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Quality of Local Scale Surface Weather Analogs in Two Climatologically and Geographically Distinct Mountainous Regions

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Abstract

An analog ensemble (AE) system is developed for realization of a set of several surface meteorological variables at three stations in the Senator Beck Basin (SBB), Colorado, U.S.A and at six stations in the  north-west Himalaya (NWH), India. Quality of local scale surface weather analogs in the NWH and SBB is evaluated and compared with surface meteorological observations of different characteristics, a set of different surface meteorological variables and for different periods of a year. The Pearson Correlation Coefficients (CCs) and Root Mean Square Differences (RMSDs) of the maximum value in analog days (MMX), minimum value in analog days (MNX) and analog ensemble mean (ME) of a 10-member analog ensemble system and climatological mean (CM) of each surface meteorological variable for d0 [lead time 0 h (0 h)] from its observed values on d0, d1 (24 h), d2 (48 h) and d3 (72 h) are computed. This is done to examine the predictability potential of the AE system for simultaneous prediction of a set of several surface meteorological variables at local scale (specific site). The ME of each surface meteorological variable for d0 (constant forecasts of the AE system) exhibiting reasonable values of the CCs and RMSDs for d1, d2 and d3 will imply that the AE system exhibits high predictive potential and it is worth to develop the AE system in future for multiple forecast lead times (d1, d2 and d3) not just for d0. Moreover, the predictability potential of the AE system is studied in two climatologically and geographically distinct mountainous regions to seek its generalization capacity. The percentage of observations on various surface meteorological variables appearing as a member of the AE system and rank histograms for d0 are also used to evaluate the quality of local scale surface weather analogs. The results show that the AE system exhibits high predictability potential and generalisation capacity, and it performs reasonably well for a complete year. The RMSDs of the AE system for realisation of a set of several surface meteorological variables comparable and/or even marginally higher as compared to the Root Mean Square Errors (RMSEs) for their prediction with the help of various other types of weather forecasting models over the NWH and elsewhere suggest that the AE predictive system can be a handy tool for real-time prediction of multiple surface meteorological variables at local scale with least requirement of resources (human, data, computation and time).

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Data availability statements

Surface meteorological data of the north-west Himalaya (NWH) used in this study is not publically available and it cannot be shared due to its belongingness to the sensitive areas. However, the surface meteorological data used in this study of the Senator Beck Basin (SBB) is publically available (https://snowstudies.org/csas-archival-data/).

References

  • Anderson JL (1996) A method for producing and evaluating probabilistic forecasts from ensemble model integration. J Clim 9(7):1518–1530

    Google Scholar 

  • Armstrong RL, Armstrong BR (1987) Snow and avalanche climates of the western United States: A comparison of maritime, intermountain and continental conditions. Avalanche formation, movement and effects. IAHS Publ No 62:281–294

    Google Scholar 

  • Atencia A, Zawadzki I (2015) A comparison of two techniques for generating nowcasting ensembles Part II: analogs selection and comparison of techniques. Mon Weather Rev 143(7):2890–2908

    Google Scholar 

  • Bannayan M, 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 Soft 23(6):703–713

    Google Scholar 

  • Barnett TP, Preisendorfer RW (1978) Multifield analog prediction of short-term climate fluctuations using a climate state vector. J Atmos Sci 35(10):1771–1787

    Google Scholar 

  • Barnston AG (1992) Correspondence among the correlation, RMSE and Heidke forecast verification measures: refinement of the Heidke score. Weather Forecast 7(4):699–709

    Google Scholar 

  • Ben Daoud A, Sauquet E, Lang M, Bontron G, Obled C (2011) Precipitation forecasting through an analog sorting technique: a comparative study. Adv Geosci 29:103–107

    Google Scholar 

  • Bergen RE, Harnack RP (1982) Long range temperature prediction using a simple analog approach. Mon Weather Rev 110(8):1083–1099

    Google Scholar 

  • Bonekamp PN, Collier JE, Immerzeel WW (2018) The impact of spatial resolution, land use, and spin up time on resolving spatial precipitation patterns in the Himalayas. J Hydrometeorol 19:1565–1581

    Google Scholar 

  • Brand S, Long JM, Blelloch JW, Hamilton GD (1975) A tropical cyclone analog program for the North IndianOcean. Mon Weather Rev 103(3):258–261

    Google Scholar 

  • Buishand TA, Brandsma T (2001) Multisite simulation of daily precipitation and temperature in the Rhine basin by nearest-neighbour resampling. Water Resour Res 37(11):2761–2776

    Google Scholar 

  • Cannon F, Carvalho Leila MV, Jones C, Hall T, Gomberg D, Dumas J, Mark J (2017) WRF simulation of down slope wind events in coastal Santa Barbara County. Atmos Res 191:57–73

