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.

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.

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.

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.

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

2017 ◽  
Vol 145 (12) ◽  
pp. 4747-4770 ◽  
Author(s):  
Alexandra M. Keclik ◽  
Clark Evans ◽  
Paul J. Roebber ◽  
Glen S. Romine
Keyword(s):  
Short Range ◽  
Wrf Model ◽  
Skill Score ◽  
Forecast Skill ◽  
Weather Research And Forecasting ◽  
Moist Convection ◽  
Ensemble Forecasts ◽  
High Bias ◽  
Deep Moist Convection ◽  
Convection Initiation

This study tests the hypothesis that assimilating mid- to upper-tropospheric, meso- α- 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 Predictability 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 × 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- α 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.

scholarly journals Convective Squalls over the Eastern Equatorial Atlantic

2012 ◽  
Vol 27 (3) ◽  
pp. 770-783 ◽  
Author(s):  
Florian Berkes ◽  
Peter Knippertz ◽  
Douglas J. Parker ◽  
Gus Jeans ◽  
Valérie Quiniou-Ramus
Keyword(s):  
Grid Point ◽  
Convective Available Potential Energy ◽  
Active Regions ◽  
Daily Basis ◽  
Congo Basin ◽  
High Temporal Resolution ◽  
Offshore Platforms ◽  
Moist Convection ◽  
Equatorial Atlantic ◽  
Deep Moist Convection

Abstract The Congo Basin and the adjacent equatorial eastern Atlantic are among the most active regions of the world in terms of intense deep moist convection, leading to frequent lightning and severe squalls. Studying the dynamics and climatology of this convection is difficult due to a very sparse operational network of ground-based observations. Here, a detailed analysis of recently available high temporal resolution meteorological observations from three oil platforms off the coast of Angola spanning the three wet seasons from 2006/07 to 2008/09 is presented. The annual cycle of squall days as identified from wind data closely follows that of convective available potential energy (CAPE) and therefore mirrors the cycle of wet and dry seasons. The diurnal cycle of squall occurrence varies from station to station, most likely related to local features such as coastlines and orography, which control the initiation of storms. An attempt to classify squalls based on the time evolution of the station meteorology and satellite imagery suggests that microbursts account for at least one-third of the strong gusts, while mesoscale squall lines appear to be quite rare. On a daily basis the probability of squall occurrence increases with increasing values of CAPE, downdraft CAPE, and 925–700-hPa wind shear, and decreases for high convective inhibition, all calculated from vertical profiles of temperature and humidity at the nearest grid point in the NCEP–NCAR and ECMWF reanalysis datasets. Both the climatological results and the stability indices can be used for local forecasting to avoid squalls impacting on operations on the offshore platforms.

scholarly journals A Case Study of Terrain Influences on Upscale Convective Growth of a Supercell

2019 ◽  
Vol 147 (12) ◽  
pp. 4305-4324 ◽  
Author(s):  
Jake P. Mulholland ◽  
Stephen W. Nesbitt ◽  
Robert J. Trapp
Keyword(s):  
Potential Energy ◽  
Indirect Effects ◽  
Complex Terrain ◽  
Wind Shear ◽  
Convective Available Potential Energy ◽  
Vertical Wind Shear ◽  
Moist Convection ◽  
Cold Pools ◽  
Deep Moist Convection

Abstract Satellite- and ground-based radar observations have shown that the northern half of Argentina, South America, is a region susceptible to rapid upscale growth of deep moist convection into larger organized mesoscale convective systems (MCSs). In particular, the complex terrain of the Sierras de Córdoba is hypothesized to be vital to this upscale-growth process. A canonical orographic supercell-to-MCS transition case study was analyzed to determine the influence that complex terrain had on processes governing upscale convective growth. High-resolution numerical modeling experiments were conducted in which the terrain height of the Sierras de Córdoba was systematically modified by raising or lowering the elevation of terrain above 1000 m. The alteration of the terrain lead to both direct and indirect effects on storm morphology. A direct effect included terrain blocking of cold pools, whereas indirect effects included terrain-induced variations in pertinent storm environmental parameters (e.g., vertical wind shear, convective available potential energy). When the terrain was raised, low-level and deep-layer vertical wind shear increased, mixed-layer convective available potential energy decreased, deep moist convection initiated earlier, and cold pools were blocked and generally became stronger and deeper. The reverse occurred when the terrain was lowered, resulting in a weaker supercell that did not grow upscale into an MCS. The control simulation supercell displayed the deepest cold pool and correspondingly fastest transition from supercell to MCS, potentially revealing that the unique terrain configuration of the Sierras de Córdoba was supportive of the observed rapid upscale convective growth of this orographic supercell.

