Progress in Tropical Cyclone Predictability and Present Status in the North Indian Ocean Region

Abstract

Tropical cyclone (TC) is an important research area since it has a significant impact on human life, properties and environment. The researchers all over the world have been studying fundamental and advanced processes to better understand and thereby predict the genesis and evolution of TCs. This review chapter provides a brief overview on TC climatology, their basic characteristics, movement and intensification, research on structure analysis and prediction of these fascinating storms, with primary emphasis to North Indian Ocean (NIO). The role of ocean and atmosphere in determining the genesis and intensification of TCs is discussed. This chapter reviews the past and current research activities including inter-annual and intra-seasonal changes in TCs, current status of TC research using numerical weather prediction, gaps identified and relevant measures taken by the meteorological and government agencies in this direction, along with future directions in order to improve the understanding and predictability over the NIO region.

A study on variability in rainfall over India contributed by cyclonic disturbances in warming climate scenario

“…The possible reasons could be weakening of CDs after making landfall because they move away from their oceanic heat source, and possibly because of the reason that rainfall intensity tends to coincide with storm intensity (Knight and Davis, 2009). As a greater number of severe storms form during post-monsoon (Singh et al, 2016 and2019a), more rainfall is observed during this season in comparison to pre-monsoon months over India. The percentage contribution is higher over AP, northeast TN, GJ (highest percentage of~70), west RJ and coastal MH during pre-monsoon season.…”

Extreme Storms

“…Notice that large number of CS formation is observed during the months of October and November in BOB and AS. A maximum of 72 [20] SCS events is observed during November [May] in BOB [AS] (see also Singh et al 2016). The larger number of TCs in BOB is possibly due to prevalence of upper-ocean warm stratification and higher sea-surface temperatures (SSTs) as compared to AS.…”

“…The extreme weather events over the Indian region have profound socio-economic implications (e.g., De et al 2005). The North Indian Ocean (NIO) rim countries (India, Bangladesh, Myanmar, Sri Lanka, Oman; countries within the Equator region-30°N; 50-100°E) comprising large coastal areas are severely affected by tropical cyclones (TCs) every year (see Singh et al 2016;Ramsay 2017;Mohapatra et al 2014Mohapatra et al , 2017. For example, the year 2018 witnessed four very severe TCs over this region during the pre-monsoon (March-May) and post-monsoon (October-December) seasons of India [Mekunu and Luban over the Arabian Sea (AS) in May and October 2018, Titli and Gaja in October and November 2018 over the Bay of Bengal (BOB); Source: Annual cyclone review report, India Meteorological Department (IMD); see also Table 8.2].…”

A New Approach to Improve the Track Prediction of Tropical Cyclones Over North Indian Ocean

“…Ensemble prediction system (EPS) is found to provide reasonably good probabilistic forecast for evolution of CS, which is a merit for operational forecast of storm tracks (Kotal & Roy Bhowmik, ; Majumdar & Finocchio, ). Despite some progress, there exists need for improvements as reliable track forecast requires accurate representation of cyclonic‐vortex in the model initial conditions (IC) to determine the most probable time and region of formation and landfall (Singh et al, , ). A major challenge in Multi‐Model Ensemble Prediction System (MMEPS) is that storm intensity gets underestimated and forecast track lags behind observations as lead time increases as numerous ensemble members from different models may give diverse path for the same system thereby increasing timing and directional errors.…”

“…Ensemble prediction system (EPS) is found to provide reasonably good probabilistic forecast for evolution of CS, which is a merit for operational forecast of storm tracks (Kotal & Roy Bhowmik, 2011;Majumdar & Finocchio, 2010). Despite some progress, there exists need for improvements as reliable track forecast requires accurate representation of cyclonic-vortex in the model initial conditions (IC) to determine the most probable time and region of formation and landfall (Singh et al, 2005(Singh et al, , 2016 track lags behind observations as lead time increases as numerous ensemble members from different models may give diverse path for the same system thereby increasing timing and directional errors. To avoid this, a probability-based approach is attempted here, by setting different dependent thresholds and giving appropriate lead-weightage to each cyclogenesis variable using bias-correction and signal amplification (BCSA).…”