Instability indices and forecasting thunderstorms:  the case of 30 April 2009

Tajbakhsh, Sahar; Ghafarian, Parvin; Sahraian, F.

doi:10.5194/nhess-12-403-2012

Cited by 38 publications

(26 citation statements)

References 20 publications

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“…e convective available potential energy (CAPE) at 12 UTC for cases A and B was 2282 J/kg and 2439 J/kg, respectively, while at 18 UTC it dropped to 1039 J/kg and 1997 J/kg, respectively. Tajbakhsh et al [43] used the Miller checklist to categorize the severity of thunderstorms. From this checklist, it is observed that most of the parameters available to determine the strength in both cases implied that the storms were weak ( Table 2).…”

Section: Wind and Moisturementioning

confidence: 99%

Meteorological Analysis of Floods in Ghana

Ansah

Ahiataku

Yorke

et al. 2020

Advances in Meteorology

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The first episodes of floods caused by heavy rainfall during the major rainy season in 2018 occurred in Accra (5.6°N and 0.17°W), a coastal town, and Kumasi (6.72°N and 1.6°W) in the forest region on the 18th and 28th of June, respectively. We applied the Weather Research and Forecasting (WRF) model to investigate and examine the meteorological dynamics, which resulted in the extreme rainfall and floods that caused 14 deaths, 34076 people being displaced with damaged properties, and economic loss estimated at $168,289 for the two cities according to the National Disaster Management Organization (NADMO). The slow-moving thunderstorms lasted for about 8 hours due to the weak African Easterly Wave (AEW) and Tropical Easterly Jet (TEJ). Results from the analysis showed that surface pressures were low with significant amount of moisture influx aiding the thunderstorms intensification, which produced 90.1 mm and 114.6 mm of rainfall over Accra and Kumasi, respectively. We compared the rainfall amount from this event to the historical rainfall data to investigate possible changes in rainfall intensities over time. A time series of annual daily maximum rainfall (ADMR) showed an increasing trend with a slope of 0.45 over Accra and a decreasing trend and a slope of –0.07 over Kumasi. The 95th percentile frequencies of extreme rainfall with thresholds of 45.10 mm and 42.16 mm were analyzed for Accra and Kumasi, respectively, based on the normal distribution of rainfall. Accra showed fewer days with more heavy rainfall, while Kumasi showed more days with less heavy rainfalls.

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Section: Wind and Moisturementioning

confidence: 99%

Meteorological Analysis of Floods in Ghana

Ansah

Ahiataku

Yorke

et al. 2020

Advances in Meteorology

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“…This study uses K index (KI), Total-totals (TT), and SWEAT to determine the condition of atmospheric stability in light and moderate rain conditions on January 28-31 and February 1-2, 2016. The indices were selected for widely used to analyse atmospheric stability conditions in tropical regions particularly in Indonesia [7,[9][10][11][12][13]].…”

Section: Instability Indicesmentioning

confidence: 99%

“…The Radiosonde data can be used for forecasting convection that can produce rain with thunderstorms [4][5][6][7]. It is essential to predict the occurrence of extreme weather as early as possible to provide security when dangerous weather threatens.…”

Section: Introductionmentioning

confidence: 99%

Data integration from intensive observation period in 2016 to detect extreme weather in the vicinity of Jakarta region

Zahroh¹,

Supatmanto²,

Ayubi³

et al. 2018

MATEC Web Conf.

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Meteorological hazard has been frequently occurred in Indonesia due to torrential rains. It is important to examine the characteristics of the atmosphere during rainy seasons for hazard mitigation. National Laboratory of Weather Modification Technology has conducted a short Intensive Observation Program (IOP) from January 18th to February 16th, 2016 to collect meteorological data in the vicinity of Jakarta Region. During that period several instruments have been used, such as Radar, Microwave Profiling Radiometer, Automatic Weather Station, and Radiosonde. This paper examines the comparison of atmospheric parameters obtained from Radiosonde and Profiling Radiometer during extreme weather days. The results showed that there were significant differences of instability indices of Radiosonde and Profiling Radiometer data: 15 points for KI, 6 points for TT and 100 points for SWEAT. The atmospheric stability indices of the Profiling Radiometer tended to be lower than Radiosonde. A radar image showing a rainstorm as well as rain rate information validates atmospheric index stability data. Radar and atmospheric instability indices data integration can be used as one of the parameters to forecast extreme weather events and as an early warning system of hazard mitigation., 02006 (2018) MATEC Web of Conferences

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“…Usually this type of thunderstorms are observed in pre-monsoon season over India. Areas like Bihar, west bengal, northeast states, Andhra Pradesh, Orissa are frequently subjected to this thunderstorms [4][5]. Now a days, satellites are playing crucial role in weather prediction.…”

Section: Introductionmentioning

confidence: 99%

ANN and ARMA based thunderstorm prediction over Andhra Pradesh using INSAT 3D Satellite

Umakanth*,

Satyanarayana,

Kiran

et al. 2019

IJRTE

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Extreme events such as severe thunderstorms, heat waves, cyclones, heavy rainfall events are increasing day by day in recent years over India. Out of all extreme events, thunderstorms are causing more damage and deaths when compared to others. Thunderstorms are tougher to be predicted in advance due to their faster development. In this paper, we tried to analyse two severe thunderstorm cases in premonsoon season for the time period 2017 and 2018 by using INSAT-3D satellite data. This satellite data helps us to monitor the convective cloud system every 30 minutes. Using this satellite data we are able to calculate the atmospheric indices like LI, KI, TTI and HI for every 30 minutes. After being analyzed by INSAT-3D satellite data, we tried to predict the peak stage of thunderstorms using ANN and ARMA techniques. The atmospheric based stability indices have been used as inputs for ANN & ARMA models inorder to achieve prediction. ANN prediction was better than ARMA prediction when compared to INSAT-3D satellite data

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Instability indices and forecasting thunderstorms: the case of 30 April 2009

Abstract: Abstract. In this paper, one meteorological case study for two Iranian airports are presented.

Cited by 38 publications

References 20 publications

Meteorological Analysis of Floods in Ghana

Meteorological Analysis of Floods in Ghana

Data integration from intensive observation period in 2016 to detect extreme weather in the vicinity of Jakarta region

ANN and ARMA based thunderstorm prediction over Andhra Pradesh using INSAT 3D Satellite

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