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Correction of mesoscale model daily precipitation data over Northwestern Himalaya

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Abstract

Maximum numerical weather prediction models have their own inherent biases and these biases have high impact on accuracy of weather forecast. Hence, bias correction is an essential part of any study for any model output datasets. The current study uses a weather research and forecasting (WRF) model, simulated daily precipitation of winter season (December to February: DJF) for the period of 2010–2011 to 2016–2017 (7 years) for the bias correction and validated against observed precipitation of Snow and Avalanche Study Establishment (SASE), India. For the first time, three different methods, i.e., empirical quantile mapping (QM), linear scaling (LS), and regression (REG) have been studied for the bias correction over the Northwest Himalaya region. In order to identify the best method out of these three, four statistical measurements, i.e., skill score (SS) and its decompositions, bias in percentage, root mean square errors (RMSE), and percentile values have been examined. Based on the analysis of SS and RMSE, it is worth to note that the QM method is found to be most suitable method for the December and February forecast of WRF model, whereas the LS approach is most suitable for the January forecast. Comparison based on Taylor’s diagram and percentiles via boxplot shows that the quantile mapping approach is most advisable for bias correction to the model simulated precipitation dataset over Northwest Himalaya region.

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References

  • Abatzoglou JT, Brown TJ (2012) A comparison of statistical downscaling methods suited for wildfire applications. Int J Climatol 32:772–780. https://doi.org/10.1002/joc.2312

    Article  Google Scholar 

  • Acharya N, Chattopadhyay S, Mohanty UC, Dash SK, Sahoo LN (2013) On the bias correction of general circulation model output for Indian summer monsoon. Meteorol Appl 20:349–356

    Article  Google Scholar 

  • Ashfaq M, Bowling LC, Cherkauer K, Pal JS, Diffenbaugh NS (2010) Influence of climate model biases and daily-scale temperature and precipitation events on hydrological impacts assessment: a case study of the United States. J Geophys Res 115:D14116. https://doi.org/10.1029/2009JD012965

    Article  Google Scholar 

  • Cannon AJ, Sobie SR, Murdock TQ (2015) Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? J Clim 28:6938–6959

    Article  Google Scholar 

  • Chakravarti IM, Laha RG (1967) Handbook of methods of applied statistics, vol 460. Wiley, New York

    Google Scholar 

  • Chen J, Brissette FP, Chaumont D, Braun M (2013) Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resour Res 49:4187–4205. https://doi.org/10.1002/wrcr.20331

    Article  Google Scholar 

  • Déqué M (2007) Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: model results and statistical correction according to observed values. Global and Planetary Change. 57 p. 16–26

  • Devi U, Shekhar MS, Singh GP, Narasimha Rao N, Bhatt US (2019) Methodological application of quantile mapping to generate precipitation data over Northwest Himalaya. Int J Climatol 39(7):3160–3170. https://doi.org/10.1002/joc.6008

    Article  Google Scholar 

  • Dimri AP, Mohanty UC (2009) Simulation of mesoscale features associated with intense western disturbances over western Himalayas. Meteorol Appl 16:289–308

    Article  Google Scholar 

  • Eum H-I, Cannon AJ (2017) Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the CMIP5 ensemble. Int J Climatol 37:3381–3397

    Article  Google Scholar 

  • Fang G, Yang J, Chen YN, Zammit C (2015) Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Published in Hydrol Earth Syst Sci. 2547–2559

  • Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM, IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation, A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, vol 582. Cambridge University Press, Cambridge

    Google Scholar 

  • Gudmundsson L, Bremnes JB, Haugen JE, Skaugen ET (2012) Technical Note: Downscaling RCM precipitation to the station scale using quantile mapping–a comparison of methods. Hydrol Earth Syst Sci Discuss 9:6185–6201. https://doi.org/10.5194/hessd-9-6185

    Article  Google Scholar 

  • Gupta HV, Sorooshian S, Yapo PO (1999) Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J Hydrol Eng 4(2):135–143

