• KRISHNA MISHRA

Articles written in Journal of Earth System Science

• Performance of numerical weather prediction models in predicting track of recurving cyclone Vayu over Arabian Sea during June 2019

A tropical cyclone (TC) Vayu developed over the Arabian Sea during June, 2019. It followed a northward track from southeast Arabian Sea to northeast Arabian Sea close to Gujarat coast during 10–12 June 2019 as a very severe cyclonic storm. It skirted south Gujarat coast by recurving west-northwestwards during 13th–14th June and again made a northeastward recurvature on 16th June towards Gujarat coast. However, it weakened over Sea on 17th. There was large divergence among various models in predicting the track of TC Vayu leading to over warning for Gujarat state and also delay in dewarning leading to evacuation of people from coastal region. Hence, a study has thus been taken up to analyze the performance of various numerical weather prediction (NWP) models in forecasting the track of TC Vayu so as to find out the reason for above limitation of NWP models. Results suggest that there is a need to relook into the existing multi-model ensemble (MME) technique which outperforms individual models in track forecasting. There is also a need to improve the individual deterministic model guidance so as to suitably represent the interaction between mid-latitude westerlies with the TC and steering anticyclone by improving the initial and boundary conditions through augmented direct and remotely sensed observations over the Arabian Sea and their assimilation in NWP models.

$\bf{Highlights}$

$\bullet$ The multiple interactions among the wind fields of TC Vayu, middle latitude westerlies and anticyclones over central India & Arabian Peninsula led to the unique track of Vayu with two recurvatures in its life cycle.

$\bullet$ The prediction of time and point of recurvature in the track of TCs is still a challenge for the NWP models and hence the operational forecast, as models could not represent the interaction of mid-latitude westerlies with the TC and steering anticyclone over either side of the TC.

$\bullet$ Comparing the average track forecast errors of different models and multi-model ensemble (MME) for the recurving TCs during 2009–2019, the MME shows minimum average track forecast error. However, the consistency in MME based track forecast decreases with increase in lead period.

$\bullet$ There is a need to look into the existing MME and improve it by re-defining the best constituent members and improving the performance of individual models through augmentation of direct & remotely sensed observations, data assimilation and the physical processes in the model.

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

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.

$\bf{Highlights}$

$\bullet$ 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.

• Correction to: Performance of numerical weather prediction models in predicting track of recurving cyclone Vayu over Arabian Sea during June 2019

• # Journal of Earth System Science

Volume 130, 2021
All articles
Continuous Article Publishing mode

• # Editorial Note on Continuous Article Publication

Posted on July 25, 2019