The appraisal of tropical cyclones in the North Indian Ocean: An overview of different approaches and the involvement of Earth’s components

This study aims to provide a comprehensive and balanced assessment of recent scientific studies on the evolution, temporal variability and prediction of tropical cyclones (TCs), focusing on the North Indian Ocean (NIO). The involvement of earth’s components in TC genesis and intensification has been elaborated in a confined way. The advancement of multidisciplinary approaches for comprehending the TCs is highlighted after a brief description of the involvement of oceanic, atmospheric, and land surface processes. Only a few studies illustrate how land surface plays a role in TC intensification; however, the role of latent heat flow, moisture, and convection in cyclogenesis is well documented. Despite two to 3 decades of advancement and significant development in forecasting techniques and satellite products, the prediction of TC’s intensity, dissipation, track, and landfall remains a challenge. The most noticeable improvements in NIO TC’s prediction have been achieved in the last couple of decades when concord techniques are utilized, especially the data assimilation methods and dynamical coupled atmosphere-ocean regional models. Through diverse methodologies, algorithms, parameterization, in-situ observational data, data mining, boundary layer, and surface fluxes, significant research has been done to increase the skills of standalone atmospheric models and air-sea coupled models. However, some crucial issues still exist, and it is suggested that they should be addressed in future studies.

[1]  N. Cartwright,et al.  Climate change impacts on tropical cyclones of the Arabian Sea: Projections and uncertainty investigations , 2022, International Journal of Climatology.

[2]  D. Sikka,et al.  Outcomes and challenges of Forecast Demonstration Project (FDP) on landfalling cyclones over the Bay of Bengal , 2022, MAUSAM.

[3]  U. C. Mohanty,et al.  A study on high resolution mesoscale modeling systems for simulation of tropical cyclones over the Bay of Bengal , 2022, MAUSAM.

[4]  Junpeng Yuan,et al.  Seasonal Variation of Tropical Cyclone Genesis and the Related Large-Scale Environments: Comparison between the Bay of Bengal and Arabian Sea Sub-Basins , 2021, Atmosphere.

[5]  A. Gupta.  Current status of tropical cyclone track prediction techniques and forecast errors , 2021, MAUSAM.

[6]  U. C. Mohanty,et al.  Recent advancements in prediction of tropical cyclone track over north Indian Ocean basin , 2021, MAUSAM.

[7]  Somnath Mandal,et al.  Spatio-temporal Behaviours of Tropical Cyclones over the Bay of Bengal Basin in last Five Decades , 2021, Tropical Cyclone Research and Review.

[8]  A. Mishra,et al.  Understanding the post‐monsoon tropical cyclone variability and trend over the Bay of Bengal during the satellite era , 2021, Quarterly Journal of the Royal Meteorological Society.

[9]  J. Bhate,et al.  Rapidly intensified, long duration North Indian Ocean tropical cyclones: Mesoscale downscaling and validation , 2021 .

[10]  C. Birch,et al.  Simulations of Bay of Bengal tropical cyclones in a regional convection-permitting atmosphere–ocean coupled model , 2021 .

[11]  V. Singh,et al.  Changing status of tropical cyclones over the north Indian Ocean , 2021, Climate Dynamics.

[12]  P. Bhaskaran,et al.  Role and influence of key atmospheric parameters in large-scale environmental flow associated with tropical cyclogenesis and ENSO in the North Indian Ocean basin , 2021, Climate Dynamics.

[13]  N. Busireddy,et al.  On occurrence of rapid intensification and rainfall changes in tropical cyclones over the North Indian Ocean , 2021, International Journal of Climatology.

[14]  U. C. Mohanty,et al.  Understanding the characteristics of rapid intensity changes of Tropical Cyclones over North Indian Ocean , 2021, SN Applied Sciences.

[15]  A. Routray,et al.  Impact of WRF Parameterization Schemes on Track and Intensity of Extremely Severe Cyclonic Storm “Fani” , 2021, Pure and Applied Geophysics.

[16]  D. Swain,et al.  Influence of eddies and tropical cyclone heat potential on intensity changes of tropical cyclones in the North Indian Ocean , 2020, Advances in Space Research.

[17]  Ramesh P. Singh,et al.  Impact of tropical cyclone "Fani" on land, ocean, atmospheric and meteorological parameters. , 2020, Marine pollution bulletin.

