The Use of Moisture Flux Convergence in Forecasting Convective Initiation: Historical and Operational Perspectives

2005 ◽  
Vol 20 (3) ◽  
pp. 351-366 ◽  
Author(s):  
Peter C. Banacos ◽  
David M. Schultz
Keyword(s):  
Numerical Models ◽  
Convective Available Potential Energy ◽  
Moisture Flux ◽  
Cumulus Parameterization ◽  
Moist Convection ◽  
Convective Initiation ◽  
Moisture Flux Convergence ◽  
Case Examples ◽  
Surface Data ◽  
Deep Moist Convection

Abstract Moisture flux convergence (MFC) is a term in the conservation of water vapor equation and was first calculated in the 1950s and 1960s as a vertically integrated quantity to predict rainfall associated with synoptic-scale systems. Vertically integrated MFC was also incorporated into the Kuo cumulus parameterization scheme for the Tropics. MFC was eventually suggested for use in forecasting convective initiation in the midlatitudes in 1970, but practical MFC usage quickly evolved to include only surface data, owing to the higher spatial and temporal resolution of surface observations. Since then, surface MFC has been widely applied as a short-term (0–3 h) prognostic quantity for forecasting convective initiation, with an emphasis on determining the favorable spatial location(s) for such development. A scale analysis shows that surface MFC is directly proportional to the horizontal mass convergence field, allowing MFC to be highly effective in highlighting mesoscale boundaries between different air masses near the earth’s surface that can be resolved by surface data and appropriate grid spacing in gridded analyses and numerical models. However, the effectiveness of boundaries in generating deep moist convection is influenced by many factors, including the depth of the vertical circulation along the boundary and the presence of convective available potential energy (CAPE) and convective inhibition (CIN) near the boundary. Moreover, lower- and upper-tropospheric jets, frontogenesis, and other forcing mechanisms may produce horizontal mass convergence above the surface, providing the necessary lift to bring elevated parcels to their level of free convection without connection to the boundary layer. Case examples elucidate these points as a context for applying horizontal mass convergence for convective initiation. Because horizontal mass convergence is a more appropriate diagnostic in an ingredients-based methodology for forecasting convective initiation, its use is recommended over MFC.

A Lagrangian Perspective on Parameterizing Deep Convection

2019 ◽  
Vol 147 (11) ◽  
pp. 4127-4149 ◽  
Author(s):  
Ron McTaggart-Cowan ◽  
Paul A. Vaillancourt ◽  
Ayrton Zadra ◽  
Leo Separovic ◽  
Shawn Corvec ◽  
...  
Keyword(s):  
Numerical Models ◽  
Spatial Scales ◽  
Deep Convection ◽  
Convective Precipitation ◽  
Moist Convection ◽  
Gray Zone ◽  
Convective Initiation ◽  
Intermittent Behavior ◽  
Cloud Properties ◽  
Deep Moist Convection

Abstract The parameterization of deep moist convection as a subgrid-scale process in numerical models of the atmosphere is required at resolutions that extend well into the convective “gray zone,” the range of grid spacings over which such convection is partially resolved. However, as model resolution approaches the gray zone, the assumptions upon which most existing convective parameterizations are based begin to break down. We focus here on one aspect of this problem that emerges as the temporal and spatial scales of the model become similar to those of deep convection itself. The common practice of static tendency application over a prescribed adjustment period leads to logical inconsistencies at resolutions approaching the gray zone, while more frequent refreshment of the convective calculations can lead to undesirable intermittent behavior. A proposed parcel-based treatment of convective initiation introduces memory into the system in a manner that is consistent with the underlying physical principles of convective triggering, thus reducing the prevalence of unrealistic gradients in convective activity in an operational model running with a 10 km grid spacing. The subsequent introduction of a framework that considers convective clouds as persistent objects, each possessing unique attributes that describe physically relevant cloud properties, appears to improve convective precipitation patterns by depicting realistic cloud memory, movement, and decay. Combined, this Lagrangian view of convection addresses one aspect of the convective gray zone problem and lays a foundation for more realistic treatments of the convective life cycle in parameterization schemes.

