Fig 2 - uploaded by Charles A. Doswell III
Content may be subject to copyright.
2. Schematic showing vorticity advection by the geostrophic wind (V ). Solid lines are height g contours (z), dashed lines are contours of absolute vorticity (in units of 10 -5 s -1 ). Where the height and vorticity contours intersect, they form quadrilaterals (with curved sides). The strength of the advection is proportional to the number of such quadrilatarals per unit area. Where vorticity and height contours are parallel, no advection is occurring. The hatched quadrilateral is in a region of negative vorticity advection (NVA) by V , since V is pointing from lower to higher vorticity. The stippled quadrilateral is in a region of g g positive vorticity advection (PVA) by V . g
Context in source publication
Similar publications
monografia sobre o clima dos Açores e o seu estudo
Dit boek geeft de inspanningen van de veiligheidsregio's weer op het gebied van digitalisering, standaardisatie,semantiek en architectuur. Het boek bevat een deel van VERA 2 en een theorie over informatiemanagement bij crisis. Het innovatieteam Inowit heeft dit boek samengesteld in samenwerking met de TU Delft onder eindverantwoordelijkheid van de...
Citations
... One of the methods uses severe weather reports collected from weather observers and media sources (Dotzek et al. 2009;Edwards et al. 2013;Elmore et al. 2014;Seimon et al. 2016;Krennert et al. 2018). However, the determination of thunderstorm intensity based on these reports is quite arbitrary (Doswell 1985), and depends, inter alia, on the population density and local reporting efficiency inducing spatial and temporal inhomogeneities (Doswell 1985;Verbout et al. 2006;Allen and Tippett 2015;Blair et al. 2017;Groenemeijer et al 2017;Edwards et al. 2018;Taszarek et al. 2019). The ESWD database for the area of Poland consists of more than 22,000 large hail, tornado and severe wind reports for the period 2008-2020. ...
... One of the methods uses severe weather reports collected from weather observers and media sources (Dotzek et al. 2009;Edwards et al. 2013;Elmore et al. 2014;Seimon et al. 2016;Krennert et al. 2018). However, the determination of thunderstorm intensity based on these reports is quite arbitrary (Doswell 1985), and depends, inter alia, on the population density and local reporting efficiency inducing spatial and temporal inhomogeneities (Doswell 1985;Verbout et al. 2006;Allen and Tippett 2015;Blair et al. 2017;Groenemeijer et al 2017;Edwards et al. 2018;Taszarek et al. 2019). The ESWD database for the area of Poland consists of more than 22,000 large hail, tornado and severe wind reports for the period 2008-2020. ...
The relationship between convective parameters derived from ERA5 and cloud-to-ground (CG) lightning flashes from the PERUN network in Poland was evaluated. All flashes detected between 2002 and 2019 were divided into intensity categories based on a peak 1-min CG lightning flash rate and were collocated with proximal profiles from ERA5 to assess their climatological variability. Thunderstorms in Poland are the most frequent in July, between 1400 and 1600 UTC and over the southeastern parts of the country. The highest median of most unstable convective available potential energy (MUCAPE) for CG lightning flash events is from June to August, between 1400 and 1600 UTC (around 900 J kg ⁻¹ ), whereas patterns in 0–6-km wind shear [deep-layer shear (DLS)] are reversed, with the highest median values during winter and night (around 25 m s ⁻¹ ). The best overlap of MUCAPE and DLS (MUWMAXSHEAR parameter) is in July–August, typically between 1400 and 2000 UTC with median values of around 850 m ² s ⁻² . Thunderstorms in Poland are the most frequent in MUCAPE below 1000 J kg ⁻¹ , and DLS between 8 and 15 m s ⁻¹ . Along with increasing MUCAPE and DLS, peak CG lightning flash rates increase as well. Compared to DLS, MUCAPE is a more important parameter in forecasting any lightning activity, but when these two are combined together (MUWMAXSHEAR) they are more reliable in distinguishing between thunderstorms producing small and high CG lightning flash rates. Our results also indicate that higher CG lightning flash rates result in thunderstorms more frequently associated with severe weather reports (hail, tornado, wind).
