermal instability forcing (e.g., horizontal convective rolls), they can be dynamically forced in sheardriven boundary layers (i.e., shear instability). Raymond (1978) suggested that parallel instability of the LLJ creates boundary layer roll structures that tilt with height in the sheared layer below the jet maximum. This concentrates the absolute vorticity at the surface into shear lines collocated with maxima of surface convergence, which produces vorticity as high as 1023 s21. While Raymond connected the parallel instability of the LLJ to squall-line formation, it may also be related to the creation of near-surface horizontal vorticity that can be tilted vertically by a storm downdraft and used in nocturnal tornadogenesis. Shallow stable boundary layers were common among the sampled weak and significant nocturnal tornadoes. Given some existing definitions of surface-based and elevated convection, the presence of a stable layer did not necessarily imply that convection was elevated. For example, Parker (2008) defined convection as surface based if any of the ingested air parcels originated near the surface—even if the air did not have the greatest potential buoyancy—and Thompson et al. (2003) defined elevated convection as having no SBCAPE. By these definitions, every significant tornadic storm and most of the weakly tornadic storms in this study were surface based because they had some measurable SBCAPE. Conversely, Thompson et al. (2007) defined some elevated supercells as those with an effective inflow base (i.e., EFFLCL) above the ground. Application of this definition marks every nocturnal tornado in this study as occurring within an elevated environment. Given these conflicting definitions, the results of the climatology fail to shed light on the surface-based or elevated nature of tornadic nocturnal convection, but they do suggest that existing definitions of what constitutes an ‘‘elevated’’ thunderstorm environment may need refinement. The results of this study suggest that parameter values and thresholds calculated from late afternoon/early evening tornado climatologies and used to aid tornado forecasting have diminished utility for forecasting nocturnal tornadoes because they do not properly represent nocturnal tornado settings. Nighttime tornado settings are typically quite different from daytime tornado settings. Thermodynamic optimal values suggested by RB98 and R03 for identifying significant tornadic supercells

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were rarely met by significant nocturnal tornadoes in this climatology, and their optimal values of shear and SREH showed little utility for discriminating between weak and significant nocturnal tornado environments because the majority of weak nocturnal tornadoes exceeded the thresholds. Additionally, Craven and Brooks’s (2004) 0–1-km AGL shear threshold used to distinguish weak tornado environments from significant tornado environments was exceeded by nearly every sampled weak nocturnal tornado. From statistical analysis in this study, vertical wind shear and SREH parameters show considerable value in separating weak nocturnal tornado environments from significant nocturnal tornado environments. Forecasters can use the data herein to assist in forecasting nocturnal tornadoes, and several new parameter threshold values based on low-level and deeper-layer shear and SREH are suggested herein to discriminate between weak and significant nocturnal tornadoes and indicate an increasing possibility of significant tornadoes. Because many nocturnal tornado events are associated with marginal instability, forecasters are urged not to use CAPE-based thresholds to discriminate between weak and significant nocturnal tornado environments, as the results of this study show that there are not statistically significant differences in CAPE between such environments.

6. Summary and recommendations Significant nocturnal tornadoes that occurred between 1 January 2004 and 31 December 2006 were sampled. The presence of environmental features unique to nocturnal boundary layers was confirmed and analyzed using RUC-2 proximity gridpoint soundings. Maximums in nocturnal tornado activity roughly followed the climatological seasonal distribution of tornado activity, and many occurred in Gulf coastal states during the cool season. Weak instability and strong vertical wind shear often characterized the inflow environments. Many of the nocturnal tornadoes formed in environments with shallow stable boundary layers, which is contrary to Leslie and Smith’s (1978) idealized numerical study of tornado development with stable layers adjacent to the surface. Quasi-linear modes of convection accounted for the majority of significant nocturnal tornadoes, and were very often accompanied by exceptionally strong LLJs. Low-level jets are likely to have produced remarkably strong low-level shear and SREH that may have dynamically promoted significant nocturnal tornadogenesis. Measures of low-level and deeper layer shear and SREH also show considerable potential in distinguishing weak nocturnal tornado environments from significant nocturnal tornado environments.

