The Influence of Assimilated Upstream, Preconvective Dropsonde

DECEMBER 2017 K E C L I K E T A L . 4747

The Inﬂuence of Assimilated Upstream, Preconvective Dropsonde Observations on Ensemble Forecasts of Convection Initiation during the Mesoscale Predictability Experiment

a ALEXANDRA M. KECLIK, CLARK EVANS, AND PAUL J. ROEBBER Atmospheric Science Program, Department of Mathematical Sciences, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin

GLEN S. ROMINE National Center for Atmospheric Research, Boulder, Colorado

(Manuscript received 6 June 2017, in ﬁnal form 25 September 2017)

ABSTRACT

This study tests the hypothesis that assimilating mid- to upper-tropospheric, meso-a- to synoptic-scale observations collected in upstream, preconvective environments is insufficient to improve short-range ensemble convection initiation (CI) forecast skill over the set of cases considered by the 2013 Mesoscale Pre- dictability Experiment (MPEX) because of a limited influence upon the lower-tropospheric phenomena that modulate CI occurrence, timing, and location. The ensemble Kalman filter implementation within the Data Assimilation Research Testbed as coupled to the Advanced Research Weather Research and Forecasting (WRF) Model is used to initialize two nearly identical 30-member ensembles of short-range forecasts for each case: one initial condition set that incorporates MPEX dropsonde observations and one that excludes these observations. All forecasts for a given mission begin at 1500 UTC and are integrated for 15 h on a convection- permitting grid encompassing much of the conterminous United States. Forecast verification is conducted probabilistically using fractions skill score and deterministically using a 2 3 2 contingency table approach at multiple neighborhood sizes and spatiotemporal event-matching thresholds to assess forecast skill and support hypothesis testing. The probabilistic verification represents the first of its kind for numerical CI forecasts. Forecasts without MPEX observations have high fractions skill score and probabilities of detection on the meso-a scale but exhibit a considerable high bias for forecast CI event count. Assimilating MPEX observations has a negligible impact upon forecast skill for the cases considered, independent of verification metric, as the MPEX observations result in only subtle differences primarily manifest in the position and intensity of atmospheric features responsible for focusing and/or triggering deep, moist convection.

1. Introduction zones, frontal boundaries, gust fronts, horizontal convective rolls, orographic circulations, sea breezes, and un- Convection initiation (CI), in the context of this study dular bores (Jorgensen and Weckwerth 2003; Weckwerth leading to the formation of deep, moist convection, is a and Parsons 2006; Burghardt et al. 2014). Further, CI is a sequence of events in which air parcels accelerate beyond classic scale interaction problem, requiring a favorable their level of free convection to create a visible cloud top interaction between phenomena from multiple scales. The with a rapid increase in cloud depth, followed by pre- synoptic and meso-a scales establish the thermodynamic cipitation development (Kain et al. 2013). CI is triggered and kinematic environment favorable for CI (Weisman by a lower-tropospheric convergence mechanism; com- et al. 2008). The meso-b scale contributes to horizontal mon examples include drylines, elevated convergence variability in the large-scale environment in which CI occurs, and meso-g to microscale phenomena determine a Current afﬁliation: National Weather Service, Chanhassen, local planetary boundary layer (PBL) lifting, moisten- Minnesota. ing, and environmental variability crucial to CI timing and location (e.g., Markowski et al. 2006; Weckwerth Corresponding author: Clark Evans, [email protected] et al. 2008; Kain et al. 2013; Burghardt et al. 2014).

DOI: 10.1175/MWR-D-17-0159.1 Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/27/21 11:36 PM UTC 4748 MONTHLY WEATHER REVIEW VOLUME 145

Consequently, CI is a locally rare event at any given et al. 2008; Romine et al. 2013). Targeted observations, location (Lock and Houston 2015). representing the augmentation of the regular observa- In a favorable environment with sufficiently large tion network with additional, specifically chosen obser- vertical wind shear, CI can lead to the development of vations, is thought to improve model ICs by improving severe thunderstorms capable of producing damaging the initial representation of atmospheric features on the surface winds, flash flooding, large hail, and tornadoes. scales of those resolved by the targeted observations Severe storms annually cause substantial loss of life and (Majumdar et al. 2011). In previous studies, obser- are responsible for the largest amount of U.S. billion- vations have mostly been targeted at synoptic-scale dollar natural disaster events during 1980–2014 (NCDC systems for the purposes of improving global model 2015). Despite the significant societal impacts that can 1–3-day forecasts (e.g., Majumdar et al. 2011; Majumdar result, accurately predicting the initiation, intensity, 2016). Targeted dropwindsonde observations from field and evolution of deep, moist convection, whether or not projects such as the NOAA Synoptic Surveillance pro- it reaches severe levels, remains a significant challenge gram (Aberson 2010) and Dropsonde Observations for numerical weather prediction models and human for Typhoon Surveillance near the Taiwan Region forecasters. Contributions to forecast error include the (DOTSTAR; Wu et al. 2007; Chou et al. 2011) have been stochastic nature of the atmospheric system, the de- found to provide statistically significant positive impacts pendence of CI and subsequent convective evolution to tropical cyclone track forecasts. Targeted observa- upon physical processes on multiple scales, shortcom- tions have, on average, shown smaller yet still positive ings in physical parameterization packages employed impacts upon extratropical, synoptic-scale forecasts. within convection-permitting numerical simulations, Representative examples include the 1997 Fronts and and data quality and availability (Weckwerth and Atlantic Storm-Track Experiment (FASTEX; e.g., Joly Parsons 2006). et al. 1999; Bergot 1999; Montani et al. 1999) and the Characteristics of convective storms are strongly tied to early-to-mid-2000s Atlantic The Observing System Re- the environment in which they develop, so it is important search and Predictability Experiment (THORPEX) to accurately represent the initiation environment when Regional Campaign (A-TReC; e.g., Fourrié et al. 2006; forecasting such events (Benjamin et al. 2010; Wandishin Rabier et al. 2008). Majumdar (2016) provides a com- et al. 2010; Weisman et al. 2015). Several recent studies prehensive summary of targeted observation studies. have been conducted using convection-permitting (CP; On the meso- and smaller scales, the Interna- horizontal grid spacing of 4 km or less) NWP simulations tional H2O Project (IHOP_2002) sampled the three- to quantify CI predictability. Duda and Gallus (2013) dimensional time-varying moisture field via in situ and investigated the relationship between large-scale forcing remote sensing techniques to better understand con- and CI predictability for 36 primarily warm-season CI vective processes (Weckwerth et al. 2008). For the events preceding mesoscale convective system (MCS) IHOP_2002 12–13 June 2002 CI event, Liu and Xue formation. Key findings include a mean absolute dis- (2008) conducted numerical sensitivity experiments to placement error of 105 km, no systematic timing bias, and assess how data assimilation frequency and targeted no significant relationship between CI forecast skill and observations influenced CI prediction. The simulation large-scale forcing magnitude, the latter of which was that assimilated the most data subjectively produced the attributed primarily to the importance of smaller-scale best forecast, as the additional observations removed features to the CI process. Similar results were obtained the resolution-related delay of CI and overly moist by Burghardt et al. (2014) for 27 warm-season CI epi- lower-tropospheric ICs (Liu and Xue 2008). However, sodes in the central High Plains in subkilometer hori- other experiments excluding targeted observations did zontal grid spacing deterministic numerical simulations. better with CI timing and location for some cell groups. Burghardtetal.(2014)also documented an over- Two more recent field projects that have targeted production of CI events within the numerical simulations, observations for mesoscale phenomena are the Hydro- particularly near higher terrain. Kain et al. (2013) found logical Cycle in the Mediterranean Experiment (HyMex) that numerical models can resolve, if crudely, physical and Deep Propagating Gravity Wave Experiment over processes important for CI. Despite no systematic en- New Zealand (DEEPWAVE), each briefly described in semble bias in CI timing, Kain et al. (2013) argued that Majumdar (2016). However, research into the forecast probabilistic numerical CI forecasts were inadequate in- impact of targeted observations collected during these dicators of subsequent convective evolution. campaigns remains in its nascent stages. In general, initial conditions (ICs) exert a greater in- The Mesoscale Predictability Experiment (MPEX; fluence on short- to medium-range (0–36 h) convective Weisman et al. 2015) hypothesized that the collection of forecast skill than does model configuration (Weisman nonroutine synoptic- and meso-a-scale observations in