    Article  Google Scholar 

  • Gutjahr O, Heinemann G (2013) Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM. Theor Appl Climatol 114:511–529. https://doi.org/10.1007/s00704-013-0834-z

    Article  Google Scholar 

  • Hart KA, Steenburgh WJ, Onton DJ, Siffert AJ (2004) An evaluation of mesoscale-model-based model output statistics (MOS) during the 2002 Olympic and Paralympic Winter Games. Wea Forecast 19:200–218

    Article  Google Scholar 

  • Hashino T, Bradley AA, Schwartz SS (2007) Evaluation of bias-correction methods for ensemble stream flow volume forecasts. Hydrol Earth Syst Sci 11:939–950

    Article  Google Scholar 

  • Hay LE, Clark MP, Wilby RL, Gutowski WJ, Leavesley GH, Pan Z, Arritt RW, Takle ES (2002) Use of regional climate model output for hydrolgic simulations. J Hydrometeorol 3:571–590. https://doi.org/10.1175/1525-7541

    Article  Google Scholar 

  • Hayhoe K, Wake C, Anderson B, Liang X-Z, Maurer E, Zhu J, Bradbury J, DeGaetano A, Stoner A, Wuebbles D (2008) Regional climate change projections for the Northeast USA. Mitig Adapt Strateg Glob Chang 13:425–436. https://doi.org/10.1007/s11027-007-9133-2

    Article  Google Scholar 

  • Ines AVM, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol 138:44–53

    Article  Google Scholar 

  • Kharin VV, Zwiers FW (2002) Notes and correspondence: climate predictions with multi-model ensembles. J Clim 15:793–799

    Article  Google Scholar 

  • Lafon T (2013) Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods. Int J Climatol 33:1367–1381. https://doi.org/10.1002/joc.3518

    Article  Google Scholar 

  • Lang TJ, Barros AP (2004) Winter storms in the Central Himalayas. J Meteorol Soc Jpn 82(3):829–844

    Article  Google Scholar 

  • Leander R, Buishand TA (2007) Resampling of regional climate model output for the simulation of extreme river flows. J Hydrol 332:487–496. https://doi.org/10.1016/j.jhydrol.2006.08.006

    Article  Google Scholar 

  • Mass CF, Ovens D, Westrick K, Colle BA (2002) Does increasing horizontal resolution produce more skillful forecasts? Bull Am Meteorol Soc 83:407–430

    Article  Google Scholar 

  • Maurer EP, Duffy PB (2005) Uncertainty in projections of streamflow changes due to climate change in California. Geophys Res Lett 32:L03704. https://doi.org/10.1029/2004GL021462

    Article  Google Scholar 

  • Maurer EP, Hidalgo HG, Das T, Dettinger MD, Cayan DR (2010) The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrol Earth Syst Sci 14:1125–1138. https://doi.org/10.5194/hess14-1125-2010

    Article  Google Scholar 

  • Murphy AH, Epstein ES (1989) Skill Scores and correlation coefficients in model verification. Mon Weather Rev 117:572–581

    Article  Google Scholar 

  • Murphy AH, Winkler RL (1992) Diagnostic verification of probability forecasts. Inter J Fore 7:435–455

    Article  Google Scholar 

  • Paegle J, Mo KC (1997) Alternating wet and dry conditions over South America during summer. Am Meteorol Soc 125:279–291

    Google Scholar 

  • Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192. https://doi.org/10.1007/s00704009-0134-9

    Article  Google Scholar 

  • Schneider SH, Semenov S, Patwardhan A, Burton I, Magadza CHD, Oppenheimer M, Pittock AB, Rahman A, Smith JB, Suarez A, Yamin F (2007) In: Parry M, Canziani O, Palutikof J, van der Linden P, Hanson C (eds) Assessing key vulnerabilities and the risk from climate change, in: Climate change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 779–810

