Advertisement

Impact of Cartosat-1 orography on weather prediction in a high-resolution NCMRWF unified model

  • Jisesh SethunadhEmail author
  • A Jayakumar
  • Saji Mohandas
  • E N Rajagopal
  • A Subbu Nagulu
Article
  • 40 Downloads

Abstract

The current study reports for the first time an application of orography from the Cartosat-1 satellite digital elevation model (DEM) generated at a source resolution of 30 m in a convection-permitting numerical weather prediction model. The effects of improvements in the representation of orography have been examined in the high-resolution regional National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model predictions for a heavy rainfall event over the city of Chennai. A time-lagged ensemble method is employed to account for the uncertainties associated with the initial conditions, which can better forecast extreme weather events than single forecasts. The simulations reveal that the predictions based on Cartosat-1 DEM capture the local details of the rainfall distribution better than the National Aeronautics and Space Administration shuttle radar topography mission DEM-based predictions, and better represent the orographic and thermal uplifting. The spatio-temporal patterns of the simulated rainfall over Chennai are superior in Cartosat-1 DEM-based simulations mainly due to the enhanced wind convergence and moisture transport. The present study reveals the role of mountains in the enhancement of heavy rainfall events over coastal cities and highlights the potential use of high-resolution orography in the improvement of the operational weather forecasting skill of the NCMRWF Unified Model.

Keywords

Orography modelling Cartosat-1 extreme rainfall clouds weather forecasting 

Notes

Acknowledgements

The authors gratefully acknowledge Dr Stuart Webster, Met Office, UK, for his valuable comments and discussions. They acknowledge the data source CartoDEM Version-3 R1, National Remote Sensing Centre, ISRO, Government of India, Hyderabad, India, NASA for the SRTM and GPM IMERG products and the JAXA GSMaP rainfall product.