Significance Statement
Each year severe thunderstorms produce considerable material losses and lead to deaths across central Europe; thus, a better understanding of local storm climatologies and their accompanying environments is important for operational forecasters, emergency managers, and risk estimation. In this research we address this issue by analyzing 18 years of lightning intensity data and collocated atmospheric environments. Thunderstorms in Poland are the most frequent in July between 1400 and 1600 UTC and form typically in environments with low atmospheric instability and moderate vertical shear of the horizontal wind. The probability for storms producing intense lightning increases when both of these environmental parameters reach higher values.
... Substantial work has also been conducted in the past regarding the dispersion and transport of seeding material in both convective and orographic clouds. Many of the potential rain-bearing clouds in tropical and semi-tropical countries are convective in nature and their tops often not exceeding the height of the freezing level (Doswell, 1985). ...
Cloud seeding technology is kind of weather modification to create rainfall artificially. Simply, it is way of dispersing cloud condensation nuclei substances into the clouds which alter the microphysical process. The concept of artificial rainmaking in super-cooled clouds with effective artificial ice nuclei (IN) was discussed by Findeisen as early 1930’s. Cloud seeding can be done either by using artillery fire shells (containing rain-inducing chemicals) or aircraft (by dropping chemicals) into the cloud cover to increase precipitation but hail and fog suppression are also practiced. Cloud seeding is economically valuable if you're trying to increase rain or snowfall at a large basin. Cloud seeding happens to be a big business now a days to the insurance companies and stock market derivatives, involved in controlling our dwindling water resources.
... In particular, we choose r fp such that the region r # r fp , where humidity increases strongly, corresponds to the typical extent of a deep convective updraft in the free troposphere. As Doswell (1985), studying the life cycle of a common convective shower, find updrafts on the order of 5 km during the early and mature stage of the life cycle, we set the radius r fp 5 2.5 km. Note that in setting r fp equal to 2.5 km, 99.5% of the increase in humidity given by Eq. (10) happens within a region with a horizontal extent of 15 km, a typical spatial extent of a deep convective cloud in its dissipating stage (Doswell 1985). ...
... As Doswell (1985), studying the life cycle of a common convective shower, find updrafts on the order of 5 km during the early and mature stage of the life cycle, we set the radius r fp 5 2.5 km. Note that in setting r fp equal to 2.5 km, 99.5% of the increase in humidity given by Eq. (10) happens within a region with a horizontal extent of 15 km, a typical spatial extent of a deep convective cloud in its dissipating stage (Doswell 1985). For the total increase in humidity associated with one cloud q 0 , we assume that the cloud initially increases the humidity from q RCE to saturation (i.e., q 5 1) within r fp throughout the free troposphere, which gives q 0 5 (1 2 q RCE )pr 2 fp 5 5:5 3 10 6 m 2 . ...
Self-aggregation in numerical simulations of tropical convection is described by an upscale growth and intensification of dry and moist regions. Previous work has focused on determining the relevant mechanism that induces moist regions to get moister and dry regions to get drier. Though different mechanisms have been identified, the spatial evolution of self-aggregation is remarkably universal. The first part of this study shows that different mechanisms can lead to a similar evolution of self-aggregation, if self-aggregation is described by a phase separation of moist and dry regions, through a process called coarsening. Though it was previously introduced based on a convection–humidity feedback, coarsening, importantly, is not tied to a specific feedback process but only requires an intensification of local humidity perturbations. Based on different feedback loops, three simple models of the evolution of the humidity field are introduced, all of which lead to coarsening. In each model, diffusive transport of humidity is assumed, which approximates a humidity increase due to convection, within a finite region around convective cores. In the second part, predictions made by coarsening are compared with atmospheric model simulations. Analyzing a set of radiative–convective equilibrium simulations shows that coarsening correctly predicts the upscale growth of the moist and dry regions in the early stages of self-aggregation. In addition, coarsening can explain why self-aggregation is not observed for small domains and why the shape of the final moist region changes with the shape of the domain.
... According to Rasmussen and Houze (2012), flash floods result from a myriad of storm types with differing structures and synoptic conditions (cf. Doswell, 1985;Doswell et al., 1996;Maddox et al., 1978). For example, the 2010 flash flood event in Ladakh (upper Indus) was caused by a large (meso-scale) rain-producing cloud system that formed over the high Himalaya and Tibetan Plateau, and received additional moisture from monsoon air masses moving northward from the Arabian Sea and Bay of Bengal (Rasmussen and Houze, 2012). ...
... Taken from Markowski & Richardson (2010). Adapted from Byers & Braham (1949) and Doswell (1985). ...