    Google Scholar 

  • Shekhar MS, Kumar S, Joshi P, Ganju A (2014) Mountain Weather research and forecasting over Western and Central Himalaya by using mesoscale models. Inter J Earth Atmos Sci 1(2):71–84

    Google Scholar 

  • Shekhar MS, Devi U, Paul S, Singh GP, Singh A (2017) Analysis of trends in extreme precipitation events over Western Himalaya Region: intensity and duration wise study. J Ind Geophys Union 21(3):225–231

    Google Scholar 

  • Singh J, Knapp HV, Demissie M (2004) Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT. Illinois Department of Natural Resources and the Illinois State Geological Survey. Illinois State Water Survey Contract Report 2004-08. http://www.isws.illinois.edu/pubdoc/CR/ISWSCR2004-08.pdf. Accessed 8 June 2007

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106(7):7183–7192

    Article  Google Scholar 

  • Teng J, Chiew FHS, Timbal B, Wang Y, Vaze J, Wang B (2012) Assessment of an analogue downscaling method for modelling climate change impacts on runoff. J Hydrol 472–473:111–125. https://doi.org/10.1016/j.jhydrol.2012.09.024

    Article  Google Scholar 

  • Teng J, Potter NJ, Chiew FHS, Zhang L, Wang B, Vaze J, Evans JP (2015) How does bias correction of regional climate model precipitation affect modelled runoff? J Hydrol Earth Syst Sci 19:711–728

    Article  Google Scholar 

  • Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol 456–457:12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052

    Article  Google Scholar 

  • Teutschbein C, Seibert J (2013) 2013: Is bias correction of regional climate model (RCM) simulations possible for nonstationary conditions? Hydrol Earth Syst Sci 17:5061–5077. https://doi.org/10.5194/hess-17-5061-2013

    Article  Google Scholar 

  • Themeßl MJ, Gobiet A, Leuprecht A (2011) Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 31:1530–1544. https://doi.org/10.1002/joc.2168

    Article  Google Scholar 

  • Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Longrange experimental hydrologic forecasting for the eastern United States. J Geophys Res 107:4429. https://doi.org/10.1029/2001JD000659

    Article  Google Scholar 

  • Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Chang 62:189–216. https://doi.org/10.1023/B:CLIM.0000013685.99609.9e

    Article  Google Scholar 

  • World Meteorological Organization (2011) Guide to climatological practices (WMO-No. 100). Geneva

  • Yèkambèssoun N’TM’P, Lawin AE, Oyerinde GT, Yao BK, Afouda AA (2016) Comparison of daily precipitation bias correction methods based on four regional climate model outputs in Ouémé Basin, Benin. Hydrology 4(6):58–71. https://doi.org/10.11648/j.hyd.20160406.11http://www.sciencepublishinggroup.com/j/hyd. Accessed 7 Jan 2017

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Acknowledgments

The authors would like to acknowledge SASE for providing observed precipitation data for this study. Modeling team of SASE is also highly appreciated for the model setup on SASE HPC and maintaining the WRF model. The authors are thankful to the reviewers for their constructive and valuable comments which improves the manuscript.

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Authors and Affiliations

  1. Department of Biotechnology, Chandigarh College of Technology, CGC, Landran, 140307, India

    Usha Devi

  2. Snow and Avalanche Study Establishment, Research and Development Centre, Sector 37, Chandigarh, 160036, India

    M. S. Shekhar

  3. Department of Geophysics, Institute of Science, Banaras Hindu University, -221005, Varanasi, India

    G. P. Singh

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  1. Usha Devi
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Correspondence to Usha Devi.

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Devi, U., Shekhar, M.S. & Singh, G.P. Correction of mesoscale model daily precipitation data over Northwestern Himalaya. Theor Appl Climatol 143, 51–60 (2021). https://doi.org/10.1007/s00704-020-03409-8

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  • DOI: https://doi.org/10.1007/s00704-020-03409-8

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