Early Warnings of Severe Convection Using the ECMWF Extreme Forecast Index

2018 ◽  
Vol 33 (3) ◽  
pp. 857-871 ◽  
Author(s):  
Ivan Tsonevsky ◽  
Charles A. Doswell ◽  
Harold E. Brooks
Keyword(s):  
Characteristic Curve ◽  
Convective Available Potential Energy ◽  
The United States ◽  
Moist Convection ◽  
Relative Operating Characteristic ◽  
Convective Initiation ◽  
Severe Convection ◽  
Medium Range ◽  
Deep Moist Convection ◽  
Two Parameters

Abstract ECMWF provides the ensemble-based extreme forecast index (EFI) and shift of tails (SOT) products to facilitate forecasting severe weather in the medium range. Exploiting the ingredients-based method of forecasting deep moist convection, two parameters, convective available potential energy (CAPE) and a composite CAPE–shear parameter, have been recently added to the EFI/SOT, targeting severe convective weather. Verification results based on the area under the relative operating characteristic curve (ROCA) show high skill of both EFIs at discriminating between severe and nonsevere convection in the medium range over Europe and the United States. In the first 7 days of the forecast ROCA values show significant skill, staying well above the no-skill threshold of 0.5. Two case studies are presented to give some practical considerations and discuss certain limitations of the EFI/SOT forecasts and how they could be overcome. In particular, both convective EFI/SOT products are good at providing guidance for where and when severe convection is possible if there is sufficient lift for convective initiation. Probability of precipitation is suggested as a suitable ensemble product for assessing whether convection is likely to be initiated. The model climate should also be considered when determining whether severe convection is possible; EFI and SOT values are related to the climatological frequency of occurrence of deep, moist convection over a given place and time of year.

Comments on “Preconditioning Deep Convection with Cumulus Congestus”

2013 ◽  
Vol 70 (11) ◽  
pp. 3689-3690 ◽  
Author(s):  
David M. Schultz
Keyword(s):  
Boundary Layer ◽  
Deep Convection ◽  
Moisture Flux ◽  
Moisture Convergence ◽  
Moist Convection ◽  
Moisture Flux Convergence ◽  
Deep Moist Convection ◽  
Cumulus Congestus ◽  
Convection Initiation ◽  
The Tropics

Abstract The hypothesis that cumulus congestus clouds in the tropics moisten dry layers above the boundary layer and promote the formation of deep moist convection was tested by Hohenegger and Stevens. This comment asks whether their hypothesis is also true for cumulus congestus clouds and deep moist convection in the midlatitudes. This comment also requests clarification on how their expression for moisture convergence is calculated and used in their article, especially in light of previous studies showing that moisture flux convergence is a less-than-adequate diagnostic for convection initiation and that deep moist convection requires sufficient lift and instability, in addition to sufficient moisture.

Evaluating the conservation of energy variables in simulations of deep moist convection

2021 ◽  
Author(s):  
John M. Peters ◽  
Daniel R. Chavas
Keyword(s):  
Heat Capacity ◽  
Numerical Models ◽  
Deep Convection ◽  
Squall Line ◽  
Moist Convection ◽  
Conservation Of Energy ◽  
Deep Moist Convection ◽  
Static Energy ◽  
Hydrostatic Balance ◽  
Latent Heats