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Based on the results of this climatology, several recommendations are made for operational forecasters in situations where nocturnal tornadoes are possible: 1) Nocturnal tornadoes often occur in environments of marginal instability with low CAPE and large CIN, and even occur in environments with stable boundary layers. Though these conditions are typically considered to be unfavorable for tornadogenesis, it appears that such unfavorable conditions are overcome by large low-level shear and SREH that is likely often caused by nocturnal LLJs. Forecasters must look for nocturnal LLJs before ruling out significant nocturnal tornadogenesis in seemingly unfavorable environments. 2) The presence of an LLJ appears to be needed for significant nocturnal tornadoes and is a common factor among weak nocturnal tornadoes. The majority of the LLJs in this study were associated with synoptic systems that often occurred during the cool season in the Southeast. Thus, nocturnal LLJs in locations east of the Great Plains should be carefully noted when forecasting nocturnal tornadoes. 3) While significant tornadoes are classically attributed to isolated supercells (e.g., Browning 1965; Marwitz 1972; Moller et al. 1994), significant nocturnal tornadoes more often occur in association with circulations embedded within quasi-linear convection rather than with isolated supercells (also see Trapp et al. 2005). Forecasters must look for nocturnal tornadoes even within quasi-linear convection and also must be aware that radar beams may pass above shallow QLCS mesovortices that may spawn strong tornadoes. 4) Forecasters should be wary of applying parameter ranges and values from existing tornado climatology studies with a daytime emphasis on nocturnal tornado forecasts. Nocturnal tornadoes can occur in environments of lower CAPE and/or larger CIN than is indicated by other tornado climatologies and may even form in environments with stable boundary layers. Nocturnal tornadoes seem to require much stronger low-level shear and SREH than values and ranges suggested by previous tornado studies dominated by late afternoon/early evening tornadoes. This study suggests some magnitudes of vertical wind shear and SREH that can signal increased potential for significant nocturnal tornadoes. Considerations of these points may help lower false alarm rates of nocturnal tornado warnings. Vulnerability in the United States to nocturnal tornadoes is increasing, and the death toll will consequently grow (Ashley et al. 2008). While increasing vulnerability

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is partly due to human reasons, a greater understanding by forecasters of the conditions promoting nocturnal tornadoes can translate to dissemination of more accurate forecasts to emergency management and the public. The authors hope that this climatology motivates further study of nocturnal tornadoes with results presented in a manner that can be easily applied by operational forecasters to nocturnal tornado forecasting. Acknowledgments. We are grateful to Mr. Jason Levit of the Storm Prediction Center for helping provide archived RUC-2 data and to Mr. David Bodine for helping with plotting. This work was supported in part by NSF Grant 0733539 and an AMS fellowship. Finally we would like to thank the reviewers, in particular Jon Davies, who provided a highly detailed review. REFERENCES Ashley, W. S., 2007: Spatial and temporal analysis of tornado fatalities in the United States: 1880–2005. Wea. Forecasting, 22, 1214–1228. ——, A. J. Krmenec, and R. Schwantes, 2008: Vulnerability due to nocturnal tornadoes. Wea. Forecasting, 23, 795–807. Beebe, R. G., and F. C. Bates, 1955: A mechanism for assisting in the release of convective instability. Mon. Wea. Rev., 83, 1–10. Benjamin, S. G., J. M. Brown, K. J. Brundage, B. E. Schwartz, T. G. Smirnova, and T. L. Smith, 2009: RUC-2—The Rapid Update Cycle version 2. Tech. Procedures Bull.—Draft. [Available online at http://ruc.noaa.gov/ruc2.tpb.html.] Bonner, W. D., 1968: Climatology of the low level jet. Mon. Wea. Rev., 96, 833–849. Brooks, H. E., C. A. Doswell, and M. P. Kay, 2003: Climatological estimates of local daily tornado probability for the United States. Wea. Forecasting, 18, 626–640. Browning, K. A., 1965: The evolution of tornadic storms. J. Atmos. Sci., 22, 664–668. 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, 61–79. Craven, J. P., and H. E. Brooks, 2004: Baseline climatology of sounding derived parameters associated with deep moist convection. Natl. Wea. Dig., 28 (December), 13–24. ——, R. E. Jewell, and H. E. Brooks, 2002: Comparison between observed convective cloud-base heights and lifting condensation level for two different lifted parcels. Wea. Forecasting, 17, 885–890. Davies, J. M., 2004: Estimations of CIN and LFC associated with tornadic and nontornadic supercells. Wea. Forecasting, 19, 714–726. ——, and R. H. Johns, 1993: Some wind and instability parameters associated with strong and violent tornadoes. I. Wind shear and helicity. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., Vol. 79, Amer. Geophys. Union, 573–582. Davies-Jones, R., 1984: Streamwise vorticity: The origin of updraft rotation in supercell storms. J. Atmos. Sci., 41, 2991–3006. ——, D. Burgess, and M. Foster, 1990: Test of helicity as a tornado forecast parameter. Preprints, 16th Conf. on Severe Local Storms, Kananaskis, AB, Canada, Amer. Meteor. Soc., 588–592.

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