Authors : Yuvashrri M.; C. Preethibha; Rajaram M.; V. Shantthi; R. Gowri; Arun Balaji S.; N. Sriraj Bommannan; Nithila G.; Gowtham P.; Veeramani K.

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/yrftny8e

Scribd : https://tinyurl.com/5n8z4h4e

DOI : https://doi.org/10.38124/ijisrt/26May1939

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The exploration of mesoscale convective systems (MCS) is a vital aspect of meteorology, particularly in understanding and predicting severe weather events. The automated identification of rain cells within MCS can significantly enhance the precision of weather forecasts. Recent advancements have led to the development of algorithms like the Tracking Algorithm for Mesoscale Convective System (TAMS), which employs area-overlapping and projected-cloud-edge tracking techniques to identify and track MCS with greater accuracy. Additionally, the integration of deep learning models, such as the deep Convolutional Neural Network for the Identification of Mesoscale Convective System (MesCoSNet), has shown promise in Identifying mMCS from satellite data. These innovative approaches, utilizing infrared imagery and meteorological data, are crucial for early warning systems, which can save lives and reduce economic losses by providing timely alerts for severe weather conditions. The ongoing research in this field aims to unravel the complex interplay between various atmospheric parameters and the dynamics of rain cell formation, ultimately contributing to a more comprehensive understanding of weather patterns and MCS behavior.

Keywords : Machine Learning, Mesoscale Convective Systems, Rain Cells.

References :

1. Kattsov,V.M.;Akentieva,E.M.;Anisimov,O.A.;Bardin,M.Y.;Zhuravlev,S.A.;Kiselev,A.A.;Klyueva,M.V.;Konstantinov,P.I.
2. Korotkov,V.N.;Kostyanoy,A.G.;etal.ThirdAssessmentReportonClimateChangeandItsConsequencesonTheTerritoryoftheRussian Federation;GeneralSummary;RoshydrometScience-IntensiveTechnologies:St.Petersburg,Russia,2022.
3. Diffenbaugh,N.S.;Scherer,M.;Trapp,R.J.Robustincreasesinseverethunderstormenvironmentsinresponsetogreenhouse forcing.Proc.Natl.Acad.Sci.USA2013,110,16361–16366. PubMed]
4. Rädler,A.T.;Groenemeijer,P.H.;Faust,E.;Sausen,R.;Púˇcik,T.FrequencyofseverethunderstormsacrossEuropeexpectedto increaseinthe21stcenturyduetorisinginstability.npjClim.Atmos.Sci.2019,2,30.
5. Chernokulsky,A.;Eliseev,A.;Kozlov,F.;Korshunova,N.;Kurgansky,M.;Mokhov, I.;Semenov,V.;Shvets’,N.;Shikhov,A.; Yarinich,Y.I.AtmosphericsevereconvectiveeventsinRussia:Changesobservedfromdifferentdata.Russ.Meteorol.Hydrol.2022, 47,343–354.
6. Meredith,E.P.;Semenov,V.A.;Maraun,D.;Park,W.;Chernokulsky,A.V.CrucialroleofBlackSeawarminginamplifyingthe2012 Krymskprecipitationextreme.Nat.Geosci.2015,8,615–619.
7. Chernokulsky,A.;Kurgansky,M.;Mokhov,I.;Shikhov,A.;Azhigov,I.;Selezneva,E.;Zakharchenko,D.;Antonescu,B.;Kühne, TornadoesinnorthernEurasia:Fromthemiddleagetotheinformationera.Mon.WeatherRev.2020,148,3081–3110.
8. Chernokulsky,A.;Shikhov,A.;Bykov,A.;Azhigov,I.Satellite-basedstudyandnumericalforecastingoftwotornadooutbreaksin theUralRegioninJune2017.Atmosphere2020,11,1146.