Unauthenticated | Downloaded 09/27/21 11:36 PM UTC DECEMBER 2017 K E C L I K E T A L . 4749 the upstream, preconvective environment and their mid- to upper-tropospheric phenomena to which sub- subsequent assimilation into CP numerical forecasts sequent deep, moist convection (and, specifically, ac- would significantly improve short-lead forecasts of the cumulated precipitation) forecasts were most sensitive timing, location, and mode of CI and subsequent con- (Romine et al. 2016), with this sensitivity primarily vective evolution. Operations involved two missions manifest in a featured location. In other words, in situ per active program day: 1) an early morning mission and upstream boundary layer phenomena to which with the NCAR Gulfstream-V aircraft, well upstream of subsequent forecasts were most sensitive were not anticipated convective storms, in which dropsonde and sampled well, if at all, by MPEX dropsonde observa- microwave temperature profiler observations were col- tions (Torn and Romine 2015; Berman et al. 2017; Torn lected; and 2) an afternoon-to-evening mission with et al. 2017). Given that meso-a- to synoptic-scale mobile sounding units to sample the preconvective en- boundaries along which CI occurs (fronts, drylines, vironment and quantify upscale convective influences etc.) typically translate in concert with upstream mid- to (Weisman et al. 2015; Trapp et al. 2016). Dropsonde upper-tropospheric cyclonic disturbances, at least in observations were collected across the U.S. Inter- part, any impact of assimilating MPEX dropsonde ob- mountain West during 15 research flights (RFs) between servations upon subsequent CI forecasts is likely to 15 May and 15 June 2013. An average of 28, a minimum be manifest in modulating the position of a subset of of 17, and a maximum of 33 dropsondes were released the lower-tropospheric boundaries along which CI ini- during each RF (hereafter, case; Weisman et al. 2015). tiates. Consequently, given that MPEX sampled a di- Ensemble sensitivity analysis (Torn and Hakim 2008) verse range of both weakly and strongly forced cases was used to identify mid- to upper-tropospheric phe- (Weisman et al. 2015), we hypothesize that assimilating nomena to which subsequent deep, moist convection MPEX dropsonde observations is insufficient to result forecasts were most sensitive for a given case, from in statistically significant improvements in short-range which targeted dropsonde observation locations (for a (0–15 h) CI forecast skill over the set of cases sampled forecast metric of accumulated precipitation) were de- by MPEX. termined. Consequently, MPEX dropsonde observa- The remainder of the manuscript is structured as fol- tions primarily sampled mid- to upper-tropospheric lows. Section 2 describes the methodology, including kinematic and thermodynamic features in upstream, ensemble analysis and simulation configuration, CI preconvective environments. identification, and forecast verification methods. Event Romine et al. (2016) investigated the impact of as- statistics and forecast skill metrics are presented in similating targeted MPEX dropsonde observations on section 3, case study analyses for three chosen MPEX probabilistic short-range forecast skill for accumulated cases are presented in section 4, and a summary and precipitation. Evaluating forecasts against rawinsonde, avenues for future work are discussed in section 5. METAR, and mesonet observations showed only small differences between ensemble forecasts that did and did not assimilate MPEX dropsondes. Despite notable 2. Methodology case-to-case variation in forecast skill and dropsonde a. Experimental design observation impact, assimilating targeted MPEX observations resulted in a small but statistically significant The forecast model and ensemble analysis system forecast skill improvement for accumulated precipita- used for this study are identical to that described in tion forecasts. Forecast skill improvement was greatest Romine et al. (2016). Summarizing, the ensemble ad- for cases that best sampled the objectively determined justment Kalman filter (EAKF; Anderson 2001, 2003) atmospheric features—nominally, in the mid- to upper implemented within the Data Assimilation Research troposphere—to which the subsequent precipitation Testbed (DART; Anderson et al. 2009) package is forecast was most sensitive. coupled with version 3.4.1 of the Advanced Research In this study, we seek to quantify the influence of as- Weather Research and Forecasting (WRF-ARW; similating MPEX dropsonde observations on short- Skamarock et al. 2008) Model to obtain two identically range (0–15 h) ensemble CI forecast skill. As noted configured 50-member ensemble analyses of the pre- above, CI is triggered by a lower-tropospheric conver- convective atmospheric state for each of the 15 MPEX gence mechanism (e.g., Weckwerth and Parsons 2006), cases. The first, or Control ensemble, does not assimi- with CI occurrence, timing, and location critically de- late MPEX dropsonde observations. The second, or pendent on PBL lifting and moistening (Markowski Updated ensemble, assimilates MPEX dropsonde ob- et al. 2006; Weckwerth et al. 2008). However, MPEX servations only for that case. Further MPEX dropsonde dropsonde observations primarily sampled upstream targeting information is provided later in this section.

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TABLE 1. WRF Model conﬁguration options.

Parameter Cycled analysis domain High-resolution forecast domain Horizontal grid 415 3 325, Dx 5 15 km 1046 3 871, Dx 5 3km

Vertical grid 40 levels, ptop 5 50 hPa Same PBL parameterization Mellor–Yamada–Janjic´ Same Cumulus parameterization Tiedtke None Microphysical parameterization Thompson Same Radiation (shortwave) RRTMG Same Radiation (longwave) RRTMG Same Land surface parameterization Noah Same

The analysis domain upon which all data assimilation that these methods for mitigating against sampling error is conducted has a horizontal grid spacing of 15 km and maintaining ensemble spread are imperfect and (415 3 325 grid points) and covers the conterminous largely ad hoc in nature. In particular, covariance local- United States, the Gulf of Mexico, and portions of ization can result in unrealistic analysis increments and Canada, Mexico, the eastern North Pacific Ocean, and imbalanced model initial conditions, whereas inflation the western North Atlantic Ocean. The domain contains dilutes the extent to which the estimated background 40 terrain-following vertical levels between the surface error statistics are truly flow dependent (Houtekamer and 50 hPa with approximately 8 levels within the and Zhang 2016). planetary boundary layer (PBL). The Mellor–Yamada– Analysis fields updated by WRF-DART include Janjic´ (MYJ; Janjic´ 1994, 2002) PBL, Thompson et al. horizontal wind components, perturbation potential (2008) hybrid double-moment microphysical, RRTM temperature, geopotential height, water vapor, instan- for GCMs (RRTMG) longwave and shortwave radia- taneous diabatic heating rate, and the mixing ratios and tion including ozone and aerosol climatologies (Mlawer number concentrations for all carried microphysical et al. 1997; Tegen et al. 1997; Iacono et al. 2008), Noah species. Routine observations assimilated by WRF- (Chen and Dudhia 2001) land surface, and Tiedtke DART include mandatory- and significant-level rawin- (Tiedtke 1989; Zhang et al. 2011) cumulus convection sonde data; surface-based METAR, buoy, and ship parameterizations are utilized by the cycled analysis observations; Aircraft Meteorological Data Relay system (Table 1). (AMDAR) reports; satellite-derived horizontal atmo- The initial 50-member ensemble analyses are pro- spheric motion vectors (AMVs; Velden et al. 2005); and duced by adding Gaussian random samples with zero thinned global positioning system (GPS)-derived radio mean and covariance derived from the NCEP back- occultation (Kursinski et al. 1997) refractivity observa- ground error covariance matrix to the 1800 UTC tions (e.g., as in Romine et al. 2013; Schwartz et al. 2015; 30 April 2013 Global Forecast System (GFS) analysis Torn and Romine 2015). AMDAR observations are interpolated to the 15-km WRF Model domain averaged over boxes with dimensions of 30 km in the (Schwartz et al. 2015) using the WRF Model’s Com- horizontal and 25 hPa in the vertical as in Torn (2010) munity Variational/Ensemble Data Assimilation Sys- and AMVs are averaged over 60 km in the horizontal. tem (WRFDA) package (Barker et al. 2012). Lateral Surface observations with model terrain and station boundary conditions for the initial and subsequent height differing by more than 300 m and/or located 6-hourly analysis times are obtained using the fixed within three grid lengths of the lateral boundaries are covariance perturbation technique of Torn et al. (2006), excluded. The characteristics of all assimilated obser- as applied to the 0-h GFS analysis and 6-h GFS forecast, vations are compared to the preassimilation atmo- respectively. Following, for example, Torn (2010), spheric state space provided by the ensemble, and Romine et al. (2013), and Schwartz et al. (2015), sam- observations whose squared difference from the en- pling error is minimized and ensemble spread is main- semble mean estimate exceeds 3 times the sum of the tained using sampling error correction (Anderson 2012), prior ensemble and observational error variances are adaptive Gaspari–Cohn observation localization (Gaspari rejected by the assimilation system (Romine et al. 2013). and Cohn 1999; Anderson 2012), and adaptive time- and Over 99% of available MPEX dropsonde observa- space-varying prior inflation (Anderson 2009). Although tions passed these internal consistency checks and were widely used in modern mesoscale ensemble atmospheric subsequently assimilated (Romine et al. 2016, see their cycled data assimilation applications using the ensem- Fig. 4). Assumed errors for each observation type match ble adjustment Kalman filter, it should be emphasized those in Romine et al. (2013).

