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Stephen F. Corfidi, Michael C. Coniglio, Ariel E. Cohen, and Corey M. Mead

Abstract

The word “derecho” was first used by Gustavus Hinrichs in 1888 to distinguish the widespread damaging windstorms that occurred on occasion over the mid–Mississippi Valley region of the United States from damaging winds associated with tornadoes. The term soon fell into disuse, however, and did not appear in the literature until Robert Johns and William Hirt resurrected it in the mid-1980s.

While the present definition of derecho served well during the early years of the term’s reintroduction to the meteorological community, it has several shortcomings. These have become more apparent in recent years as various studies shed light on the physical processes responsible for the production of widespread damaging winds. In particular, the current definition’s emphasis on the coverage of storm reports at the expense of identifying the convective structures and physical processes deemed responsible for the reports has led to the term being applied to wind events beyond those for which it originally was intended.

The revised definition of a derecho proposed herein is intended to focus more specifically on those types of windstorms that are the most damaging and potentially life threatening because of their intensity, sustenance, and degree of organization. The proposal is not intended to be final or all encompassing, but rather an initial step toward ultimately realizing a more complete physically based taxonomy that also addresses other forms of damaging-wind-producing convective systems.

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Ariel E. Cohen, Steven M. Cavallo, Michael C. Coniglio, Harold E. Brooks, and Israel L. Jirak

Abstract

Southeast U.S. cold season severe weather events can be difficult to predict because of the marginality of the supporting thermodynamic instability in this regime. The sensitivity of this environment to prognoses of instability encourages additional research on ways in which mesoscale models represent turbulent processes within the lower atmosphere that directly influence thermodynamic profiles and forecasts of instability. This work summarizes characteristics of the southeast U.S. cold season severe weather environment and planetary boundary layer (PBL) parameterization schemes used in mesoscale modeling and proceeds with a focused investigation of the performance of nine different representations of the PBL in this environment by comparing simulated thermodynamic and kinematic profiles to observationally influenced ones. It is demonstrated that simultaneous representation of both nonlocal and local mixing in the Asymmetric Convective Model, version 2 (ACM2), scheme has the lowest overall errors for the southeast U.S. cold season tornado regime. For storm-relative helicity, strictly nonlocal schemes provide the largest overall differences from observationally influenced datasets (underforecast). Meanwhile, strictly local schemes yield the most extreme differences from these observationally influenced datasets (underforecast) in a mean sense for the low-level lapse rate and depth of the PBL, on average. A hybrid local–nonlocal scheme is found to mitigate these mean difference extremes. These findings are traced to a tendency for local schemes to incompletely mix the PBL while nonlocal schemes overmix the PBL, whereas the hybrid schemes represent more intermediate mixing in a regime where vertical shear enhances mixing and limited instability suppresses mixing.

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Michael C. Coniglio, Harold E. Brooks, Steven J. Weiss, and Stephen F. Corfidi

Abstract

The problem of forecasting the maintenance of mesoscale convective systems (MCSs) is investigated through an examination of observed proximity soundings. Furthermore, environmental variables that are statistically different between mature and weakening MCSs are input into a logistic regression procedure to develop probabilistic guidance on MCS maintenance, focusing on warm-season quasi-linear systems that persist for several hours. Between the mature and weakening MCSs, shear vector magnitudes over very deep layers are the best discriminators among hundreds of kinematic and thermodynamic variables. An analysis of the shear profiles reveals that the shear component perpendicular to MCS motion (usually parallel to the leading line) accounts for much of this difference in low levels and the shear component parallel to MCS motion accounts for much of this difference in mid- to upper levels. The lapse rates over a significant portion of the convective cloud layer, the convective available potential energy, and the deep-layer mean wind speed are also very good discriminators and collectively provide a high level of discrimination between the mature and dissipation soundings as revealed by linear discriminant analysis. Probabilistic equations developed from these variables used with short-term numerical model output show utility in forecasting the transition of an MCS with a solid line of 50+ dBZ echoes to a more disorganized system with unsteady changes in structure and propagation. This study shows that empirical forecast tools based on environmental relationships still have the potential to provide forecasters with improved information on the qualitative characteristics of MCS structure and longevity. This is especially important since the current and near-term value added by explicit numerical forecasts of convection is still uncertain.

