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Selection of suitable predictors and predictor domain for statistical downscaling over the Western Himalayan region of India

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Abstract

Selection of suitable predictor(s) from the NCEP/NCAR reanalysis datasets for downscaling annual and seasonal rainfall over the Western Himalayas has been carried out in the present study. Size of the domain on downscaling was also judged by considering three different sizes of domains, namely Western Himalayan region (WHR), India and South Asia. Statistical measures like spatial correlation maps, product-moment correlations, and adjusted R2 of regression analysis were used to evaluate the skills of the predictors. Results showed predictors were sensitive to the method of analysis, choice of season, and size of the domain. A majority of the predictors exhibited stronger spatial correlations (±) in annual and monsoon season compared to the winter. It was found that the first principal components (PCs) of most of the predictors were consistently well correlated (RE) with the annual and monsoon rainfall in all domains, whereas, in the winter season, none of the PCs showed such consistent results. During the monsoon season, the predictors had higher RE values than the winter and annual time scale. Geopotential height at 850 hPa, relative humidity at 500 and 1000 hPa, and precipitation rate emerged as good predictors for downscaling precipitation over different predictor domains. On the other hand, the geopotential height at 500 and 850 hPa, v at 500 hPa, specific humidity at 500 hPa, and divergence at 850 hPa resulted as least affected predictors based on analysis of ranks of the predictors. Finally, WHR was considered as a suitable predictor domain for downscaling monsoon rainfall for the Western Himalayan region compared to other domains as ranks obtained for different predictors in this domain are not very sensitive to statistical measures used to evaluate the skills of predictors.

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References

  • Akhter J, Das L, Meher JK, Deb A (2019) Evaluation of different large-scale predictor-based statistical downscaling models in simulating zone-wise monsoon precipitation over India. Int J Climatol 39(1):465–482

    Article  Google Scholar 

  • Anandhi A, Srinivas VV, Nagesh Kumar D, Nanjundiah RS (2009) Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine. Int J Climatol 29:583–603. https://doi.org/10.1002/joc.1719

    Article  Google Scholar 

  • Anandhi A, Srinivas VV, Nanjundiah RS, Kumar DN (2008) Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. Int J Climatol 28:401–420. https://doi.org/10.1002/joc.1529

    Article  Google Scholar 

  • Bawiskar SM, Chipade MD, Puranik PV, Bhide UV (2005) Energetics of lower tropospheric planetary waves over mid latitudes: precursor for Indian summer monsoon. J Earth Syst Sci 114:557–564. https://doi.org/10.1007/bf02702031

    Article  Google Scholar 

  • Blazak A (2012) Statistical downscaling of precipitation projections in Southeast Queensland catchments. PhD diss., University of Southern Queensland. Available at https://eprints.usq.edu.au/23571/1/Blazak_2012_whole.pdf (Assessed on 29 May 2017)

  • Das L, Meher JK, Dutta M (2016) Construction of rainfall change scenarios over the Chilka lagoon in India. Atmos Res 182:36–45. https://doi.org/10.1016/j.atmosres.2016.07.013

    Article  Google Scholar 

  • Das L, Meher JK (2019) Drivers of climate over the Western Himalayan region of India: A review. Earth Sci Rev. https://doi.org/10.1016/j.earscirev.2019.102935

    Article  Google Scholar 

  • Dabanlı İ, Şen Z (2017) Precipitation projections under GCMs perspective and Turkish Water Foundation (TWF) statistical downscaling model procedures. Theor Appl Climatol 132((1-2):153–166. https://doi.org/10.1007/s00704-017-2070-4

    Article  Google Scholar 

  • Devak M, Dhanya CT (2014) Downscaling of precipitation in Mahanadi basin, India. Int J Civil Eng Res 5:111–120

