Skip to main content

Advertisement

Log in

A pathway for multi-stage cyclone-induced hazard tracking—case study for Yaas

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

A Very Severe Cyclonic Storm ‘Yaas’ developed over the Bay of Bengal (BoB) on 23 May 2021 and crossed over the Odisha coast on 26 May with maximum sustained wind speed of 75 kts. Herein, a pathway has been developed and exemplified for ‘Yaas’ through three-stage cyclone-induced hazard tracking. Days before the cyclone formation, cyclone genesis potential parameter, sea surface temperature (SST) (> 30 °C) and tropical cyclone heat potential (anomaly of 40–80 kJ/cm2) indicated a strong possibility of cyclogenesis in the BoB. A Lagrangian advection model used for its track prediction with 24-h lead-time provided an accuracy of ~ 19 km and ~ 6 h in its landfall location and time. Further, intensity prediction was done using numerical weather prediction model. Geostationary satellites, INSAT-3D/3DR, were used to visualize cyclone structure. Passing of cyclone had its reverbarations in oceans, which are observed in SST drop of ~ 3 °C, salinity and density increase by ~ 1 psu and ~ 2 kg/m3, respectively. During the period, 23–26 May 2021, the Ekman suction velocity and chlorophyll concentration were found significantly high at ~ 5 m/day and > 0.5 mg/m3, respectively. Forecast of storm surge was found to be between 3.5 and 4 m at coastal locations. Significant wave height was found to be 5.5–9.2 m. The coastal inundation forecast for 24 May 2021 provided its quantitative maximum inland extent. Finally, loss of the crop, fishery and forest areas by strong winds and inundation/ingress of saline water associated with storm surge were examined using SAR and optical data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Ajadi OA, Liao H, Jaacks J, Delos Santos A, Kumpatla SP, Patel R, Swatantran A (2020) Landscape-scale crop lodging assessment across iowa and illinois using synthetic aperture radar (SAR) images. Remote Sens 12(23):3885

    Article  Google Scholar 

  • Bell J, Gebremichael E, Molthan A, Schultz L, Meyer F, Shrestha S, (2019) Synthetic aperture radar and optical remote sensing of crop damage attributed to severe weather in the central United States. In: IGARSS 2019–2019 IEEE international geoscience and remote sensing symposium (pp. 9938–9941). IEEE

  • Biswas MK, Carson L, Newman K, Stark D, Kalina E, Grell E, Frimel J (2018) Community HWRF Users’ Guide v4.0a, 162 pp. (Available at: https://dtcentre.org/sites/default/files/community-code/hwrf/docs/users_guide/HWRF-UG-2018.pdf)

  • Brand S, Buenafe CA, Hamilton HD (2018) Comparison of tropical cyclone motion and environmental steering. Mon Weather Rev 109(4):908–909

    Article  Google Scholar 

  • Bronner E, Carrere L (2011) SARAL AltiKa Products Handbook, SALP-MU-M-OP-15984-CN, Issue 1, Rev 2, CNES Publication, p74

  • Chan JCL, Williams RT (1987) Tropical cyclone movement and surrounding flow relationship. Mon Weather Rev 110:1354–1374

    Article  Google Scholar 

  • Chaudhuri D, Sengupta D, D’Asaro E, Venkatesan R, Ravichandran M (2019) Response of the salinity-stratified Bay of Bengal to cyclone Phailin. J Phys Oceanogr 49(5):1121–1140

    Article  Google Scholar 

  • Cione JJ, Uhlhorn EW (2003) Sea surface temperature variability in hurricanes: Implications with respect to intensity change. Mon Weather Rev 131(8):1783–1796

    Article  Google Scholar 

  • Deshpande M, Singh VK, Ganadhi MK et al (2021) Changing status of tropical cyclones over the north Indian Ocean. Clim Dyn. https://doi.org/10.1007/s00382-021-05880-z

    Article  Google Scholar 

  • Dickey T, Frye D, McNeil J, Manov D, Nelson N, Sigurdson D, Jannasch H, Siegel D, Michaels T, Johnson R (1998) Upper-ocean temperature response to hurricane Felix as measured by the Bermuda testbed mooring. Mon Weather Rev 126:1195–1201

    Article  Google Scholar 

  • Dietrich JC, Zijlema M, Westerink JJ, Holthuijsen LH, Dawson C, Luettich RA Jr, Stone GW (2011) Modeling hurricane waves and storm surge using integrally-coupled, scalable computations. Coast Eng 58(1):45–65

    Article  Google Scholar 

  • Emanuel K (2004) Tropical cyclone energetics and structure Chapter 8. In: Fedorovich E, Rotunnos R, Stevens B (eds) Atmospheric Turbulence and Mesoscale Meteorology. Cambridge University Press, Cambridge, pp 165–192

