Probing for overshooting as extreme event of thunderstorms

Chaudhuri, Sutapa; Khan, F.; Das, Debesh K.; Mondal, Paramita; Dey, Swarnali

doi:10.1007/s11069-020-03977-y

Cited by 4 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As awareness has increased of the potential disasters of downbursts, researchers have carried out extensive investigations on thunderstorm wind characteristics through field measurements (Chaudhuri et al, 2020;Jiang et al, 2022), wind tunnel tests (Asano et al, 2019;Fang et al, 2023) and numerical simulations (Selvam and Holmes, 1992;Iida et al, 2015). Specifically, the collaborative team of Hangan and Romanic have conducted extensive research on the climatic environment of downbursts and their interaction with boundary layer winds by comparing downburst models with full-size downbursts (Romanic et al, 2020(Romanic et al, , 2022Canepa et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Observation of downburst wind characteristics using the Doppler profiler and near-ground measurements

He,

Cai,

Wang

et al. 2024

Nat Hazards

View full text Add to dashboard Cite

A downburst is a strong downdraft that causes radiating, catastrophic winds at or near the ground. Relevant research conducted in the past decades shows that downbursts not only bring damage to the built environment but also have surprisingly destructive effects on aircraft and other flying objects. However, due to the complexity of atmospheric phenomena and limited measurement datasets, many uncertainties remain to be clarified in the understanding and modelling of the downburst wind field.To further explore the evolution characteristics of thunderstorms, this study uses data recorded during thunderstorms by a Doppler profiler of the Hong Kong Observatory and a near-ground anemometer installed at the Hong Kong International Airport to conduct a detailed feature analysis of the vertical profile and turbulence characteristics of downbursts within the atmospheric boundary layer. Based on long-term field measurements during thunderstorm events, the occurrence of a thunderstorm should be determined by a combination of simultaneous variations in multiple meteorological factors, including wind speed and direction, vertical wind speed, and signal-to-noise ratio (SNR). During thunderstorms, extreme wind speeds occurred in the lower atmosphere and the height of the local peaks seems to be stable in the thunderstorm wind profiles. In addition, both methods, which consider a constant mean component (stationary) and a slowly varying mean component (non-stationary), are used to decompose the measured wind speed and are then compared to investigate the turbulence characteristics of downbursts. It is found that although the gust factor and turbulence intensity show some deviation due to the influence of atmospheric stratification instability, this effect does not appear to be reflected in the turbulence integral scale.

show abstract

Section: Introductionmentioning

confidence: 99%

Observation of downburst wind characteristics using the Doppler profiler and near-ground measurements

He,

Cai,

Wang

et al. 2024

Nat Hazards

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…BPN and RBFN give suitable solutions for the prediction of long-range weather forecasting 30 . Chaudhuri et al, has showed in their studies the use of multilayer perceptron logic and fuzzy logic to analyse the role of different weather parameters for thunderstorm prediction purpose 24,31,32 .The Multilayer Perceptron 33 and KNN (K-Nearest Neighbor) all have been applied on different weather parameters (such as moisture data) for severe thunderstorm prediction purpose previously. There are many studies where statistical and different machine learning techniques have been applied on different weather parameters to predict severe thunderstorm.…”

mentioning

confidence: 99%

A comparative study of severe thunderstorm among statistical and ANN methodologies

Bhattacharya,

Bhattacharyya

2023

Sci Rep

View full text Add to dashboard Cite

Severe Thunderstorms are the extreme weather convective features. It causes local calamities in various ways. Proper prediction with lead time is an important factor to prevent such calamities from saving people. Here, both probabilistic and machine learning techniques are applied to weather data to obtain proper predictions. Traditional methodologies are already available for such prediction purposes. However, Naïve Bayes and RBFN (Radial Basis Function Network) methodology have been introduced here with some specific weather parameters that has not done before remarkably. A comparative study was performed on weather data including Naïve Bayes, Multilayer Perceptron (MLP), K-nearest neighbor (KNN) and Radial Basis Function Network (RBFN). All these data have been procured from Kolkata located in north-east India. The result obtained by applying the Radial Basis Function Network is better among the three methods, yielding a correct prediction of 95% for severe “squall-storms” and 94% for “no storm”. The predictions have a sufficient lead time of 10- 12 h.

show abstract

Thermodynamic Changes in the Atmosphere Associated with Pre-monsoon Thunderstorms Over Eastern and North-Eastern India

Tyagi

Sahu

Hari

et al. 2022

Extreme Natural Events

View full text Add to dashboard Cite

Probing for overshooting as extreme event of thunderstorms

Cited by 4 publications

References 31 publications

Observation of downburst wind characteristics using the Doppler profiler and near-ground measurements

Observation of downburst wind characteristics using the Doppler profiler and near-ground measurements

A comparative study of severe thunderstorm among statistical and ANN methodologies

Thermodynamic Changes in the Atmosphere Associated with Pre-monsoon Thunderstorms Over Eastern and North-Eastern India

Contact Info

Product

Resources

About