Characterization of warm season convective systems over US in terms of Cloud to Ground lightning, cloud kinematics, and precipitation

Tadesse, Alemu; Anagnostou, Emmanouil N.

doi:10.1016/j.atmosres.2008.05.009

Cited by 13 publications

(11 citation statements)

References 33 publications

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“…Petersen and Rutledge, 1998;Rivas Soriano et al, 2001;Rivas Soriano and De Pablo, 2003;Pineda et al, 2007;Barnolas et al, 2008;Tadesse and Anagnostou, 2009;Yair et al, 2010;Koutroulis et al, 2012), we propose to calibrate the threshold parameter I * at every station as a function of the presence or absence of lightning. In Sect.…”

Section: Methods Of Intense Storm Event Selectionmentioning

confidence: 99%

“…The premise is that summer thunderstorms with convective activity are commonly associated with lightning (e.g. Tapia et al, 1998;Rivas Soriano et al, 2001;Rivas Soriano and De Pablo, 2003;Barnolas et al, 2008;Tadesse and Anagnostou, 2009;Yair et al, 2010;Koutroulis et al, 2012). A number of recent studies have demonstrated the connection between intra-cloud and cloudto-ground lightning and convective precipitation using rainfall yield (i.e.…”

Section: Gaál Et Al: Selection Of Intense Storms By Rainfall Intementioning

confidence: 99%

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Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

Gaál

Molnár

Szolgay

2014

Hydrol. Earth Syst. Sci.

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Abstract. This paper presents a method to identify intense warm season storms with convective character based on intensity thresholds and the presence of lightning, and analyzes their statistical properties. Long records of precipitation and lightning data at 4 stations and 10 min resolution in different climatological regions in Switzerland are used. Our premise is that thunderstorms associated with lightning generate bursts of high rainfall intensity. We divided all recorded storms into those accompanied by lightning and those without lightning and found the threshold I * that separates intense events based on peak 10 min intensity I p ≥ I * for a chosen misclassification rate α. The performance and robustness of the selection method was tested by investigating the inter-annual variability of I * and its relation to the frequency of lightning strikes. The probability distributions of the main storm properties (rainfall depth R, event duration D, average storm intensity I a and peak 10 min intensity I p ) for the intense storm subsets show that the event average and peak intensities are significantly different between the stations. Nonparametric correlations between the main storm properties were estimated for intense storms and all storms including stratiform rain. The differences in the correlations between storm subsets are greater than those between stations, which indicates that care must be exercised not to mix events of different origin when they are sampled for multivariate analysis, for example, copula fitting to rainfall data.

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Section: Methods Of Intense Storm Event Selectionmentioning

confidence: 99%

Section: Gaál Et Al: Selection Of Intense Storms By Rainfall Intementioning

confidence: 99%

Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

Gaál

Molnár

Szolgay

2014

Hydrol. Earth Syst. Sci.

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“…The greatest flash density occurs in the tallest convective clouds where 40 dBZ reflectivity values are present at an altitude above 5 km (Proctor 1991;Steiger et al 2007;Tapia et al 1998;Toracinta et al 1996). Tadesse and Anagnostou (2009) found that a higher density of flashes coincides with a longer duration of the storm. Due to the strong relationship between radar reflectivity and lightning flash occurrence, Tapia et al (1998) concluded that lightning flash data could be utilized to detect heavy rainfall events associated with convective rainfall in locations where radar data are not available.…”

Section: Radar-based Climatologies Of Hazardous Weather Events and Opmentioning

confidence: 86%

“…1996). Tadesse and Anagnostou (2009) found that a higher density of flashes coincides with a longer duration of the storm. Due to the strong relationship between radar reflectivity and lightning flash occurrence, Tapia et al.…”

Section: Radar‐based Climatologies Of Hazardous Weather Events and Opmentioning

confidence: 94%

“…Incorporating lightning data into radar-based thunderstorm climatologies can help to separate stratiform and convective rainfall and indicate the stage of development of a storm cell (Steinacker et al 2000;Tadesse and Anagnostou 2009). Lightning activity commences when updrafts reach the )10°C isotherm (Gremillion and Orville 1999;Lhermitte and Krehbiel 1979;Toracinta et al 1996), and once reflectivity values increase above 35 dBZ (Tapia et al 1998;Toracinta et al 1996).…”

Section: Radar-based Climatologies Of Hazardous Weather Events and Opmentioning

confidence: 99%

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Use of Ground‐based Radar for Climate‐Scale Studies of Weather and Rainfall

Matyas

2010

Geography Compass

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Reflectivity data from weather radar provide information on the location and quantity of water and ice in the atmosphere at high spatial and temporal resolutions. Although the analysis of radar data facilitates spatially accurate climatologies of weather events and rainfall, relatively few studies have utilized data from the US Next Generation Radar (NEXRAD) network for climate‐scale research. Towards the goal of increasing the use of these data by geographers, this article details the collection of radar data, their limitations, and conversion into rainfall rates. Examples of climate‐scale research incorporating NEXRAD data are also presented. Although its capabilities to analyze temporal data are limited, the use of Geographical Information Systems (GIS) by non‐geographers is growing. This suggests that the collaboration of geographers specializing in geospatial techniques with those pursuing research in climatology could develop new GIS‐based methods for the spatial analysis of radar data that facilitate climate‐scale research of weather patterns.

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Ambient Systems for the Environmental Monitoring: Characteristic Examples at Different Spatial Scales

Kolios

Stylios

2013

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Characterization of warm season convective systems over US in terms of Cloud to Ground lightning, cloud kinematics, and precipitation

Cited by 13 publications

References 33 publications

Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

Use of Ground‐based Radar for Climate‐Scale Studies of Weather and Rainfall

Ambient Systems for the Environmental Monitoring: Characteristic Examples at Different Spatial Scales

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