    Google Scholar 

  • Dimri AP, Yasunari T, Kotlia BS, Mohanty UC, Sikka DR (2016) Indian winter monsoon: present and past. Earth-Sci Rev 163:297–322. https://doi.org/10.1016/j.earscirev.2016.10.008

    Article  Google Scholar 

  • Dimri AP, Niyogi D, Barros AP, Ridley J, Mohanty UC, Yasunari T, Sikka DR (2015) Western disturbances: a review. Rev Geophys 53:225–246. https://doi.org/10.1002/2014RG000460

    Article  Google Scholar 

  • Douville H (2006) Impact of regional SST anomalies on the Indian monsoon response to global warming in the CNRM climate model. J Clim 19(10):2008–2024. https://doi.org/10.1175/JCLI3727.1

    Article  Google Scholar 

  • Eden JM, Widmann M (2014) Downscaling of GCM-simulated precipitation using model output statistics. J Clim 27(1):312–324. https://doi.org/10.1175/JCLI-D-13-00063.1

    Article  Google Scholar 

  • Førland EJ, Benestad R, Hanssen-Bauer I, Haugen JE, Skaugen TE (2011) Temperature and precipitation development at Svalbard 1900–2100. Adv Meteorol. https://doi.org/10.1155/2011/893790

    Article  Google Scholar 

  • Gaur A, Simonovic SP (2017) Application of physical scaling towards downscaling climate model precipitation data. Theor Appl Climatol 132(1-2):287–300. https://doi.org/10.1007/s00704-017-2088-7

    Article  Google Scholar 

  • Ghosh S, Mujumdar PP (2006) Future rainfall scenario over Orissa with GCM projections by statistical downscaling. Curr Sci 90(3):396–404

    Google Scholar 

  • Goyal MK, Ojha CSP (2012) Downscaling of surface temperature for lake catchment in an arid region in India using linear multiple regression and neural networks. Int J Climatol 32(4):552–566. https://doi.org/10.1002/joc.2286

    Article  Google Scholar 

  • Goyal MK, Ojha CSP (2010) Evaluation of various linear regression methods for downscaling of mean monthly precipitation in arid Pichola watershed. Nat Res Forum 1(01):11–18. https://doi.org/10.4236/nr.2010.11002

    Article  Google Scholar 

  • Hannachi A, Jolliffe IT, Stephenson DB (2007) Empirical orthogonal functions and related techniques in atmospheric science: a review. Int J Climatol 27(9):1119–1152. https://doi.org/10.1002/joc.1499

    Article  Google Scholar 

  • Hanssen-Bauer I, Førland EJ, Haugen JE, Tveito OE (2003) Temperature and precipitation scenarios for Norway: comparison of results from dynamical and empirical downscaling. Clim Res 25(1):15–27

    Article  Google Scholar 

  • Hanssen-Bauer I, Achberger C, Benestad RE, Chen D, Førland EJ (2005) Statistical downscaling of climate scenarios over Scandinavia. Clim Res 29(3):255–268

    Article  Google Scholar 

  • Hofer M, Mölg T, Marzeion B, Kaser G (2010) Empirical-statistical downscaling of reanalysis data to high-resolution air temperature and specific humidity above a glacier surface (Cordillera Blanca, Peru). J Geophys Res Atmos 115(D12). https://doi.org/10.1029/2009JD012556

  • Hu Y, Maskey S, Uhlenbrook S (2013) Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods. Theor Appl Climatol 112(3-4):447–460. https://doi.org/10.1007/s00704-012-0745-4

    Article  Google Scholar 

  • Huang J, Zhang J, Zhang Z, Xu C, Wang B, Yao J (2011) Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method. Stoch Env Res Risk A 25(6):781–792

    Article  Google Scholar 

  • Huth R (1999) Statistical downscaling in Central Europe: evaluation of methods and potential predictors. Clim Res 13(2):91–101

    Article  Google Scholar 

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y (1996) The NCEP-NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471. https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2

