Quality of local scale surface weather analogs over the north-west Himalaya (NWH), India

  • Dan SinghEmail author
  • Ashavani Kumar
  • M S Shekhar


An analog ensemble system was developed for the realisation of local-scale surface meteorological variables for independent test data (test data) at six stations over the north-west Himalaya (NWH), India. Extreme values (the maximum value and the minimum value) and the mean value in 10 analog days (the analog mean) and the climatological mean of each surface meteorological variable were compared with its corresponding observed values on the same day (d0, lead time 0 hour (h)), d1 (d0 + 1, lead time 24 h), d2 (d0 + 2, lead time 48 h) and d3 (d0 + 3, lead time 72 h) of test data. Pearson correlation coefficients (CCs), Mean Absolute Differences (MADs) and Root Mean Square Differences (RMSDs) of the extreme values in analog days, and the analog mean and climatological mean of each meteorological variable on d0 with its corresponding observed values on d0, d1, d2 and d3 of test data were computed at six stations over the NWH. CCs of extreme values in analog days and the analog mean of each meteorological variable on d0 with its observed values on d0, d1, d2 and d3 were found to be higher than the CCs of the climatological mean of each meteorological variable on d0 with its observed values on d0, d1, d2 and d3. MADs (RMSDs) of extreme values in analog days and the analog mean of each meteorological variable on d0 with its observed values on d0, d1, d2 and d3 were found to be lesser than the MADs (RMSDs) of the climatological mean of each meteorological variable on d0 with its observed values on d0, d1, d2 and d3. However, the MADs (RMSDs) of the extreme values of each meteorological variable in analog days were found to be higher than the MADs (RMSDs) of its analog mean. Results show that the analog mean of each meteorological variable holds better predictive skill than the extreme values in analog days and its climatological mean. MADs (RMSDs) of different surface meteorological variables in surface weather analogs comparable to Mean Absolute Errors (MAEs) and Root Mean Square Errors (RMSEs) for their prediction with the help of different types of weather forecast models show that the surface weather analogs hold good promise for the local-scale prediction of surface meteorological variables over the NWH.


Weather weather forecasting analog ensemble system north-west Himalaya 



The authors are thankful to the director, SASE, for his constant encouragement during this study. The invaluable suggestions from the anonymous reviewers on improving the quality of the manuscript is gratefully acknowledged. Efforts made by the Scientists and Technical Staff of SASE towards the collection of surface meteorological observations from remote areas of the NWH under harsh climatic conditions are acknowledged.


  1. Bannayan M and Hoogenboom G 2008 Weather analogue: A tool for real time prediction of daily weather data realization based on a modified k-nearest neighbor approach; Environ. Model Softw. 23 703–713.Google Scholar
  2. Barnston A G 1992 Correspondence among the correlation, RMSE and Heidke forecast verification measures: Refinement of the Heidke score; Wea. Forecasting 7 699–709.Google Scholar
  3. Barnett T P and Preisendorfer R W 1978 Multifield analog prediction of short-term climate fluctuations using a climate state vector; J. Atmos. Sci. 35 1771–1787.Google Scholar
  4. Buizza R, Hollingsworth A, Lalaurette F and Ghelli A 1999 Probabilistic predictions of precipitation using the ECMWF ensemble prediction system; Wea. Forecasting 14 168–189.Google Scholar
  5. Charles A, Timbal B, Fernandez E and Hendon H 2013 Analog downscaling of seasonal rainfall forecasts in the Murray Darling Basin; Mon. Weather Rev. 141 1099–1117.Google Scholar
