Search Results

You are looking at 1 - 10 of 17 items for :

  • Author or Editor: Michael C. Coniglio x
  • Weather and Forecasting x
  • Refine by Access: Content accessible to me x
Clear All Modify Search
Michael C. Coniglio

Abstract

This study uses radiosonde observations obtained during the second phase of the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) to verify base-state variables and severe-weather-related parameters calculated from Rapid Update Cycle (RUC) analyses and 1-h forecasts, as well as those calculated from the operational surface objective analysis system used at the Storm Prediction Center (the SFCOA). The rapid growth in temperature, humidity, and wind errors from 0 to 1 h seen at all levels in a past RUC verification study by Benjamin et al. is not seen in the present study. This could be because the verification observations are also assimilated into the RUC in the Benjamin et al. study, whereas the verification observations in the present study are not. In the upper troposphere, the present study shows large errors in relative humidity, mostly related to a large moist bias. The planetary boundary layer tends to be too shallow in the RUC analyses and 1-h forecasts. Wind speeds tend to be too fast in the lowest 1 km and too slow in the 2–4-km layer. RUC and SFCOA 1-h forecast errors for many important severe weather parameters are large relative to their potential impact on convective evolution. However, the SFCOA significantly improves upon the biases seen in most of the 1-h RUC forecasts for the base-state surface variables and most of the other severe-weather-related parameters, indicating that the SFCOA has a more significant impact in reducing the biases in the 1-h RUC forecasts than on the root-mean-squared errors.

Full access
Michael C. Coniglio and David J. Stensrud

Abstract

Past studies have examined the climatology of derechos and suggest very different distributions of derechos within the United States. This uncertainty in the climatology of derechos is a concern for forecasters, since knowledge of the relevant climatological information is a key piece in the forecast process. A 16-yr dataset from 1986 to 2001 is used to examine the effects that changing the method of identifying derechos may have on the interpretation of the derecho climatology. In addition, an attempt is made to visualize the favored regions of particularly intense derecho events.

The results show aspects seen in earlier climatologies, including a southern axis in the southern plains that is favored in the mid-1980s and early 1990s and a northern axis centered from the upper Mississippi River valley into Ohio that is favored in more recent years. However, altering the criteria to not require three 33 m s−1 gust reports or F1-type damage (low-end events) significantly increases the number of events that are identified in the lower Appalachians, the Ohio valley, and in portions of the southern axis, particularly in the earlier period. To a lesser extent, the inclusion of low-end events also increases the frequency values in the northern axis in the later period. The overall effect of including the low-end events is to create a distribution that still suggests both a southern and northern axis, and a shift of the primary axis from the southern plains in the early period to the upper Mississippi valley in the later period. However, the frequency values of the maxima are noticeably reduced when the low-end events are excluded. Therefore, both the length of the dataset and the criteria used to define derechos can significantly influence the resulting climatology.

High-end derechos, which require three wind gust reports (or comparable damage) exceeding 38 m s−1, appear to be favored in the northern corridor during the warm season, particularly in the later period, and are favored along the lower Mississippi River valley during the colder months in both periods.

Full access
Diego A. Alfaro and Michael C. Coniglio

Abstract

The environmental factors that drive the dissipation of linear severe-wind-producing mesoscale convective systems (MCSs) are investigated. Layer-lifting indices are emphasized, which measure convective instability in forward-propagating MCSs by considering that deep convective latent heating depends on 1) the potential latent heating within the atmospheric column, measured by the integrated CAPE (ICAPE), and 2) the dilution of buoyancy due to midtropospheric inflow, measured by the inflow fraction (IF) of convectively unstable air to total system-relative inflow. These elements are integrated to define the layer-lifting CAPE (CAPEll), which depends on environmental thermodynamics, kinematics, and the MCS’s movement vector. Radar reflectivity plots are used to subjectively identify and classify MCSs in terms of their stage (mature or dissipating) and degree of organization (highly or weakly organized). Nonparametric statistical inferences are performed on several metrics computed at maturity and dissipation from RUC/RAP analysis data, aiming to identify the most skillful indices for diagnosing three different aspects of MCS dissipation: 1) the transition from maturity to dissipation, 2) the stage of an MCS, and 3) the disorganization that characterizes the dissipating stage. In terms of MCS dissipation CAPEll is the best diagnostic. A close approximation to CAPEll is accomplished by estimating an MCS’s movement with Corfidi vectors, providing a potentially useful index in operational settings. ICAPE is the most skillful thermodynamic metric, while IF is the best kinematic discriminator of MCS stage and stage transition, suggesting the fundamental importance of layer-lifting convective instability for MCS maintenance. Layer-lifting indices are not particularly skillful at distinguishing the degree of MCS organization at maturity, which is best diagnosed by deep vertical wind shear.

Full access
Michael C. Coniglio, David J. Stensrud, and Michael B. Richman

Abstract

This study identifies the common large-scale environments associated with the development of derecho- producing convective systems (DCSs) from a large number of events. Patterns are identified using statistical clustering of the 500-mb geopotential heights as guidance. The majority of the events (72%) fall into three main patterns that include a well-defined upstream trough (40%), a ridge (20%), and a zonal, low-amplitude flow (12%), which is identified as an additional warm-season pattern. Consequently, the environmental large-scale patterns idealized in past studies only depict a portion of the full spectrum of the possibilities associated with the development of DCSs.

