Forecast Verification Analysis of Rainfall for Southern Districts of Tamil Nadu, India

Joseph, M.; Murugan, E.; Hemalatha, M.

doi:10.20546/ijcmas.2017.605.034

Cited by 4 publications

(6 citation statements)

References 3 publications

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“…There were no values for post monsoon season as neither rainfall was observed nor was the forecast made for the said season (Table 15). Similar observations were reported by Joseph et al, (2017).…”

Section: Resultssupporting

confidence: 92%

Verification of Medium Range Weather Forecast Issued for Jammu Region to Generate Agromet Advisory

Sharma¹,

Singh²

2018

Int.J.Curr.Microbiol.App.Sci

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Section: Resultssupporting

confidence: 92%

Verification of Medium Range Weather Forecast Issued for Jammu Region to Generate Agromet Advisory

Sharma¹,

Singh²

2018

Int.J.Curr.Microbiol.App.Sci

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“…In either case, it is necessary to provide information on the nature of forecasting errors, due to their strengths or weaknesses of a forecasting system. Verification of precipitation is very important as it is one of the important end products with a more practical application for the user community" [3]. "Since these are very discontinuous parameters and the representativeness of the ground truth on which to carry out the verification depends mainly on the quality, density and pre-processing of the observed data.…”

Section: Introductionmentioning

confidence: 99%

“…A weather element may be either a continuous or a categorical variable. Parameters like rainfall take discrete values like 0 (no rain) and 1 (rain) and hence is a categorical variable" [3]. "The agromet advisory bulletin is prepared for the next five days for the district and different blocks of respective district based on crop and weather forecast information received from the field visit, whatsapp group and IMD website.…”

Section: Introductionmentioning

confidence: 99%

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Rainfall Forecast Verification in Different Blocks of Banka District of Bihar

Sahu

Prasad

Pandit³

et al. 2022

IJECC

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Accuracy in rainfall forecast plays an important role in farming operations. Here, a study has been done to verify the forecast and actual rainfall data of 11 blocks of Banka district at KVK, Banka. To verify the accuracy of rainfall forecast for the financial year 2020-21 several statistical tools has been used i.e., Forecast Accuracy (ACC) or Ratio Score or Hit Score, Hanssen and Kuipers Scores or True Skill Score (HK score), Probability of detection (POD), Heidke Skill Score (HSS), False alarm ratio, Critical Success index (CSI), Missing Rate (MR) and BIAS score. Forecast Accuracy (ACC) ranged from 0.81 to 0.87 and the highest accuracy of rainfall prediction was observed in Katoriya block and lowest accuracy observed in Amarpur, Dhoraiya and Rajoun block. Similarly, Critical Success Index (CSI) was recorded with the values range of 0.4 to 0.6 for respective blocks of Banka district. Heidke Skill Score (HSS) was accounted as 0.50 for Amarpur, Banka, Barahat, Bounsi, Belhar, Dhoraiya, Rajoun and Shambhuganj and for rest of the blocks it was 0.6. The higher accuracy (87.1 per cent) was recorded in Katoriya block followed by Fullidumar (86.3 per cent) and Chandan (85.4 per cent). The highest no. of rainy days was observed in Katoriya block (75 no.) followed by Chandan (74 no.). POD value ranges between 0.8-0.9, FAR value between 0.3-0.5, MR and BIAS value ranges between 0.8-0.9 and 1.3 -2.0 respectively.

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“…Verification of model forecasts is very imperative, because, it says how to make the best use of the forecasts at any weather prediction (Karuna Sagar, 2017). Verification of rainfall is crucial, as it is one of the important products with more practical application to the user community (Joseph et al, 2017). The present study explored the verification of location-specific seasonal rainfall predicted Australian Rainman software V(4.3) with the observed during both southwest and northeast monsoon by using different skill scores for Tamil Nadu…”

Section: Introductionmentioning

confidence: 99%

Corroboration of Location Specific Seasonal Rainfall Forecast Using Australian Rainman for Tamil Nadu

Sembanan¹,

Geethalakshmi²,

Ramanathan³

2022

MAJ

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Skillful weather and seasonal predictions have significant socio-economic potential and could provide evocative information to farmers and decisionmakers towards agricultural planning. The present study evaluates the Australian seasonal rainfall forecast model for the southwest and northeast monsoon of Tamil Nadu over the period from 2016 to 2020.. Different skill scores viz., Nash-Sutcliffe Efficiency (NSE), Ratio of Root Mean Square Error (RMSE) to the standard deviation of the observations (RSR), Per cent Bias (PBIAS), and Kling-Gupta Efficiency (KGE) were used for the present study to evaluate the performance of seasonal rainfall prediction by the Australian Rainman Software. The verification skill scores for both monsoons indicated that for southwest monsoon, especially the drought incidence (2016) was well captured by the Software. Inculcation of new climatic drivers, the performance of the northeast monsoon was also found to be within the acceptable range limit in recent years.

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Forecast Verification Analysis of Rainfall for Southern Districts of Tamil Nadu, India

Cited by 4 publications

References 3 publications

Verification of Medium Range Weather Forecast Issued for Jammu Region to Generate Agromet Advisory

Verification of Medium Range Weather Forecast Issued for Jammu Region to Generate Agromet Advisory

Rainfall Forecast Verification in Different Blocks of Banka District of Bihar

Corroboration of Location Specific Seasonal Rainfall Forecast Using Australian Rainman for Tamil Nadu

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