Articles written in Journal of Earth System Science

    • Evaluation of heavy rainfall warnings of India National Weather Forecasting Service for monsoon season (2002–2018)


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      The major objective of any national weather forecasting services is to provide weather forecast and warnings and other meteorological related information to the public and government for the safety of life and property and economic activities. The heavy rainfall causes huge loss to the public in form of flood and landslide in varying severity mainly during monsoon season (June–September). Hence its accurate prediction is essential and the accuracy of prediction needs to be verified quantitatively to evaluate its strength and weakness. The National Weather Forecasting Centre (NWFC) of India Meteorological Department (IMD) issues heavy rainfall (HR) warnings for the safety of life and property of the public. In this study, verification of operational heavy rainfall (HR) warning issued by NWFC of IMD for 36 sub-divisions of India is carried out. The verification scores presented in the study are for 24 hrs (D1), 48 hrs (D2) and 72 hrs (D3) lead period average warning skills during 2014–2018 and year-wise trend of the HR warnings for the period 2002–2018. In general, it is observed that there are significant improvements in skill scores in recent years. The improvement in D3 is at higher rate as compared to D1 scores. The improvement in the recent years is mainly due to improvement in model resolution and data assimilation in the Numerical Prediction (NWP) Models runs by Ministry of Earth Sciences (MoES), Government of India and their interpretation and utilization by the forecasters for objective consensus forecast using an objective decision support system and synoptic value addition.


      $\bullet$ There is significant improvement in heavy rainfall warning skill of India Meteorological Department during monsoon season in recent two years (2017 and 2018) as compared to 2002–2016.

      $\bullet$ The skill scores namely, Probability of Detection (PoD), Critical Success Index (CSI) and Heidke Skill Score (HSI) has improved by 48%, 46% and 33%, respectively, as compared to mean of scores between 2002–2016 for Day 1 (D1) warning.

      $\bullet$ In Day 3 (D3) warning, there is an improvement by 69%, 54% and 54% in PoD, CSI and HSS respectively during 2017–2018 as compared to mean of 2013–2015. The improvement in D3 warning is at higher rate as compared to D1 warning.

      $\bullet$ In general, the skill scores are higher over the regions with higher frequency of heavy rainfall and lower over less prone regions of heavy rainfall.

      $\bullet$ These improvements in the forecast warning skill may be attributed to availability and use of latest forecasting models with high resolution and better data assimilation. Apart from the above, the structured monitoring of the monsoon circulations parameters, interpretation of NWP models guidance through Forecast Demonstration Project (FDP), objective consensus through decision support system and subjective consensus amongst the forecasters through video conference contributed significantly improved HR warning in recent years.

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