    Google Scholar 

  • Carter RG, Keislar RE (2000) Emergency response transport forecasting using historical wind field pattern matching. J Appl Meteorol 39(3):446–462

    Google Scholar 

  • Charles A, Timbal B, Fernandez E, Hendon H (2013) Analog downscaling of seasonal rainfall forecasts in theMurray Darling Basin. Mon Weather Rev 141(3):1099–1117

    Google Scholar 

  • Chawla I, Osuri Krishna K, Mujumdar PP, Niyogi D (2018) Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin. Hydrol Earth Sys Sci 22(2):1095–1117

    Google Scholar 

  • Daoud AB, Sauquet E, Lang M, Bontron G, Obled C (2011) Precipitation forecasting through an analog sorting technique: a comparative study. Adv Geosci 29:103–107

    Google Scholar 

  • Das S, Singh SV, Rajagopal EN, Gall R (2003) Mesoscale modeling for mountain weather forecasting over the Himalayas. Bull Am Meteorol Soc 84(9):1237–2124

    Google Scholar 

  • Delle Monache L, Nipen T, Liu Y, Roux G, Stull R (2011) Kalman filter and analog schemes to post process numerical weather predictions. Mon Weather Rev 139(11):3554–3570

    Google Scholar 

  • Delle Monache L, Eckel FA, Rife DL, Nagarajan B, Searight K (2013) Probabilistic weather prediction with an Analog Ensemble. Mon Weather Rev 141(10):3498–3516

    Google Scholar 

  • Dimri AP, Mohanty UC (2007) Location-specific prediction of maximum and minimum temperature over theWestern Himalayas. Meteorol Appl 14(1):79–93

    Google Scholar 

  • Dimri AP, Niyogi D (2013) Regional climate model application at subgrid scale on Indian Winter Monsoon over the Western Himalayas. Int J Climatol 33(9):2185–2205

    Google Scholar 

  • Diomede T, Nerozzi F, Paccagnella T, Todini E (2008) The use of meteorological analogues to account for LAM QPF uncertainty. Hydrol Earth Sys Sci 12:141–157

    Google Scholar 

  • Durai VR, Bhradwaj R (2014) Evaluation of statistical bias correction methods for numerical weather prediction model forecasts of maximum and minimum temperatures. Nat Hazards 73(3):1229–1254

    Google Scholar 

  • Eccel E, Ghielmi L, Granitto P, Barbiero R, Grazzini F, Cesari D (2007) Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models. Nonlin Processes Geophys 14:211–222

    Google Scholar 

  • Fiddes J, Gruber S (2014) TopoSCALE v.1.0: downscaling gridded climate data in complex terrain. Geosci Model Dev 7(1):387–405

    Google Scholar 

  • Foresti L, Panziera L, Mandapaka PV, Germannb U, Seeda A (2015) Retrieval of analogue radar images for ensemble nowcasting of orographic rainfall. Meteorol Appl 22(2):141–155

    Google Scholar 

  • Gangopadhyay S, Clark M (2005) Statistical downscaling using K-nearest neighbors. Water Resour Res. https://doi.org/10.1029/2004WR003444

    Article  Google Scholar 

  • Gibergans-Baguena J, Llasat MC (2007) Improvement of the analog forecasting method by using local thermodynamic data. Application to autumn precipitation in Catalonia. Atmos Res 86(3–4):173–193

    Google Scholar 

  • Gutzler DS, Shukla J (1984) Analogs in the wintertime 500 mb height field. J Atmos Sci 41(2):177–189

    Google Scholar 

  • Hall TJ, Thessin RN, Bloy GJ, Mutchler CN (2010) Analog sky condition forecasting based on a k-nn algorithm. Weather Forecast 25(5):1463–1478

    Google Scholar 

  • Hamill TM (2001) Interpretation of rank histograms for verifying ensemble forecasts. Mon Weather Rev 129(3):550–560

    Google Scholar 

  • Hamill TM, Whitaker JS (2006) Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Mon Weather Rev 134(11):3209–3229

    Google Scholar 

  • Jarrell JD, Mauck CJ, Renard RJ (1975) Forecasting tropical cyclone motion over the Northeastern Pacific Ocean by an analog scheme. Mon Weather Rev 103(8):674–684

    Google Scholar 

  • Jensenius JRJS (1990) A statistical comparison of the forecasts produced by the NGM and LFM for the 1987/88 cool season. Weather Forecast 5(1):116–127

    Google Scholar 

  • Joshi P, Ganju A (2012) Maximum and minimum temperature prediction over Western Himalaya using artificial neural network. Mausam 63(2):283–290

    Google Scholar 

  • Joshi P, Ganju A (2013) Downscaling of MM5 model output using artificial neural network over western Himalaya. Mausam 64(2):221–230

    Google Scholar 

  • Joshi JC, Kumar T, Srivastava S, 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. https://doi.org/10.1007/s12040-016-0780-0