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Cycled analysis continues at an interval of 6 h through 0000 UTC 15 June. However, at 0000 UTC on the day of each RF, two distinct forks of the ensemble analyses are obtained. Each utilizes a 1-h cycling interval through 1500 UTC, with Control assimilating only the observations listed above and Updated also assimilating MPEX dropsonde observations. The hourly cycling is performed to reduce time-dependent background errors (Romine et al. 2016). All MPEX data are quality controlled by the staff of NCAR’s Earth Observing Laboratory prior to assimilation and dropsonde observation quality is assessed by evaluating them against Control posterior analyses, as described in Romine et al. (2016, see their section 2c). Note that the dropsonde observations were recently found to be significantly dry biased under cold and dry conditions (Vömel FIG. 1. Approximate extent of the 3-km simulation domain, with the inner black rectangle representing the domain over which ob- et al. 2016) that, during MPEX, is most commonly observed and modeled CI events are identified. Labeled black dots served in the middle to upper troposphere (Romine indicate locations of the 42 NEXRAD sites used to identify et al. 2016). As the potential for a nascent thermal to observed CI events. remain positively buoyant for a sufficient duration as to initiate deep, moist convection is partially influenced by entrainment from its surroundings, it is possible that two-way-interacting 15-km–3-km nested domain. The this bias could influence the results presented herein. outer 15-km domain is identical to the analysis domain. However, any such impact is expected to be minor given The inner 3-km domain (1046 3 871 grid points) extends the nature of the bias and the overriding influence from the U.S. Intermountain West to the Appalachian of boundary layer processes in triggering CI. Relative Mountains and from Baja California to the Canadian to the routine observation vertical distribution, the border (Fig. 1). The model configuration for the free greatest proportional increase in observation counts forecasts is identical to that utilized by the cycled anal- resulting from assimilating MPEX targeted dropsonde ysis system with the exception that cumulus convection observations occurs in the middle troposphere (Romine is treated explicitly on the 3-km domain. Simulations for et al. 2016). the Control and Updated ensembles are initialized at Observation targeting is determined from ensemble 1500 UTC and are integrated forward 15 h. sensitivity analysis (Torn and Hakim 2008). Ensemble b. CI event identification sensitivity analysis provides an estimate of the linear relationship between a chosen forecast metric and Following Kain et al. (2013) and Burghardt et al. model state variables at earlier forecast times. During (2014), CI events in this study are defined as objects in MPEX, 24–48-h output from daily 1200 UTC real-time the radar reflectivity field with reflectivity $35 dBZ at ensemble forecasts is used to identify regions where a the 2108C isotherm for a minimum of 30 min. The chosen forecast metric—here, accumulated precipita- 35-dBZ reflectivity threshold is chosen following tion averaged over time and space windows that varied Gremillion and Orville (1999), who found this thresh- between cases, and not a CI-specific metric—had nota- old to effectively distinguish observed thunderstorms ble variability between ensemble members. Ensemble from non-thunder-producing precipitation. The 2108C sensitivity analysis is used to identify the meteorological level is selected to prevent contamination of the results variables and features at the times of next-day MPEX from melting hydrometeors (bright banding) primarily flight missions to which the forecast metric is most sen- within stratiform precipitation regions. The 30-min cri- sitive. The collected observations are then assimilated terion is used to ensure only sustained CI events are into later model cycles, as described above, from which considered. observation impact upon subsequent forecast evolution To identify observed CI events, Level II Next is quantified. Further details are provided in Weisman Generation Weather Radar (NEXRAD) reflectivity is et al. (2015) and Romine et al. (2016). obtained for 42 radars across the central United States For each RF, the first 30 ensemble analyses from each for the duration of each simulation (Fig. 1). Warning 50-member ensemble analysis system are used to ini- Decision Support System–Integrated Information tialize free forecasts. Forecasts are conducted using a (WDSS-II; Lakshmanan et al. 2007) spatial analysis

Unauthenticated | Downloaded 09/27/21 11:36 PM UTC 4752 MONTHLY WEATHER REVIEW VOLUME 145 tools (Lakshmanan 2012) are used to merge (w2merger; fields over the period 1800–0600 UTC on the 0.0383 Lakshmanan et al. 2006; Lakshmanan and Humphrey 0.038 evaluation grid used to identify CI events (Fig. 1). 2014) the NEXRAD data to a uniform 0.038 latitude 3 Event fractions (or probabilities; the number of CI 0.038 longitude gridded domain (black square in Fig. 1) events divided by the number of neighborhood grid and interpolate the result to the height of the 2108C points) are then computed at every grid point for both isotherm extracted from the nearest-in-time 13-km 0-h time-independent (ignoring CI event time differences) Rapid Update Cycle (RUC; Benjamin et al. 2004) and time-dependent neighborhoods. For the time- hourly analyses. Next, the watershed transform tech- independent verification, square neighborhoods of 50-, nique implemented in the WDSS-II w2segmotionll tool 100-, and 200-km side half-lengths are considered. For (Lakshmanan et al. 2009) is used to identify individ- the time-dependent verification, a square neighborhood ual features that encompass at least four contiguous of 100-km side half-length and 61-h time window is pixels with reflectivity $35 dBZ on a single WDSS-II considered for consistency with the deterministic veri- scale with no data smoothing. These features are then fication described below. This completes the process tracked forward in time (Lakshmanan and Smith 2010), of obtaining the observed neighborhood probabilities. from which feature motion estimates are obtained For the Control and Updated ensembles, however, a (Lakshmanan and Smith 2010; Lakshmanan et al. 2014). neighborhood ensemble probability is then obtained for Unique features that persist for at least 30 min are each case following Schwartz et al. (2010) by averaging classified as CI events, with CI time and location set to the individual ensemble member modeled event frac- their specific values at the start of the 30-min evalua- tions. From these quantities, FSS is computed following tion period. While this procedure primarily identifies Roberts and Lean (2008): isolated CI events (e.g., separate from ongoing deep, moist convection), distinct features within or adjacent MSE FSS 5 1 2 , (1) to existing regions of deep, moist convection meeting MSEref the thresholds described above are also identified as