Die satelliten-basierte Erkennung und Kurzfristvorhersage der Initiierung von Konvektion (CI) anhand des Cb-TRAM Algorithmus kann erheblich durch die Nutzung zusätzlicher Datenquellen aus unabhängigen Beobachtungen und numerischen Vorhersagemodellen verbessert werden. Dazu werden die für die Entstehung eines Gewitters wichtigen Größen wie Feuchte, Instabilität und Hebung innerhalb einer von Cb-TRAM erkannten CI Zelle genutzt.
Die verschiedenen Datenquellen werden mit Hilfe von Fuzzy Logik miteinander kombiniert, um für jede von Cb-TRAM detektierte CI Erkennung eine „CI Forcierung“ und daraus eine Wahrscheinlichkeit zu bestimmen, mit der sich die CI Zelle in ein Gewitter weiter entwickelt. So können Zellen mit niedriger Weiterentwicklungswahrscheinlichkeit gefiltert und damit die Anzahl der Fehlalarme für CI verringert werden.
Das Verfahren ist mit Hilfe einer statistischen Auswertung für eine ganze Sommerperiode (15. Mai 2009 – 31. August 2009) über Mitteleuropa verifiziert worden. Die Auswertung ergibt, dass die Anzahl der Fehlalarme für die Weiterentwicklung von CI Zellen in ein Gewitter mit dem neuen Verfahren um bis zu 65 % verringert werden kann.
... The high Sweat Index values are indications of high variation in wind directions both at ground and in upper atmospheric levels. The PW values of above 30 exceeded the threshold level set by Doswell (1982) to indicate potential for severe rainfall. 123 presents areas where the PW values reached maximums and it is clear that the areas flooded during 7-10 September were characterized with the highest PW values. ...
Turkey often suffers from flood-related damages and causalities as a result of intense and prolonged storms that are usually convective or cyclonic in origin. The impact is more distinctive in Aegean and Mediterranean coasts of the country where quantity and distribution of rainfall is influenced by Mediterranean cyclones, especially in late autumn and early winter. The floods sometimes became very hazardous when combined with urbanization effects, especially in the densely populated coastal communities and major cities. Severe weather was marked in the early parts of September 2009 that produced record-setting rainfall amounts across the Marmara region of Turkey and led a series of flash floods which affected İstanbul and Tekirdağ provinces especially. The overall flooding was the result of successive and persistent intense rainfall episodes over a 3-day period which produced more than 250-mm rainfall over portions of the region. The floods resulted in death of 32 people and caused extensive environmental and infrastructural damage in the region. This study provides in-depth analysis of hydrometeorological conditions that led to the occurrence of flash floods in Marmara region during 7–10 September 2009 period and also discusses non-meteorological factors that exacerbated the flooding conditions. Main meteorological settings that led to intense storms were presence of cold air in the upper atmosphere, a slow-moving quasi-stationary trough, and continuous resupply of moisture to the surface low from the warm Aegean Sea. Radar images showed the development of clusters of convective cells that remained quasi-stationary over portions of the region. The 24-h rainfall amounts varied between 100 and 253 mm in most parts of the region during the flooding period with diverse spatial patterns. The southern locations received the highest amount of the rainfall as compared to stations located in northern slopes of the region. Typical effects of orography that enhance rainfall in the coastal areas, however, were not observed during the Marmara flood. Some features of the synoptic pattern observed prior and during the flooding period, supported the back door cold front concept. This is characterized with easterly to northeasterly surface flows forced by an anticyclone, advection of cold continental air over the warm Black Sea which provided anomalous moisture to trigger cyclogenesis over the Marmara region, and falling of core of the intense rainfall over the Marmara Sea. The study concluded that although the meteorological settings were favorable for the convective rainfalls, urbanization factors, such as land use changes and occupation of flood plains, played major role in aggravating the worst flood observed in the region in recent decades.
... ANNs are trainable self-adaptive systems that can "learn" to solve complex problems from a set of examples and generalize the "acquired knowledge" to solve unforeseen problems as in stock market and environmental prediction. Previously, most weather prediction systems used a combination of empirical and dynamical techniques [2, 3, 4]. However, a little attention was paid to the use of ANNs in weather forecasting [5, 6, 7, 8, 9, 10]. ...