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Ariel E. Cohen, Michael C. Coniglio, Stephen F. Corfidi, and Sarah J. Corfidi

Abstract

The prediction of the strength of mesoscale convective systems (MCSs) is a major concern to operational meteorologists and the public. To address this forecast problem, this study examines meteorological variables derived from sounding observations taken in the environment of quasi-linear MCSs. A set of 186 soundings that sampled the beginning and mature stages of the MCSs are categorized by their production of severe surface winds into weak, severe, and derecho-producing MCSs. Differences in the variables among these three MCS categories are identified and discussed. Mean low- to upper-level wind speeds and deep-layer vertical wind shear, especially the component perpendicular to the convective line, are excellent discriminators among all three categories. Low-level inflow relative to the system is found to be an excellent discriminator, largely because of the strong relationship of system severity to system speed. Examination of the mean wind and shear vectors relative to MCS motion suggests that cell propagation along the direction of cell advection is a trait that separates severe, long-lived MCSs from the slower-moving, nonsevere variety and that this is favored when both the deep-layer shear vector and the mean deep-layer wind are large and nearly parallel. Midlevel environmental lapse rates are found to be very good discriminators among all three MCS categories, while vertical differences in equivalent potential temperature and CAPE only discriminate well between weak and severe/derecho MCS environments. Knowledge of these variables and their distribution among the different categories of MCS intensity can be used to improve forecasts and convective watches for organized convective wind events.

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Ariel E. Cohen, Steven M. Cavallo, Michael C. Coniglio, and Harold E. Brooks

Abstract

The representation of turbulent mixing within the lower troposphere is needed to accurately portray the vertical thermodynamic and kinematic profiles of the atmosphere in mesoscale model forecasts. For mesoscale models, turbulence is mostly a subgrid-scale process, but its presence in the planetary boundary layer (PBL) can directly modulate a simulation’s depiction of mass fields relevant for forecast problems. The primary goal of this work is to review the various parameterization schemes that the Weather Research and Forecasting Model employs in its depiction of turbulent mixing (PBL schemes) in general, and is followed by an application to a severe weather environment. Each scheme represents mixing on a local and/or nonlocal basis. Local schemes only consider immediately adjacent vertical levels in the model, whereas nonlocal schemes can consider a deeper layer covering multiple levels in representing the effects of vertical mixing through the PBL. As an application, a pair of cold season severe weather events that occurred in the southeastern United States are examined. Such cases highlight the ambiguities of classically defined PBL schemes in a cold season severe weather environment, though characteristics of the PBL schemes are apparent in this case. Low-level lapse rates and storm-relative helicity are typically steeper and slightly smaller for nonlocal than local schemes, respectively. Nonlocal mixing is necessary to more accurately forecast the lower-tropospheric lapse rates within the warm sector of these events. While all schemes yield overestimations of mixed-layer convective available potential energy (MLCAPE), nonlocal schemes more strongly overestimate MLCAPE than do local schemes.

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Michael C. Coniglio, James Correia Jr., Patrick T. Marsh, and Fanyou Kong

Abstract

This study evaluates forecasts of thermodynamic variables from five convection-allowing configurations of the Weather Research and Forecasting Model (WRF) with the Advanced Research core (WRF-ARW). The forecasts vary only in their planetary boundary layer (PBL) scheme, including three “local” schemes [Mellor–Yamada–Janjić (MYJ), quasi-normal scale elimination (QNSE), and Mellor–Yamada–Nakanishi–Niino (MYNN)] and two schemes that include “nonlocal” mixing [the asymmetric cloud model version 2 (ACM2) and the Yonei University (YSU) scheme]. The forecasts are compared to springtime radiosonde observations upstream from deep convection to gain a better understanding of the thermodynamic characteristics of these PBL schemes in this regime. The morning PBLs are all too cool and dry despite having little bias in PBL depth (except for YSU). In the evening, the local schemes produce shallower PBLs that are often too shallow and too moist compared to nonlocal schemes. However, MYNN is nearly unbiased in PBL depth, moisture, and potential temperature, which is comparable to the background North American Mesoscale model (NAM) forecasts. This result gives confidence in the use of the MYNN scheme in convection-allowing configurations of WRF-ARW to alleviate the typical cool, moist bias of the MYJ scheme in convective boundary layers upstream from convection. The morning cool and dry biases lead to an underprediction of mixed-layer CAPE (MLCAPE) and an overprediction of mixed-layer convective inhibition (MLCIN) at that time in all schemes. MLCAPE and MLCIN forecasts improve in the evening, with MYJ, QNSE, and MYNN having small mean errors, but ACM2 and YSU having a somewhat low bias. Strong observed capping inversions tend to be associated with an underprediction of MLCIN in the evening, as the model profiles are too smooth. MLCAPE tends to be overpredicted (underpredicted) by MYJ and QNSE (MYNN, ACM2, and YSU) when the observed MLCAPE is relatively small (large).

