1. Introduction
Tornadoes in the southeast (SE) region of the United States can occur under different conditions than are typically seen in the Southern Great Plains (SGP) region. For instance, in the SE United States, tornadic storms have been observed to form and persist when analyzed CAPE is less than 500 J kg−1 provided sufficient shear is also present; this is the so-called “high shear, low CAPE” (HSLC) regime (Sherburn and Parker 2014; Anderson-Frey et al. 2016; Sherburn et al. 2016; King et al. 2017). Such low-CAPE tornadic storms are most common during the cold season (Childs et al. 2018) or at night (Kis and Straka 2010; Sherburn and Parker 2014; Ashley 2007). While these environments and associated storms can occur anywhere, the SE United States experiences a relatively high fraction of these apparent low-CAPE storms and is therefore at greater risk from them. Another difficulty with standard mesoscale analyses, specifically the SPC mesoanalysis, is that their relatively coarse temporal resolution (typically 1 h) may miss situations where CAPE can increase abruptly owing to rapid (<1 h) destabilization of the environment (Hart et al. 2016; King et al. 2017). The desire for subhourly resolution is part of the motivation of the present study. The goals of the present study are to assess the inaccuracies in CAPE analyses in such rapid-recovery situations that stem directly from inadequate temporal resolution, as well as to understand the physical mechanisms responsible for the rapid CAPE recovery. We will examine the time evolution of CAPE and its sources preceding an outbreak of severe storms on 31 March 2016 in northern Alabama (AL) that occurred during the Verification of the Origin of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE) field program (Rasmussen et al. 2015; Koch and Rasmussen 2016).
VORTEX-SE is an ongoing research program to study tornadoes and tornado environments in the SE United States. Its stated objectives include understanding the environments of the SE United States that influence the structure and path of the region’s tornadoes, as well as how to best communicate warnings of these storms to the public (Rasmussen et al. 2015). The tornadoes in the SE United States often occur at night, in forested areas, and/or outside of the perceived tornado season. Additionally, they often occur in areas with limited visibility, inadequate shelter, and/or a large population. These factors are believed to contribute to the disproportionately large number of killer tornadoes in this area (Ashley 2007), making the accurate assessment of tornadic potential in storms of high importance.
In this study, we apply the methods of Emanuel (1994) and Agard (2017) to examine various sources of CAPE in the environment and their contributions to its time tendency that will complement forecast models and operational analyses that are relatively temporally (∼1 h) coarse. We apply this technique to a low-CAPE tornadic storm case from VORTEX-SE on 31 March 2016 in northern Alabama to determine the relative importance of CAPE sources prior to the development of a tornadic storm with a focus on the temporal evolution. We evaluated the rate of convective destabilization and its causes using a combination of VORTEX-SE observations (Rasmussen and Koch 2016) and EnKF-based numerical analyses. We focus our analysis on Belle Mina because our main instruments, the University of Massachusetts (UMass) S-Band frequency-modulated, continuous-wave (FMCW) radar and NOAA National Severe Storms Laboratory (NSSL) Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS; Wagner et al. 2019), were collocated there.
2. Data and methodology
a. Observation data
In addition to the UMass radar and the CLAMPS ATDD also participated in VORTEX-SE and operated a 10-m meteorological tower at Belle Mina. This tower collected meteorological, soil, and vertical flux observations (Lee et al. 2016a), of which we used surface temperature T, dewpoint Td, and net radiation R. Table 1 details the instruments on the tower that we utilized. The tower sampled data every 5 s and reported the 30-min mean, except for the sonic anemometers and gas analyzers that sampled at 10 Hz (Lee et al. 2016a).
Meteorological variables with their corresponding instrument and sampling height from the meteorological tower at Belle Mina, AL (Lee et al. 2016a).
The Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS; Geerts et al. 2017; Wagner et al. 2019) provided boundary layer profiles of temperature T and dewpoint temperature Td at times intermediate to those of the soundings. The Atmospheric Emitted Radiance Interferometer (AERI; Knuteson et al. 2004a,b), which is one of the principal instruments of the CLAMPS, is an operational ground-based infrared spectrometer that measures the downwelling infrared (3–19 μm) radiance emitted by the atmosphere at a high temporal (∼1 s) and spectral (cm−1) resolution. The AERI observations in the 15-μm carbon dioxide band and the 18-μm water vapor band are used to infer profiles of temperature and water mixing ratio, respectively.
The optimal estimation-based approach of Turner and Löhnert (2014) and Turner and Blumberg (2019) is used to invert the AERI radiance data, constrained by a climatological radiosonde dataset, to provide the retrieved thermodynamic profiles with their uncertainties (known as “AERIoe” profiles). This retrieval technique has been evaluated against radiosondes in multiple locations and seasons, demonstrating that the AERI has a good ability to characterize the evolution of the boundary layer (Blumberg et al. 2015; Wulfmeyer et al. 2015; Klein et al. 2015; Weckwerth et al. 2016; Turner and Blumberg 2019). A study of the accuracy of CAPE derived from AERI-retrieved profiles demonstrated that the remotely sensed CAPE was able to accurately capture destabilization trends (Blumberg et al. 2017). The convective boundary layer profiles CAPE calculations from the AERI had correlation coefficients above 0.92 when compared with those calculated from collocated soundings. Additionally, during the daytime MLCAPE was the most accurate and during the night SBCAPE was the most accurate. However, all the correlations, as mentioned above, were high (>0.9) and therefore useful.