Severe thunderstorms frequently occur over the eastern and northeastern states of India during the pre-monsoon season (March-May). Forecasting thunderstorm is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent non-linearity of their dynamics and physics. In this paper, experiments are conducted on artificial neural network (ANN) model to predict severe thunderstorms that occurred over Kolkata on 3 using thunderstorm affected parameters and validated the model results with observation. The performance of ANN model in predicting hourly surface temperature during thunderstorm days using different learning algorithms are evaluated. A statistical analysis based on mean absolute error, root mean square error, correlation coefficient and percentage of correctness is performed to compare the predicted and observed data. The results show that the ANN model with Levenberg Marquardt algorithm predicted the thunderstorm activities well in terms of sudden fall of temperature and intensity as compared to other learning algorithms.
... The intent is to provide a broad overview and global survey of radar processing systems for the provision of severe weather warning services. There is a considerable literature in convective weather forecasting and warning, this contribution can only explore a few aspects of this topic (Doswell, 1982: Doswell, 1985 Johns and Doswell, 1992; Wilson et al, 1998). The forecasting and the warning of severe weather are very briefly described. ...
... This is often called the ingredients approach as one looks to see where the various ingredients come together and that is where severe weather will occur. Historically, this is based on the original Fawbush and Miller Technique (1953) but it has gone through significant evolution (Doswell, , 1982Doswell, , 1985 Johns and Doswell, 1992; Moller, 2001; Moninger et al, 1991; Monteverdi et al, 2003; Rasmussen, 2003; Weiss et al 1980).Fig. 2 shows the morphology of thunderstorms that theoretically develop under different wind shear and convective available potential energy (CAPE) situations (Brooks et al, 1993; Markowski et al, 1998b; Weisman and Klemp, 1984; Weisman and Rotunno, 2000). Dynamics is represented by the 0-3 km magnitude of the wind shear. ...
... This uncertainty is reflected in the wide range of proximity criteria that have been employed in previous studies (Table 1). The more restrictive of these criteria implicitly acknowledge the large mesoscale variability that is often observed on severe weather (particularly tornado outbreak) days (Doswell 1982; Davies-Jones 1993, Markowski et al. 1998). It is tempting to presume that conditions nearer a severe weather event are generally more representative of the region of the atmosphere that fostered the development of the parent storm. ...
Proximity sounding studies typically seek to optimize several trade-offs that involve somewhat arbitrary definitions of how to define a “proximity sounding.” More restrictive proximity criteria, which presumably produce results that are more characteristic of the near-storm environment, typically result in smaller sample sizes that can reduce the statistical significance of the results. Conversely, the use of broad proximity criteria will typically increase the sample size and the apparent robustness of the statistical analysis, but the sounding data may not necessarily be representative of near-storm environments, given the presence of mesoscale variability in the atmosphere. Previous investigations have used a wide range of spatial and temporal proximity criteria to analyze severe storm environments. However, the sensitivity of storm environment climatologies to the proximity definition has not yet been rigorously examined.
In this study, a very large set (1200) of proximity soundings associated with significant tornado reports is used to generate distributions of several parameters typically used to characterize severe weather environments. Statistical tests are used to assess the sensitivity of the parameter distributions to the proximity criteria. The results indicate that while soundings collected too far in space and time from significant tornadoes tend to be more representative of the larger-scale environment than of the storm environment, soundings collected too close to the tornado also tend to be less representative due to the convective feedback process. The storm environment itself is thus optimally sampled at an intermediate spatiotemporal range referred to here as the Goldilocks zone. Implications of these results for future proximity sounding studies are discussed.
... One finding not revealed by previous case studies regards the rapid decrease and sign reversal of the air density difference (or ) across the dryline near sunset ( Fig. 9), although forecasters have long been aware of a reversal of the temperature gradient across the dryline near sunset (Doswell 1982). This reversal is largely due to the more rapid evening cooling on the dry side, compared to the moist side of the dryline (section 3c). ...
Two months of Lubbock, Texas, radar reflectivity data and West Texas Mesonet data are examined to detect dryline finelines and to describe their thermodynamic and propagation characteristics. Before sunset the moist air mass east of the dryline was consistently denser than the dry air mass. This air density difference waned and even reversed after sunset, because of more rapid cooling on the dry side.
This study provides further evidence that the formation and propagation of the dryline convergence zone is driven by the daytime air density difference, that is, that the dryline behaves as a density current. The implication for forecasters is that the air density (or virtual potential temperature) difference across the dryline should be monitored, as a measure of dryline strength and as an additional indicator for the likelihood of convective initiation along the dryline.