Quasi-idealized numerical simulations of processes involved in orogenic convection initiation over the Sierras de Córdoba mountains

2021 ◽  
Author(s):  
Christopher A. Davis
Keyword(s):  
Numerical Simulations ◽  
Mountain Range ◽  
Moist Convection ◽  
Low Level ◽  
Physical Mechanisms ◽  
Convergence Line ◽  
Synoptic Conditions ◽  
Deep Moist Convection ◽  
Outflow Boundary ◽  
Convection Initiation

Abstract The Sierras de Córdoba (SDC) mountain range in Argentina is a hotspot of deep moist convection initiation (CI). Radar climatology indicates that 44% of daytime CI events that occur near the SDC in spring and summer seasons and that are not associated with the passage of a cold front or an outflow boundary involve a northerly LLJ, and these events tend to preferentially occur over the southeast quadrant of the main ridge of the SDC. To investigate the physical mechanisms acting to cause CI, idealized convection-permitting numerical simulations with a horizontal grid spacing of 1 km were conducted using CM1. The sounding used for initializing the model featured a strong northerly LLJ, with synoptic conditions resembling those in a previously postulated conceptual model of CI over the region, making it a canonical case study. Differential heating of the mountain caused by solar insolation in conjunction with the low-level northerly flow sets up a convergence line on the eastern slopes of the SDC. The southern portion of this line experiences significant reduction in convective inhibition, and CI occurs over the SDC southeast quadrant. Thesimulated storm soon acquires supercellular characteristics, as observed. Additional simulations with varying LLJ strength also show CI over the southeast quadrant. A simulation without background flow generated convergence over the ridgeline, with widespread CI across the entire ridgeline. A simulation with mid- and upper-tropospheric westerlies removed indicates that CI is minimally influenced by gravity waves. We conclude that the low-level jet is sufficient to focus convection initiation over the southeast quadrant of the ridge.

scholarly journals The Application of Artificial Neural Networks in Predicting Children’s Giftedness

2016 ◽  
Vol 19 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Nina Pavlin-Bernardić ◽  
◽  
Silvija Ravić ◽  
Ivan Pavao Matić ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
School Readiness ◽  
Multilayer Perceptron ◽  
Gifted Students ◽  
Test Sample ◽  
Hyperbolic Tangent ◽  
Readiness Assessment ◽  
Artificial Neural

Artificial neural networks have a wide use in the prediction and classification of different variables, but their application in the area of educational psychology is still relatively rare. The aim of this study was to examine the accuracy of artificial neural networks in predicting students’ general giftedness. The participants were 221 fourth grade students from one Croatian elementary school. The input variables for artificial neural networks were teachers’ and peers’ nominations, school grades, earlier school readiness assessment and parents’ education. The output variable was the result on the Standard Progressive Matrices (Raven, 1994), according to which students were classified as gifted or non-gifted. We tested two artificial neural networks’ algorithms: multilayer perceptron and radial basis function. Within each algorithm, a number of different types of activation functions were tested. 80% of the sample was used for training the network and the remaining 20% to test the network. For a criterion according to which students were classified as gifted if their result on the Standard Progressive Matrices was in the 95th centile or above, the best model was obtained by the hyperbolic tangent multilayer perceptron, which had a high accuracy of 100% of correctly classified non-gifted students and 75% correctly classified gifted students in the test sample. When the criterion was the 90th centile or above, the best model was also obtained by the hyperbolic tangent multilayer perceptron, but the accuracy was lower: 94.7% in the classification of non-gifted students and 66.7% in the classification of gifted students. The study has shown artificial neural networks’ potential in this area, which should be further explored. Keywords: gifted students, identification of gifted students, artificial neural networks

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