Assessing the skill of NCMRWF global ensemble prediction system in predicting Indian summer monsoon during 2018

Chakraborty, Paromita; Sarkar, Abhijit; Bhatla, R.; Singh, Rahul

doi:10.1016/j.atmosres.2020.105255

Cited by 15 publications

(8 citation statements)

References 95 publications

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“…The challenges of forecasting heavy precipitation associated with the monsoon for the Indian region have been pointed out in Mitra et al (2011), who investigated multimodel ensembles and found low skill for lead times longer than three days for individual ensemble members as well as for the ensemble mean. Chakraborty et al (2021) showed that the NEPS-G system had relatively good skill at forecasting low to moderate daily precipitation but had difficulty accurately representing higher amounts. This is likely because the 12-km resolution NEPS-G system is unable to properly capture regional convective processes, which are highly relevant for heavy precipitation (Dirmeyer et al, 2012;Konduru & Takahashi, 2020;Sillmann et al, 2017;Willetts et al, 2017), and also does not simulate critical interactions with small-scale orographic features (Baisya & Pattnaik, 2019;Rotunno & Houze, 2007;Webster et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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Section: Introductionmentioning

confidence: 99%

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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show abstract

“…They concluded that the updated EPS performs much better in probabilistic forecasts of the chosen extreme weather events than the old EPS, especially across the Indian region. Chakraborty et al (2021) and Sarkar et al (2021) also assessed the forecast skill of NEPS‐G in predicting the Indian summer monsoon and tropical cyclones respectively, and reported that upgraded NEPS‐G possesses good skill in predicting extreme rainfall up to day‐5 lead time. In a recent study, Shanker et al (2022) have shown that there is a significant improvement in the forecast quality of NEPS‐G due to the addition of lagged 11 members running from the previous day 1200 UTC initial conditions (IC).…”

Section: Introductionmentioning

confidence: 99%

Relative economic value of global ensemble prediction system of NCMRWF, India, for extreme weather events

Shanker,

Sarkar,

Mamgain

et al. 2024

Quart J Royal Meteoro Soc

Self Cite

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An ensemble prediction system quantifies the uncertainty in the forecast by a numerical weather prediction model and helps in decision‐making. The global ensemble prediction system (NEPS‐G) of NCMRWF, India, has 1 control and 22 perturbed ensemble members. In the present study, the relative economic value of NEPS‐G forecast has been investigated based on a simple decision‐analytic model. Temperature at 850 hPa (T850), maximum surface temperature (Tmax) and precipitation (rf) over the Indian domain have been studied. Ensemble mean forecast offers a larger value to the users than the control forecast. For rare extreme events, the area under the curve (T‐AUC) of a relative operating characteristic (ROC) obtained using the trapezoidal approximation for 22‐member NEPS‐G (NEPS‐22) is significantly smaller than the area under the curve (Z‐AUC) of the full ROC obtained using binormal model and Z‐transformation. As a result, the discrimination ability and relative economic value of NEPS‐G are underestimated. A method (SUB‐EM) which subdivides the lowest probability category into several subcategories and uses ensemble mean forecast as the secondary decision variable to enhance T‐AUC and improve relative economic value of NEPS‐G has been investigated. Tmax > 45°C and rf > 65 mm·day−1 have been considered as extreme events. The impact of increasing the number of perturbed ensemble members from 22 to 33 (NEPS‐33) by running additional 11 perturbed members from 0000 UTC initial condition has also been investigated and compared with the results obtained using SUB‐EM. SUB‐EM offers larger economic value to the users with small cost–loss ratio (α) without engaging any extra computational resources. An experiment is also carried out to study the impact of using different ensemble summary statistics as a secondary decision variable. Ensemble Maximum provides maximum value and benefits the largest range of users.

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“…Accurate rainfall forecasts beyond a few days can be challenging. For example, Chakraborty et al (2021) verified global ensemble rainfall forecasts during the 2018 summer monsoon and found that although forecasts for light to moderate rainfall had good skill out to 7 days, forecasts for extreme rainfall (>65.5 mm in 24 h) generally had poor skill beyond a few days. This can make it difficult for operational meteorologists to assess the likelihood of extreme rainfall within the medium‐range.…”

Section: Introductionmentioning

confidence: 99%

The application of predefined weather patterns over India within probabilistic medium‐range forecasting tools for high‐impact weather

Neal

Guentchev

Arulalan

et al. 2022

Meteorological Applications

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A method is presented for deriving probabilistic medium‐range (1‐to‐2‐week) weather pattern forecasts for India. This method uses an existing set of 30 objectively derived daily weather patterns, which provide climatological representations for unique states in the large‐scale circulation over India. Weather pattern forecast probabilities are based on the number of ensemble members objectively assigned to each weather pattern. Two summer monsoon case studies illustrate the best use of the forecasting tool within medium‐range guidance, such as highlighting the most likely weather pattern transitions and relating these to the likelihood of weather impacts. Forecast skill is evident out to at least 10–15 days. Winter dry period weather patterns have the highest forecast skill, closely followed by retreating monsoon weather patterns. In contrast, monsoon onset and break monsoon weather patterns have the lowest forecast skill. Finally, a prototype weather pattern forecast climatology application is presented for use in highlighting when extreme rainfall is more likely than normal. This application is based on weather pattern empirical probabilities of threshold exceedances using a high‐resolution regional reanalysis. The transitional pre‐ and post‐monsoon seasons have the greatest variability in rainfall across all possible weather patterns, with a slight dip in variability during the main summer monsoon season. In contrast, very little variability across weather patterns is evident during the relatively dry winter months. This highlights the times of year when a climatology‐based weather pattern forecasting approach may have its greatest benefits over that of a basic daily climatology.

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Assessing the skill of NCMRWF global ensemble prediction system in predicting Indian summer monsoon during 2018

Cited by 15 publications

References 95 publications

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

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

Relative economic value of global ensemble prediction system of NCMRWF, India, for extreme weather events

The application of predefined weather patterns over India within probabilistic medium‐range forecasting tools for high‐impact weather

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