[18]  Akhil Srivastava,et al.  Impact of INSAT-3D/3DR Radiance Data Assimilation in Predicting Tropical Cyclone Titli Over the Bay of Bengal , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Weimin Zhang,et al.  Machine Learning in Tropical Cyclone Forecast Modeling: A Review , 2020, Atmosphere.

[20]  U. C. Mohanty,et al.  Quasi-operational forecast guidance of extremely severe cyclonic storm Fani over the Bay of Bengal using high-resolution mesoscale models , 2020, Meteorology and Atmospheric Physics.

[21]  Shivani,et al.  Tropical Cyclones Over the North Indian Ocean in Changing Climate , 2020 .

[22]  E. Coppola,et al.  Thermodynamical properties associated with the Indian summer monsoon rainfall using a regional climate model , 2020, Theoretical and Applied Climatology.

[23]  M. Tippett,et al.  Statistical–Dynamical Downscaling Projections of Tropical Cyclone Activity in a Warming Climate: Two Diverging Genesis Scenarios , 2020 .

[24]  K. S. Singh,et al.  Prediction of landfalling Bay of Bengal cyclones during 2013 using the high resolution Weather Research and Forecasting model , 2019, Meteorological Applications.

[25]  R. Mahmood,et al.  Influence of Land Cover and Soil Moisture based Brown Ocean Effect on an Extreme Rainfall Event from a Louisiana Gulf Coast Tropical System , 2019, Scientific Reports.

[26]  J. Knaff,et al.  Operational Perspectives on Tropical Cyclone Intensity Change Part 1: recent advances in intensity guidance , 2019, Tropical Cyclone Research and Review.

[27]  U. C. Mohanty,et al.  Forecasting tropical cyclones in the Bay of Bengal using quasi-operational WRF and HWRF modeling systems: an assessment study , 2019, Meteorology and Atmospheric Physics.

[28]  Sushil Kumar,et al.  A High-Resolution Mesoscale Model Approach to Reproduce Super Typhoon Maysak (2015) Over Northwestern Pacific Ocean , 2019, Earth Systems and Environment.

[29]  Prashant Kumar,et al.  Assimilating INSAT‐3D Thermal Infrared Window Imager Observation With the Particle Filter: A Case Study for Vardah Cyclone , 2019, Journal of Geophysical Research: Atmospheres.

[30]  Sam Ganzfried,et al.  Predicting Hurricane Trajectories using a Recurrent Neural Network , 2018, AAAI.

[31]  A. Pradhan,et al.  Application of weather forecasting model WRF for operational electric power network management—a case study for Phailin cyclone , 2018, Theoretical and Applied Climatology.

[32]  M. Lal,et al.  Impact of Climate Change on Tropical Cyclones Frequency and Intensity on Indian Coasts , 2018, Springer Series in Geomechanics and Geoengineering.

[33]  Deepak Gopalakrishnan,et al.  On the Improved Predictive Skill of WRF Model With Regional 4DVar Initialization: A Study With North Indian Ocean Tropical Cyclones , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[34]  M. Mandal,et al.  Changes in tropical cyclone activity in north Indian Ocean during satellite era (1981–2014) , 2018 .

[35]  U. C. Mohanty,et al.  Land surface sensitivity of monsoon depressions formed over Bay of Bengal using improved high-resolution land state , 2017 .

[36]  Michael B. Richman,et al.  Reducing Tropical Cyclone Prediction Errors Using Machine Learning Approaches , 2017 .

[37]  R. Toumi,et al.  A self‐weakening mechanism for tropical cyclones , 2017 .

[38]  V. S. Prasad,et al.  Quality and Impact of Indian Doppler Weather Radar Wind Profiles: A Diagnostic Study , 2017, Pure and Applied Geophysics.

[39]  C. Reason,et al.  Variability of upper-ocean characteristics and tropical cyclones in the South West Indian Ocean , 2017 .

[40]  U. C. Mohanty,et al.  Improved prediction of severe thunderstorms over the Indian Monsoon region using high-resolution soil moisture and temperature initialization , 2017, Scientific Reports.

[41]  M. Rajeevan,et al.  Impact of Assimilation of Conventional and Satellite Radiance GTS Observations on Simulation of Mesoscale Convective System Over Southeast India Using WRF-3DVar , 2017, Pure and Applied Geophysics.