AbstractIt is often assumed in parcel theory calculations, numerical models, and cumulus parameterizations that moist static energy (MSE) is adiabatically conserved. However, the adiabatic conservation of MSE is only approximate because of the assumption of hydrostatic balance. Two alternative variables are evaluated here: MSE −IB and MSE +KE, wherein IB is the path integral of buoyancy (B) and KE is kinetic energy. Both of these variables relax the hydrostatic assumption and are more precisely conserved than MSE. This article quantifies the errors that result from assuming that the aforementioned variables are conserved in large eddy simulations (LES) of both disorganized and organized deep convection. Results show that both MSE −IB and MSE +KE better predict quantities along trajectories than MSE alone. MSE −IB is better conserved in isolated deep convection, whereas MSE −IB and MSE +KE perform comparably in squall line simulations. These results are explained by differences between the pressure perturbation behavior of squall lines and isolated convection. Errors in updraft B diagnoses are universally minimized when MSE−IB is assumed to be adiabatically conserved, but only when moisture dependencies of heat capacity and temperature dependency of latent heating are accounted for. When less accurate latent heat and heat capacity formulae were used, MSE−IB yielded poorer B predictions than MSE due to compensating errors. Our results suggest that various applications would benefit from using either MSE −IB or MSE +KE instead of MSE with properly formulated heat capacities and latent heats.

scholarly journals Image Processing of Radar Mosaics for the Climatology of Convection Initiation in South China

2020 ◽  
Vol 59 (1) ◽  
pp. 65-81 ◽  
Author(s):  
Lanqiang Bai ◽  
Guixing Chen ◽  
Ling Huang
Keyword(s):  
South China ◽  
Numerical Models ◽  
Early Morning ◽  
Moist Convection ◽  
Late Night ◽  
Morning Peak ◽  
Spatial Modes ◽  
Triggering Mechanisms ◽  
Deep Moist Convection ◽  
Convection Initiation

AbstractA dataset of convection initiation (CI) is of great value in studying the triggering mechanisms of deep moist convection and evaluating the performances of numerical models. In recent years, the data quality of the operationally generated radar mosaics over China has been greatly improved, which provides an opportunity to retrieve a CI dataset from that region. In this work, an attempt is made to reveal the potential of applying a simple framework of objective CI detection for the study of CI climatology in China. The framework was tested using radar mosaic maps in South China that were accessible online. The identified CI events were validated in both direct and indirect ways. On the basis of a direct manual check, nearly all of the identified CI cells had an organized motion. The precipitation echoes of the cells had a median duration of approximately 2.5 h. The CI occurrences were further compared with rainfall estimates to ensure physical consistency. The diurnal cycle of CI occurrence exhibits three major modes: a late-night-to-morning peak at the windward coasts and offshore, a noon-to-late-afternoon peak on the coastal land, and an evening-to-early-morning peak over the northwestern highland. These spatial modes agree well with those of rainfall, indirectly suggesting the reliability of the CI statistics. By processing radar mosaic maps, such a framework could be applied for studying CI climatology over China and other regions.

A High-Resolution Modeling Study of the 24 May 2002 Dryline Case during IHOP. Part I: Numerical Simulation and General Evolution of the Dryline and Convection

2006 ◽  
Vol 134 (1) ◽  
pp. 149-171 ◽  
Author(s):  
Ming Xue ◽  
William J. Martin
Keyword(s):  
Numerical Simulation ◽  
Boundary Layer ◽  
High Resolution ◽  
Companion Paper ◽  
Horizontal Resolution ◽  
Initial Condition ◽  
Moist Convection ◽  
Convective Initiation ◽  
Deep Moist Convection ◽  
Localized Forcing

Abstract Results from a high-resolution numerical simulation of the 24 May 2002 dryline convective initiation (CI) case are presented. The simulation uses a 400 km × 700 km domain with a 1-km horizontal resolution grid nested inside a 3-km domain and starts from an assimilated initial condition at 1800 UTC. Routine as well as special upper-air and surface observations collected during the International H2O Project (IHOP_2002) are assimilated into the initial condition. The initiation of convective storms at around 2015 UTC along a section of the dryline south of the Texas panhandle is correctly predicted, as is the noninitiation of convection at a cold-front–dryline intersection (triple point) located farther north. The timing and location of predicted CI are accurate to within 20 min and 25 km, respectively. The general evolution of the predicted convective line up to 6 h of model time also verifies well. Mesoscale convergence associated with the confluent flow around the dryline is shown to produce an upward moisture bulge, while surface heating and boundary layer mixing are responsible for the general deepening of the boundary layer. These processes produce favorable conditions for convection but the actual triggering of deep moist convection at specific locations along the dryline depends on localized forcing. Interaction of the primary dryline convergence boundary with horizontal convective rolls on its west side provides such localized forcing, while convective eddies on the immediate east side are suppressed by a downward mesoscale dryline circulation. A companion paper analyzes in detail the exact processes of convective initiation along this dryline.