    Article  Google Scholar 

  • Kannan S, Ghosh S (2013) A nonparametric kernel regression model for downscaling multisite daily precipitation in the Mahanadi basin. Water Resour Res 49(3):1360–1385. https://doi.org/10.1002/wrcr.20118

    Article  Google Scholar 

  • Lorenz E (1956) Empirical orthogonal functions and statistical weather prediction. Scientific Report No. 1, Statistical Forecasting Project, Massachusetts Institute of Technology, Department of Meteorology, Cambridge, Mass., 49 pp

  • Lorenzo-Seva U (2013) How to report the percentage of explained common variance in exploratory factor analysis Available at http://psico.fcep.urv.es/utilitats/factor/documentation/Percentage_of_explained_common_variance.pdf (Assessed on 29 Sep 2017)

  • Mahmood R, Babel MS (2013) Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India. Theor Appl Climatol 113(1-2):27–44

    Article  Google Scholar 

  • Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VK (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(3). https://doi.org/10.1029/2009RG000314

  • Meher JK (2019) Estimation of rainfall statistics over the Western Himalaya region through empirical-statistical downscaling. Doctoral dissertation. Department of Agricultural Meteorology and Physics, Bidhan Chandra Krishi Viswavidyalaya

  • Meher JK, Das L (2019) Gridded data as a source of missing data replacement in station records. J Earth Syst Sci 128(3). https://doi.org/10.1007/s12040-019-1079-8

  • Meher JK, Das L, Akhter J, Benestad RE, Mezghani A (2017) Performance of CMIP3 and CMIP5 GCMs to simulate observed rainfall characteristics over the Western Himalayan region. J Clim 30:7777–7799. https://doi.org/10.1175/JCLI-D-16-0774.1

    Article  Google Scholar 

  • Nicholas RE, Battisti DS (2012) Empirical downscaling of high-resolution regional precipitation from large-scale reanalysis fields. J Appl Meteorol Climatol 51(1):100–114. https://doi.org/10.1175/JAMC-D-11-04.1

    Article  Google Scholar 

  • Ojha CS, Goyal MK, Adeloye AJ (2010) Downscaling of precipitation for lake catchment in arid region in India using linear multiple regression and neural networks. Int J Climatol 4(1):122–136. https://doi.org/10.1002/joc.2286

    Article  Google Scholar 

  • Pai DS, Sridhar L, Rajeevan M, Sreejith OP, Satbhai NS, Mukhopadhyay B (2014) Development of a new high spatial resolution (0.25× 0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam 65(1):1–18

    Google Scholar 

  • Parding KM, Benestad R, Mezghani A, Erlandsen HB (2019) Statistical projection of the North Atlantic storm tracks. J Appl Meteorol Climatol 58(7):1509–1522

    Article  Google Scholar 

  • Pervez MS, Henebry GM (2014) Projections of the Ganges–Brahmaputra precipitation—downscaled from GCM predictors. J Hydrol 517:120–134. https://doi.org/10.1016/j.jhydrol.2014.05.016

    Article  Google Scholar 

  • Parthasarathy B, Kumar KR, Deshpande VR (1991) Indian summer monsoon rainfall and 200-mbar meridional wind index: Application for long-range prediction. Int J Climatol 11(2):165–176

    Article  Google Scholar 

  • Priya P, Krishnan R, Mujumdar M, Houze RA (2016) Changing monsoon and midlatitude circulation interactions over the Western Himalayas and possible links to occurrences of extreme precipitation. Clim Dyn 49:2351–2364. https://doi.org/10.1007/s00382-016-3458-z

    Article  Google Scholar 

  • R Core Team (2002) R: A Language and Environment for Statistical Computing. R Core Team R Foundation for Statistical Computing, Vienna, Austria

  • Ripley BD (2002) Modern applied statistics with S. Springer-Verlag, New York. https://doi.org/10.1007/978-0-387-21706-2