[42]  Marshall Shepherd,et al.  Inland Tropical Cyclones and the “Brown Ocean” Concept , 2017 .

[43]  P. K. Pal,et al.  Tropical Cyclogenesis Prediction in the North Indian Ocean During 2013 Using OSCAT Derived Surface Wind Observations , 2017 .

[44]  Sushil Kumar,et al.  Simulation of Tropical Cyclone ‘Phailin’ Using WRF Modeling System , 2017 .

[45]  S. Al-Yahyai,et al.  Seasonal probabilistic forecasting of tropical cyclone activity in the North Indian Ocean , 2016 .

[46]  John Derber,et al.  All-Sky Microwave Radiance Assimilation in NCEP’s GSI Analysis System , 2016 .

[47]  Jinzhong Min,et al.  AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system , 2016 .

[48]  J. Mahfouf,et al.  Towards the assimilation of all-sky microwave radiances from the SAPHIR humidity sounder in a limited area NWP model over tropical regions , 2016 .

[49]  K. Prakash,et al.  Upper oceanic response to tropical cyclone Phailin in the Bay of Bengal using a coupled atmosphere-ocean model , 2016, Ocean Dynamics.

[50]  U. C. Mohanty,et al.  Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Prediction , 2016 .

[51]  M. Mohapatra.  Monitoring and Forecasting of Tropical Cyclones over North Indian Ocean , 2016 .

[52]  Vijay Tallapragada,et al.  A Great Escape from the Bay of Bengal ''Super Sapphire-Phailin'' Tropical Cyclone: A Case of Improved Weather Forecast and Societal Response for Disaster Mitigation , 2015 .

[53]  M. Mohapatra,et al.  Modeling Storm Surge and its Associated Inland Inundation Extent Due to Very Severe Cyclonic Storm Phailin , 2015 .

[54]  Yuqing Wang,et al.  A Statistical Analysis of the Effects of Vertical Wind Shear on Tropical Cyclone Intensity Change over the Western North Pacific , 2015 .

[55]  P. Salvekar,et al.  Effect of physical parameterization schemes on track and intensity of cyclone LAILA using WRF model , 2015, Asia-Pacific Journal of Atmospheric Sciences.

[56]  Michael K. Tippett,et al.  Probabilistic Multiple Linear Regression Modeling for Tropical Cyclone Intensity , 2015 .

[57]  Tanvir Islam,et al.  Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics , 2015, Natural Hazards.

[58]  F. Jose,et al.  A coupled hydrodynamic modeling system for PHAILIN cyclone in the Bay of Bengal , 2014 .

[59]  K. Prasad,et al.  East India Coastal Current induced eddies and their interaction with tropical storms over Bay of Bengal , 2014 .

[60]  U. C. Mohanty,et al.  Modeling on the Aspects of Thermal Response of Bay of Bengal to Tropical Cyclone TC05B 1999 using Princeton Ocean Model (POM): Preliminary Results , 2014 .

[61]  U. C. Mohanty,et al.  Mesoscale Modelling for Tropical Cyclone Forecasting over the North Indian Ocean , 2014 .

[62]  M. Mohapatra,et al.  Status and Plans for Operational Tropical Cyclone Forecasting and Warning Systems in the North Indian Ocean Region , 2014 .

[63]  U. C. Mohanty,et al.  Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model , 2013 .

[64]  Fuzhong Weng,et al.  Impacts of assimilation of ATMS data in HWRF on track and intensity forecasts of 2012 four landfall hurricanes , 2013 .

[65]  P. K. Pal,et al.  Quality assessment and assimilation of Megha‐Tropiques SAPHIR radiances into WRF assimilation system , 2013 .

[66]  Milija Zupanski,et al.  All-Sky Satellite Radiance Data Assimilation: Methodology and Challenges , 2013 .

[67]  J. Knaff,et al.  Ocean heat content for tropical cyclone intensity forecasting and its impact on storm surge , 2013, Natural Hazards.

[68]  C. M. Kishtawal,et al.  Prediction of tropical cyclogenesis in North Indian Ocean using Oceansat-2 scatterometer (OSCAT) winds , 2013, Meteorology and Atmospheric Physics.