The AERI does have some limitations compared to radiosondes due to being a passive instrument. For our data the vertical resolution of the AERI ranges from 70 to 200 m at the surface to a little over 3 km at the top for the water vapor and about 6 m to 4–5 km at the top for temperature. While it still has a lower vertical resolution than radiosondes it has been found that by supplementing the AERI profiles with data from the mid- to upper troposphere, destabilization trends can be identified (Blumberg et al. 2017). We supplemented our AERI calculated soundings in this study with sounding data from the NOAA/ATDD group, and then calculated CAPE from the soundings and AERIoe profiles (Fig. 1, Table 2).
Skew T–logp plots of the radiosonde soundings from (a) 1200 UTC at Birmingham, AL; (b) 2000 UTC at Belle Mina, AL; (c) 2100 UTC at Belle Mina, AL; and (d) 2200 UTC 31 Mar at Belle Mina, AL. The yellow trace shows the temperature, and the cyan trace shows the dewpoint (°C); the black line shows the surface parcel trajectory. Dashed red and blue contours are dry and moist adiabats, respectively, and dashed green contours are isohumes of saturation mixing ratio. The winds are in m s−1.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
Surfaced-based, mixed-layer, and most-unstable CAPE and CIN (J kg−1) from three ATDD radiosonde soundings taken at Belle Mina, AL, on 31 Mar 2016 (Lee et al. 2016b).
Unless otherwise specified, we used MetPy (May et al. 2021) for the calculations of CAPE and related quantities in this study. MetPy is an open-source, Python-based library that contains methods to calculate and plot soundings and associated thermodynamic and kinematic parameters given vertical profiles of temperature, dewpoint, pressure, height, wind speed, and wind direction.
b. Numerical modeling
Many of the quantities required to compute the terms in the CAPE tendency equation were either not available or are otherwise difficult or impossible to obtain from the single-site observation platforms in our study. These include information on horizontal and vertical advective tendencies and surface sensible and latent heat fluxes. Therefore, we utilized a numerical model to compute these quantities. We used the Advanced Regional Prediction System (ARPS; Xue et al. 2000, 2001) coupled with an EnKF-based data assimilation system (ARPS-EnKF; Tong and Xue 2005; Dawson et al. 2013) with 40 ensemble members to model the atmospheric conditions over the VORTEX-SE domain on 31 March 2016. In addition to the benefits afforded by the EnKF in regard to generating accurate mean analyses, by utilizing the ensemble of model states we can additionally obtain information regarding the uncertainty in the various factors affecting the development of CAPE in the region. The ARPS is a nonhydrostatic numerical cloud model designed to be used on the regional-to-storm scale (Xue et al. 2000, 2001). We used a telescoping one-way nested domain configuration centered over northern Alabama with an outer 1800 × 1800 km2 grid at 6-km grid spacing and an inner 450 × 450 km2 grid at 3-km grid spacing (Fig. 2). The outer 6-km grid used initial and boundary conditions (including soil moisture and temperature) interpolated from the 0600 UTC North American Mesoscale Forecast System (NAM; Janjić 2003) with 12-km grid spacing. We generated a set of 40 ensemble members from this interpolated NAM analysis by applying random Gaussian perturbations smoothed with a 2D recursive filter (Tong and Xue 2008; Jung et al. 2012) with correlation length scales of 36 km in the horizontal and 7.2 km in the vertical to the model potential temperature, water vapor specific humidity, and horizontal wind component fields. The inner 3-km grid used initial and boundary conditions from the parent 6-km grid at 5-min intervals. The simulation period started at 0600 UTC 31 March with an initial 6-h free-forecast “spinup period” on the 6-km grid assimilating conventional surface observations every 15 min using the ARPS-EnKF until 0300 UTC 1 April. The 3-km experiment was initialized from the 6-km ensemble analysis at 1200 UTC and assimilated conventional surface observations and reflectivity and radial velocity data from area NEXRAD radars (cf. Fig. 2) at 15-min intervals until 0300 UTC. Covariance cutoff radii of 300 and 6 km in the horizontal and vertical, respectively, were applied to assimilated surface observations, while a radius of 6 km was used in both the horizontal and vertical for radar data. To maintain ensemble spread, a combination of multiplicative inflation (Anderson 2001) with a factor of 1.2 applied in regions of reflectivity > 5 dBZ and adaptive relaxation to prior spread (RTPS; Whitaker and Hamill 2012) with a factor of 0.9 were used. The model parameterization configuration included a nonlocal PBL parameterization based on a 1.5-order TKE scheme (Xue et al. 1996, 2003) and the triple-moment version of the NSSL precipitation microphysics scheme (Mansell et al. 2010; Dawson et al. 2014). No convective parameterization was used on either of the grids. A two-layer land surface model based on Noilhan and Planton (1989) and Pleim and Xiu (1995) was used, with stability-dependent drag coefficients computed for the surface fluxes. The radiation scheme for surface long- and short-wave fluxes and atmospheric heating rates was based on the NASA Goddard Space Flight Center scheme (Chou 1990, 1992; Chou and Suarez 1994).
