Abstract
This study presents an assessment on the impact of different Land Use/Land Cover datasets in simulating the movement and severity of three extremely severe cyclonic storms, namely Phailin (2013), Hudhud (2014) and Fani (2019), which developed over the Bay of Bengal (BoB). For this purpose, the Advanced Research Weather and Forecasting System (WRF) is selected and the model is forced with necessary input parameters. Two sets of numerical experiment are conducted. The first set of experiments uses U.S. Geological Survey Land Use/Land Cover datasets, and the WRF model is integrated with four different land surface parameterization schemes. The second set of experiments uses the Indian satellite IRS P6 AWiFS-derived Land Use/Land Cover obtained from the National Remote Sensing Centre (here after; AWiFS), and WRF model is integrated with four different land surface parameterization schemes. The model simulated track, mean sea-level pressure, wind, and rainfall are analysed and verified with available observation as obtained from India Meteorological Department and NASA Global Precipitation Measurement. The dynamics and thermodynamic structure are analysed in terms of model simulated vorticity and equivalent potential temperature during the landfall of the system. The results suggested that the use of AWiFS Land Use/Land Cover improves the simulation of track of all the cyclones during and after the landfall of the system. Also, it significantly reduces the landfall point error for all the land surface parameterization schemes except with thermal diffusion scheme. The AWiFS experiments could simulate both the spatial and station rainfall reasonably well. Also, it could simulate the intensity and thermodynamic structure of the cyclone reasonably well.
Similar content being viewed by others
Data availability
The National Centres for Environmental Prediction data (https://rda.ucar.edu/datasets/ds083.2/) is utilised for the model’s initial and boundary conditions. The USGS (https://www.usgs.gov/) provides the USGS-LULC data sets, and the National Remote Sensing Centre (https://www.nrsc.gov.in/) provides the AWiFS datasets.
References
Alapaty K, Niyogi D, Chen F, Pyle P, Chand S, N, (2008) Development of the flux-adjusting surface data assimilation system for mesoscale models. J of Appl Met and Clim 47:2331–2350. https://doi.org/10.1175/2008JAMC1831.1
Anantharaj VG, Fitzpatrick PJ, Li Y, King RL, Mostovoy GV (2006) Impact of land use and land cover changes in the surface fuxes of an atmospheric model. In International Geoscience and Remote Sensing Symposium (IGARSS) 2369–2372, https://doi.org/10.1109/IGARSS.2006.613.
Anthes RA (1984) Enhancement of convective precipitation by mesoscale variation in vegetative covering in semiarid regions. J Appl Meteorol 23:541–554. https://doi.org/10.1175/1520-0450(1984)023<0541:EOCPBM>2.0.CO;2
Badarinath KVS, Mahalakshmi DV, Ratna SB (2012) Influence of land use land cover on cyclone track prediction—a study during Aila cyclone. Open Atmos Sci J 6:33–41. https://doi.org/10.2174/1874282301206010033
Betts AK, Ball JH, Beljaars ACM, Miller MJ, Viterbo PA (1996) The land surface–atmosphere interaction: a review based on observational and global modeling perspectives. J Geophys Res 101(B):7209–7225. https://doi.org/10.1029/95JD02135
Bosart LF, Velden CS, Bracken WE, Molinari J, Black P (2000) Environmental influence on the rapid intensification stage of hurricane Opal (1995) over the Gulf of Mexico. Mon Weather Rev 128:322–352. https://doi.org/10.1175/1520-0493(2000)128%3c0322:EIOTRI%3e2.0.CO;2
Chauhan A, Kumar R, Singh RP (2018) Coupling between Land–Ocean–Atmosphere and Pronounced Changes in Atmospheric/Meteorological Parameters Associated with the Hudhud Cyclone of October 2014”. Int J Environ Res Public Health 15:2759. https://doi.org/10.3390/ijerph15122759