CI events. where MSE, or mean-square error, and MSEref,ora To identify simulated CI events, forecast reflectivity at reference MSE that represents the maximum-possible the 2108C level of each ensemble member is computed in- MSE for the observed neighborhood and modeled line with model execution every 5 min. These data are neighborhood ensemble probabilities, are given by then bilinearly interpolated to the same grid as the ob- N served data, upon which the same WDSS-II algorithms Nx y 5 1 å å 2 2 described above are used to identify and track model- MSE (Oi,j Mi,j) , (2a) N N i51 j51 simulated CI events. As implemented here, bilinear in- x y 2 3 N N terpolation computes the average of the four simulated Nx y Nx y 5 1 4å å 2 1 å å 25 grid points nearest the grid point to which the data are MSEref (Oi,j) (Mi,j) , (2b) N N i51 j51 i51 j51 being interpolated, with the contribution of each simu- x y lated grid point weighted by its distance from the interpolated grid point. It should be noted, however, that no where Nx is the number of east–west grid points in the further attempt is made to sample the model data in a verification domain, Ny is the number of north–south grid similar manner to that of the observations (e.g., to account points in the verification domain, Oi,j is the observed for radar gaps, terrain blocking, and nonuniform vertical neighborhood probability at grid point (i, j), and Mi,j is the data distribution). As most observed and modeled CI modeled neighborhood ensemble probability at grid point events for the set of MPEX cases considered occur east of (i, j). To our knowledge, this study is the first attempt to elevated terrain, this is believed to have minimal effect on probabilistically verify CI forecasts, independent of fore- the results. cast lead time, although Mecikalski et al. (2015) describe a probabilistic, very-short-range CI nowcast algorithm that c. Verification metrics is largely verified deterministically. Both probabilistic and deterministic forecast verifi- Deterministic verification uses the flow-dependent cation metrics are used in this study. Probabilistic fore- error metric of Burghardt et al. (2014) to quantify the cast skill is evaluated using fractions skill score (FSS; proximity between modeled and observed events in Roberts and Lean 2008; Schwartz et al. 2010). To com- both time and space for each ensemble member and pute FSS, observed and modeled CI event locations, the event. The error metric takes the following form: latter for all ensemble members for both Control and 2 5 2 1 3 2 Updated ensembles, are first used to define binary event C (Errord) (Velocityc Errort) , (3)

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TABLE 2. 2 3 2 contingency table. ranges from 0 to 1, with higher values indicating a greater number of false positives. Bias [Eq. (6)] is the Event observed ratio of total forecast CI events to total observed CI Yes No events; values less (greater) than 1 indicate fewer (more) Event forecast Yes abforecast than observed events. Finally, critical success No cdindex [CSI; Eq. (7)], or threat score, is the ratio of correctly forecast CI events to the total number of observed and forecast CI events, with a value of 1 representing a where C is the spatiotemporal error (km), Errord is the perfect forecast: spatial difference (km) between the modeled and ob- a served CI events, and Errort is the temporal error (h) POD 5 , (4) a 1 c between the modeled and observed CI events. Velocityc 2 is the translation velocity (km h 1) of the observed re- 5 b FAR 1 , (5) flectivity object associated with the observed CI event a b a 1 b being considered; this differs from Burghardt et al. bias 5 , (6) (2014), which estimated translation velocity using a a 1 c layer-mean wind. The flow dependence allows for tim- 5 a CSI 1 1 . (7) ing errors to be collapsed into the spatial dimension a b c while making allowance for storm motion variation (Burghardt et al. 2014). Next, spatiotemporal thresholds of maximum Errord and maximum Errort of 50 km 2 2 2 (0.5 h) 1, 100 km (1 h) 1, and 200 km (2 h) 1 are applied. 3. Forecast evaluation 2 Particular focus is given to the 100 km (1 h) 1 threshold a. Overall CI event characteristics as it represents a balance of space and time errors on the mesoscale and allows for comparison to the results of Both observed and modeled CI events most fre- Burghardt et al. (2014). The pairing between observed quently occur during the local late afternoon to early and modeled CI events with the lowest C is designated evening hours (Fig. 2a). The Control and Updated en- as the match or hit for the observed event, presuming the sembles each forecast approximately 2 times more CI modeled CI event is not a better match or hit to a dif- events than are observed, with slightly increased spread ferent observed event. Thus, the C metric provides a in event count within the Updated ensemble relative to measure of match goodness (i.e., a lower C error the Control that may result from an increase in analysis implies a modeled CI event closer in time and space to spread in the Updated relative to Control ensemble near an observed CI event). dropsonde locations (Romine et al. 2016). For a 2 Deterministic verification is performed over the entire matching threshold of 100 km (1 h) 1, hits most fre- radar analysis domain in Fig. 1 using a 2 3 2 contingency quently occur during the local late afternoon hours table for dichotomous yes–no forecasts (Wilks 1995; (2000–2300 UTC; Figs. 2c,d). Misses most frequently Table 2). Multiple previous studies have used this ap- occur during the local early evening hours (2200–0000 proach to verify forecasts of convection occurrence or UTC), while false alarms most frequently occur during initiation (e.g., Fowle and Roebber 2003; Kain et al. the local middle to late afternoon hours (1900–2200 2013; Burghardt et al. 2014). Herein, true positives (or UTC). Hit, miss, and false alarm probabilities and event matches; a), false positives (or false alarms; b), and false counts are approximately constant in the first 2–3 h fol- negatives (or misses; c) are identified. True negatives lowing sunset, after which time each become smaller. (d), or correct forecasts of nonevents, are not evaluated Given the high forecast bias in Fig. 2a, it is perhaps due to the ambiguity in defining nonevents from an unsurprising that false alarms occur nearly twice as object-based event identification method. Following frequently as hits at all forecast times (Fig. 2d). Hits Roebber (2009), four quality measures are derived from occur slightly more frequently than misses during the contingency table classifications. Probability of de- local daytime hours; the inverse is true after sunset. tection [POD; Eq. (4)] is the ratio of correctly forecast There are no significant differences in event probability CI events to the total number of observed CI events and or count between the Control and Updated ensembles ranges from 0 to 1, with higher values indicating a higher over the set of MPEX cases (Figs. 2c,d). The ambient proportion of correctly forecast CI events. False alarm environments supporting CI vary substantially between ratio [FAR; Eq. (5)] is the ratio of unobserved forecast cases, resulting in significant CI timing, location, and CI events to the total number of forecast CI events and frequency differences from one MPEX case to another

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FIG. 2. (a) CI event cumulative distribution, in 5-min intervals, averaged over all 15 MPEX events for observations (black), the Control ensemble (blue), and the Updated ensemble (red). Bars represent 61standard deviation about the mean value. The Control and Updated ensemble values represent the mean of all ensemble member forecasts over all MPEX events. (b) As in (a), but for RF6, RF10, and RF12, as labeled at the end of each line. (c) Probability distribution functions averaged over all 15 MPEX events for hits (black), misses (blue), 2 and false alarms (red), each as deﬁned at the 100 km (1 h) 1 spatiotemporal matching threshold, for the Control (dashed) and Updated (solid) ensembles. (d) As in (c), but for event counts rather than probabilities.