Configuration for the outer 6-km (black bounding box) and inner 3-km (red box) ARPS-EnKF domains. Also shown are the NEXRAD radars assimilated on the 3-km grid with 240-km range rings for reference.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
c. Time tendency of CAPE calculation
Term B of (1) is the free-troposphere radiative heating term and is integrated from the LFC to the EL, i.e., only over the layer of positive parcel buoyancy (Emanuel 1994). It can be seen from Eq. (1) that radiative cooling of the cloud layer, or the free troposphere, increases SBCAPE, while warming decreases SBCAPE. Term C in (1), the relative advection (both horizontal, C1, and vertical, C2) term, is computed using a second-order centered spatial finite difference approximation and is also integrated from the LFC to the EL. Term C represents the effects of temperature advection in the free troposphere on SBCAPE; cooling this layer will increase the environmental lapse rate, which in turn will increase SBCAPE.
It is important to note that the direct effects of deep convection in the column are not accounted for in (1). The time frame over which we analyzed CAPE tendency was largely free of deep convection over the Belle Mina site in both the observations and model simulations until near the end of the period (∼0100 UTC). However, the effects of deep convection upstream of the Belle Mina site may potentially be captured by the advective part of Term A as well as the free-tropospheric relative advection term (Term C).
Results from Agard (2017) and Agard and Emanuel (2017) found that Term A is likely to be dominant, and changes in the boundary layer (also can be referred to as subcloud layer), such as increases in temperature, will likely contribute most to changes in SBCAPE. In other words, their work suggests that boundary layer diabatic heating through solar insolation and associated fluxes of heat and moisture is the primary driver of SBCAPE buildup in many severe storm environments (Agard 2017; Agard and Emanuel 2017). This study is testing the applicability of this equation to our case by evaluating (1) on model output and comparing with a synthesis of conventionally available and special VORTEX-SE observations.
3. Case overview
a. Description of VORTEX-SE IOP on 31 March 2016
On 31 March 2016, a VORTEX-SE IOP (the third of 2016; hereafter IOP3) was declared in anticipation of convective storms in the domain. Nonsevere morning convection was expected in association with weak low-level (∼0–3 km) CAPE, but in the afternoon the environment was expected to support stronger convection, including supercells, owing to rapid destabilization and moderate low-level wind shear (NWS Storm Prediction Center 2016a; Rasmussen et al. 2015). The focus of VORTEX-SE field observations during the preconvective period of IOP3 was the rapid northward advection of warm, humid near-surface air as a morning convective system departed. Examining the conceptual model that moisture advection is the primary driver of destabilization in the SE United States was one objective of this work.
Figure 3 provides context of the upper-level winds, temperatures, and humidity for the region before the morning storms (12 UTC 31 March) and during the evening convection (01 UTC 1 April 2016). At 1200 UTC 31 March, a positively tilted, upper-level trough extended from central Alberta in Canada southwestward toward the four corners (Fig. 3a). An attendant subtropical jet streak, stretching from the Baja peninsula of Mexico to western Mississippi at 1200 UTC, rounded the base of this trough such that its right exit region was situated over northern Alabama (the VORTEX-SE domain) by 0000 UTC 1 April (Fig. 3b). While the trough progressed sluggishly eastward from 1200 UTC 31 March to 0000 UTC 1 April, its progress was sufficient to amplify the upper-level geopotential height gradient between the trough to the west and the mid-Atlantic ridge to the east, increasing the maximum winds in the subtropical jet streak (Fig. 3b). This, in turn, amplified upper-level flow and deep-layer shear over the VORTEX-SE domain (Fig. 3b). At the surface, a low pressure system remained nearly stationary over lower Michigan with a cold front extending southwestward through Texas (Figs. 3c,d). Like its attendant upper-level trough, this surface cold front progressed slowly eastward through the day, reaching the Mississippi River by 0000 UTC 1 April. It follows that this front was not the primary source of environmental heterogeneity over the VORTEX-SE domain in northern Alabama. East of the front, across nearly all of Alabama, southerly flow advected near-surface moisture northward from the Gulf of Mexico (Figs. 3c,d).
