Forecasting of thunderstorms in the pre‐monsoon season at Delhi

Ravi, N.; Mohanty, U. C.; Madan, O. P.; Paliwal, R. K.

doi:10.1002/met.19996103

Cited by 33 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The discriminant analysis led to classify few selected, morning and afternoon thunderstorms with 53.33 and 42.20% accuracy and 20 variables. In the present study, more than 90% accurate forecasts are observed for thunderstorms with lead time 12 to 6 h. Ravi et al (2006) applied a multiple regression method to give a potential forecast in probabilistic terms using nine predictors for pre‐monsoon thunderstorms over Delhi, India. This was a potential method for operational use.…”

Section: Applicability and Limitationsmentioning

confidence: 66%

Nowcasting thunderstorms with graph spectral distance and entropy estimation

Chaudhuri

Middey

2011

Meteorological Applications

View full text Add to dashboard Cite

ABSTRACT:The aim of the present study is to forecast thunderstorms over Kolkata (22°32 N, 88°20 E), India, during the pre-monsoon season (April-May) with graph spectral distance and entropy analysis. Graph vertices represent points connected by lines or edges, and lifting condensation level, convective condensation level, level of free convection, freezing level, level of neutral buoyancy and the surface level are taken as the input of the graph vertices. The variation in the most probable distance between the vertices is investigated. The result reveals a particular orientation of the vertex distances for thunderstorm days which is significantly different from the non-thunderstorm days. The reference graphs for thunderstorm and non-thunderstorm days are formed using the most probable vertex distances. The spectral distance between the reference graph and the graphs corresponding to thunderstorms are computed with the data collected during the period 1997-2009. The entropies, or the measure of disorderliness or uncertainty, are estimated for the graph distance matrices. The result shows that the thunderstorm days possess lower distance entropy than the non-thunderstorm days. This indicates that the reference graph that has been constructed for thunderstorms is more consistent. The result further depicts that the forecast accuracy through the present method is 98% with 1 h lead time, whereas the accuracy is 93% with 6 h lead time. The forecast is validated with the India Meteorological Department observations for the years

show abstract

Section: Applicability and Limitationsmentioning

confidence: 66%

Nowcasting thunderstorms with graph spectral distance and entropy estimation

Chaudhuri

Middey

2011

Meteorological Applications

View full text Add to dashboard Cite

show abstract

“…Thunderstorm study was mainly carried out using techniques/observations such as radars (Leslie et al 1977;Sen et al 2010;Srivastava et al 2010;Litta et al 2012), radiosonde (Ravi et al 1999;Basu and Mondal 2002;Chaudhari et al 2010;Tajbakhsh et al 2012;Pucik et al 2015), lightning network (Dieter 2014), electric field mill (Pawar and Kamra 2009;Pawar et al 2014) and numerical models like WRF (Litta and Mohanty 2008;Litta et al 2012 and references there within), where the extrapolation of radar reflectivity echoes to forecast the location of convective storms was used widely. But the accuracy of these forecasts generally decreases rapidly during the first 30 min because of the very short lifetime of individual convective storms.…”

Section: Introductionmentioning

confidence: 99%

Analysing the characteristic features of a pre-monsoon thunderstorm event over Pune, India, using ground-based observations and WRF model

et al. 2019

View full text Add to dashboard Cite

show abstract

“…On the other hand, south‐west monsoon (June to September) cloud systems over the same region are relatively widespread and have three to four times lower cloud base, with less tall clouds as compared with the pre‐monsoon season (Chaudhuri, ). Over Northwest Indian plain regions, these systems are less organized, mesoscale in temporal and spatial extent and often accompanied by hail during the pre‐monsoon season (Ravi et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of monthly cloud climatology of the Indian subcontinent as observed by TRMM precipitation radar

Roy

Saha

Bhowmik

et al. 2014

Intl Journal of Climatology

View full text Add to dashboard Cite

Tropical Rainfall Measuring Mission Precipitation Radar data (2A23 V7), for a period of 9 years (2002–2010), is used to analyse the evolution of the monthly cloud climatology over the Indian subcontinent (lat. 6.5°–38.5°N, long. 66.5°–100.5°E). The analysis of the cloud data suggests that over most regions and seasons, the stratiform cloud fraction is associated with outflows from and remnants of convective cells. However, the stratiform cloud cover over the North Indian subcontinent during winter season is not accompanied by any convective cloud maximum zone, indicating minimal role of ground processes in the growth of cloud cover over the region during this season. Mesoscale factors and land surface processes play a major role in the development of cloud maxima during March to May over the Indian subcontinent, over the eastern Himalayas and adjoining plains, as well as southwest peninsular India. As the summer monsoon season approaches, the spatial spread and growth of both cloud fractions gradually increase (stratiform fraction of cloud cover increases rapidly, while the convective fraction rises gradually) over east India, Bay of Bengal and over south Arabian Sea, indicating increased organization of the cloud systems with longer lasting stratiform outflows with the advancement of the season. The largest cloud maxima during the monsoon season are observed at the windward side of hills along the west coast of peninsular India and the west coast of Myanmar. The respective values of convective and stratiform cloud fractions indicate that the cloud cover over Myanmar is associated with more organized cloud systems with larger stratiform outflows, as compared with clouds along the west coast of peninsular India. Similarly, during October to December, the increasing stratiform cloud fractions over southeast peninsular India indicate increasing organization of clouds, compared with values during the monsoon season over this region.

show abstract

Forecasting of thunderstorms in the pre‐monsoon season at Delhi

Abstract: Accurate prediction of thunderstorms during the pre-monsoon season (April-June)

Cited by 33 publications

References 4 publications

Nowcasting thunderstorms with graph spectral distance and entropy estimation

Nowcasting thunderstorms with graph spectral distance and entropy estimation

Analysing the characteristic features of a pre-monsoon thunderstorm event over Pune, India, using ground-based observations and WRF model

Analysis of monthly cloud climatology of the Indian subcontinent as observed by TRMM precipitation radar

Contact Info

Product

Resources

About