Prediction of the August 2018 heavy rainfall events over Kerala with high‐resolution <scp>NWP</scp> models

Ashrit, Raghavendra; Sharma, Kuldeep; Kumar, Sushant; Dube, Anumeha; Karunasagar, S.; Arulalan, T.; Mamgain, Ashu; Chakraborty, Paromita; Lodh, Abhishek; Dutta, Devajyoti; Momin, Imranali M.; Bushair, M. T.; Prakash, Buddhi J.; Jayakumar, A.; Rajagopal, E. N.

doi:10.1002/met.1906

Cited by 23 publications

(5 citation statements)

References 18 publications

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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%

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IMDAA: High Resolution Satellite-era Reanalysis for the Indian Monsoon Region

et al. 2021

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A high resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12 km resolution reanalysis covering the satellite-era from 1979 to 2018 using 4D-Var data assimilation method and the UK Met Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some were not used in any previous reanalyses. Various aspects of this reanalysis, like quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semi-permanent summer monsoon features (e.g., Low-level Jet and Tropical Easterly Jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, inter-annual, and intra-seasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.

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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%

IMDAA: High Resolution Satellite-era Reanalysis for the Indian Monsoon Region

et al. 2021

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“…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%

The impact of time‐varying sea surface temperature on UK regional atmosphere forecasts

Mahmood

Lewis

Castillo

et al. 2021

Meteorological Applications

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A new approach to improve the ocean surface boundary condition used in regional numerical weather prediction is proposed. Typically, regional atmosphere forecast systems assume a fixed sea surface temperature during a simulation. The study assesses the use of ocean temperature from an operational regional ocean model as an evolving lower boundary in a kilometrescale regional atmosphere configuration centred on the UK. Simulations of a winter and two five day duration summer case studies associated with anomalously warm temperatures are considered. The largest impact is found in summer, when a growing cold bias in mean temperature over land compared with observations is apparent when using a fixed global-scale analysis lower boundary condition. The mean error is improved by 0.1 K when using a fixed temperature boundary condition from a kilometre-scale regional ocean model initial condition. When using hourly surface temperature data from the same regional ocean model, the error is improved by 0.5 K for this case. Prediction of daytime maximum air temperature is also improved during the summer heat wave cases. A winter case study shows marginal improvement over the ocean and negligible changes over land.These results are confirmed in longer duration experiments using an hourly cycling regional forecast system with data assimilation for summer and winter periods. A systematic and statistically significant improvement of near-surface temperature verification relative to the observations over land is demonstrated for both summer and winter using the new approach. This study supports future operational implementation of a time-varying lower boundary for regional numerical weather prediction.

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“…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%

A comparison of two statistical postprocessing methods for heavy‐precipitation forecasts over India during the summer monsoon

Angus,

Widmann,

Orr

et al. 2024

Quart J Royal Meteoro Soc

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Accurate ensemble forecasts of heavy precipitation in India are vital for many applications and essential for early warning of damaging flood events, especially during the monsoon season. In this study we investigate to what extent Quantile Mapping (QM) and Ensemble Model Output Statistics (EMOS) statistical postprocessing reduce errors in precipitation ensemble forecasts over India, in particular for heavy precipitation. Both methods are applied to day‐1 forecasts at 12‐km resolution from the 23‐member National Centre for Medium Range Weather Forecasting (NCMRWF) global ensemble prediction system (NEPS‐G). By construction, QM leads to distributions close to the observed ones, while EMOS optimizes the ensemble spread, and it is not a priori clear which is better suited for practical applications. The methods are therefore compared with respect to several key aspects of the forecasts: local distributions, ensemble spread, and skill for forecasting precipitation amounts and the exceedance of heavy‐precipitation thresholds. The evaluation includes rank histograms, Continuous Ranked Probability Skill Scores (CRPSS), Brier Skill Scores (BSS), reliability diagrams, and receiver operating characteristic. EMOS performs best not only with respect to correcting under‐ or overdispersive ensembles, but also in terms of forecast skill for precipitation amounts and heavy precipitation events, with positive CRPSS and BSS in most regions (both up to about 0.4 in some areas), while QM in many regions performs worse than the raw forecast. QM performs best with respect to the overall local precipitation distributions. Which aspects of the forecasts are most relevant depends to some extent on how the forecasts are used. If the main criteria are the correction of under‐ or overdispersion, forecast reliability, match between the forecasted distribution for individual days and observations (CRPSS), and the skill in forecasting heavy‐precipitation events (BSS), then EMOS is the better choice for postprocessing NEPS‐G forecasts for short lead times.

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Prediction of the August 2018 heavy rainfall events over Kerala with high‐resolution NWP models

Cited by 23 publications

References 18 publications

IMDAA: High Resolution Satellite-era Reanalysis for the Indian Monsoon Region

IMDAA: High Resolution Satellite-era Reanalysis for the Indian Monsoon Region

The impact of time‐varying sea surface temperature on UK regional atmosphere forecasts

A comparison of two statistical postprocessing methods for heavy‐precipitation forecasts over India during the summer monsoon

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