Analyzed 250-mb (1 mb = 1 hPa) heights and winds at (a) 1200 UTC 31 Mar 2016 and (b) 0100 UTC 1 Apr 2016 and surface analysis from (c) 2100 UTC 31 Mar 2016 and (d) 0000 UTC 1 Apr 2016. Panels (a) and (b) are courtesy of UCAR; panels (c) and (d) are from the NWS Weather Prediction Center.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
Widespread nonsevere storms did indeed move through the VORTEX-SE domain early in the morning on 31 March (Fig. 4). These storms were part of a remnant mesoscale convective system that moved eastward through Alabama and Tennessee the previous night. This morning rain and its associated cool outflow stabilized the boundary layer over northern Alabama. Owing to predicted destabilization over the VORTEX-SE domain and increasing deep-layer wind shear, an intensive observing period (IOP; Rasmussen and Koch 2016) was declared for the afternoon and evening of 31 March 2016.
MRMS reflectivity mosaic (dBZ) at 0 m MSL at 1330 UTC 31 Mar 2016, depicting the morning storms passing over the Belle Mina, AL, observation site (black star) from west to east. The magenta box encompasses the VORTEX-SE domain. Radar image created using Py-ART (Helmus and Collis 2016).
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
Between 1500 and 2000 UTC, the 0–6-km shear over northern Alabama increased from westerly at ∼40–55 kt (1 kt ≈ 0.51 m s−1) to west-southwesterly at ∼60–70 kt as depicted in Fig. 5. At 1900 UTC, thunderstorms developed over north central Mississippi (Fig. 6a). These afternoon storms were more discrete in nature when compared to the morning convection. The difference in the structure of the morning and afternoon convection is believed to be due to an absence of low-level CAPE for the morning convection (Rasmussen et al. 2015). These storms moved rapidly to the northeast (Fig. 6b), with some exhibiting weak rotation (not shown). Between 1900 and 2000 UTC the 0–6-km shear suggested possible heterogeneities in the environment in northern Alabama, as evident in the green contour on Figs. 5b and 5c. This area, which encloses shear values greater than 55 kt, grew from 1900 to 2000 UTC.
Operational mesoanalysis (from the NWS Storm Prediction Center; Hart et al. 2014) of surface-to-6 km AGL shear vectors (kt) at (a) 1500, (b) 1900, and (c) 2000 UTC 31 Mar 2016. Pennants correspond to 50 kt, full barbs correspond to 10 kt, and half-barbs correspond to 5 kt. Contours are drawn every 10 kt, starting from 30 kt. The green annotations encompass the region of interest where shear values are above 55 kt.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
MRMS reflectivity (dBZ) mosaic at 0 m MSL at (a) 1900, (b) 2100, (c) 2200, and (d) 2300 UTC 31 Mar 2016; and (e) 0000, (f) 0100, (g) 0200, and (h) 0300 UTC 1 Apr 2016. The black star in the center of (h) is the location of Belle Mina, AL. Radar image created using Py-ART (Helmus and Collis 2016).
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
After 2100 UTC, the storms moved into air that was too stable to support low-level rotation (NWS Storm Prediction Center 2016b). The morning convection left a large-scale outflow boundary (Fig. 7c) extending from northern Mississippi to central Alabama, in an area which later exhibited strong speed shear at and above 2 km AGL, 0–1-km SRH around 200 m2 s−2 (NWS Storm Prediction Center 2016a), and 0–6-km shear of 50–60 kt.
Operational mesoscale analysis (from the NWS Storm Prediction Center) of SBCAPE and SBCIN (J kg−1) over the SE United States at (a) 1600 UTC, just after the morning rain ended; (b) 2000 UTC, showing a relative minimum in CAPE over northern Alabama; and the recovery of CAPE from (c) 2200 UTC 31 Mar 2016 to (d) 0000 UTC 1 Apr 2016. SBCAPE is contoured in red every 500 J kg−1, and SBCIN is contoured in blue every 50 J kg−1. Surface winds are depicted as beige barbs; full barbs correspond to 10 kt, and half-barbs correspond to 5 kt. The brown annotation in (c) is an outflow boundary analyzed by the SPC.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
By 2200 UTC, storms with persistent low-level rotation (velocity not shown) had reached the Alabama–Tennessee border (Figs. 6c and 8), despite near-surface conditions being relatively cool and stable (Fig. 7c). At 2300 UTC, the NWS SPC issued a tornado watch covering northern Alabama and southern middle Tennessee, citing the potential for “supercells capable of hail, locally damaging winds, and tornadoes…to develop and move eastward to east-northeastward across the area overnight” (NWS Storm Prediction Center 2016e). Two areas on the Alabama–Mississippi and Alabama–Georgia borders, respectively, had developed a mix of discrete supercells and multicell clusters (Figs. 6d–f), supporting a tornado risk across northern Alabama (NWS Storm Prediction Center 2016c,d).