Collins DC, Avissar R (1994) An evaluation with the Fourier amplitude sensitivity test (FAST) of which land surface parameters are of greatest importance in atmospheric modeling. J Clim 7:681–703. https://doi.org/10.1175/1520-0442(1994)007%3c0681:AEWTFA%3e2.0.CO;2
Gharai B (2014) IRS-P6 AWiFS Derived LU/LC Data Compatible to Mesoscale (WRF) Models Over Indian Region, NRSC Oct 2014, NRSC-ECSA-ACSG-OCT-2014-TR-651
Gogoi PP, Vinoj V, Swain D, Roberts G, Dash J, Tripathy S (2019) Land use and land cover change effect on surface temperature over Eastern India. Sci Rep 9:8859. https://doi.org/10.1038/s41598-019-45213-z
Hong X, Chang SW, Raman S, Shay LK, Hodur R (2000) The interaction between hurricane Opal (1995) and a warm-core ring in the Gulf of Mexico. Mon Weather Rev 128:1347–1365. https://doi.org/10.1175/1520-0493(2000)128%3c1347:TIBHOA%3e2.0.CO;2
IMD report (2014) Very severe cyclonic storm, HUDHUD over the Bay of Bengal (07–14 October 2014): A Report, Cyclone Warning Division, India Meteorological Department, New Delhi, pp 66
IMD report (2019) Extremely severe cyclonic storm, FANI over the Bay of Bengal (26 April-4 May 2019): a Report, Cyclone Warning Division, India Meteorological Department, New Delhi, pp 71
Johari P, Kumar S, Pattanayak S, Routray A (2022) Performance of land surface schemes on simulation of land falling tropical cyclones over Bay of Bengal using ARW model. Mausam. (In Press)
Karri S, Gharai B, Krishna, SVSS, Rao PVN (2016) Impact of AWiFS derived land use land cover on simulation of heavy rainfall. In: Proc. SPIE 9882, Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions VI, 98821M; https://doi.org/10.1117/12.2223627
Kimball JS (2009) A satellite approach to estimate land-atmosphere CO2 exchange for boreal and arctic biomes using MODIS and AMSR-E. IEEE Trans Geol Remote Sens 47:2. https://doi.org/10.1109/TGRS.2008.2003248
Loveland TR, Reed BC, Brown JF, Ohlen DO, Zhu Z, Yang L, Merchant JW (2020) Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR. Int J Remote Sens 21(6 & 7):1303–1330. https://doi.org/10.1080/014311600210191
Maxwell RM, Chow FK, Kollet SJ (2007) The groundwater-land surface-atmosphere connection: soil moisture effects on the atmospheric boundary layer in fully-coupled simulations. Adv Water Resour 30:2447–2466. https://doi.org/10.1016/j.advwatres.2007.05.018
Mohan M, Kandya A (2015) Impact of urbanization and land-use/land-cover change on diurnal temperature range: A case study of tropical urban airshed of India using remote sensing data. Sci Total Environ 506–507:453–465. https://doi.org/10.1016/j.scitotenv.2014.11.006
Mohanty UC, Osuri KK, Routray A, Mohapatra M, Pattanayak S (2010) Simulation of Bay of Bengal tropical cyclones with WRF model: Impact of initial and boundary conditions. Mar Geodesy 33:294–314. https://doi.org/10.1080/01490419.2010.518061
Narisma GT, Pitman AJ (2003) The impact of 200 years land cover change on the Australian near-surface climate. J Hydrometeorol 4:424–436. https://doi.org/10.1175/1525-7541(2003)4%3c424:TIOYOL%3e2.0.CO;2
NRSC (2014) Land Use/Land Cover database on 1:50,000 scale, Natural Resources Census Project, LUCMD, LRUMG, RSAA, National Remote Sensing Centre, ISRO, Hyderabad
Osuri KK, Mohanty UC, Routray A, Mohapatra M (2012) Real-time track prediction of tropical cyclones over the North Indian Ocean using the ARW model. J Appl Meteorol Climatol 52(11):2476–2492. https://doi.org/10.1175/JAMC-D-12-0313.1
Pitman AJ, Narisma GT, Pielke Sr RA, Holbrook NJ (2004) Impact of land cover change on the climate of southwest Western Australia. J Geophys Res 109:1–12. https://doi.org/10.1029/2003JD004347
Raju PVS, Potty J, Mohanty UC (2011) Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model. Meteorol Atmos Phys 113:125–137. https://doi.org/10.1007/s00703-011-0151-y
Raupach MR (2000) Equilibrium evaporation and the convective boundary layer”. Bound Layer Meteor 96:107–141. https://doi.org/10.1023/A:1002675729075