  6. Daoud A B, Sauquet E, Lang M, Bontron G and Obled C 2011 Precipitation forecasting through an analog sorting technique: A comparative study; Adv. Geosci. 29 103–107.Google Scholar
  7. Das S 2005 Mountain weather forecasting using MM5 modelling system; Curr. Sci. 88 899–905.Google Scholar
  8. Das S, Mitra A K, Iyengar G R and Singh J 2002 Skill of medium-range forecasts over the Indian monsoon region using different parameterizations of deep convection; Wea. Forecasting 17 1194–1210.Google Scholar
  9. Das S, Singh S V, Rajagopal E N and Gall R 2003 Mesoscale modeling for mountain weather forecasting over the Himalayas; Bull. Am. Meteor. Soc. 84(9), Scholar
  10. Das S, Ashrit R, Iyengar G R, Mohandas S, Gupta M D, George J P, Rajagopal E N and Dutta S K 2008 Skills of different meso scale models over Indian region during monsoon season: Forecast errors; J. Earth Syst. Sci. 117 603–620.Google Scholar
  11. Delle Monache L, Nipen T, Liu Y, Roux G and Stull R 2011 Kalman filter and analog schemes to post process numerical weather predictions; Mon. Weather Rev. 139 3554–3570.Google Scholar
  12. Delle Monache L, Eckel F A, Rife D L, Nagarajan B and Searight K 2013 Probabilistic weather prediction with an analog ensemble; Mon. Weather Rev. 141 3498–3516.Google Scholar
  13. Dimri A P 2006 Surface and upper air fields during extreme winter precipitation over the western Himalayas; Pure Appl. Geophys. 163 1679–1698.Google Scholar
  14. Dimri A P and Mohanty U C 2007 Location-specific prediction of maximum and minimum temperature over the Western Himalayas; Meteorol. Appl. 14 79–93.Google Scholar
  15. Dimri A P and Das S K 2011 Wintertime climatic trends in the western Himalayas; Clim. Change 111 775–800.Google Scholar
  16. Dimri A P and Chevuturi A 2014 Model sensitivity analysis study for western disturbances over the Himalayas; Meteorol. Atmos. Phys. 123(3–4) 155–180, Scholar
  17. Dimri A P and Niyogi D 2013 Regional climate model application at subgrid scale on Indian Winter Monsoon over the Western Himalayas; Int. J. Climatol. 33 2185–2205.Google Scholar
  18. Dimri A P, Yasunari T, Wiltshire A, Kumar P, Mathison C, Ridley J and Jacob D 2013 Application of regional climate models to the Indian winter monsoon over the Western Himalayas; Sci. Total Env. 468 S36–S47, Scholar
  19. Eccel E, Ghielmi L, Granitto P, Barbiero R, Grazzini F and Cesari D 2007 Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models; Nonlinear Process. Geophys. 14 211–222.Google Scholar
  20. Fiddes J and Gruber S 2014 TopoSCALE v.1.0: Downscaling gridded climate data in complex terrain; Geosci. Model Dev. 7 387–405.Google Scholar
  21. Gibergans-Baguena J and Llasat M C 2007 Improvement of the analog forecasting method by using local thermodynamic data. Application to autum precipitation in Catalonia; Atmos. Res. 86(3–4) 173–193, Scholar
  22. Gutzler D S and Shukla J 1984 Analogs in the wintertime 500 mb height field; J. Atmos. Sci. 41 177–189.Google Scholar
  23. Hall T J, Thessin R N, Bloy G J and Mutchler C N 2010 Analog sky condition forecasting based on a k-nn algorithm; Wea. Forecasting 25 1463–1478.Google Scholar
  24. Hamill T M and Colucci S J 1997 Verification of Eta–RSM short range ensemble forecasts; Mon. Weather Rev. 125 1312–1327.Google Scholar
  25. Hamil T M and Whitaker J S 2006 Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application; Mon. Weather Rev. 134 3209–3229.Google Scholar
  26. Hamil T M and Whitaker J S 2007 Ensemble calibration of 500-hPa geopotential height and 850-hPa and 2-m temperatures using reforecasts; Mon. Weather Rev. 135 3273–3280.Google Scholar
  27. Hatwar H R, Yadav B P and Rama Rao Y V 2005 Prediction of western disturbances and associated weather over Western Himalayas; Curr. Sci. 88 913–920.Google Scholar
  28. Jaun S and Ahrens B 2009 Evaluation of a probabilistic hydrometeorological forecast system; Hydrol. Earth Syst. Sci. 13 1843–1877.Google Scholar