(e.g., Fig. 2b for frequency). Despite this, the Control individual cases (Fig. 3b). When event timing is con- and Updated ensembles each forecast too many CI sidered, average FSS is largest in the local evening hours events relative to observations for all 15 cases, as is and lowest in the local afternoon and overnight hours demonstrated further in section 3b. In addition, no sig- (Fig. 3c). Significant forecast skill variation about the nificant differences between the Control and Updated average FSS exists between cases at all forecast times ensembles exist for any of the metrics presented above (Figs. 3c,d). Despite the use of a neighborhood half-width (not shown), such that in the verification, the Control twice as large as Romine et al. (2016), average FSS with and Updated ensembles are more like each other than time is lower for CI than for accumulated precipitation at 2 they are to observations, as in Romine et al. (2016). 0.25, 1.0, and 10.0 mm h 1 thresholds [cf. Fig. 3c to Fig. 15 of Romine et al. (2016)]. However, it should be noted that b. Forecast skill evaluation the verification region is more expansive in the present The time-independent average FSS increases with study [cf. Fig. 1 to Fig. 11 of Romine et al. (2016)], which neighborhood size (Fig. 3a). Although the average FSS may account for part of the FSS differences. Control and is relatively high for all neighborhood sizes, significant Updated ensemble performance is nearly identical at all forecast skill variation about the average FSS exists forecast times when averaged over all 15 MPEX cases between MPEX cases. Control and Updated FSS per- (Fig. 3c), but larger differences emerge for individual ca- formance when averaged over all cases is nearly iden- ses at selected times (Fig. 3d). tical for each neighborhood, with small differences For a specific spatiotemporal matching threshold, noted between the Control and Updated ensembles for there is modest variability in measures of deterministic

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FIG. 3. (a) Time-independent fractions skill score vs neighborhood size (as assessed using square side half- length, such that the 50-km neighborhood represents a square with sides of 100 km), averaged over all 15 MPEX events, for the Control (blue) and Updated (red) ensembles. Boxes represent 61 standard deviation from the mean value and whiskers represent the maximum and minimum values over the set of 15 MPEX events considered. (b) Time-independent fractions skill score difference (Updated 2 Control) for the 50-, 100-, and 200-km neighborhood sizes. (c) Fractions skill score, averaged over all 15 MPEX events, for the Control (blue) and Updated (red) ensembles within a 100-km and 1-h neighborhood. Box-and-whisker plots have the same meaning as in (a). (d) Fractions skill score for RFs 6, 10, and 12 for the Control (blue) and Updated (red) ensembles within a 100-km and 1-h neighborhood. Numbers on the lines in (a), (b), and (d) refer to the corresponding MPEX event. forecast skill, including POD, FAR, CSI, and C error becomes larger (Fig. 5). This was not seen in the cases (Figs. 4 and 5; Table 3), for a given MPEX case. For most considered by Burghardt et al. (2014), where greater cases, greater variability exists between individual en- variability between cases was noted albeit with de- semble members for a given case than between cases terministic forecasts, and merits further investigation. (Fig. 5) and between the Control and Updated ensem- For all 15 RFs, mean ensemble bias is greater than unity, bles for a given case (Figs. 4 and 5; Table 3). Ensemble- indicating a mean model overproduction of CI events averaged CSI is ;0.1 at the strictest spatiotemporal that is consistent with Burghardt et al. (2014) and 2 matching threshold [50 km (0.5 h) 1], ;0.25 at an in- Coniglio et al. (2016). Mean skill of the Updated en- 2 termediate threshold [100 km (1 h) 1], and ;0.4 at the semble is nearly identical to the Control ensemble for all 2 most lenient threshold [200 km (2 h) 1]. However, as cases and veriﬁcation thresholds (Figs. 4 and 5; Table 3), the time and space thresholds become more lenient, the leading to a preliminary conclusion that the assimila- subjective utility of the forecast (such as to a forecaster) tion of MPEX dropsonde observations is not sufﬁcient decreases. Notably, ensemble-averaged CSI is approxi- to result in increased CI timing and location forecast 2 mately equal across cases at the 50 km (0.5 h) 1 and skill. 2 2 100 km (1 h) 1 matching thresholds; it is only at the At the 100 km (1 h) 1 spatiotemporal matching 2 200 km (2 h) 1 threshold where case-to-case variation threshold, average ensemble distance error and timing

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neighborhood half-width) improvements in the Up- dated ensemble are 0.03 and 0.06, respectively, for RF12 (Figs. 3b and 5). The largest mean CSI and FSS degradations in the Updated ensemble are 0.02 and 0.045, respectively, for RF6 (Figs. 3b and 5). These small differences are not statistically signiﬁcant. For each case, CSI variation between individual ensemble members is similar within the Control and Updated ensembles. The magnitude of intraensemble CSI variation is larger than the interensemble difference in ensemble-averaged CSI difference (Fig. 5; Table 3). Despite small probabilistic and deterministic skill differences between the Control and Updated ensembles, three cases are selected for further investigation: RF6 (23 May 2013, Figs. 6a,d; Romine et al. 2016), RF10 (31 May 2013, Figs. 6b,e; Torn and Romine 2015), and RF12 (8 June 2013, Figs. 6c,f; Burlingame et al. 2017). These cases differ in regards to the meso-a- to synoptic- scale environments in which CI occurs and the upstream mid- to upper-tropospheric features sampled by MPEX

FIG. 4. Performance diagram, following Roebber (2009), with dropsonde observations. As previously noted, the larg- ensemble mean skill metrics for all 15 MPEX RFs (labeled) for the est mean FSS and CSI improvement (degradation) over Updated (red) and Control (blue) ensembles at each of three the set of cases considered occurs with RF12 (RF6). RF6 21 spatiotemporal matching thresholds: 50 km (0.5 h) (squares), is a relatively poorly forecast case, consistent with the 21 21 100 km (1 h) (circles), and 200 km (2 h) (triangles). Bias is in- result in Romine et al. (2016), whereas RF12 forecast dicated by diagonal straight lines (labels along top), while CSI is indicated by curved lines (labels along right). skill is near average. In contrast, RF10 is a relatively well-forecast case, but one with a similar level of FSS and CSI degradation to RF6. error and bias for matched events are all small (Table 3). a. RF6: 23 May 2013 Mean distance error and timing bias over all matched events for the Control and Updated ensembles are RF6 sampled (Fig. 6a) the environments surrounding 2 2 45.7 km (0.9 min) 1 and 45.5 km (1.1 min) 1, respec- a lower-tropospheric cold front in the northern Texas tively, where a positive timing bias denotes the model is and western Oklahoma Panhandles (Fig. 6d) and a weak later than observations. The near-zero timing biases are short-wave trough in northern New Mexico (Fig. 6a). consistent with Kain et al. (2013), Duda and Gallus Through ensemble sensitivity analysis, these features (2013), and Burghardt et al. (2014), providing further were objectively identiﬁed to result in the greatest evidence that CP ensemble forecasts are capable of ac- forecast impact (for accumulated precipitation) over the curately simulating the CI diurnal cycle. Mean absolute Texas Panhandle later that day (Romine et al. 2016). time error for matched events for the Control and Lower-tropospheric ﬂow originating from the Gulf of Updated ensembles is 26.7 and 26.6 min, respectively. Mexico allowed for robust lower-tropospheric moisture However, these results should be interpreted in light of (e.g., as implied by 850-hPa equivalent potential the substantial frequency bias that exists for both Con- temperature $340 K) to be present across west central trol and Updated ensembles for all cases (section 3a) Texas (Fig. 6d). This, in conjunction with steep midlevel (i.e., subjective forecast utility is less than what it would lapse rates associated with an elevated mixed layer ad- be in the presence of a near-unity frequency bias). vected downwind of the southern Rockies, contributed to surface-based convective available potential energy 2 (SBCAPE) greater than 3000 J kg 1 (not shown). Con- 4. Case studies vection initiated after 1900 UTC (Fig. 2b) between Whether in a probabilistic or deterministic context, Lubbock, Texas, and Childress, Texas, along the lower- mean skill score differences between the Control tropospheric front and in southeastern New Mexico and and Updated ensembles are small. For example, the southwestern Texas along a dryline. Most observed CI 2 largest mean CSI [100 km (1 h) 1 spatiotemporal match- events on this day occurred from Childress southwest to ing threshold] and FSS (time independent; 100-km the Texas–Mexico border (Fig. 6d).