References

  1. Aonashi K, Awaka J, Hirose M, Kozu T, Kubota T, Liu G, Shige S, Kida S, Seto S, Takahashi N and Takayabu Y N 2009 GSMap passive microwave precipitation retrieval algorithm: Algorithm description and validation; J. Meteorol. Soc. Japan Ser. II 87A 119–136,  https://doi.org/10.2151/jmsj.87A.119.CrossRefGoogle Scholar
  2. Arun P, Katiyar S K and Prasad V 2016 Performances evaluation of different open source DEM using differential global positioning system (DGPS); Egypt. J. Remote Sens. Space Sci. 19(1) 7–16,  https://doi.org/10.1016/j.ejrs.2015.12.004.CrossRefGoogle Scholar
  3. Baral S S, Das J, Saraf A K, Borgohain S and Singh G 2016 Comparison of Cartosat, ASTER and SRTM DEMs of different terrains; Asian J. Geoinformatics 16(1) 1–7.Google Scholar
  4. Boutle I A, Belcher S E and Plant R S 2011 Moisture transport in midlatitude cyclones; Quart. J. Roy. Meteor. Soc. 137 360–373,  https://doi.org/10.1002/qj.783.CrossRefGoogle Scholar
  5. Boutle I A, Eyre J E J and Lock A P 2014 Seamless stratocumulus simulation across the turbulent grey zone; Mon. Weather Rev. 142 1655–1668.CrossRefGoogle Scholar
  6. Chakraborty A 2016 A synoptic-scale perspective of heavy rainfall over chennai in November 2015; Curr. Sci. 111(1) 201–207,  https://doi.org/10.18520/cs/v111/i1/201-207.CrossRefGoogle Scholar
  7. Chen C-S, Lin Y-L, Zeng H-T, Chen C-Y and Liu C-L 2013 Orographic effects on heavy rainfall events over northeastern Taiwan during the northeasterly monsoon season; Atmos. Res. 122 310–335,  https://doi.org/10.1016/j.atmosres.2012.10.008.CrossRefGoogle Scholar
  8. Dimri A P 2004 Impact of horizontal model resolution and orography on the simulation of a western disturbance and its associated precipitation; Meteorol. Appl. 11 115–127,  https://doi.org/10.1017/S1350482704001227.CrossRefGoogle Scholar
  9. Edwards J M and Slingo A 1996 Studies with a flexible New radiation code. I: Choosing a configuration for a large-scale model; Quart. J. Roy. Meteor. Soc. 122 689–719.CrossRefGoogle Scholar
  10. Gregory D and Rowntree P R 1990 A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure; Mon. Weather Rev. 118 1483–1506,  https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2.CrossRefGoogle Scholar
  11. Henneberg O, Henneberger J and Lohmann U 2017 Formation and development of orographic mixed-phase clouds; J. Atmos. Sci. 74(11) 3703–3724.CrossRefGoogle Scholar
  12. Hoffman R N and Kalnay E 1983 Lagged average forecasting, an alternative to Monte Carlo forecasting; Tellus A 35A 100–118,  https://doi.org/10.1111/j.1600-0870.1983.tb00189.x.CrossRefGoogle Scholar
  13. Houze R A Jr 2012 Orographic effects on precipitating clouds; Rev. Geophys. 50 RG1001,  https://doi.org/10.1029/2011RG000365.CrossRefGoogle Scholar
  14. Jayakumar A, Sethunadh J, Rakhi R, Arulalan T, Mohandas S, Iyengar G R and Rajagopal E N 2017 Behavior of predicted convective clouds and precipitation in the high-resolution unified model over the Indian summer monsoon region; Earth Space Sci. 4 303–313,  https://doi.org/10.1002/2016EA000242.CrossRefGoogle Scholar
  15. Jiang Q 2003 Moist dynamics and orographic precipitation; Tellus A 55 301–316,  https://doi.org/10.1034/j.1600-0870.2003.00025.x.CrossRefGoogle Scholar
  16. Kitoh A 1997 Mountain uplift and surface temperature changes; Geophys. Res. Lett. 24(2) 185–188,  https://doi.org/10.1029/96GL03953.CrossRefGoogle Scholar
  17. Krishnamurti T N, Dubey S, Kumar V, Deepa R and Bhardwaj A 2017 Scale interaction during an extreme rain event over South-East India; Quart. J. Roy. Meteor. Soc. 143 1442–1458,  https://doi.org/10.1002/qj.3016.CrossRefGoogle Scholar
  18. Lott F 1999 Alleviation of stationary biases in a GCM through a mountain drag parameterization scheme and a simple representation of mountain lift forces; Mon. Weather Rev. 127 788–801,  https://doi.org/10.1175/1520-0493(1999)127<0788:AOSBIA>2.0.CO;2.CrossRefGoogle Scholar
  19. Nagalakshmi R and Prasanna K 2016 2015 flood assessment in Kanchipuram District of Tamilnadu using GIS; Rasayan J. Chem. 9 798–805.Google Scholar
  20. Narasimhan B, Murty Bhallamudi S, Mondal A, Ghosh S and Mujumdar P 2016 Chennai floods 2015 – A rapid assessment; Interdisciplinary Centre for Water Research, Indian Institute of Science, Bangalore, 49p.Google Scholar
  21. Ning S, Song F, Udmale P, Jin J, Thapa B R and Ishidaira H 2017 Error analysis and evaluation of the latest GSMap and IMERG precipitation products over eastern China; Adv. Meteorol. 2017(11) 1–16,  https://doi.org/10.1155/2017/1803492.CrossRefGoogle Scholar
