Bias‐free rainfall forecast and temperature trend‐based temperature forecast using T‐170 model output during the monsoon season

Bhardwaj, Rashmi; Kumar, Ashok; Maini, Parvinder; Kar, S. C.; Rathore, L. S.

doi:10.1002/met.35

Cited by 22 publications

(7 citation statements)

References 4 publications

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“…For Bhutan, Nepal and Thailand, the bias is negative. By applying statistical post-processing methods, such a bias can be improved and thereby the forecast skill can also improve (Bhardwaj et al, 2007). Area-averaged correlation between observed and forecast rainfall (Figure 11(c)) indicates a rapid drop in correlation values from day 1 to day 3.…”

Section: Gujarat Heavy Rainfallmentioning

confidence: 99%

Medium‐range forecasts of extreme rainfall events during the Indian summer monsoon

Satyanarayana

Kar

2016

Meteorological Applications

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Extreme rainfall events occur rarely but are integral elements of climatic conditions in any region. The frequency of occurrence of extreme events has been studied using 57 years of observed rainfall data over India and its neighbouring countries. In this study, the rank method has been used to compute the threshold rainfall amount based on past observations. If the model forecasted rainfall is above the climatological threshold, then that event is identified as an extreme at that grid point and for that specific day. The results show an increasing trend in the number of extreme weather events during monsoons. The National Centre for Medium Range Weather Forecasting (NCMRWF) uses the Unified Model (NCUM) for operational weather forecasting on a medium-range timescale. The capability of the model in predicting extreme rainfall events over India and its surrounding regions has not been evaluated so far. Therefore, this study has been undertaken to identify extreme rainfall events from the medium-range rainfall forecasts and compare the same with observations so that forecasts for extreme events can be obtained. The rank method has been applied to the NCUM forecast of rainfall during the period June to September 2014. Several extreme rainfall cases are identified during this monsoon. The frequency of occurrence of extreme events is compared with observations, and it is found that the model has reasonable skill to predict up to day three forecasts. However, this skill reduces when the forecast period length increases.

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Section: Gujarat Heavy Rainfallmentioning

confidence: 99%

Medium‐range forecasts of extreme rainfall events during the Indian summer monsoon

Satyanarayana

Kar

2016

Meteorological Applications

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“…Currently, temperature forecasting is mainly issued in qualitative terms with the use of conventional methods, assisted by the data projected images taken by meteorological satellites to assess future trends (Bhardwaj et al, 2007;Paras et al, 2007). Yet, it is still far from being satisfactory, mostly due to the current techniques that involve sophisticated mathematical models to justify the use of such empirical rules.…”

Section: Problem Statementsmentioning

confidence: 99%

Jordan Pi-Sigma Neural Network for Temperature Prediction

Husaini

Ghazali

Nawi

et al. 2011

Communications in Computer and Information Science

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In recent years, various temperature forecasting models have been proposed, which broadly can be classified into physically-based approaches and statistically-based approaches. Hitherto, those approaches involve sophisticated mathematical models to justify the use of empirical rules which make them less desirable for some applications. Therefore, in this respect, Neural Networks (NN) have been successfully applied and with no doubt, they provide the ability and potentials to predict the temperature events. However, the ordinary NN adopts computationally intensive training algorithms and can easily get trapped into local minima.To overcome such drawbacks in ordinary NN, this research focuses on using a

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“…Small deviations in model forecasts from the observations can often cause mis‐representation of the useful model forecast skill. Several approaches have been applied to deal with the deficiencies of traditional verification methods (Davis et al , , ; Bhardwaj et al , ; Dube et al , ; Durai and Bhradwaj, ). Few studies have been made related to the evaluation of the economic values to the forecast based on the cost/loss model (Jolliffe and Stephenson, ; Considine et al , ; Kar et al , ).There have been no studies, so far, to evaluate the medium‐range rainfall forecasts from models run at the NCMRWF from the users' perspective.…”

Section: Introductionmentioning

confidence: 99%

Value‐added quantitative medium‐range rainfall forecasts for the BIMSTEC region

Joshi

Kar

2016

Meteorological Applications

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Daily operational forecasts from the global models run at the National Centre for Medium Range Weather Forecasting (NCMRWF), India were assessed for their skill during the southwest monsoon for member countries of the Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC). The rainfall forecasts from two models, NCMRWF Global Forecast System (NGFS) and NCMRWF Unified Model (NCUM), were used to carry out the verification study for July and August 2013 and 2014. The skills were analysed in terms of equitable threat score (ETS), Hansen-Kuiper skill score (HKS) and the economic value for various rainfall thresholds. The ETS had a positive value for all of the thresholds up to the Day 10 forecast for both years. The HKS and economic value for binary forecast of Yes rain (prediction that rainfall event will occur) suggest that the model has significant positive skills. Deterministic forecast for four rainfall thresholds show negative skills, suggesting that false alarms increase as forecasts are made for smaller rainfall thresholds. To improve the skill scores, forecasted data were processed using simple bias correction, relative co-efficient of variance (Rvar) method and the best easy systematic error (BESE) correction method. Correlation of the processed forecasts with model forecasts suggests an improvement using the bias correction and Rvar method but not using the BESE method. Significant improvement in the HKS and economic value of processed forecasts using the bias correction method has been noticed over most parts of the BIMSTEC region, except for the Western Ghats.

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Bias‐free rainfall forecast and temperature trend‐based temperature forecast using T‐170 model output during the monsoon season

Cited by 22 publications

References 4 publications

Medium‐range forecasts of extreme rainfall events during the Indian summer monsoon

Medium‐range forecasts of extreme rainfall events during the Indian summer monsoon

Jordan Pi-Sigma Neural Network for Temperature Prediction

Value‐added quantitative medium‐range rainfall forecasts for the BIMSTEC region

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