    Book  Google Scholar 

  • Saeed F, Hagemann S, Saeed S, Jacob D (2013) Influence of mid-latitude circulation on upper Indus basin precipitation: the explicit role of irrigation. Clim Dyn 40(1-2):21–38. https://doi.org/10.1007/s00382-012-1480-3

    Article  Google Scholar 

  • Salvi K, Ghosh S (2013) High-resolution multisite daily rainfall projections in India with statistical downscaling for climate change impacts assessment. J Geophys Res-Atmos 118(9):3557–3578. https://doi.org/10.1002/jgrd.50280

    Article  Google Scholar 

  • Satyanarayana P, Srinivas VV (2008) Regional frequency analysis of precipitation using large-scale atmospheric variables. J Geophys Res-Atmos 113(D24). https://doi.org/10.1029/2008JD010412

  • Sauter T, Venema V (2011) Natural three-dimensional predictor domains for statistical precipitation downscaling. J Clim 24(23):6132–6145. https://doi.org/10.1175/2011JCLI4155.1

    Article  Google Scholar 

  • Shashikanth K, Ghosh S (2013) Fine Resolution Indian Summer Monsoon Rainfall Projection with statistical Downscaling. Int J Chem Environ Biol Sci 1(4):615–618

    Google Scholar 

  • Sinha P, Mohanty UC, Kar SC, Dash SK, Robertson AW, Tippett MK (2013) Seasonal prediction of the Indian summer monsoon rainfall using canonical correlation analysis of the NCMRWF global model products. Int J Climatol 33(7):1601–1614. https://doi.org/10.1002/joc.3536

    Article  Google Scholar 

  • Srinivasan G, Hulme M, Jones CG (1995) An evaluation of the spatial and interannual variability of tropical precipitation as simulated by GCMs. Geophys Res Lett 22(16):2139–2142

    Article  Google Scholar 

  • Wilby R, Wigley T (2000) Precipitation predictors for downscaling: observed and general circulation model relationships. Int J Climatol 20:641–661

    Article  Google Scholar 

  • Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change. Available http://www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf (Assessed on 29 May 2017)

  • Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Progress in physical geography 21(4): 530–548. https://doi.org/10.1177/030913339702100403

    Article  Google Scholar 

  • Wilby RL, Hay LE, Leavesley GH (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado. J Hydrol 225(1):67–91. https://doi.org/10.1016/S0022-1694(99)00136-5

    Article  Google Scholar 

  • Wilks DS (2011) Statistical methods in the atmospheric sciences. 100. Academic press

Download references

Acknowledgments

The authors would like to thank India Meteorological Department, and NCEP/NCAR for providing the required data for the present study. JKM would like to thank all the members of climate simulation lab, Bidhan Chandra Krishi Viswavidyalaya, West Bengal, for their constant support in preparing the work. The authors would like to thank the anonymous reviewers for their critical comments on the present work and improvements suggested by them.

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  1. Department of Agricultural Meteorology and Physics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, West Bengal, 741252, India

    Jitendra Kumar Meher & Lalu Das

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  1. Jitendra Kumar Meher
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Correspondence to Lalu Das.

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Appendix

Appendix

z = geopotential height

t2 m = air temperature at 2 m

mslp = mean sea level pressure

sf = surface airflow strength

t = air temperature

su = surface zonal wind velocity

u = zonal wind velocity

sv = surface meridional wind velocity

v = meridional velocity

sƱ = surface vorticity

prw = precipitable water

sw = surface wind direction

sp = surface pressure

s▽ = surface divergence

s = specific humidity

sr = surface relative humidity

r = relative humidity

ss = surface specific humidity

NB numbers associated with each variable shows the pressure level at that hPa

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Meher, J.K., Das, L. Selection of suitable predictors and predictor domain for statistical downscaling over the Western Himalayan region of India. Theor Appl Climatol 139, 431–446 (2020). https://doi.org/10.1007/s00704-019-02980-z

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