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FIG. 5. Box-and-whisker plots of average ensemble CSI scores vs average ensemble CSI difference (deﬁned as 2 2 2 Updated-minus-Control) for all 15 RFs at the (a) 50 km (0.5 h) 1, (b) 100 km (1 h) 1, and (c) 200 km (2 h) 1 spatiotemporal matching thresholds for the Control (blue) and Updated (red) ensembles. The mean CSI score for each ensemble is given by the position of the number label within each box. Boxes depict CSI values 61 standard deviation from the mean. Diamonds indicate the minimum and maximum CSI values across an ensemble.

Observed CI event neighborhood probability is more similar to each other than either ensemble is to highest in far western Texas, with a secondary maxi- observations (cf. Figs. 7a,b). mum to the east from the southern Texas Panhandle Assimilating MPEX dropsonde observations for RF6 southward to the Rio Grande River (Fig. 7a). Both increases surface-based CAPE in west Texas and east- Control and Updated ensembles poorly forecast CI ern New Mexico in the Updated ensemble mean relative event probabilities, with each predicting the highest to the Control ensemble mean (Figs. 8a–c and 9). Both neighborhood ensemble probabilities in far northern are associated with higher surface-based CAPE than the Mexico, west-central Texas, and along the northeastern corresponding Rapid Refresh model analysis (Fig. 8d). Oklahoma–southeastern Kansas border (Fig. 7b). Note, Consequently, CI occurs farther to the west (e.g., Fig. 8c), however, that terrain blocking and radar proximity on average, in Updated relative to both Control and hinder observed CI event identiﬁcation in northern observations (Figs. 7b and 8d). Subsequent simulated CI Mexico, with satellite imagery (not shown) suggesting events that occur along the southward-spreading out- an observed CI frequency in northern Mexico similar to ﬂow boundary from this initial convection (not shown) the Control and Updated ensembles. Forecast skill occur farther to the northwest in Updated relative to differences between ensembles (Fig. 3) are primarily both Control and observations (Fig. 7b). Assimilating manifest in southwestern Texas, where Control en- MPEX dropsonde observations for RF6 also decreases semble neighborhood probability is higher, and west- surface-based CAPE, while increasing surface-based central Texas, where maximum Control ensemble CIN, in far southwestern Texas (Figs. 8a–c and 9). neighborhood probabilities are located farther south- This mitigates CI potential relative to Control and, east (Fig. 7b). The Control and Updated ensembles are again, observations (Fig. 7a). Whereas surface-based

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21 TABLE 3. Average ensemble CSI and C error (km) for both Control and Updated ensembles at the 100 km (1 h) spatiotemporal matching threshold for each of the 15 MPEX RFs considered. The average values over all 15 events and all ensemble members at the 2 2 2 50 km (0.5 h) 1, 100 km (1 h) 1, and 200 km (2 h) 1 thresholds for both Control and Updated Ensembles are provided at bottom.

RF Date (2013) Dropsondes Control CSI Updated CSI Control C error (km) Updated C error (km) 1 15 May 27 0.26 0.29 53.9 53.6 2 16 May 30 0.25 0.25 50.1 48.6 3 18 May 17 0.29 0.28 52.5 52.5 4 19 May 29 0.33 0.34 51.5 50.0 5 21 May 27 0.24 0.26 53.4 53.7 6 23 May 29 0.14 0.13 54.6 56.6 7 27 May 29 0.17 0.16 52.6 52.6 8 28 May 21 0.27 0.28 52.8 52.2 9 30 May 26 0.26 0.26 52.4 52.7 10 31 May 28 0.30 0.29 49.2 50.3 11 03 Jun 32 0.23 0.23 46.0 43.7 12 08 Jun 31 0.22 0.25 54.7 53.3 13 11 Jun 33 0.26 0.27 47.9 48.2 14 12 Jun 33 0.15 0.15 52.3 51.8 15 14 Jun 33 0.29 0.29 45.6 45.2 2 100 km (1 h) 1 0.24 0.24 51.3 51.0 2 50 km (0.5 h) 1 0.09 0.10 31.4 31.6 2 200 km (2 h) 1 0.40 0.40 73.8 72.9

CAPE differences between the Updated and Control MCS that caused deadly flash flooding near Oklahoma ensemble means are relatively consistent with time City, Oklahoma, during the evening (Torn and Romine (Figs. 8a–c), coherent differences in the surface-based 2015; Schumacher 2015). CIN field in southwest Texas and southeast New Mexico Observed CI event neighborhood probability is at the time of CI emerge do not emerge until later highest along the cold front (Fig. 6e) from southwest (cf. Figs. 9a,b). Further investigation is needed to elu- Missouri west-southwestward to west-central Oklahoma cidate the causes of the disparate response between (Fig. 7c). Along this well-defined triggering mechanism, these two thermodynamic fields. both Updated and Control ensembles reasonably forecast CI event probability (Fig. 7d). Both the Control and b. RF10: 31 May 2013 Updated ensembles have high forecast event probabil- RF10 sampled (Fig. 6b) a strong upper-tropospheric ities in northern Iowa (Fig. 7d), however, where ob- westerly jet streak over the central high plains to the served event probability was comparatively low. south of a vertically stacked cyclone over South Dakota Forecast skill differences between ensembles (Fig. 3) (Fig. 6b; Torn and Romine 2015). In the lower tropo- primarily manifest in northern Iowa and far southwest- sphere, a cold front extended south-southwestward ern Oklahoma, with lower ensemble neighborhood from the vertically stacked cyclone through northwest- probability in Control relative to Updated in both lo- ern Oklahoma to the southern Texas Panhandle, with cations (Fig. 7d). Albeit to lesser extent than with RF6, lower-tropospheric flow originating in the western Gulf the Control and Updated ensembles are again more of Mexico promoting a warm, moist boundary layer similar to each other than either is to observations ahead of the cold front (Fig. 6e). By 2100 UTC, (cf. Figs. 7c,d). Over the set of cases considered, the SBCAPE values along this front reached upward of fewest number of observed CI events occurs with RF10 2 4500 J kg 1 in Oklahoma (not shown). On this day, CI (Figs. 2a,b), contributing in part to the comparatively first occurred in eastern Kansas and Missouri along the high CSI spread across the Control and Updated en- cold front by 1930 UTC (Figs. 2b and 6e), with sub- sembles for this case (Fig. 5). sequent events occurring in west-central Oklahoma af- Assimilating MPEX dropsonde observations for ter 2100 UTC near the intersection of the cold front and RF10 shifts the simulated cold front slightly westward, dryline. It is along this corridor where most observed CI shifts a simulated dryline bulge in the southeastern events occurred on this day (Fig. 6e). Discrete supercells Texas Panhandle southward, and sharpens the simu- produced by the initial convection in west-central lated horizontal surface-based CAPE gradient across Oklahoma led to destructive tornadoes near El Reno, the dryline along the Oklahoma–Texas border in Up- Oklahoma, before transitioning into a quasi-stationary dated relative to Control (Fig. 10). This results in

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FIG. 6. Rapid Refresh–derived (a)–(c) 500-hPa height (contours; every 30 dam), horizontal wind speed (shaded per top color bar in kt), and horizontal wind (barbs; half flag: 5 kt, flag: 10 kt, and pennant: 50 kt) and (d)–(f) mean sea level pressure (contours; every 4 hPa), 850-hPa equivalent potential temperature (shaded per bottom color bar in K), and 10-m horizontal wind (barbs; half flag: 5 kt, flag: 10 kt, and pennant: 50 kt) at 1800 UTC (a),(d) 23 May 2013 (RF6); (b),(e) 31 May 2013 (RF10); and (c),(f) 8 Jun 2013 (RF12). The flight path followed to collect targeted dropsonde observations for each case is depicted in (a)–(c) (black line, last hour in red). Observed CI events over the period 1500–0600 UTC for each case are depicted in (d)–(f) (black dots). In (a), (c), and (f), trough axes are depicted by the thick black dashed lines. In (b), an upper-tropospheric jet streak is depicted by the curved white vector. In (d),(e), cold fronts and drylines are depicted by the thick solid blue and brown lines, respectively. Please refer to the text for further details.