[69]  Bin Wang,et al.  Intensified Arabian Sea tropical storms , 2012, Nature.

[70]  U. C. Mohanty,et al.  Impact of Parameterization of Physical Processes on Simulation of Track and Intensity of Tropical Cyclone Nargis (2008) with WRF-NMM Model , 2012, TheScientificWorldJournal.

[71]  U. C. Mohanty,et al.  Simulation of very severe cyclone Mala over Bay of Bengal with HWRF modeling system , 2012, Natural Hazards.

[72]  U. C. Mohanty,et al.  Customization of WRF-ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean , 2012, Natural Hazards.

[73]  U. C. Mohanty,et al.  An observational perspective on tropical cyclone activity over Indian seas in a warming environment , 2012, Natural Hazards.

[74]  C. Kishtawal,et al.  Sensitivity of inland decay of North Atlantic tropical cyclones to soil parameters , 2012, Natural Hazards.

[75]  Chandra M. Kishtawal,et al.  Prediction of Tropical Cyclogenesis Using Scatterometer Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[76]  R. Sriver.  Climate change: Man-made cyclones , 2011, Nature.

[77]  P. Bauer,et al.  The use of cloud and precipitation observations in data assimilation (CPDA) , 2011 .

[78]  U. C. Mohanty,et al.  Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model , 2011 .

[79]  U. C. Mohanty,et al.  Simulation of Severe Land-Falling Bay of Bengal Cyclones During 1995–1999 Using Mesoscale Model MM5 , 2010 .

[80]  A. Chandrasekar,et al.  Impact of 3DVAR assimilation of Doppler Weather Radar wind data and IMD observation for the prediction of a tropical cyclone , 2010 .

[81]  U. C. Mohanty,et al.  Simulation of Tropical Cyclones Over Indian Seas: Data Impact Study Using WRF-Var Assimilation System , 2010 .

[82]  U. C. Mohanty,et al.  Simulation of Track and Intensity of Gonu and Sidr with WRF-NMM Modeling System , 2010 .

[83]  U. C. Mohanty,et al.  Possible relation between land surface feedback and the post‐landfall structure of monsoon depressions , 2009 .

[84]  W. Menzel,et al.  Inferring Convective Weather Characteristics with Geostationary High Spectral Resolution IR Window Measurements: A Look into the Future , 2009 .

[85]  A. Munot,et al.  Warming SST of Bay of Bengal and decrease in formation of cyclonic disturbances over the Indian region during southwest monsoon season , 2009 .

[86]  Akhilesh Gupta,et al.  Deterministic Methods for Prediction of Tropical Cyclone Tracks , 2008 .

[87]  T. Marchok,et al.  The Operational GFDL Coupled Hurricane–Ocean Prediction System and a Summary of Its Performance , 2007 .

[88]  G. Young,et al.  The Interannual Variability of Tropical Cyclones , 2007 .

[89]  Florence Rabier,et al.  Relative impact of polar‐orbiting and geostationary satellite radiances in the Aladin/France numerical weather prediction system , 2007 .

[90]  P. Webster,et al.  Changes in Tropical Cyclone Number, Duration, and Intensity in a Warming Environment , 2005, Science.

[91]  John A. Knaff,et al.  Further improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS) , 2005 .

[92]  J. Chan.  THE PHYSICS OF TROPICAL CYCLONE MOTION , 2005 .

[93]  Chun‐Chieh Wu,et al.  Current understanding of tropical cyclone structure and intensity changes – a review , 2004 .

[94]  Bin Wang,et al.  Satellite data analysis and numerical simulation of tropical cyclone formation , 2003 .

[95]  Mark A. Bourassa,et al.  EARLY DETECTION OF TROPICAL CYCLONES USING SEAWINDS-DERIVED VORTICITY , 2001 .

[96]  U. C. Mohanty,et al.  Numerical study of the role of land-air-sea interactions for the northeasterly monsoon circulations over Indian Ocean during INDOEX , 2001 .

[97]  S. Patwardhan,et al.  A study of cyclonic disturbances over India and the adjacent ocean , 2001 .

[98]  I. Ginis,et al.  Real-Case Simulations of Hurricane-Ocean Interaction Using A High-Resolution Coupled Model: Effects on Hurricane Intensity , 2000 .

[99]  Mark DeMaria,et al.  A Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic Basin , 1994 .