Convective and non-convective wind gusts in Poland, 2001-2015

Kolendowicz, Leszek; Taszarek, Mateusz; Czernecki, Bartosz

doi:10.26491/mhwm/63636

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…(DeCosmo et al 1996;Smith et al 1996). In this way, air continuously exchanges and equilibrates water vapor partial pressures and relative humidity acts as an interactive factor controlling both upward and downward directed water vapor fluxes (Kraus 1972;Yu 2007;Kolendowicz et al 2016;Szwejkowski et al 2017).…”

Section: Water Vapor Impact On Airborne Eddiesmentioning

confidence: 99%

Water Vapor Induced Airborne Rotational Features

Marks¹

2019

Meteorol. Hydrol. Water Manage.

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In order to study the rotational features induced by evaporating water several laboratory experiments were conducted with airborne rotary detection discs made of absorbent cotton wool and ultralight polyurethane foam discs.Measurements indicated that water vapor develops and transits into the air an upwardly directed counterclockwise rotary motion in the Northern Hemisphere, and a clockwise motion in the Southern Hemisphere. Additionally, measurements of the thermal structure of the air/water interface indicated that evaporating water may gather rotational momentum in the warmer subsurface layer. The conducted observations suggest that the process of water evaporation may be based on the grouping of some coherently spiraling molecules in the liquid phase. The interacting molecules combine their partial rotational momentums, thus allowing the top molecule to transit via the surface tension microlayer and become airborne. At the moment of evaporation, the gathered rotational energy is taken over by the free bi-hydrogen rotor-arm of the evaporating molecule that starts to revolve. Next, the water vapor-induced rotational share of the kinetic energy is likely to be redistributed among other gaseous molecules and transferred into heat (during condensation) that further energizes the airborne convective loops. In order to confirm the rotary effects induced by water vapor, several field experiments were conducted with airborne rotary detection ribbons in the Northern Hemisphere. The observations confirmed that a more enhanced counterclockwise spiraling motion of air is found with air currents under atmospheric lows of a higher relative humidity, while weaker and clockwise directed rotary dominates under atmospheric highs.

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Section: Water Vapor Impact On Airborne Eddiesmentioning

confidence: 99%

Water Vapor Induced Airborne Rotational Features

Marks¹

2019

Meteorol. Hydrol. Water Manage.

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“…Convective wind gusts (CGs) are nontronadic, straight-line winds (Mohr et al, 2017;Yu and Zheng, 2020). They predominantly occur in Eastern China in the warm summer months (Yang et al, 2017), and they are usually associated with thunderstorm clouds, in particular squall lines and supercells, which generate conditions conducive to the appearance of whirlwinds and squalls (Kolendowicz et al, 2016). Severe convective gusts are caused either by mesoscale cold pools associated with horizontal pressure gradients large enough to produce high wind speeds in the absence of strong downdrafts or by local-scale downbursts that create strong divergent horizontal winds near the ground (Wakimoto, 2001).…”

Section: Introductionmentioning

confidence: 99%

Convective Gusts Nowcasting Based on Radar Reflectivity and a Deep Learning Algorithm

Xiao

Wang

Zheng

et al. 2023

Preprint

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Abstract. Convective wind gusts (CGs) are usually related to thunderstorms, and they may cause great structural damage and serious hazards, such as train derailment, service interruption, and building collapse. Due to the small-scale and nonstationary nature of CGs, reliable CGs nowcasting with high spatial and temporal resolutions has remained unattainable. In this study, a novel nowcasting model based on deep learning – namely, CGsNet – is developed for 0–2 h of quantitative CGs nowcasting, first achieving minute-kilometer-level forecasts. CGsNet is a physics-constrained model established by training on large corpora of average surface wind speed (ASWS) and radar observations, it can produce realistic and spatiotemporally consistent ASWS predictions in CGs events. By combining the gust factor (1.77, the ratio of the observed peak wind gust speed (PWGS) to the ASWS) with the ASWS predictions, the PWGS forecasts are estimated with a spatial resolution of 0.01° × 0.01° and a 6-minute temporal resolution. CGsNet is shown to be effective, and it has an essential advantage in learning the spatiotemporal features of CGs. In addition, quantitative evaluation experiments indicate that CGsNet exhibits higher generalization performance for CGs than the traditional nowcasting method based on numerical weather prediction models. CGs nowcasting technology can be applied to provide real-time quantitative CGs forecasts and alerts the damaging wind events in meteorological services.

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“…Convective wind gusts (CGs) are non-tornadic, straight-line winds (Mohr et al, 2017;Yu and Zheng, 2020). They predominantly occur in eastern China in the warm summer months (Yang et al, 2017), and they are usually associated with thunderstorm clouds, particularly squall lines and supercells, which generate conditions conducive to the appearance of whirlwinds and squalls (Kolendowicz et al, 2016). Severe convective gusts are caused either by mesoscale cold pools associated with horizontal pressure gradients large enough to produce high wind speeds in the absence of strong downdrafts or by local-scale downbursts that create strong divergent horizontal winds near the ground (Wakimoto, 2001).…”

Section: Introductionmentioning

confidence: 99%

Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm

et al. 2023

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Abstract. Convective wind gusts (CGs) are usually related to thunderstorms, and they may cause great structural damage and serious hazards, such as train derailment, service interruption, and building collapse. Due to the small-scale and nonstationary nature of CGs, reliable CG nowcasting with high spatial and temporal resolutions has remained unattainable. In this study, a novel nowcasting model based on deep learning – namely, CGsNet – is developed for 0–2 h lead times of quantitative CG nowcasting, achieving minute–kilometer-level forecasts. CGsNet is a physics-constrained model established by training on large corpora of average surface wind speed (ASWS) and radar observations; it can produce realistic and spatiotemporally consistent ASWS predictions in CG events. By combining the gust factor (1.77, the ratio of the observed peak wind gust speed (PWGS) to the ASWS) with the ASWS predictions, the PWGS forecasts are estimated with a spatial resolution of 0.01∘ × 0.01∘ and a 6 min temporal resolution. CGsNet is shown to be effective, and it has an essential advantage in learning the spatiotemporal features of CGs. In addition, quantitative evaluation experiments indicate that CGsNet exhibits higher generalization performance for CGs than the traditional nowcasting method based on numerical weather prediction models. CG-nowcasting technology can be applied to provide real-time quantitative CG forecasts.

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Convective and non-convective wind gusts in Poland, 2001-2015

Cited by 5 publications

References 11 publications

Water Vapor Induced Airborne Rotational Features

Water Vapor Induced Airborne Rotational Features

Convective Gusts Nowcasting Based on Radar Reflectivity and a Deep Learning Algorithm

Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm

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