scholarly journals Radiosonde observations of environments supporting deep moist convection initiation during RELAMPAGO-CACTI

2020 ◽  
Author(s):  
T. Connor Nelson ◽  
James Marquis ◽  
Adam Varble ◽  
Katja Friedrich
Keyword(s):  
Complex Terrain ◽  
Deep Convection ◽  
Convective Available Potential Energy ◽  
Environmental Parameters ◽  
Convective Precipitation ◽  
Moist Convection ◽  
Spatiotemporal Resolution ◽  
Close Proximity ◽  
Deep Moist Convection ◽  
Convection Initiation

AbstractThe Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) and Cloud, Aerosol, and Complex Terrain Interactions (CACTI) projects deployed a high-spatiotemporal-resolution radiosonde network to examine environments supporting deep convection in the complex terrain of central Argentina. This study aims to characterize atmospheric profiles most representative of the near-cloud environment (in time and space) to identify the mesoscale ingredients affecting storm initiation and growth. Spatiotemporal autocorrelation analysis of the soundings reveals that there is considerable environmental heterogeneity, with boundary layer thermodynamic and kinematic fields becoming statistically uncorrelated on scales of 1–2 hr and 30 km. Using this as guidance, we examine a variety of environmental parameters derived from soundings collected within close proximity (30 km and 30 min in space and time) of 44 events over 9 days where the atmosphere either: 1) supported the initiation of sustained precipitating convection, 2) yielded weak and short-lived precipitating convection, or 3) produced no precipitating convection in disagreement with numerical forecasts from convection-allowing models (i.e., Null events). There are large statistical differences between the Null event environments and those supporting any convective precipitation. Null event profiles contained larger convective available potential energy, but had low free tropospheric relative humidity, higher freezing levels, and evidence of limited horizontal convergence near the terrain at low levels that likely suppressed deep convective growth. We also present evidence from the radiosonde and satellite measurements that flow-terrain interactions may yield gravity wave activity that affects CI outcome.

scholarly journals Low-level Mesoscale and Cloud-scale Interactions Promoting Deep Convection Initiation

2021 ◽  
Author(s):  
James N. Marquis ◽  
Adam C. Varble ◽  
Paul Robinson ◽  
T. Connor. Nelson ◽  
Katja Friedrich
Keyword(s):  
Boundary Layer ◽  
Deep Convection ◽  
Convective Available Potential Energy ◽  
Surface Wind ◽  
Horizontal Wind ◽  
Moist Convection ◽  
Surface Precipitation ◽  
Low Level ◽  
Deep Moist Convection ◽  
Convection Initiation

AbstractData from scanning radars, radiosondes, and vertical profilers deployed during three field campaigns are analyzed to study interactions between cloud-scale updrafts associated with initiating deep moist convection and the surrounding environment. Three cases are analyzed in which the radar networks permitted dual-Doppler wind retrievals in clear air preceding and during the onset of surface precipitation. These observations capture the evolution of: i) the mesoscale and boundary layer flow, and ii) low-level updrafts associated with deep moist convection initiation (CI) events yielding sustained or short-lived precipitating storms.The elimination of convective inhibition did not distinguish between sustained and unsustained CI events, though the vertical distribution of convective available potential energy may have played a role. The clearest signal differentiating the initiation of sustained versus unsustained precipitating deep convection was the depth of the low-level horizontal wind convergence associated with the mesoscale flow feature triggering CI, a sharp surface wind shift boundary or orographic upslope flow. The depth of the boundary layer relative to the height of the LFC failed to be a consistent indicator of CI potential. Widths of the earliest detectable low-level updrafts associated with sustained precipitating deep convection were ~3-5 km, larger than updrafts associated with surrounding boundary layer turbulence (~1-3-km wide). It is hypothesized that updrafts of this larger size are important for initiating cells to survive the destructive effects of buoyancy dilution via entrainment.

