2020
DOI: 10.1002/met.1906
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Prediction of the August 2018 heavy rainfall events over Kerala with high‐resolution NWP models

Abstract: The southern Indian state of Kerala experienced exceptionally high rainfall during August 2018, which led to devastating floods in many parts of the state. Prediction and early warning of severe weather events in vulnerable areas is crucial for disaster management agencies in order to protect life and property. In recent years, state-of-the-art numerical weather prediction (NWP) models have been used operationally to predict rainfall over different spatial and temporal scales. In the present paper, predictions… Show more

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Cited by 25 publications
(5 citation statements)
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“…Kerala has a complex terrain with large heterogeneity, bounded by the Arabian Sea to the west and the Western Ghats mountains with peaks of more than 2500 m to the east. Details of the 2018 Kerala flood events and the underlying synoptic conditions are discussed in Hunt and Menon (2020), Ashrit et al (2020b), andMohandas et al (2020). The rain episode was mostly due to a monsoon depression formed over the Bay of Bengal from 13 to 17 August 2018 that immediately followed a monsoon low pressure system from 6 to 9 August 2018.…”
Section: Comparison Of Imdaa Rainfall With Era5 and Imd Gridded Observationsmentioning
confidence: 99%
See 1 more Smart Citation
“…Kerala has a complex terrain with large heterogeneity, bounded by the Arabian Sea to the west and the Western Ghats mountains with peaks of more than 2500 m to the east. Details of the 2018 Kerala flood events and the underlying synoptic conditions are discussed in Hunt and Menon (2020), Ashrit et al (2020b), andMohandas et al (2020). The rain episode was mostly due to a monsoon depression formed over the Bay of Bengal from 13 to 17 August 2018 that immediately followed a monsoon low pressure system from 6 to 9 August 2018.…”
Section: Comparison Of Imdaa Rainfall With Era5 and Imd Gridded Observationsmentioning
confidence: 99%
“…The state-of-the-art Met Office Unified Model (UM) and its 4D-Var data assimilation system are used to produce the IMDAA reanalysis. Performance of the pilot phase of IMDAA reanalysis is described in Mahmood et al (2018), and an initial evaluation of the first 15 years of the production run is described in Ashrit et al (2020a).…”
Section: Introductionmentioning
confidence: 99%
“…The routine application of regional atmosphere forecast systems at the kilometre scale, in which convection is not parameterized and local heterogeneity in the land surface can be represented, has led to substantial improvements in forecast skill at increasingly local scales (e.g. Clark et al, 2016;Bengtsson et al, 2017;Bush et al, 2020;Ashrit et al, 2020). The present paper assesses whether further performance improvements can be achieved through better representing the SST lower boundary condition.…”
Section: Introductionmentioning
confidence: 96%
“…Because deterministic forecasting of precipitation, and in particular of extreme events, is challenging due to the chaotic nature of weather and the associated exponential growth of forecast errors (caused, e.g., by model limitations in the representation of moist convection and errors in the initial conditions) ensemble forecasts are the preferred approach. They provide estimates for the range of plausible future states and thus quantify uncertainties, as well as yield probabilities for the occurrence of extreme weather events (Ashrit et al ., 2020; Mukhopadhyay et al ., 2021). The NCMRWF ensemble prediction system (NEPS) currently consists of (a) global forecasts (NCMRWF global ensemble prediction system [NEPS‐G]) with 23 members (one control and 22 perturbed members) with lead times of up to 10 days at 12‐km resolution in which convection is partially resolved as well as parametrized, and (b) regional forecasts (NEPS‐R) with 12 members (one control and 11 perturbed members) with lead times of up to 75 hr at 4‐km resolution in which convection is explicitly resolved instead of parametrized (Ashrit et al ., 2020; Chakraborty et al ., 2021; Mamgain et al ., 2020a, 2020b; Mukhopadhyay et al ., 2021; Sarkar et al ., 2021).…”
Section: Introductionmentioning
confidence: 99%