Spatio-Temporal Analysis of Intense Convective Storms Tracks in a Densely Urbanized Italian Basin

Sangiorgio, Matteo; Barindelli, Stefano

doi:10.3390/ijgi9030183

Cited by 7 publications

(6 citation statements)

References 48 publications

(53 reference statements)

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“…For flood impacts reduction, convective spells need to be monitored. In fact, as shown by Sangiorgio and Barindelli [63], long-lasting convective cells present a higher probability of generating hydrometeorological risks. Events containing such convective spells produced very important rainfall.…”

Section: Discussionmentioning

confidence: 89%

The Phenomenology of West African Coastal Rainfall Events Based on a New Rain Gauge Network over Abidjan (Côte d’Ivoire)

Kacou,

Zahiri,

Yao

et al. 2023

Atmosphere

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In the District of Abidjan, flooding typically occurs suddenly during intense rainfall events. The individual rainfall event provides the basic element for the study. Its analysis is required to develop solutions for managing the impact of extreme rainfall occurrences. Our study proposes to identify individual rainfall events that occurred in the District of Abidjan from a densified network and analyze some of their characteristics related to the amount of rainfall they provided, their duration, and their level of intensity. A total of 1240 individual rainfall events were identified between 2018 and 2021 using a network of 21 rain gauges. Rainfall events were identified based on criteria such as a minimum inter-event time without rainfall of 30 min, a detection threshold of 0.02 mm/5 min, a minimum duration of 30 min applicable to the average hyetograph, and a minimum of 1 mm of rainfall in at least one rain gauge. The analysis of characteristics related to accumulation, intensity, and duration showed that the rainfall events were essentially convective, with an average duration of more than 2 h and a rainfall of 11.30 mm/event. For 70% of the rainfall events of a mixed nature, the convective episodes last up to 33.33% of the total duration of the event and produce an average of 80% of the cumulative rainfall. The 30-min peak intensities generally occur in the first half of the event. Less than 13.5% of the events have peaks greater than 50 mm/h. The probability of observing more than two, three, or four events per day is high in June and October, the core of the two rainy seasons.

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Section: Discussionmentioning

confidence: 89%

The Phenomenology of West African Coastal Rainfall Events Based on a New Rain Gauge Network over Abidjan (Côte d’Ivoire)

Kacou,

Zahiri,

Yao

et al. 2023

Atmosphere

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“…Hitchcock et al (2021) also studied the characteristics of linear precipitation systems and their contributions in heavy rainfall over Melbourne by applying an object-based approach over a weather radar data near Melbourne. Similar object-based radar studies have been conducted in Sydney (Potts et al 2000), Southeast Queensland (Peter et al 2015) and other parts of the world such as Germany (Thomassen et al 2020), Italy (Sangiorgio and Barindelli 2020) and Spain (Rigo et al 2010). This technique has also been applied over satellite datasets to study the global climatology of different storm types (Nesbitt et al 2000;Jiang et al 2011;Wall et al 2013).…”

Section: Introductionmentioning

confidence: 96%

An object-based climatology of precipitation systems in Sydney, Australia

et al. 2022

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The climate is warming and this is changing some aspects of storms, but we have relatively little knowledge of storm characteristics beyond intensity, which limits our understanding of storms overall. In this study, we apply a cell-tracking algorithm to 20 years of radar data at a mid-latitude coastal-site (Sydney, Australia), to establish a regional precipitation system climatology. The results show that extreme storms in terms of translation-speed, size and rainfall intensity usually occur in the warm season, and are slower and more intense over land between ~ 10 am and ~ 8 pm (AEST), peaking in the afternoon. Precipitation systems are more frequent in the cold season and often initiate over the ocean and move northward, leading to precipitation mostly over the ocean. Using clustering algorithms, we have found five precipitation system types with distinct properties, occurring throughout the year but peaking in different seasons. While overall rainfall statistics don't show any link to climate modes, links do appear for some system types using a multivariate approach. This climatology for a variety of precipitation system characteristics will allow future study of any changes in these characteristics due to climate change.

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“…The effect of adding an input parameter, on the extreme rainfall event multiclass classification, was inspected by Sangiorgio et al [17] They compared the performance of logistic regression and DNN with weather parameters as inputs and found that with the addition of an input parameter selected by them, there was an improvement in the accuracy of the classification. Many researchers have used parameters derived from the GNSS and radar for analysis [18], classification [8], and nowcasting [6] of the rainfall, storms, thunderstorms. The parameters derived from GNSS include zenith tropospheric delay (ZTD) [8], [17], precipitable water vapor [11], [19], [20], IWV [21], and IWV with vertical profiles of wet refractivity [6], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Binary classification of rainfall time-series using machine learning algorithms

Hudnurkar¹,

Rayavarapu²

2022

IJECE

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Summer monsoon rainfall contributes more than 75% of the annual rainfall in India. For the state of Maharashtra, India, this is more than 80% for almost all regions of the state. The high variability of rainfall during this period necessitates the classification of rainy and non-rainy days. While there are various approaches to rainfall classification, this paper proposes rainfall classification based on weather variables. This paper explores the use of support vector machine (SVM) and artificial neural network (ANN) algorithms for the binary classification of summer monsoon rainfall using common weather variables such as relative humidity, temperature, pressure. The daily data, for the summer monsoon months, for nineteen years, was collected for the Shivajinagar station of Pune in the state of Maharashtra, India. Classification accuracy of 82.1 and 82.8%, respectively, was achieved with SVM and ANN algorithms, for an imbalanced dataset. While performance parameters such as misclassification rate, F1 score indicate that better results were achieved with ANN, model parameter selection for SVM was less involved than ANN. Domain adaptation technique was used for rainfall classification at the other two stations of Maharashtra with the network trained for the Shivajinagar station. Satisfactory results for these two stations were obtained only after changing the training method for SVM and ANN.

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Spatio-Temporal Analysis of Intense Convective Storms Tracks in a Densely Urbanized Italian Basin

Cited by 7 publications

References 48 publications

The Phenomenology of West African Coastal Rainfall Events Based on a New Rain Gauge Network over Abidjan (Côte d’Ivoire)

The Phenomenology of West African Coastal Rainfall Events Based on a New Rain Gauge Network over Abidjan (Côte d’Ivoire)

An object-based climatology of precipitation systems in Sydney, Australia

Binary classification of rainfall time-series using machine learning algorithms

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