Implementation of the Unified Representation of Deep Moist Convection in the CWBGFS

2021 ◽  
Vol 149 (10) ◽  
pp. 3525-3539
Author(s):  
Chun-Yian Su ◽  
Chien-Ming Wu ◽  
Wei-Ting Chen ◽  
Jen-Her Chen
Keyword(s):  
Multiple Scales ◽  
Horizontal Resolution ◽  
Cumulus Parameterization ◽  
Moist Convection ◽  
Short Term ◽  
Local Circulation ◽  
Convective Systems ◽  
Simulated Precipitation ◽  
Deep Moist Convection ◽  
Central Weather Bureau

AbstractThis study implements the unified parameterization (UP) in the Central Weather Bureau Global Forecast System (CWBGFS) based on the relaxed Arakawa–Schubert scheme (RAS) at a horizontal resolution of 15 km. The new cumulus parameterization that incorporates the UP framework is called URAS. The UP generalizes the representation of moist convection between the parameterized and the explicitly resolved processes according to the process-dependent convective updraft fraction (σ). Short-term hindcasts are performed to investigate the impacts of the UP on the simulated precipitation variability and organized convective systems over the Maritime Continent when multiple scales of convection occurred. The result shows that σ is generally larger when convective systems develop, which adaptively reduces the parameterized convection and increases the spatial variation of moisture. In the URAS experiment, the moisture hotspots within organized convective systems contribute to the enhanced local circulation and the more significant variability of precipitation. Consequently, the URAS has a more realistic precipitation spectrum, an improved relationship between the maximum precipitation and the horizontal scale of the convective systems, and an improved column water vapor–precipitation relationship.

scholarly journals Evaluation of Thunderstorm Predictors for Finland Using Reanalyses and Neural Networks

2017 ◽  
Vol 56 (8) ◽  
pp. 2335-2352 ◽  
Author(s):  
Peter Ukkonen ◽  
Agostino Manzato ◽  
Antti Mäkelä
Keyword(s):  
Neural Networks ◽  
Convective Available Potential Energy ◽  
Test Sample ◽  
Skill Score ◽  
Substantial Improvement ◽  
False Alarms ◽  
Large Sample Size ◽  
Moist Convection ◽  
Probability Curve ◽  
Deep Moist Convection

AbstractThis work evaluates numerous thunderstorm predictors and investigates the use of artificial neural networks (ANNs) for identifying occurrences of thunderstorms in reanalysis data. Environmental conditions favorable for deep, moist convection are derived from 6-hourly ERA-Interim reanalyses, while thunderstorm occurrence in the following 6 h over Finland is derived from lightning location data. By taking advantage of the consistency and large sample size (14 summers) provided by the reanalysis, complex multivariate models can be trained for a robust estimation of convective weather events from model data. This and other methods are used to yield information on the most effective convective predictors in a multivariate setting, which can also benefit the forecasting community. The best ANN found uses 15 inputs and received a Heidke skill score (HSS) of 0.51 on an independent test sample. This is a substantial improvement over the best predictor when used alone, the most unstable lifted index (MULI) with HSS = 0.40, the multivariate model having fewer false alarms in particular. After MULI, the most important ANN input was relative humidity near 700 hPa. Dry air aloft was associated with significantly lower thunderstorm probability and flash density regardless of convective available potential energy (CAPE). Other important parameters for thunderstorm development were vertical velocity and low-level θe advection. Finally, the Peirce skill score indicates a clear meridional gradient in skill for categorical forecasts, with higher skill in northern Finland. This analysis suggests that the difference in skill is real and associated with a steeper thunderstorm probability curve in the north, but further studies are needed for a physical explanation.

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