Please use this identifier to cite or link to this item: https://elibrary.tucl.edu.np/handle/123456789/11328
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPrajapati, Krishna Ram-
dc.date.accessioned2022-06-16T07:17:20Z-
dc.date.available2022-06-16T07:17:20Z-
dc.date.issued2021-
dc.identifier.urihttps://elibrary.tucl.edu.np/handle/123456789/11328-
dc.description.abstractDuring the pre-monsoon (March, April and May) in Nepal, severe thunderstorms and hailstorms cause significant property and agricultural damage, in addition to loss of life from lightening. In the present study, Severe thunderstorm event occurred at Pokhara, 800 asl, in Central Nepal on 29 th April, 2019, during early afternoon is performed. The event was lasted half hour and produced the golf ball-sized hailstones that destroyed vehicles windshields, damaged the crops worth millions of rupees and more. The Advanced Research-Weather Research and Forecasting (WRF-ARW) model was used to simulate the features associated with a severe thunderstorm and examined its sensitivity to six different micro-physical (MP) schemes (Thompson, Morrison, Goddard, Lin, WSM6 and Ferrier). The three nested domains with the innermost domain of 1km horizontal resolution and integrated for 36hr with the spin-up time of 24 hr. Numerical study of thunderstorms have been discussed with its antecedent thermodynamic stability indices that include K Index, Total totals index (TTI), Convective Available Potential Energy (CAPE), Convective Inhibition Energy (CINE), and Precipitable Water (PW) used for the short range prediction of thunderstorms. For validating simulated features of the thunderstorm, Automatic Weather Station data of Lumle station and observed data of Pokhara synoptic station were used with statistic error analysis using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Correlation Coefficient (CC). All the microphysics scheme well predicts the instability indices required for the thunderstorm occurrence. Overall Morrison and Thompson scheme performed well with same correlation coefficient of 0.80, whereas WSM6 has least results with mean correlation coefficient of 0.46 compared to the observed. Keywords: WRF-ARW, Microphysics, Thunderstorm, Instability Indices.en_US
dc.language.isoen_USen_US
dc.publisherDepartment of Hydrology and Meteorologyen_US
dc.subjectWRF-ARWen_US
dc.subjectMicrophysicsen_US
dc.subjectThunderstormen_US
dc.subjectInstability Indicesen_US
dc.titleSimulation of Severe Thunderstorm Event on 29 th April 2019 in Pokhara Using Advance Research Weather Research and Forecasting Model (WRF-ARW)en_US
dc.typeThesisen_US
local.institute.titleCentral Department of Hydrology and Meteorologyen_US
local.academic.levelMastersen_US
Appears in Collections:Hydrology & Meteorology

Files in This Item:
File Description SizeFormat 
All thesis.pdf4.55 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.