    Chapter  Google Scholar 

  • Faghmous JH, Frenger I, Yao Y, Warmka R, Lindell A, Kumar V (2015) A daily global mesoscale eddy dataset from satellite altimetry. Sci Data 2:150028

    Article  Google Scholar 

  • Fan X, Li Y, Lyu A, Liu L (2020) Statistical and comparative analysis of tropical cyclone activity in the Arabian Sea and Bay of Bengal (1977–2018). J Trop Meteorol 26(4):441–452

    Google Scholar 

  • Gangwar RK, Thapliyal PK (2020) Variational based estimation of sea surface temperature from split-window observations of insat-3d/3dr imager. Remote Sens 12(19):3142

    Article  Google Scholar 

  • Gray WM (1975) Cyclone genesis. Department of Atmospheric Science, Paper# 234, Colorado State University, Collins, CO, pp 121

  • Gupta PK, Pradhan R, Singh RP, Misra A (2019) Scatterometry for land hydrology science and its applications. Curr Sci 117(6):1014–1021

    Article  Google Scholar 

  • Halpern D (2002) Offshore Ekman transport and Ekman pumping off Peru during the 19971998 El Nino. Geophys Res Lett 29(5):19-1-19–4

    Article  Google Scholar 

  • Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High-resolution global maps of 21st-centuray forest cover change. Science 342(6160):850–853

    Article  Google Scholar 

  • Hoover BT, Morgan MC (2006) Effects of cumulus parameterization on tropical cyclone potential vorticty structure and steering flow. In: Preprints of the 27th AMS Conference on Hurricanes and Tropical Meteorology. April 23–28 Monterey, CA, paper 8B.5

  • Jaiswal N, Kishtawal CM (2011) Prediction of tropical cyclogenesis using scatterometer data. IEEE Trans Geosci Remote Sens 49(12):4904–4909

    Article  Google Scholar 

  • Jaiswal N, Kishtawal CM, Pal PK (2013) Prediction of tropical cyclogenesis in North Indian Ocean using Oceansat-2 scatterometer (OSCAT) winds. Meteorol Atmos Phys 119:137–149. https://doi.org/10.1007/s00703-012-0230-8

    Article  Google Scholar 

  • Jaiswal N, Kishtawal CM (2016) Structural analysis of tropical cyclone using INSAT-3D observations, In: Proc. SPIE 9876, Remote Sensing of the Atmosphere, Clouds, and Precipitation VI, https://doi.org/10.1117/12.2223508

  • Kotal SD, Kundu PK, Roy Bhowmik SK (2009) Analysis of cyclogenesis parameter for developing and non-developing low pressure systems over the Indian Sea. Nat Hazards 50:389–402

    Article  Google Scholar 

  • Kumar P, Varma AK (2016) Assimilation of INSAT-3D hydro-estimator method retrieved rainfall on short range weather prediction. Quart J Royal Meteorol Soc 143:384–394. https://doi.org/10.1002/qj.2929

    Article  Google Scholar 

  • Lin II, Goni GJ, Knaff JA, Forbes C, Ali MM (2013) Ocean heat content for tropical cyclone intensity forecasting and its impact on storm surge. Nat Hazards 66(3):1481–1500

    Article  Google Scholar 

  • Luettich RA, Westerink JJ (2004) Formulation and numerical implementation of the 2D/3D ADCIRC finite element model version 44. XX (p. 74)

  • Mallick SK, Agarwal N, Sharma R, Prasad KVSR, Weller RA (2019) Impact of satellite-derived diffuse attenuation coefficient on upper ocean simulation using high-resolution numerical ocean model: case study for the Bay of Bengal. Mar Geodesy 42(6):535–557

    Article  Google Scholar 

  • Mallick SK, Agarwal N, Sharma R, Prasad K, Ramakrishna S (2020) Thermodynamic response of a high-resolution tropical Indian Ocean model to TOGA COARE bulk air–sea flux parameterization: case study for the Bay of Bengal (BoB). Pure Appl Geophys 177(8):4025–4044

    Article  Google Scholar 

  • Mandal AK, Ramakrishnan R, Pandey S, Rao AD, Kumar P (2020) An early warning system for inundation forecast due to a tropical cyclone along the east coast of India. Nat Hazards 103:2277–2293

    Article  Google Scholar 

  • Mandal AK, Ramakrishnan R (2019) Surge residual validation using INCOIS tide gauge data for past cyclones in the Bay of Bengal basin. SAC/EPSA/AOSG/SR/04/2019

  • Murakami H, Vecchi GA, Underwood S (2017) Increasing frequency of extremely severe cyclonic storms over the Arabian Sea. Nat Climate Change 7(12):885–889

    Article  Google Scholar 

  • Njoku EG, Jackson TJ, Lakshmi V, Chan TK (2003) Soil moisture retrieval from AMSR-E. IEEE Trans Geosci Remote Sens 41:215–229