  29. Jensenius J S 1990 A statistical comparison of the forecasts produced by the NGM and LFM for the 1987/88 cool season; Wea. Forecasting 5 116–127.Google Scholar
  30. Joshi P and Ganju A 2012 Maximum and minimum temperature prediction over Western Himalaya using artificial neural network; Mausam 63 283–290.Google Scholar
  31. Joshi P and Ganju A 2013 Downscaling of MM5 model output using artificial neural network over western Himalaya; Mausam 64 221–230.Google Scholar
  32. Joshi J C, Kumar T, Srivastava S and Sachdeva D 2017 Optimisation of hidden Markov model using Baum–Welch algorithm for prediction of maximum and minimum temperature over Indian Himalaya; J. Earth Syst. Sci. 126,
  33. Junk C, Delle Monache L and Alessandrini S 2015 Analog-based ensemble model output statistics; Mon. Weather Rev. 143 2909–2917, Scholar
  34. Kalnay E, Kanamitsu M and Baker W E 1990 Global numerical weather prediction at NMC; Bull. Am. Meteorol. Soc. 71 1410–1428.Google Scholar
  35. Kumar T S V V and Krishnamurti T N 2006 High resolution numerical weather prediction over the Indian subcontinent; J. Earth Syst. Sci. 115 529–555.Google Scholar
  36. Livezey R E 1994 The evaluation of forecasts; In: Analysis of climate variability: Application of statistical techniques (eds) vonStorch H and Navarra A, Spinger-Verlag, pp. 177–196.Google Scholar
  37. Lorenz E N 1969 Atmospheric predictability as revealed by naturally occurring analogues; J. Atmos. Sci. 26 636–646.Google Scholar
  38. Maini P, Kumar A, Rathore L S and Singh S V 2003 Forecasting maximum and minimum temperatures by statistical interpretation of numerical weather prediction model output; Wea. Forecasting 18 938–952.Google Scholar
  39. Menegoz M, Gallee H and Jacobi H W 2013 Precipitation and snow cover in the Himalaya: From reanalysis to regional climate simulations; Hydrol. Earth Syst. Sci. 17 3921–3936.Google Scholar
  40. Mohanty U C and Dimri A P 2004 Location specific prediction of probability of occurrence and quantity of precipitation over Western Himalayas; Wea. Forecasting 19 520–533.Google Scholar
  41. Murphy A H 1988 Skill scores based on mean square error and their relationship to the correlation coefficient; Mon. Weather Rev. 116 2417–2424.Google Scholar
  42. Murphy A H and Winkler R L 1987 A general framework for forecast verification; Mon. Weather Rev. 115 1330–1338.Google Scholar
  43. Nagarajan B, Delle Monache L D, Hacker J P, Rife D L, Searight K, Knievel J C and Nipen T N 2015 An evaluation of analog-based postprocessing methods across several variables and forecast models; Wea. Forecasting 30 1623–1643.Google Scholar
  44. Novak D R, Bailey C, Brill K F, Burke P, Hogsett W A, Rausch R and Schichte M 2014 Precipitation and temperature forecast performance at the Weather Prediction Center; Wea. Forecasting 29 489–504.Google Scholar
  45. Obled C, Bontron G and Garcon R 2002 Quantitative precipitation forecasts: A statistical adaptation of model outputs through an analogues sorting approach; Atmos. Res. 63 303–324.Google Scholar
  46. Palazzi E, Von Hardenberg J and Provenzale A 2013 Precipitation in The Hindu-Kush Karakoram Himalaya: Observations and future scenarios; J. Geophys. Res. 118 85–100.Google Scholar
  47. Radinovic D 1975 An analogue method for weather forecasting using 500/1000 mb relative topography; Mon. Weather Rev. 103 639–649.Google Scholar
  48. Raje D and Mujumdar P P 2011 A comparison of three methods for downscaling daily precipitation in the Punjab region; Hydrol. Process. 25 3575–3589, Scholar
  49. Rangachary N and Bandyopadhyay B K 1987 An analysis of the synoptic weather pattern associated with extensive avalanching in Western Himalaya; Avalanche formation, movement and effects, IAHS Publ no. 162, pp. 311–316.Google Scholar
  50. Roy Bhowmik S K and Durai V R 2008 Multi-model ensemble forecasting of rainfall over Indian monsoon region; Atmosfera 21 225–239.Google Scholar