Ravindranath M, Ashrit R (2010) Experiment on utilization of AWiFS LU/LC data in WRF mesoscale model. NMRF Research Report, NMRF/RR/01/2010
Routray A, Mohanty UC, Osuri KK, Kar SC, Niyogi D (2016) Impact of satellite radiance data on simulations of Bay of Bengal tropical cyclones using the WRF-3DVAR modeling system. IEEE Trans Geosci Remote Sens 54:2285–2303
Sateesh M, Srinivas CV, Raju PVS (2017) Numerical simulation of tropical cyclone Thane: role of boundary layer and surface drag parameterization schemes. Nat Hazards 89(3):1255–1271. https://doi.org/10.1007/s11069-017-3020-2
Shenoy M, Raju PVS, Prasad VS, Hariprasad KBRR (2022) Sensitivity of physical schemes on simulation of severe cyclones over Bay of Bengal using WRF-ARW model. Theor Appl Climatol. https://doi.org/10.1007/s00704-022-04102-8
Singh P, Kikon N, Verma P (2017) Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustain Cities Soc 32:100–114. https://doi.org/10.1016/j.scs.2017.02.018
Tiwari G, Kumar P (2022) Predictive skill comparative assessment of WRF 4DVar and 3DVar data assimilation: an Indian Ocean tropical cyclone case study. Atmos Res. https://doi.org/10.1016/j.atmosres.2022.106288
Tiwari G, Kumar P (2023) Pertaining the application of the 4DVar and 4DEnVar WRFDA techniques to simulate tropical cyclones in the Bay of Bengal. Adv Sp Res. https://doi.org/10.1016/j.asr.2023.03.015. (ISSN 0273-1177)
Wang Y (2002) Vortex Rossby waves in a numerically simulated tropical cyclone. Part II: the role in tropical cyclone structure and intensity change. J Atmos Sci 59:1239–2126. https://doi.org/10.1175/1520-0469(2002)059%3c1213:VRWIAN%3e2.0.CO;2
Wang B, Elsberry RL, Wang Y, Wu L (1998) Dynamics in tropical cyclone motion: a review. Chinese J Atmos Sci 22:535–547
Wong MLM, Chan JCL (2006) Tropical cyclone motion in response to land surface friction. J Atmos Sci. https://doi.org/10.1175/JAS3683.1
Wu C-C, Cheng H-J (1999) An observational study of environmental influences on the intensity changes of typhoons Flo (1990) and Gene (1990). Mon Wea Rev 127:3003–3031. https://doi.org/10.1175/1520-0493(1999)127%3c3003:AOSOEI%3e2.0.CO;2
Wu C-C, Emanuel KA (1993) Interaction of a baroclinic vortex with background shear: application to hurricane movement. J Atmos Sci 50:62–76. https://doi.org/10.1175/1520-0469(1993)050%3c0062:IOABVW%3e2.0.CO;2
Wu C-C, Kurihara Y (1996) A numerical study of the feedback mechanism of hurricane-environment interaction on hurricane movement from the potential vorticity perspective. J Atmos Sci 53:2264–2282. https://doi.org/10.1175/1520-0469(1996)053%3c2264:ANSOTF%3e2.0.CO;2
Acknowledgements
The authors sincerely acknowledge the National Oceanic and Atmospheric Administration (NOAA) for providing Global Forecasting System (GFS) initial and boundary data to run WRF model and the National Aeronautics and Space Administration for Global Precipitation Measurement (GPM) rainfall data. The authors are thankful to India Meteorological Department for the observational data and National Remote Sensing Centre (NRSC), ISRO, India for the Land Use/Land Cover data.
Funding
The authors declare that there is no financial support received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
All authors contributed to the conceptualization of the study. Data acquisition, model run and the first draft of the manuscript were done by PJ. Result analyses were performed by SP and AR. Editing and finalisation of the manuscript by Sushil Kumar and P.V.S. Raju.
Corresponding authors
Ethics declarations
Conflict of Interest
The authors have no relevant financial or non-financial interests to disclose.
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.
About this article
Cite this article
Johari, P., Kumar, S., Pattanayak, S. et al. Impact of different land use and land cover in simulation of tropical cyclones over Bay of Bengal. Earth Syst Environ 7, 661–678 (2023). https://doi.org/10.1007/s41748-023-00350-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41748-023-00350-4