  22. Phadtare J 2018 Role of Eastern Ghats orography and cold pool in an extreme rainfall event over Chennai on 1 December 2015; Mon. Weather Rev. 146 943–965,  https://doi.org/10.1175/MWR-D-16-0473.1.CrossRefGoogle Scholar
  23. Rapp R 1997 Use of potential coefficient models for geoid undulation determinations using a spherical harmonic representation of the height anomaly/geoid undulation difference; J. Geodesy 71 282–289,  https://doi.org/10.1007/s001900050096.CrossRefGoogle Scholar
  24. Sato T 2013 Mechanism of orographic precipitation around the Meghalaya Plateau associated with intraseasonal oscillation and the diurnal cycle; Mon. Weather Rev. 141 2451–2466,  https://doi.org/10.1175/MWR-D-12-00321.1.CrossRefGoogle Scholar
  25. Sironi A, Tekin B, Rigamonti R, Lepetit V and Fua P 2015 Learning separable filters; IEEE Pattern Anal. 37(1) 94–106,  https://doi.org/10.1109/TPAMI.2014.2343229.CrossRefGoogle Scholar
  26. Smith S A, Vosper S B and Field P R 2015 Sensitivity of orographic precipitation enhancement to horizontal resolution in the operational Met office weather forecasts; Met. Apps. 22 14–24,  https://doi.org/10.1002/met.1352.CrossRefGoogle Scholar
  27. Trenberth K E, Dai A, Rasmussen R M and Parsons D B 2003 The changing character of precipitation; Bull. Am. Meteor. Soc. 84 1205–1218,  https://doi.org/10.1175/BAMS-84-9-1205.CrossRefGoogle Scholar
  28. Unnikrishnan C K, Gharai B, Mohandas S, Mamgain A, Rajagopal E N, Iyengar G R and Rao P V 2016 Recent changes on land use/land cover over Indian region and its impact on the weather prediction using unified model; Atmos. Sci. Lett. 17 294–300,  https://doi.org/10.1002/asl.658.CrossRefGoogle Scholar
  29. Van Oldenborgh G, Otto F L, Haustein K and Achuta Rao K 2016 The heavy precipitation event of December 2015 in Chennai, India; Bull. Am. Meteor. Soc. 97(12) S87–S91,  https://doi.org/10.1175/BAMS-D-16-0129.1.CrossRefGoogle Scholar
  30. Vogel H, Förstner J, Vogel B, Hanisch T, Mühr B, Schättler U and Schad T 2014 Time-lagged ensemble simulations of the dispersion of the Eyjafjallajökull plume over Europe with COSMO-ART; Atmos. Chem. Phys. 14 7837–7845.CrossRefGoogle Scholar
  31. Walcek C J 1994 Cloud cover and Its relationship to relative humidity during a springtime midlatitude cyclone; Mon. Weather Rev. 122(6) 1021–1035,  https://doi.org/10.1175/1520-0493(1994)122<1021:CCAIRT>2.0.CO;2.CrossRefGoogle Scholar
  32. Walters D, Boutle I and Brooks M et al. 2017 The Met office unified model global atmosphere 6.0/6.1 and JULES global land 6.0/6.1 configurations; Geo. Sci. Model Dev. 10(4) 1487–1520,  https://doi.org/10.5194/gmd-10-1487-2017.CrossRefGoogle Scholar
  33. Webster S, Brown A R, Cameron D R and Jones C P 2003 Improvements to the representation of orography in the Met office unified model; Quart. J. Roy. Meteor. Soc. 129 1989–2010,  https://doi.org/10.1256/qj.02.133.CrossRefGoogle Scholar
  34. Wilson D R and Ballard S P 1999 Amicrophysically based precipitation scheme for the UK meteorological office unified model; Quart. J. Roy. Meteor. Soc. 125 1607–1636,  https://doi.org/10.1002/qj.49712555707.CrossRefGoogle Scholar
  35. Wilson D R, Bushell A C, Kerr-Munslow A M, Price J D and Morcrette C J 2008 PC2: A prognostic cloud fraction and condensation scheme. I: Scheme description; Quart. J. Roy. Meteor. Soc. 134 2093–2107,  https://doi.org/10.1002/qj.333.CrossRefGoogle Scholar
  36. Wood N, Staniforth A, White A, Allen T, Diamantakis M, Gross M, Melvin T, Smith C, Vosper S, Zerroukat M and Thuburn J 2014 An inherently mass-conserving semi-implicit semi-Lagrangian discretization of the deep-atmosphere global non-hydrostatic equations; Quart. J. Roy. Meteor. Soc. 140 1505–1520,  https://doi.org/10.1002/qj.2235.CrossRefGoogle Scholar
  37. Yadav S and Indu J 2016 Estimation of vertical accuracy of digital elevation models over complex terrains of Indian subcontinent; Int. Geosci. Remote Sens. 6036–6039,  https://doi.org/10.1109/IGARSS.2016.7730577.
  38. Yarrakula K, Deb D and Samanta B 2013 Comparative evaluation of Cartosat-1 and SRTM imageries for digital elevation modelling; Geo-spatial Inf. Sci. 16(2) 75–82,  https://doi.org/10.1080/10095020.2012.747645.CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  • Jisesh Sethunadh
    • 1
    Email author
  • A Jayakumar
    • 1
  • Saji Mohandas
    • 1
  • E N Rajagopal
    • 1
  • A Subbu Nagulu
    • 2
  1. 1.National Centre for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth SciencesNoidaIndia
  2. 2.National Remote Sensing Centre (NRSC)Balanagar, HyderabadIndia

Personalised recommendations