Tropical cyclone forecast using NCMRWF Global (12 km)  and regional (4 km) models

Ashrit, Raghavendra; Kumar, Sushant; Dube, Anumeha; Arulalan, T.; Karunasagar, S.; Routray, Aurobinda; Mohandas, Saji; George, John P.; Mitra, Ashim K.

doi:10.54302/mausam.v72i1.125

Cited by 7 publications

(2 citation statements)

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“…Further, the operational forecasts of various leading meteorological agencies were also compared by Chen et al (2022). The authors further concluded that all the operational deterministic models were e cient enough for predicting landfall point of super typhoon 'Lekima', as reported by Ashrit et al (2021) for Super Cyclone (SuCS) 'Amphan' using NCMRWF's operational model. The nested nite volume solver (FV-3) based Hurricane Analysis and Forecast System (HAFS) of National Oceanic and atmospheric administration (NOAA) is a hurricane modelling system that is in evolution phase at present.…”

Section: Resultsmentioning

confidence: 75%

“…The e cacy of NCUM global and regional models was further proved in simulation of a Super Cyclone (SuCS) 'Amphan' over the same ocean basin (i.e. Bay of Bengal; Ashrit et al 2021). The performance of a non-hydrostatic limited-area atmospheric model "Consortium for Small-scale Modelling (COSMO)" was evaluated and found commendable in detection of early genesis of a very severe cyclonic storm 'Ockhi' (2017) over Arabian Sea (Subrahamanyam et 2021) evaluated the skill of global and regional models only (i.e.…”

Section: Resultsmentioning

confidence: 99%

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Scientific strategies associated with mitigating the challenges in predicting track and intensity of cyclones across the world basins

Singh

2022

Preprint

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The present study analyses the scientific strategies associated with mitigating the challenges in predicting track and intensity of cyclones across the world basins. The relevant ‘51’ original publications on predicting track and intensity of cyclones across the world basins utilising numerical weather prediction (NWP) models, machine learning and satellite based approach were chosen using a focused orderly search and were used to develop the selection benchmarks for the systematic literature review. According to our review, out of selected ‘51’ studies, a total of ‘19’(37.25%) and ‘12’ (23.52%) and ‘9’ (17.64%) studies were focused on Pacific (including south China sea) Atlantic and North Indian Ocean (NIO) basins respectively. Three studies were conducted over both the Pacific and Atlantic Basins. However, the remaining ‘8’ studies were not over any specific basin. After 2015, the widespread use of machine based algorithm and analysis began, with a focus on the improvement in intensity prediction of TCs. Satellite based approach was employed in nearly all of the studies, and data assimilation techniques were in nascent stage during 2006-2013. To create detailed cyclone prediction track and intensity of cyclones across the world basins, a balance among data assimilation systems, modelling systems, and machine learning to uncover predictability drivers is necessary. Globally, predicting track and intensity of cyclones should take into account future climate change scenarios in the world basins. In order to gain improvement in this direction, further research is needed to fill in the knowledge gaps that have been identified.

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Section: Resultsmentioning

confidence: 75%

Section: Resultsmentioning

confidence: 99%