The 1-km visible satellite imagery at 2207 UTC 31 Mar 2016. Image courtesy of NCAR.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
At 0000 UTC 1 April, the VORTEX-SE domain environment was characterized by backing and strengthening winds above 1 km AGL, 0–1 km AGL storm relative helicity (SRH) of 250 to 300 m2 s−2, and SBCAPE around 500 J kg−1 in Belle Mina and surrounding areas (Fig. 7d). Low-level warm air advection was also present closer to the Alabama–Georgia border (NWS Storm Prediction Center 2016c,d).
Over the next few hours, the northern storms (Figs. 6d–f) continued to exhibit low-level rotation (not shown). These storms moved over the VORTEX-SE domain, and one generated a tornado near Priceville, AL, approximately 22 km south of Belle Mina, at 0200 UTC (Fig. 6g). The tornado was rated an EF2 on the enhanced Fujita scale with maximum winds of 51.4 m s−1. Its damage track was approximately 14 km long and 180 m wide (National Weather Service 2016). VORTEX-SE IOP3 field observations ceased at 0300 UTC, when the storms exited the Huntsville domain (Fig. 6h).
b. Boundary layer development
The vertically pointing, UMass, S-band, FMCW radar (İnce et al. 2003) located in Belle Mina, AL (34.6568°N, 86.8792°W), observed high reflectivity in the morning precipitation, followed by refractive index turbulence (Bragg scatter) in the hydrometeor-free period that followed (Fig. 9a). After the cessation of rainfall at Belle Mina around 1650 UTC, the convective boundary layer (CBL) began to redevelop, reaching a depth of about 500 m by 1740 UTC (Fig. 9a). The boundary layer height then fluctuated between 500 and 750 m until 2100 UTC and remained above 750 m thereafter (Fig. 9a). The exception to that is between 2300 and 0000 UTC where the boundary layer height was closer to 500 m again when lightly precipitating clouds were present (Fig. 9a). While vertical velocity in the CBL (Fig. 9b) fluctuated in intensity and sign (Fig. 9c), indicating the unsteadiness of CBL thermal plumes, the overall mean vertical velocity was weakly positive (+0.07 m s−1) over the hydrometeor-free period (1700 UTC 31 March–0100 UTC 1 April).
Observations of (a) reflectivity (dBZ) and (b) Doppler velocity (m s−1) by the vertically pointing UMass FMCW radar at Belle Mina, AL, from 1700 UTC 31 Mar 2016 to 0100 UTC 1 Apr 2016. In both (a) and (b), a dashed gray line follows an automatically detected boundary layer height (Lange et al. 2014, 2015). The reflectivity maximum appearing at 1.3 km AGL, and associated velocity discontinuities, are artifacts of power spurs in the UMass FMCW transmitter (Tanamachi et al. 2019). (c) Mean boundary layer vertical velocity (m s−1) as a function of time (light blue line), with a 15-point rolling mean applied (heavy black line), and the overall mean for the plotted 8-h period (red horizonal line at +0.07 m s−1).
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
The boundary layer height time series depicted in Fig. 9a was objectively calculated using an extended Kalman filter (EKF)-based method (Lange et al. 2014, 2015). This algorithm iteratively fits an error function (erf) to the vertical profile of reflectivity, returning the boundary layer height as the function’s inflection point and recalculating the error covariance matrix at each time step. This approach assumes that Bragg scatter (and hence reflectivity) will be maximized at or near the top of the precipitation-free boundary layer, before decreasing in the relatively scatterer-free troposphere (Lange et al. 2014). One weakness of this EKF-based approach is that erf-like reflectivity profiles can also be produced by phenomena other than Bragg scatter, such as clouds and precipitation. Layer misattribution error (Araújo da Silva et al. 2022) may cause the EKF method to give erroneous results when multiple scatterer layers are present. In the present case, however, the EKF method follows the top of the CBL accurately from 1800 to 2230 UTC (Fig. 9a), under clear sky conditions. After 2300 UTC, a lightly precipitating cloud layer developed between 1.0 and 1.5 km AGL (blue box in Fig. 9a). This cloud layer complicated CBL identification by intermittently suppressing convection, yielding CBL top heights that are likely to be underestimates of the true boundary layer depth from 2230 to 0100 UTC (Fig. 9a). Similar results were obtained by Araújo da Silva et al. (2022) on cloudy days during an observation campaign in Germany. However, during the period under discussion, the objectively identified boundary layer top remained below the cloud layer, indicating that the algorithm avoided misidentifying the cloud layer (as well as an artificial reflectivity maximum at 1.3 km AGL; Tanamachi et al. 2019) as the top of the CBL.
4. Results: Analysis of CAPE development
a. Observed and modeled SBCAPE development
Following the morning rain on 31 March, the SBCAPE at Belle Mina increased from near zero to over 500 J kg−1 between 2000 and 2200 UTC according to the observed ATDD soundings. During that time, shortwave solar insolation (measured by the ATDD instrumented tower) increased over the Belle Mina site (Fig. 10), contributing to surface heating over the area (Fig. 11). The increased near-surface air temperatures contributed to the increase in SBCAPE.
