Improved skill of NCMRWF Unified Model (NCUM-G) in forecasting tropical cyclones over NIO during 2015–2019
Tóm tắt
Operational forecasting of tropical cyclone (TC) track and intensity in the India Meteorological Department (IMD) relies more and more on the numerical weather prediction (NWP) model guidance from national and international agencies particularly, on the medium range (24–120 h). Any improvement in TC forecasts by the NWP models enhances the operational forecaster's confidence and capability. The real-time information from the National Centre for Medium Range Weather Forecasting (NCMRWF) global NWP model (NCUM-G) is routinely used by operational forecasters at IMD as model guidance. The present study documents the improved skill of NCUM-G in forecasting the North Indian Ocean (NIO) TCs during 2015–2019, based on a collection of 1810 forecasts involving 22 TC cases. The study highlights three significant changes in the modelling system during the recent five years, namely (i) increased grid resolution from 17 to 12 km, (ii) use of hybrid 4D-Var data assimilation (DA), and (iii) increased volume of assimilated data. The study results indicate a consistent improvement in the NCUM-G model forecasts during the pre-monsoon (April–May, AM) and post-monsoon (October–December, OND) TC seasons. In addition to a 44% reduction in the initial position error, the study also reports a statistically significant decrease in the direct position error (DPE) and error in the intensity forecast, resulting in a forecast gain of 24 hrs. Comparing NWP models with IMDs official track error shows that NCUM-G and ECMWF model forecasts feature lower DPE than IMD in 2019, particularly at higher (96, 108, and 120 h) lead times.
Tài liệu tham khảo
Auligne T, McNally A P and Dee D P 2007 Adaptive bias correction for satellite data in a numerical weather prediction system; Quart. J. Roy. Meteorol. Soc. 133 631–642, https://doi.org/10.1002/qj.56.
Barker D M and Clayton A M 2011 Hybrid variational-ensemble data assimilation; Met Office 14.
Bender M A, Ginis I, Tuleya R, Thomas B and Marchok T 2007 The operational GFDL coupled hurricane-ocean prediction system and a summary of its performance; Mon. Wea. Rev. 135 3965–3989, https://doi.org/10.1175/2007MWR2032.1.
Cameron J and Bell W 2018 The testing and planned implementation of variational bias correction (VarBC) at the Met Office; Met Office 21.
Clayton A M, Lorenc A C and Barker D M 2013 Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office; Quart. J. Roy. Meteorol. Soc. 139 1445–1461, https://doi.org/10.1002/qj.2054.
Deo A A and Ganer D W 2013 Variability in tropical cyclone activity over Indian Seas in changing climate; IJSR 4(5) 2319–7064.
Dube S K, Rao A D, Poulose J, Mohapatra M and Murty T S 2014 Storm surge inundation in South Asia under climate change scenarios; In: Monitoring and prediction of tropical cyclones in the Indian Ocean and climate change (eds) Mohanty U C, Mohapatra M, Singh O P, Bandyopadhyay B K and Rathore L S, Springer, Netherlands, Dordrecht, pp. 355–363.
Edwards J M and Slingo A 1996 Studies with a flexible new radiation code. I: Choosing a configuration for a largescale model; Quart. J. Roy. Meteorol. Soc. 122 689–719, https://doi.org/10.1002/qj.49712253107.
Emanuel K 2005 Increasing destructiveness of tropical cyclones over the past 30 years; Nature 436 686–688.
George J P, Indira Rani S, Jayakumar A, Saji Mohandas, Mallick S, Rakhi R, Sreevathsa M N R and Rajagopal E N 2016 NCUM-G data assimilation system; NMRF/TR/01/2016, 20p.
Gopalakrishnan S G, Goldenberg S, Quirino T, Zhang X, Marks F, Yeh K S, Atlas R and Tallapragada V 2012 Toward improving high-resolution numerical hurricane forecasting: Influence of model horizontal grid resolution, initialization, and physics; Wea. Forecasting 27 647–666, https://doi.org/10.1175/WAF-D-11-00055.1.
Harris B A and Kelly G 2001 A satellite radiance-bias correction scheme for data assimilation; Quart. J. Roy. Meteorol. Soc. 127 1453–1468, https://doi.org/10.1002/qj.49712757418.