In addition, statistics of derecho proximity-sounding parameters are presented relative to the derecho life cycle as well as relative to the forcing for upward motion. It is found that the environments ahead of maturing derechos tend to moisten at low levels while remaining relatively dry aloft. In addition, derechos tend to decay as they move into environments with less instability and smaller deep-layer shear. Low-level shear (instability) is found to be significantly higher (lower) for the more strongly forced events, while the low-level storm-relative inflow tends to be much deeper for the more weakly forced events. Furthermore, discrepancies are found in both low- level and deep-tropospheric shear parameters between observations and the shear profiles considered favorable for strong, long-lived convective systems in idealized simulations. This study highlights the need to examine DCS simulations within more realistic environments to help reconcile these disparities in observations and idealized models and to provide improved information to forecasters.

Full access
Michael A. VandenBerg, Michael C. Coniglio, and Adam J. Clark

Abstract

This study compares next-day forecasts of storm motion from convection-allowing models with 1- and 4-km grid spacing. A tracking algorithm is used to determine the motion of discrete storms in both the model forecasts and an analysis of radar observations. The distributions of both the raw storm motions and the deviations of these motions from the environmental flow are examined to determine the overall biases of the 1- and 4-km forecasts and how they compare to the observed storm motions. The mean storm speeds for the 1-km forecasts are significantly closer to the observed mean than those for the 4-km forecasts when viewed relative to the environmental flow/shear, but mostly for the shorter-lived storms. For storm directions, the 1-km forecast storms move similarly to the 4-km forecast storms on average. However, for the raw storm motions and those relative to the 0–6-km shear, results suggest that the 1-km forecasts may alleviate some of a clockwise (rightward) bias of the 4-km forecasts, particularly for those that do not deviate strongly from the 0–6-km shear vector. This improvement in a clockwise bias also is seen for the longer-lived storms, but is not seen when viewing the storm motions relative to the 850–300-hPa mean wind or Bunkers motion vector. These results suggest that a reduction from 4- to 1-km grid spacing can potentially improve forecasts of storm motion, but further analysis of closer storm analogs are needed to confirm these results and to explore specific hypotheses for their differences.

Full access
Derek R. Stratman, Michael C. Coniglio, Steven E. Koch, and Ming Xue

Abstract

This study uses both traditional and newer verification methods to evaluate two 4-km grid-spacing Weather Research and Forecasting Model (WRF) forecasts: a “cold start” forecast that uses the 12-km North American Mesoscale Model (NAM) analysis and forecast cycle to derive the initial and boundary conditions (C0) and a “hot start” forecast that adds radar data into the initial conditions using a three-dimensional variational data assimilation (3DVAR)/cloud analysis technique (CN). These forecasts were evaluated as part of 2009 and 2010 NOAA Hazardous Weather Test Bed (HWT) Spring Forecasting Experiments. The Spring Forecasting Experiment participants noted that the skill of CN’s explicit forecasts of convection estimated by some traditional objective metrics often seemed large compared to the subjectively determined skill. The Gilbert skill score (GSS) reveals CN scores higher than C0 at lower thresholds likely due to CN having higher-frequency biases than C0, but the difference is negligible at higher thresholds, where CN’s and C0’s frequency biases are similar. This suggests that if traditional skill scores are used to quantify convective forecasts, then higher (>35 dBZ) reflectivity thresholds should be used to be consistent with expert’s subjective assessments of the lack of forecast skill for individual convective cells. The spatial verification methods show that both CN and C0 generally have little to no skill at scales <8–12Δx starting at forecast hour 1, but CN has more skill at larger spatial scales (40–320 km) than C0 for the majority of the forecasting period. This indicates that the hot start provides little to no benefit for forecasts of convective cells, but that it has some benefit for larger mesoscale precipitation systems.

Full access
Adam J. Clark, Michael C. Coniglio, Brice E. Coffer, Greg Thompson, Ming Xue, and Fanyou Kong

Abstract

Recent NOAA Hazardous Weather Testbed Spring Forecasting Experiments have emphasized the sensitivity of forecast sensible weather fields to how boundary layer processes are represented in the Weather Research and Forecasting (WRF) Model. Thus, since 2010, the Center for Analysis and Prediction of Storms has configured at least three members of their WRF-based Storm-Scale Ensemble Forecast (SSEF) system specifically for examination of sensitivities to parameterizations of turbulent mixing, including the Mellor–Yamada–Janjić (MYJ); quasi-normal scale elimination (QNSE); Asymmetrical Convective Model, version 2 (ACM2); Yonsei University (YSU); and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes (hereafter PBL members). In postexperiment analyses, significant differences in forecast boundary layer structure and evolution have been observed, and for preconvective environments MYNN was found to have a superior depiction of temperature and moisture profiles. This study evaluates the 24-h forecast dryline positions in the SSEF system PBL members during the period April–June 2010–12 and documents sensitivities of the vertical distribution of thermodynamic and kinematic variables in near-dryline environments. Main results include the following. Despite having superior temperature and moisture profiles, as indicated by a previous study, MYNN was one of the worst-performing PBL members, exhibiting large eastward errors in forecast dryline position. During April–June 2010–11, a dry bias in the North American Mesoscale Forecast System (NAM) initial conditions largely contributed to eastward dryline errors in all PBL members. An upgrade to the NAM and assimilation system in October 2011 apparently fixed the dry bias, reducing eastward errors. Large sensitivities of CAPE and low-level shear to the PBL schemes were found, which were largest between 1.0° and 3.0° to the east of drylines. Finally, modifications to YSU to decrease vertical mixing and mitigate its warm and dry bias greatly reduced eastward dryline errors.

Full access
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).

Full access
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.

Full access
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.

Full access