Shortwave radiation data from a 2.5 m AGL meteorological tower at Belle Mina, AL. Data from Lee et al. (2016a). For reference, sunrise was at 1135 UTC 31 Mar and sunset was at 0008 UTC 1 Apr.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
Temperature (°C) at 3 and 10 m AGL and dewpoint at 10 m from the meteorological tower at Belle Mina, AL (Lee et al. 2016a), and temperature and dewpoint at 10 m from the ARPS model at the grid point closest to Belle Mina, AL.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
While hourly soundings are useful, they are still too coarse temporally to resolve rapid changes in CAPE. To estimate subhourly CAPE changes, the CLAMPS temperature and moisture profiles, which extended from the surface to 4 km AGL, were combined with temporally interpolated ATDD soundings above 4 km AGL to create constructed soundings every five minutes from 2000 to 2200 UTC 31 March. The linear temporal interpolation between the hourly ATDD soundings was done to match the temporal resolution of the CLAMPS data, and to avoid large discontinuities in CAPE resulting from sudden changes in the profile above 4 km AGL. The results of this interpolation are shown in Fig. 12 (purple line). From these constructed soundings, CAPE and other parameters were calculated at 5-min intervals over the period. It was found that MLCAPE was initially 400 J kg−1 and peaked at about 750 J kg−1 at 2015 UTC (Fig. 12). SBCAPE started out around 1000 J kg−1 and fell to around 500 J kg after 2030 UTC. After that time CAPE hovered around the MLCAPE values. The only exception to this is at two-time steps where SBCAPE fell to 0 J kg−1 (gaps in dark purple line; likely due to a known issue in the Metpy calculation routine). Since MLCAPE is calculated over a deeper layer, it is less sensitive to changes in surface temperature and dewpoint than SBCAPE.
ML CAPE values (J kg−1) calculated from the ARPS ensemble mean (blue), ATDD soundings (gray), constructed ATDD/CLAMPS soundings (purple), and the model ensemble mean integrated CAPE tendency equation (green) from 2000 UTC 31 Mar 2016 to 0200 UTC 1 Apr 2016. The shaded areas are the standard deviation of the values.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
We also calculated SBCAPE from the model grid column closest to the Belle Mina site (Fig. 12, green) and compared this with the SBCAPE computed from integrating the CAPE tendency equation (Fig. 12, blue). This comparison was done to quantify how well the SBCAPE integrated from the SBCAPE tendency equation agreed with that computed directly from the model profiles. The two are in reasonable agreement; discrepancies arise from a combination of discretization error and the effects of data assimilation. In more detail surface observations were assimilated every 15 min, so any errors in the EnKF analysis may have cumulative effects independent from the forward model. The maximum SBCAPE calculated from the model ensemble mean during the late afternoon was ∼1400 J kg−1, which is substantially larger than that computed from the ATDD soundings and the hybrid ATDD/CLAMPS-constructed soundings. The lower SBCAPE values in the hybrid soundings resulted from greater low-level moisture in the ATDD soundings than in the CLAMPS-derived soundings. The SBCAPE computed directly from the model and from integrating (1) using model data both rise steadily until about 2000 UTC, after which they remain relatively constant until ∼2300 UTC. Then, after 2300 UTC, the model derived SBCAPE decreases slowly and at roughly the same rate as that computed from the ATDD soundings until ∼0030 UTC. Overall, the model predicted higher SBCAPE peaking later than the observations. This is at least partially explained by the fact that the model predicted surface temperatures were consistently ∼2 K higher than the observations (Fig. 11). Another experiment was performed on the 3-km grid in which radar data were withheld from the assimilation; this experiment resulted in lower SBCAPE closer to that of the observations and peaking at around the same time (not shown), although with much greater spread across the ensemble. This experiment also produced a large amount of spurious convection over northern Alabama during the mid-to-late afternoon (not shown) and was thus not analyzed further.
To better understand the role of both boundary layer and free-troposphere radiative heating/cooling, we calculated the vertical profile of the total radiative heating rate over the Belle Mina site from the CLAMPS observations using the longwave and shortwave versions of the Rapid Radiative Transfer Model (RRTM; Mlawer et al. 1997; Clough et al. 2005). We then compared the time evolution of these radiative heating rates directly with the radiative heating profile from the ARPS ensemble mean interpolated to the Belle Mina location (Fig. 13). The BL radiatively warmed until about 2200 UTC (driven primarily by shortwave absorption), then radiatively cooled for about an hour when the clouds were optically thin (i.e., had liquid water paths smaller than about 20 g m−2), and then began to warm again below the optically thicker clouds at about 2300 UTC (Fig. 13a). The radiative heating rate from the ARPS model agreed qualitatively with the RRTM calculations using the CLAMPS observations (Fig. 13b) below the cloud layer, especially between 1800 and 2100 UTC, with a general cooling trend in both the model and observations. Otherwise, the CLAMPS exhibits a somewhat deeper layer of heating above the cloud layer than the ARPS, especially early in the period. Some of this discrepancy may be due to the relatively coarse resolution of the AERI profiles above the boundary layer.