Heming J T 2009 Evaluation of and improvements to the Met Office tropical cyclone initialisation scheme; Met Apps. 16 339–351, https://doi.org/10.1002/met.129.
Heming J T 2016 Met office unified model tropical cyclone performance following major changes to the initialization scheme and a model upgrade; Wea. Forecasting 31 1433–1449, https://doi.org/10.1175/WAF-D-16-0040.1.
Heming J T 2017 Tropical cyclone tracking and verification techniques for Met Office numerical weather prediction models; Met Apps. 24 1–8, https://doi.org/10.1002/met.1599.
Kotal S D, Bhattacharya S K and Bhowmik S K R 2014 Development of NWP based objective cyclone prediction system (CPS) for North Indian Ocean tropical cyclones – Evaluation of performance; TCCR 3(3) 162–177.
Knutson T R, McBride J L, Chan J, Emanuel K, Holland G, Landsea C, Held I, Kossin J P, Srivastava A K and Sugi M 2010 Tropical cyclones and climate change; Nat. Geosci. 3 157–163, https://doi.org/10.1038/ngeo779.
Kumar S, Jayakumar A, Bushair M T, Jangid B P, George G, Lodh A, Rani S I, Mohandas S, George J P and Rajagopal E N 2018 Implementation of new high resolution NCUM-G analysis-forecast system in Mihir HPCS; NMRF/TR/01/2018, 17p.
Kutty G, Gogoi R, Rakesh V and Pateria M 2020 Comparison of the performance of HYBRID ETKF-3DVAR and 3DVAR data assimilation scheme on the forecast of tropical cyclones formed over the Bay of Bengal; J. Earth Syst. Sci. 129 233.
Kutty G, Muraleedharan R and Kesarkar A P 2018 Impact of representing model error in a hybrid ensemble-variational data assimilation system for track forecast of tropical cyclones over the Bay of Bengal; Pure Appl. Geophys. 175 1155–1167.
Lock A P, Brown A R, Bush M R, Martin G M and Smith R N B 2000 A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests; Mon. Wea. Rev. 128 3187–3199.
Lorenc A C 2003 Modelling of error covariances by 4D-Var data assimilation; Quart. J. Roy. Meteorol. Soc. 129 3167–3182, https://doi.org/10.1256/qj.02.131.
Mamgain A, Sarkar A, Dube A, Arulalan T, Chakraborty P, George J P and Rajagopal E N 2018 Implementation of very high resolution (12 km) global ensemble prediction system at NCMRWF and its initial validation; NMRF/TR/02/2018, 21p.
Mohanty U C, Nadimpalli R, Mohanty S and Osuri K K 2019 Recent advancements in prediction of tropical cyclone track over north Indian Ocean basin; Mausam 70(1).
Mohanty U C, Osuri K K and Pattanayak S 2014 Mesoscale modelling for tropical cyclone forecasting over the North Indian Ocean; In: Monitoring and prediction of tropical cyclones in the Indian Ocean and climate change (eds) Mohanty U C, Mohapatra M, Singh O P, Bandyopadhyay B K and Rathore L S, Springer, Netherlands, Dordrecht, pp. 274–286.
Mohapatra M 2014 Tropical cyclone forecast verification by India Meteorological Department for North Indian Ocean: A review; TCCR 3(4) 229–242, https://doi.org/10.6057/2014TCRR04.03.
Mohapatra M and Sharma M 2019 Cyclone warning services in India during recent years: A review; Mausam 70 635–666.
Mohapatra M, Bandyopadhyay B K and Tyagi A 2012a Best track parameters of tropical cyclones over the North Indian Ocean: A review; Nat. Hazards 63 1285–1317, https://doi.org/10.1007/s11069-011-9935-0.
Mohapatra M, Nayak D P and Bandyopadhyay B K 2012b Evaluation of cone of uncertainty in tropical cyclone track forecast over North Indian Ocean issued by India Meteorological Department; TCCR 1(3) 331–339, https://doi.org/10.6057/2012TCRR03.02.
Mohapatra M, Bandyopadhyay B K and Nayak D P 2013a Evaluation of operational tropical cyclone intensity forecasts over north Indian Ocean issued by India Meteorological Department; Nat. Hazards 68 433–451, https://doi.org/10.1007/s11069-013-0624-z.