    Article  Google Scholar 

  • O’Reilly JE, Maritorena S, Mitchell BG, Siegel DA, Carder KL, Garver SA, Kahru M, McClain C (1998) Ocean color chlorophyll algorithms for SeaWifs. J Geophys Res 103(C11):24937–24953

    Article  Google Scholar 

  • O’Reilly JE, Werdell PJ (2019) Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sens Environ 229:32–47

    Article  Google Scholar 

  • Pan J, Huang L, Devlin AT, Lin H (2018) Quantification of typhoon-induced phytoplankton blooms using satellite multi-sensor data. Remote Sens 10:318. https://doi.org/10.3390/rs10020318

    Article  Google Scholar 

  • Picot N, Marechal C, Couhert A, Desai S, Scharroo R, Egido A, (2018) Jason-3 Products Handbook, SALP-MU-M-OP-16118-CN

  • Pottier E, Cloude SR (1996) A review of target decomposition theorems in radar polarimetry. IEEE Trans Geosci Remote Sens 34(2):498–518

    Article  Google Scholar 

  • Price JF, Sanford TB, Forristall GZ (1994) Forced stage response to a moving hurricane. J Phys Oceanogr 24:233–260

    Article  Google Scholar 

  • Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS, Schlax MG (2007) Daily hihj-resolution-blended analysis for sea surface temperature. J Climate 20:5473–5496

    Article  Google Scholar 

  • Seemanth M, Bhowmick SA, (2018) Operational implementation of very high resolution data assimilative wave forecasting system for Indian Ocean. Scientific Report. SAC/EPSA/AOSG/SR/23/2018

  • Singh SK, Kishtawal CM, Pal PK (2012a) Track prediction of Indian ocean cyclones using Lagrangian advection model. Nat Hazards 62:745–778. https://doi.org/10.1007/s11069-012-0121-9

    Article  Google Scholar 

  • Singh SK, Kishtawal CM, Jaiswal N, Singh R, Pal PK (2012b) Impact of vortex-removal from environmental flow in cyclone track prediction using Lagrangian advection model. Meteorol Atmos Phys 117:103–120. https://doi.org/10.1007/s00703-012-0198-4

    Article  Google Scholar 

  • Singh SK, Jaiswal N, Kishtawal CM, Singh R, Pal PK (2013) Early detection of cyclogenesis signature using global model products. IEEE Trans Geosci Remote Sens 52(8):5116–5121

    Article  Google Scholar 

  • Sun L, Yang YJ, Xian T, Lu Z, Fu YF (2010) Strong enhancement of chlorophyll-A concentration by a weak typhoon. Marine Ecol Progr Ser 404:39–50

    Article  Google Scholar 

  • Tolman HL (2009) User manual and system documentation of WAVEWATCH III version 3.14, NOAA/NWS/NCEP/MMAB Technical Note# 276, 194 p. (Available at: https://polar.ncep.noaa.gov/mmab/papers/tn276/MMAB_276.pdf

  • Varma AK, Gairola RM (2015), Algorithm theoretical basis document hydro-estimator (Modified). SAC/EPSA/AOSG/SR/04/2015

  • Varma AK, Mangalsinh NR, Piyush DN (2020) Precipitation measurement from SARAL AltiKa and passive microwave radiometer observations. Int J Remote Sens 41(23):8948–8964

    Article  Google Scholar 

  • Walker ND, Leben RR, Balasubramanian S (2005) Hurricane-forced upwelling and chlorophyll-a enhancement within cold-core cyclones in the Gulf of Mexico. Geophys Res Lett 32:L18610. https://doi.org/10.1029/2005GL023716

    Article  Google Scholar 

  • Zheng W, Sun D, Li S (2016) Coastal flood monitoring based on AMSR-E data. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 4399–4401. https://doi.org/10.1109/IGARSS.2016.7730146

Download references

Acknowledgements

In the present study, we use the data from different agencies cited in Section 2 on ‘Data Used’. Authors are thankful to all of them. Authors are also thankful to the anonymous reviewers for their valuable suggestions/comments.

Funding

No funds, grants or other support were received.

Author information

Authors and Affiliations

Authors

Contributions

All the authors have contributed to the study conception, its data processing and compiling the results. The final version is read by all the authors and approved.

Corresponding author

Correspondence to Neeru Jaiswal.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare. All co-authors are agreed with the contents of manuscript, and there are no relevant financial or non-financial interests to disclose.

Consent for publication

We certify that the submission is an original work and is not submitted anywhere else.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Varma, A.K., Jaiswal, N., Das, A. et al. A pathway for multi-stage cyclone-induced hazard tracking—case study for Yaas. Nat Hazards 117, 1035–1067 (2023). https://doi.org/10.1007/s11069-023-05893-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-023-05893-3

Keywords

Navigation