The total (longwave plus shortwave) radiative heating rate (K day−1) computed from the (a) CLAMPS observations using the RRTM on 31 Mar 2016 and (b) ARPS model. The thin layer of larger radiative cooling with a lot of noise in (a) is associated with the radiative cooling at the top of the thin stratiform cloud layer.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
b. Time tendency/components of SBCAPE
The ARPS simulations produced southerly winds (not shown) and increasing θe over northern Alabama over the course of the afternoon (Figs. 14a–c), consistent with heating and moistening of the boundary layer via advection or surface fluxes or a combination of the two. In both the simulations (Fig. 15) and observations from CLAMPS (Fig. 16), low-level temperature and moisture (below 500 m AGL) at the Belle Mina site increased steadily from midmorning (∼1500 UTC) to late evening (0000 UTC). This heating coincides with increased incoming solar (shortwave) radiation (Fig. 10). Additionally, the increase in the moisture at low levels in the ARPS at 1800 UTC agrees with the AERI retrievals (Figs. 15 and 16); however, the AERI shows that the second increase of moisture comes at ∼2100 UTC whereas the ARPS has that second increase occurring somewhat earlier (∼2000 UTC).
ARPS ensemble mean surface θe (color fill; K) and radar reflectivity (dBZ; black contours, 10-dBZ increment, starting at 30 dBZ) on the 3-km domain at (a) 1300, (b) 1600, (c) 1900, and (d) 2200 UTC.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
Time–height plots of (top) temperature (°C) and (bottom) water vapor mixing ratio (g kg−1) interpolated to the location of Belle Mina, AL, from the ARPS ensemble mean from 1500 UTC 31 Mar 2016 to 0000 UTC 1 Apr 2016.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
Time–height plot of (a) temperature (°C) and (b) water vapor mixing ratio (g kg−1) retrieved from the CLAMPS. The black dots represent cloud base heights. The vertical white areas indicate times when the AERI’s hatch was closed due to precipitation, and thus no retrievals were possible.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
To examine the contributions to SBCAPE in more detail, we evaluated each of the terms of the time tendency of SBCAPE [Eq. (1)] using the model output, for each fifteen-minute interval from 1200 UTC 31 March to 0200 UTC 1 April (Fig. 17). Prior to ∼1745 UTC, the modeled SBCAPE was 0 J kg−1, as were all the components of the SBCAPE tendency. SBCAPE calculated from the reconstructed ATDD/CLAMPS soundings reached its peak after 2015 UTC, which was before the peak in the ATDD soundings (2200 UTC) and that in the ARPS model (∼2300 UTC) (Fig. 12). We hypothesize a subsidence inversion between 800 and 900 hPa (Figs. 1b,c) played a role in reducing the observed SBCAPE. This inversion was not captured in the model and may have contributed to the higher model derived SBCAPE over the event.
Integrated contribution to CAPE (J kg−1) of terms in the CAPE tendency equation (1), calculated from the ARPS simulations at the grid point closest to Belle Mina. The shaded areas encompass the standard deviation of the values.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
An analysis of the individual integrated terms of the SBCAPE tendency equation across the ARPS ensemble (Fig. 17) demonstrates that the greatest positive contributor to SBCAPE was BLE tendency [i.e., Term A in Eq. (1)]. In the mean, the free-tropospheric vertical advection term (C2) contributed weakly to decreasing SBCAPE especially after 2100 UTC. In contrast, the free-tropospheric horizontal advection term (C1) contributed weakly negatively prior to ∼2100 UTC and then increasingly positively (in the mean) to SBCAPE thereafter. The contributions from terms C1 and C2 tended to compensate for each other. While the contribution of CAPE from C1 was not as relevant for our study, it could be a contributor to destabilization after dark in the SE United States, as explored by King et al. (2017). They found that low-level positive θe advection was the primary reason surface temperatures increased in their simulations of various HSLC environments. Along with the surface warming, they also found that cooling aloft (due to several mechanisms including both horizontal cold air advection and lift) also contributed to destabilization of the environment in many cases. (King et al. 2017).
The contribution from the free-tropospheric radiative heating term (Term B) was negligible throughout the period. The ensemble spread tended to increase somewhat after ∼0000 UTC for each term except the radiative heating term. The increased spread appears to be a result of deep convection overspreading the Belle Mina site (not shown) that varies substantially in its intensity and specific timing—and therefore its effects on the model state variables that constitute the terms in (1)—across the ensemble during this period. As mentioned previously, the CAPE tendency equation (1) does not account for the effects of deep convection; the increase in ensemble spread is a helpful indicator of the loss of reliability after 0000 UTC. Additionally, in the observed soundings it is evident subsidence is influencing the region; however, the model did not capture this subsidence after 2000 UTC.