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FIG. 7. Observed CI event neighborhood probability within 6100 km (shaded; units: % of grid points within the neighborhood) between 1800 and 0600 UTC for (a) RF6, (c) RF10, and (e) RF12. Control CI event neighborhood ensemble probability within 6100 km (shaded; units: % of grid points within the neighborhood) between 1800 and 0600 UTC and Updated-minus-Control CI event neighborhood ensemble probability (contours; units: % of grid points within the neighborhood) for (b) RF6, (d) RF10, and (f) RF12. elevated CI probabilities in southwest Oklahoma in vorticity (Fig. 11a) differences between the Updated Updated relative to both Control and observations and Control ensemble means are small at initialization (Figs. 7c,d). In Iowa, assimilating MPEX dropsonde and grow in both spatial extent and magnitude with time observations for RF10 shifts a simulated short wave (Figs. 10b,c and 11b,c). embedded within the synoptic-scale upper-tropospheric c. RF12: 8 June 2013 cyclonic ﬂow slightly eastward relative to Control (Fig. 11a); however, both Updated and Control are too RF12 sampled (Fig. 6c) the environment surrounding fast, on average, with the eastward translation of this an upper-tropospheric short-wave trough and accom- feature relative to Rapid Refresh analyses (Fig. 11d). panying northwesterly ﬂow across the northern In- Convection initiated ahead of this feature in northern termountain West and high plains (Fig. 6c). Ahead of Iowa during the local afternoon hours in both Updated this trough, a lower-tropospheric wind shift is noted, and Control ensembles (Figs. 7d and 11a), whereas only extending south-southwest from central South Dakota congested cumulus occurred in association with the to western Kansas, from which it extended west along observed feature (not shown). In contrast to RF6, the Colorado–New Mexico border (Fig. 6f). Ahead of surface-based CAPE (Fig. 10a) and 300 hPa potential this feature, the boundary layer was not as warm or as

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21 FIG. 8. Updated-minus-Control ensemble mean surface-based CAPE (shaded per top color bar; units: J kg ), 2 Control ensemble mean surface-based CAPE (contours; units: J kg 1), Control ensemble mean 10-m wind (gray barbs; half flag: 5 kt, flag: 10 kt, pennant: 50 kt), and Control ensemble member 1 reflectivity on the 2108C surface (shaded per bottom color bar; units: dBZ) at (a) 1500 UTC, (b) 1800 UTC, and (c) 2000 UTC 23 May 2013 for RF6. 2 (d) Rapid Refresh–analyzed surface-based CAPE (contours; units: J kg 1), 10-m wind (gray barbs; half flag: 5 kt, flag: 10 kt, pennant: 50 kt), and observed reflectivity on the 2108C surface (shaded per bottom color bar; units: dBZ) at 2000 UTC 23 May 2013 for RF6. moist as in RF6 and RF10, with 850-hPa equivalent southeast Nebraska (Fig. 7e). Although the Control potential temperature generally between 330 and 340 K and Updated ensembles each forecast CI along this in the warm sector in Nebraska and Kansas (Fig. 6f), boundary, the highest ensemble neighborhood proba- 2 contributing to SBCAPE of up to 2000 J kg 1 (not bilities are found between central Kansas and southeast shown). Widespread CI occurred along this feature from Nebraska in each, with secondary maxima farther far southwestern Iowa to the Texas Panhandle (Fig. 6f; southwest from central Kansas to the Texas Panhandle Burlingame et al. 2017), with the first observed CI events (Fig. 7f). Furthermore, both Control and Updated en- along this feature occurring in northern Kansas by 2000 sembles have high forecast event probabilities in west- UTC. A few instances of CI occurred before 2000 UTC ern Nebraska (Fig. 7f) near the surface low (Fig. 6f) (Fig. 2b) in northwestern Nebraska with the upper- where observed event probability was comparatively tropospheric trough itself. low. Forecast skill differences between ensembles Observed CI event probability is highest along the (Fig. 3) primarily manifest in western Nebraska, where southeastward-advancing wind shift (Fig. 6f) from the Updated ensemble neighborhood probability is lower, Texas Panhandle to central Kansas, with a secondary and from southwestern Kansas to western Oklahoma, maximum farther northeast from central Kansas to where Updated ensemble neighborhood probability is

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FIG. 9. Updated-minus-Control ensemble mean surface-based convective inhibition (CIN, shaded per bottom 2 2 color bar; units: J kg 1). Control ensemble mean surface-based CIN (contoured at 10, 25, and 50 J kg 1), Control ensemble mean 10-m wind (gray barbs; half flag: 5 kt, flag: 10 kt, pennant: 50 kt), and Control ensemble member 1 reflectivity on the 2108C surface (shaded per top color bar; units: dBZ) at (a) 1500 UTC and (b) 1800 UTC 23 May 2013 for RF6. Simulated Control ensemble mean dryline positions are indicated by the thick brown lines in each panel. higher (Fig. 7f). As with RF6 and RF10, the Control and whereas only shallow convection is found in observa- Updated ensembles are more similar to each other than tions (Fig. 13). This may be related to the chosen mi- either is to observations (cf. Figs. 7e,f). crophysics parameterization and its influence upon the Assimilating MPEX dropsonde observations for computation of simulated reflectivity (e.g., Stratman RF12 reduces simulated surface-based CAPE along et al. 2013), although further investigation is warranted. and ahead of the simulated lower-tropospheric wind shift in Nebraska and north-central Kansas (Fig. 12), 5. Summary and discussion mitigating simulated CI frequency in Nebraska in Up- dated relative to Control (Fig. 7f). Further, assimilating This study tested the hypothesis that assimilating mid- MPEX dropsonde observations for RF12 increases to upper-tropospheric, meso-a- to synoptic-scale MPEX simulated surface-based CAPE in southwestern Kansas, dropsonde observations collected in upstream, pre- the Oklahoma and Texas Panhandles, and eastern New convective environments is insufficient to improve Mexico in Updated relative to Control (Fig. 12). This short-range (3–15 h) CI forecast skill due to a limited results in the increased CI probabilities in Updated influence upon the lower-tropospheric phenomena that relative to Control (Fig. 7f) from southwestern Kansas modulate CI occurrence, timing, and location. Output to western Oklahoma during the forecast. Surface-based from two 30-member, convection-permitting WRF- CAPE differences between the Updated and Control ARW ensembles, one each in which MPEX dropsonde ensemble means at initialization (Fig. 12a) quickly grow observations were (Updated) and were not (Control) in magnitude and scale (Figs. 12b,c), with the largest assimilated, was used to verify simulated CI forecasts difference magnitudes found along ensemble mean and test this hypothesis. Verification was conducted surface-based CAPE gradients (e.g., southeast Colo- over multiple mesoscale spatiotemporal thresholds us- rado, northeast Nebraska) or the lower-tropospheric ing FSS to assess probabilistic forecast skill and a flow- wind shift (e.g., central Nebraska, west Kansas). Note dependent metric to match modeled CI events from also that both Updated and Control forecast isolated CI each ensemble member to observations, from which events near and ahead of the upper-tropospheric trough, measures of deterministic forecast skill (POD, FAR,