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Michael C. Coniglio, Glen S. Romine, David D. Turner, and Ryan D. Torn

Abstract

The ability of Atmospheric Emitted Radiance Interferometer (AERI) and Doppler lidar (DL) wind profile observations to impact short-term forecasts of convection is explored by assimilating retrievals into a partially cycled convection-allowing ensemble analysis and forecast system. AERI and DL retrievals were obtained over 12 days using a mobile platform that was deployed in the preconvective and near-storm environments of thunderstorms during the afternoon in the U.S. Great Plains. The observation locations were guided by real-time ensemble sensitivity analysis (ESA) fields. AERI retrievals of temperature and dewpoint and DL retrievals of the horizontal wind components were assimilated into a control experiment that only assimilated conventional observations. Using the fractions skill score within 25-km neighborhoods, it is found that the assimilation of the AERI and DL retrievals results in far more times when the forecasts are improved than degraded in the 6-h forecast period. However, statistical confidence in the improvements often is not high and little to no relationships between the ESA fields and the actual changes in spread and skill is found. But, the focus on convective initiation and early convective evolution—a challenging forecast problem—and the fact that frequent improvements were seen despite observations from only one system over a limited period, provides encouragement to continue exploring the benefits of ground-based profilers to supplement the current upper-air observing system for severe weather forecasting applications.

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Robert J. Trapp, David J. Stensrud, Michael C. Coniglio, Russ S. Schumacher, Michael E. Baldwin, Sean Waugh, and Don T. Conlee

Abstract

The Mesoscale Predictability Experiment (MPEX) was a field campaign conducted 15 May through 15 June 2013 within the Great Plains region of the United States. One of the research foci of MPEX regarded the upscaling effects of deep convective storms on their environment, and how these feed back to the convective-scale dynamics and predictability. Balloon-borne GPS radiosondes, or “upsondes,” were used to sample such environmental feedbacks. Two of the upsonde teams employed dual-frequency sounding systems that allowed for upsonde observations at intervals as fast as 15 min. Because these dual-frequency systems also had the capacity for full mobility during sonde reception, highly adaptive and rapid storm-relative sampling of the convectively modified environment was possible. This article documents the mobile sounding capabilities and unique sampling strategies employed during MPEX.

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Stacey M. Hitchcock, Russ S. Schumacher, Gregory R. Herman, Michael C. Coniglio, Matthew D. Parker, and Conrad L. Ziegler

Abstract

During the Plains Elevated Convection at Night (PECAN) field campaign, 15 mesoscale convective system (MCS) environments were sampled by an array of instruments including radiosondes launched by three mobile sounding teams. Additional soundings were collected by fixed and mobile PECAN integrated sounding array (PISA) groups for a number of cases. Cluster analysis of observed vertical profiles established three primary preconvective categories: 1) those with an elevated maximum in equivalent potential temperature below a layer of potential instability; 2) those that maintain a daytime-like planetary boundary layer (PBL) and nearly potentially neutral low levels, sometimes even well after sunset despite the existence of a southerly low-level wind maximum; and 3) those that are potentially neutral at low levels, but have very weak or no southerly low-level winds. Profiles of equivalent potential temperature in elevated instability cases tend to evolve rapidly in time, while cases in the potentially neutral categories do not. Analysis of composite Rapid Refresh (RAP) environments indicate greater moisture content and moisture advection in an elevated layer in the elevated instability cases than in their potentially neutral counterparts. Postconvective soundings demonstrate significantly more variability, but cold pools were observed in nearly every PECAN MCS case. Following convection, perturbations range between −1.9 and −9.1 K over depths between 150 m and 4.35 km, but stronger, deeper stable layers lead to structures where the largest cold pool temperature perturbation is observed above the surface.

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Ryan A. Sobash, John S. Kain, David R. Bright, Andrew R. Dean, Michael C. Coniglio, and Steven J. Weiss

Abstract

With the advent of convection-allowing NWP models (CAMs) comes the potential for new forms of forecast guidance. While CAMs lack the required resolution to simulate many severe phenomena associated with convection (e.g., large hail, downburst winds, and tornadoes), they can still provide unique guidance for the occurrence of these phenomena if “extreme” patterns of behavior in simulated storms are strongly correlated with observed severe phenomena. This concept is explored using output from a series of CAM forecasts generated on a daily basis during the spring of 2008. This output is mined for the presence of extreme values of updraft helicity (UH), a diagnostic field used to identify supercellular storms. Extreme values of the UH field are flagged as simulated “surrogate” severe weather reports and the spatial correspondence between these surrogate reports and actual observed severe reports is determined. In addition, probabilistic forecasts [surrogate severe probabilistic forecasts (SSPFs)] are created from each field’s simulated surrogate severe reports using a Gaussian smoother. The simulated surrogate reports are capable of reproducing the seasonal climatology observed within the field of actual reports. The SSPFs created from the surrogates are verified using ROC curves and reliability diagrams and the sensitivity of the verification metrics to the smoothing parameter in the Gaussian distribution is tested. The SSPFs produce reliable forecast probabilities with minimal calibration. These results demonstrate that a relatively straightforward postprocessing procedure, which focuses on the characteristics of explicitly predicted convective entities, can provide reliable severe weather forecast guidance. It is anticipated that this technique will be even more valuable when implemented within a convection-allowing ensemble forecasting system.

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