As in Fig. 17, but for terms in the BLE tendency equation (3). The BLE (LHS) in green refers to the explicit time integral of the lhs of (3) and is identical to the BLE term plotted in Fig. 17. The shaded areas are the standard deviation of the values.
Citation: Monthly Weather Review 151, 6; 10.1175/MWR-D-22-0051.1
Recall that the boundary layer has the highest radiative heating rates during the period of the highest SBCAPE increase (Fig. 13). The integral of the radiative heating term in (3) confirms that this term positively contributed to SBCAPE (Fig. 18, gray). However, by far the largest contribution to increasing SBCAPE from increasing BLE comes from the surface sensible and latent heat fluxes (Fig. 18, blue), in agreement with the results of Agard (2017). Advection also positively contributes to increasing SBCAPE (Fig. 18, red) to roughly the same degree as direct radiative heating. It should be noted that the direct radiative heating term in (3) is not the same as that in (1), the latter of which is computed in the free troposphere and integrated between the LFC and EL.
5. Conclusions
We conclude with high confidence that an increase in boundary layer entropy was the largest contributor to SBCAPE recovery at Belle Mina, AL, on 31 March 2016. Specifically, we are moderately confident that entropy increase due to surface heating was the primary source of SBCAPE increase, as supported by an analysis of the boundary layer entropy tendency equation (Fig. 18). This result agrees with those of Agard (2017) and stands somewhat in contrast with conventional wisdom suggesting that moisture advection should be the primary driver of destabilization close to the Gulf coast. We observed substantial variations in SBCAPE tendency on subhourly scales in both observations and numerical simulations (Fig. 12). This result underscores the need for frequent observations of the atmospheric boundary layer on days when severe weather is deemed likely. A system such as the CLAMPS can serve as a high temporal resolution substitute for radiosonde soundings in the boundary layer when soundings are not available. Optimally, several CLAMPS would be deployed in a network across the inflow region in advance of severe weather, so that temporal trends and the spatial pattern of CAPE and other stability parameters can be monitored in real-time. While the thermodynamic retrievals from the AERI in CLAMPS has some vertical resolution limitations, Blumberg et al. (2017) showed that there is high correlation (r > 0.9) between radiosonde-observed and AERI-derived CAPE values.
Despite having supplemental VORTEX-SE observations over Belle Mina, our analysis is only valid at one point, and our methodology required numerical model fields to estimate the contribution of advection to CAPE recovery. During this IOP, additional soundings were launched at other sites in the VORTEX-SE domain, but these were primarily launched to the west of Belle Mina, whereas our primary interest was in boundary layer conditions south of Belle Mina, closer to the Gulf of Mexico moisture source. Additional soundings south of the VORTEX-SE domain would have mitigated the need for numerical model estimates of advection, although these would still be coarse in temporal resolution. In short, current operational observations are not at a high enough temporal resolution for this analysis. Spatial resolution was not directly addressed in this study, because the field campaign design already addressed this issue to some extent. To better resolve these rapid, small-scale CAPE variations, surface and upper-air observations need to be collected subhourly and with sufficiently high spatial resolution (∼5 km or less) for such small-scale influences to be characterized and their impacts on subsequent severe weather anticipated.
Acknowledgments.
Collection and analysis of VORTEX-SE 2016 data were funded by National Oceanic and Atmospheric Administration Grant NA15OAR4590231. We gratefully acknowledge other VORTEX-SE participants for the sharing of their observations, particularly NOAA/ATD and NCAR/EOL for the Belle Mina sounding and radiation data. We also acknowledge Kevin Knupp and Tony Lyza for suggesting the Belle Mina deployment site for UMass FMCW and CLAMPS, and to Temple Lee and Bruce Baker for arranging land use permission, power, and internet connectivity with the Tennessee Valley Authority. We are indebted to Ming Xue, Youngsun Jung, Nathan Snook, Tim Supinie, and the rest of the team at the Center for Analysis and Prediction of Storms (CAPS) for providing the ARPS-EnKF code and assistance with running the system. Stephen Frasier and Joseph Waldinger (University of Massachusetts—Amherst) provided the UMass FMCW radar observations. We thank Sherman Fredrickson and Doug Kennedy for their help in deploying the CLAMPS at Belle Mina. We also acknowledge Connor Belak for his support in plotting the ARPS model data. The constructive comments of three anonymous reviewers greatly improved the manuscript. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the NOAA or the U.S. Department of Commerce.
Data availability statement.
Data from this project can be found on the VORTEX-SE NCAR EOL archive, found at https://data.eol.ucar.edu/master_lists/generated/vortex-se_2016/. The CLAMPS data can be found at doi:10.5065/D6154FFP as referenced in Turner (2016). The micrometeorological tower data and sounding data can be found at doi:10.5065/d6bg2mbj and doi:10.5065/d68k77fn respectively, as cited in Lee et al. (2016a,b). Birmingham sounding data and KTHX radar data are openly available from the National Center for Environmental Information (NCEI).
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