  51. Shank D B, Hoogenboom G and McClendon R W 2008 Dew point temperature prediction using artificial neural networks; J. Appl. Meteorol. Clim. 47 1757–1769, Scholar
  52. Shekhar M S, Kumar M S, Joshi P and Ganju A 2014 Mountain weather research and forecasting over western and central Himalaya by using mesoscale models; Int. J. Earth Atmos. Sci. 1 71–84.Google Scholar
  53. Sievers O, Fraedrich K and Raible C 2000 Self-adapting analog ensemble predictions of tropical cyclone tracks; Wea. Forecasting 15 623–629.Google Scholar
  54. Singh D and Ganju A 2005 Expert system for prediction of avalanches; Curr. Sci. 94 1076–1081.Google Scholar
  55. Singh D, Dimri A P and Ganju A 2008 An analogue method for simultaneous prediction of surface weather parameters at a specific location in the Western Himalaya in India; Meteorol. Appl. 15 491–496.Google Scholar
  56. Singh D, Srinivasan K, Ganju and A and Snehmani 2010 Comparative study of performance of different weather forecast models at specific sites in northwest Himalaya in India; Meteorol. Atmos. Phys. 107(3) 137–147, Scholar
  57. Singh D, Sharma V and Juyal V 2015 Observed linear trend in few surface weather elements over the northwest Himalayas (NWH) during winter season; J. Earth Syst. Sci. 124 553–565.Google Scholar
  58. Singh D, Kumar A and Shekhar M S 2019 Spatiotemporal variability of binary weather patterns and precipitation amounts of short time intervals during winter period over the Northwest Himalaya (NWH); J. Earth Syst. Sci. (in press).Google Scholar
  59. Srinivasan K, Ganju A and Sharma S S 2005 Usefulness of meso-scale weather forecast for avalanche forecasting; Curr. Sci. 88 921–926.Google Scholar
  60. Srinivasan K, Kumar A, Verma J and Ganju A 2010 Statistical downscaling of MM5 model output to better assess avalanche threats; Ann. Glaciol. 51 14–18.Google Scholar
  61. Storch H and Zwiers F W 1999 Statistical analysis in climate research; Cambridge University Press, ISBN 0521450713.Google Scholar
  62. Suranjana S and Van Den Dool H M 1988 A measure of the practical limit of predictability; Mon. Weather Rev. 116 2522–2526.Google Scholar
  63. Terzago S, Hardenberg J V, Palazzi E and Provenzale A 2014 Snowpack changes in The Hindu Kush–Karakoram–Himalaya from CMIP5 Global Climate Models; J. Hydrometeorol. 15 2293–2313, Scholar
  64. Tiwari P R, Kar S C, Mohanty U C, Dey S, Sinha P, Raju P V S and Shekhar M S 2014 Dynamical downscaling approach for wintertime seasonal scale simulation over the western Himalayas; Acta Geophys. 62(4) 930–952, Scholar
  65. Toth Z 1989 Long-range weather forecasting using an analog approach; J. Clim. 2 594–607.Google Scholar
  66. Toth Z and Kalnay E 1993 Ensemble forecasting at NMC: The generation of perturbations; Bull. Am. Meteorol. Soc. 74 2317–2330.Google Scholar
  67. Tracton M S and Kalnay E 1993 Operational ensemble prediction at the National Meteorological Center: Practical aspects; Wea. Forecasting 8 379–398.Google Scholar
  68. Van Den Dool H M 1989 A new look at weather forecasting through analogues; Mon. Weather Rev. 117 2230–2247.Google Scholar
  69. Whitaker J S, Wee X and Vitart F 2006 Improving week-2 forecasts with multimodel reforecast ensembles; Mon. Weather Rev. 34 2279–2284.Google Scholar
  70. Woodcock F 1980 On use of analogues to improve regression forecasts; Mon. Weather Rev. 108 292–297.Google Scholar
  71. Xavier P K and Goswami B N 2007 An analog method for real-time forecasting of summer monsoon subseasonal variability; Mon. Weather Rev. 135 4149–4160.Google Scholar
  72. Zorita E and Stroch H V 1999 The analog method as a simple statistical downscaling technique: Comparison with more complicated methods; J. Clim. 12 2474–2489.Google Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Research and Development Centre, Snow and Avalanche Study EstablishmentChandigarhIndia
  2. 2.Department of PhysicsNational Institute of Technology KurukshetraKurukshetraIndia

Personalised recommendations