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21 FIG. 10. Updated-minus-Control ensemble mean surface-based CAPE (shaded per bottom color bar; units: J kg ). Control ensemble 2 mean surface-based CAPE (contoured; units: J kg 1), Control ensemble mean 10-m wind (gray barbs; half flag: 5 kt, flag: 10 kt, pennant: 50 kt), and Control ensemble member 1 reflectivity on the 2108C surface (shaded per top color bar; units: dBZ) at (a) 1500 UTC, (b) 1800 UTC, and (c) 2100 UTC 31 May 2013 for RF10. Simulated Control ensemble mean cold front and dryline positions are indicated by the thick blue and brown lines, respectively, in each panel. and CSI) were computed. Consistent with prior studies et al. 2017). In an evaluation of simulations conducted (e.g., Duda and Gallus 2013; Burghardt et al. 2014), the during the 2009 and 2010 Hazardous Weather Testbed CI diurnal cycle was well predicted for all cases. Spring Forecasting Experiments, Stratman et al. (2013) Forecast CI event count was large relative to obser- demonstrated a high-frequency bias for the occurrence vations in both ensembles for all cases considered, a result of .40-dBZ simulated reflectivity values in 0–12-h that is consistent with prior studies (e.g., Burghardt et al. WRF-ARW simulations. A similar high-frequency 2014; Coniglio et al. 2016). There likely exist multiple bias is noted for RF6, RF10, and RF12 in this study causes for this high-frequency bias. For 3 of the 15 cases (not shown). Notably, the simulations of Stratman et al. considered herein, Burlingame et al. (2017) demon- (2013), Burghardt et al. (2014), Coniglio et al. (2016), strated this result to be robust to the choice of PBL pa- and this study each use the Thompson et al. (2008) mi- rameterization, with largest frequency bias for local crophysics parameterization. This suggests that at least closure parameterizations that forecast moister yet part of the high forecast CI event frequency bias is related slightly cooler daytime boundary layers (Burlingame to microphysics parameterization and its influence on

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FIG. 11. Updated-minus-Control ensemble mean 300-hPa potential vorticity (shaded per top color bar; units: 2 2 2 PVU; 1 PVU 5 10 6 Kkg 1 m2 s 1), Control ensemble mean 300-hPa potential vorticity (contoured; units: PVU), Control ensemble mean 300-hPa wind (gray barbs; half flag: 5 kt, flag: 10 kt, pennant: 50 kt), and Control ensemble member 1 reflectivity on the 2108C surface (shaded per bottom color bar; units: dBZ) at (a) 1500 UTC, (b) 1800 UTC, and (c) 2200 UTC 31 May 2013 for RF10. (d) Rapid Refresh–analyzed 300-hPa potential vorticity (contoured; units: PVU), 300-hPa wind (gray barbs; half flag: 5 kt, flag: 10 kt, pennant: 50 kt), and observed reflectivity on the 2108C surface (shaded per bottom color bar; units: dBZ) at 2200 UTC 31 May 2013 for RF10. The upper-tropospheric short-wave trough referred to in the text is indicated by black circles in (b)–(d). simulated reflectivity. However, it is likely that other probabilistic CI forecast skill was lower than for accu- modeling system components such as horizontal grid mulated precipitation for the 15 cases considered herein spacing (e.g., Bryan and Morrison 2012) also contribute and by Romine et al. (2016). Probabilistic forecast skill to this result. Further investigation is warranted into was largest near the peak of the CI diurnal cycle in the the relative contributions of different sources of model local evening hours, with lesser skill noted in the after- error, including its manifestation in the cycled analysis noon and overnight. Though ensemble forecasts could system as well as subsequent free forecasts, toward the reasonably predict the mesoscale regions in which CI forecast CI event bias (and other forecast characteristics) would occur at approximately the correct time, they documented herein. were unable to predict precisely when and where a given Deterministic forecast skill for the cases considered CI event would occur within those corridors. This is herein was of similar magnitude to that described in generally consistent with the findings of previous CI previous works that used similar methodologies to study predictability investigations with convection-allowing CI predictability (e.g., Duda and Gallus 2013; Kain et al. models (Duda and Gallus 2013; Kain et al. 2013; 2013; Burghardt et al. 2014). Although subjectively high, Burghardt et al. 2014). Further, while forecast skill was

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FIG. 12. As in Fig. 10, but at (a) 1500 UTC, (b) 1800 UTC, and (c) 2100 UTC 8 Jun 2013 for RF12. Thick dashed black lines indicate the Control ensemble mean position of a wind shift referred to in the text. higher at more lenient verification thresholds, such the CI event locations relative to the areas covered by the forecasts are expected to have limited forecaster utility. accumulated precipitation thresholds of Romine et al. On average and for each case considered, assimilating (2016) indicates substantial differences, with lower- MPEX dropsonde observations had negligible impact on threshold regions including light precipitation associ- all CI forecast skill measures considered. This impact is ated with larger-scale phenomena and higher-thresholds similar if somewhat reduced to the small positive impact regions including only heavy precipitation associated (#0.02 FSS) in Romine et al. (2016) for accumulated with organized convective systems (not shown). Al- precipitation over the same cases using identical simula- though this too requires further investigation, we hy- tion methods. This likely results, in part, from the dif- pothesize that the greater implied synoptic-scale control ferent forecast feature of interest between the two on the accumulated precipitation field than the CI event studies, with accumulated precipitation being that for field (as described in the introduction) is responsible for which observations were specifically targeted to improve. the small increase in forecast skill in Romine et al. (2016) Further investigation is necessary to determine the extent but no change in skill in the present study. to which forecast sensitivity for CI is different from that The forecast impact from assimilating MPEX drop- for accumulated precipitation. However, an analysis of sonde observations is also similar if somewhat reduced

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FIG. 13. (a) Control ensemble mean 500-hPa height (contoured; units: m) at 2000 UTC 8 Jun 2013 and Control ensemble member 1 reflectivity on the 2108C surface (shaded; units: dBZ) at 1930 UTC 8 Jun 2013 for RF12. (b) Rapid Refresh–analyzed 500-hPa height (contoured; units: m) at 2000 UTC 8 Jun 2013 and observed reflectivity on the 2108C surface (shaded; units: dBZ) at 1930 UTC 8 Jun 2013 for RF12. The Control ensemble mean 500-hPa trough axis referred to in the text is depicted by the thick dashed line in each panel. to that identified by prior observation targeting studies occurred, with case-to-case variability noted in the for tropical cyclones and synoptic-scale midlatitude cy- magnitude and spatial extent of initial differences as clones (e.g., Bergot 1999; Montani et al. 1999; Fourrié well as their subsequent growth between the Updated et al. 2006; Wu et al. 2007; Rabier et al. 2008; Aberson and Control ensembles. Assimilating MPEX dropsonde 2010; Chou et al. 2011; Majumdar 2016). Targeted observations also modified ambient thermodynamic observation assimilation did not always improve short- conditions along and ahead of these lower-tropospheric range CI predictions for the 12–13 June 2002 IHOP boundaries, manifest primarily on the meso-b scale for event studied by Liu and Xue (2008), which is also con- RF6 and RF10 and on the meso-a to synoptic scale for sistent with the results presented here despite significant RF12. RF6 was a case where MPEX dropsonde obser- differences in modeling systems, data assimilation and vations poorly sampled the preconvective upstream observation targeting methods, collected observation disturbance and its environment (Romine et al. 2016), types, and case characteristics between the two studies. whereas RF12 was a case where MPEX dropsonde ob- However, it should be noted that the specific findings servations better sampled the preconvective upstream presented here are formally only valid for the model disturbance and its environment. The extent to which configuration, selected cases, observation targeting the spatial scale of analysis and forecast differences re- strategies, and verification metrics used herein. sulting from the assimilation of MPEX dropsonde ob- Three cases were investigated in more detail: RF6, a servations is directly correlated with how well a given poorly forecast case with reduced skill in Updated rel- feature was sampled merits further investigation with ative to Control; RF10, a well-forecast case with reduced a larger sample of cases. For all cases, however, the skill in Updated relative to Control; and RF12, an av- Updated and Control ensembles were more alike than erage skill case with increased skill in Updated relative either was to observations. to Control. For each of the three cases considered, as- As finite large-scale initial condition errors are a sig- similating MPEX dropsonde observations resulted in nificant contributor to short-range convective-scale er- small shifts in the positions of lower-tropospheric at- ror growth (e.g., Durran and Weyn 2016), it is probable mospheric boundaries along which CI preferentially that the synoptic-scale phenomena to which CI forecasts

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