Mohapatra M, Nayak D P, Sharma R P and Bandyopadhyay B K 2013b Evaluation of official tropical cyclone track forecast over north Indian Ocean issued by India Meteorological Department; J. Earth Syst. Sci. 122 589–601, https://doi.org/10.1007/s12040-013-0291-1.
Mohapatra M, Geetha B, Balachandran S and Rathore L S 2015 On the tropical cyclone activity and associated environmental features over North Indian Ocean in the context of climate change; J. Clim. Change 1 1–26.
Neumann C J 1993 Global Overview, Chapter 1 Global guide to tropical cyclone forecasting; WMO TC 560.
Rabier F, Järvinen H, Klinker E, Mahfouf J F and Simmons A 2007 The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics; Quart. J. Roy. Meteorol. Soc. 126 1143–1170, https://doi.org/10.1002/qj.49712455005.
Rabier F, Thépaut J N and Courtier P 1998 Extended assimilation and forecast experiments with a four-dimensional variational assimilation system; Quart. J. Roy. Meteorol. Soc. 124 1861–1887, https://doi.org/10.1002/qj.49712656415.
Rajagopal E N, Iyengar G R, George J P, Gupta M D, Mohandas S, Siddharth R, Gupta A, Chourasia M, Prasad V S, Sharma A and Ashish K A 2012 Implementation of the UM model based analysis–forecast system at NCMRWF; NMRF/TR/2012, 45p.
Rakesh V and Goswami P 2011 Impact of background error statistics on forecasting of tropical cyclones over the north Indian Ocean; J. Geophys. Res. 116 D20130, https://doi.org/10.1029/2011JD015751.
Rani S I, Taylor R, Sharma P, Bushair M T, Jangid B P, George J P and Rajagopal E N 2019 Assimilation of INSAT-3D imager water vapour clear sky brightness temperature in the NCMRWF’s assimilation and forecast system; J. Earth Syst. Sci. 128 197, https://doi.org/10.1007/s12040-019-1230-6.
Rawlins F, Ballard S P, Bovis K J, Clayton A M, Li D, Inverarity G W, Lorenc A C and Payne T J 2007 The Met Office global four-dimensional variational data assimilation scheme; Quart. J. Roy. Meteorol. Soc. 133 347–362, https://doi.org/10.1002/qj.32.
Routray A, Singh V, Singh H, Dutta D, George J P and Rakhi R 2017 Evaluation of different versions of NCUM global model for simulation of track and intensity of tropical cyclones over Bay of Bengal; Dry Atmos. Oceans 78 71–88, https://doi.org/10.1016/j.dynatmoce.2017.04.001.
Sarkar A, Chakraborty P, George J P and Rajagopal E N 2016 Implementation of unified model based ensemble prediction system at NCMRWF (NEPS); NMRF/TR/02/2016, 26p.
Singh O P, Ali Khan T M and Rahman Md S 2000 Changes in the frequency of tropical cyclones over the North Indian Ocean; Meteorol. Atmos. Phys. 75 11–20, https://doi.org/10.1007/s007030070011.
Singh V, Konduru R T, Srivastava A K, Momin I M, Kumar S, Singh A K, Bisht D S, Tiwari S and Sinha A K 2021 Predicting the rapid intensification and dynamics of pre-monsoon extremely severe cyclonic storm ‘Fani’ (2019) over the Bay of Bengal in a 12-km global model; Atmos. Res. 247 105222, https://doi.org/10.1016/j.atmosres.2020.105222.
Walters D, Boutle I, Brooks M, Melvin T, Stratton R, Vosper S, Wells H, Williams K, Wood N, Allen T, Bushell A, Copsey D, Earnshaw P, Edwards J, Gross M, Hardiman S, Harris C, Heming J, Klingaman N, Levine R, Manners J, Martin G, Milton S, Mittermaier M, Morcrette C, Riddick T, Roberts M, Sanchez C, Selwood P, Stirling A, Smith C, Suri D, Tennant W, Vidale P L, Wilkinson J, Willett M, Woolnough S and Xavier P 2017 The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations; Geosci. Model Dev. 10 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017.
Wilson D R and Ballard S P 1999 A microphysically based precipitation scheme for the UK Meteorological Office Unified Model; Quart. J. Roy. Meteorol. Soc. 125 1607–1636, https://doi.org/10.1002/qj.49712555707.
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. Meteorol. Soc. 140 1505–1520, https://doi.org/10.1002/qj.2235.