    Article  Google Scholar 

  • Junk C, Delle Monache L, Alessandrini S (2015) Analog-based ensemble model output statistics. Mon Weather Rev 143(7):2909–2917

    Google Scholar 

  • Kalnay E, Kanamitsu M, Baker WE (1990) Global numerical weather prediction at NMC. Bull Am Meteorol Soc 71(10):1410–1428

    Google Scholar 

  • Kendzierski S, Czernecki B, Kolendowicz L, Adam J (2018) Air temperature forecasts’ accuracy of selected short-term and long-term numerical weather prediction models over Poland. Geofizika 35:67–85

    Google Scholar 

  • Lall U, Sharma A (1996) A nearest neighbor bootstrap for resampling hydrologic time series. Water Resour Res 32(3):769–693

    Google Scholar 

  • Landry CC, Buck KA, Raleigh MS, Clark MP (2014) Mountain system monitoring at Senator Beck Basin, San Juan Mountains, Colorado: A new integrative data source to develop and evaluate models of snow and hydrologic processes. Water Resour Res https://doi.org/10.1002/2013WR013711

  • Livezey RE, Barnston AG, Gruza GV, Rankova EYA (1994) Comparative skill of two analog seasonal temperature prediction systems: Objective selection of predictors. J Clim 7(4):608–615

    Google Scholar 

  • Lorenz EN (1969) Atmospheric predictability as revealed by naturally occuring analogues. J Atmos Sci 26(4):636–646

    Google Scholar 

  • Mahmud M (2004) Skill of a superensemble forecast over equatorial Southeast Asia. Int J Climatol 24(15):1963–1972

    Google Scholar 

  • Maini P, Kumar A, Rathore LS, Singh SV (2003) Forecasting maximum and minimum temperatures by statistical interpretation of Numerical Weather Prediction Model Output. Weather Forecast 18(5):938–952

    Google Scholar 

  • McClung D, Schaerer P (1993) The Avalanche Handbook. Mountaineers, 1001 SW Klickitat Way, Seattle, WA 98134, USA

  • Mccollor D, Stull R (2008) Hydrometeorological Short-Range Ensemble Forecasts in Complex Terrain. Part I Meteorol Evalu Weather Forecast 23(4):533–556

    Google Scholar 

  • Menegoz M, Gallee H, Jacobi HW (2013) Precipitation and snow cover in the Himalaya: from reanalysis to regional climate simulations. Hydrol Earth Sys Sci 17(10):3921–3936

    Google Scholar 

  • Mohanty UC, Dimri AP (2004) Location specific prediction of probability of occurrence and quantity of precipitation over Western Himalayas. Weather Forecast 19(3):520–533

    Google Scholar 

  • Mughal MO, Lynch M, Frank Yu, Jeanneret MB, F, Sutton J, (2017) Wind modelling, validation and sensitivity study using Weather Research and Forecasting model in complex terrain. Environ Model Soft 90:107–125

    Google Scholar 

  • Murphy AH (1988) Skill scores based on mean square error and their relationship to the correlation coefficient. Mon Weather Rev 116(12):2417–2424

    Google Scholar 

  • Murphy AH, Winkler RL (1987) A general framework for forecast verification. Mon Weather Rev 115(7):1330–1338

    Google Scholar 

  • Nagarajan B, Delle Monache LD, Hacker JP, Rife DL, Searight K, Knievel JC, Nipen TN (2015) An evaluation of analog-based post processing methods across several variables and forecast models. Weather Forecast 30(6):1623–1642

    Google Scholar 

  • Novak DR, Bailey C, Brill KF, Burke P, Hogsett WA, Rausch R, Schichte M (2014) Precipitation and temperature forecast performance at the Weather Prediction Center. Weather Forecast 29(3):489–504

    Google Scholar 

  • Palazzi E, Von Hardenberg J, Provenzale A (2013) Precipitation in the Hindu-Kush Karakoram Himalaya: Observations and future scenarios. J Geophys Res 118(1):85–100

    Google Scholar 

  • Panziera L, Germann U, Gabella M, Mandapaka PV (2011) NORA - Nowcasting of orographic rainfall by means of Analogues. Q J R Meteorol Soc 137(661):2106–2123

    Google Scholar 

  • Odak P, Delle Monache L, Horvath K, Hrastinski M (2018) Deterministic wind speed predictions with analog-based methods over complex topography. J Appl Meteorol Climatol 57(9):2047–2070

    Google Scholar 

  • Radinovic D (1975) An analogue method for weather forecasting using 500/1000 mb relative topography. Mon Weather Rev 103(7):639–649

    Google Scholar 

  • Rajagopalan B, Lall U (1999) A k-nearest-neighbor simulator for daily precipitation and other weather variables. Water Resour Res 35(10):3089–3101

    Google Scholar 

  • Raje D, Mujumdar PP (2011) A comparison of three methods for downscaling daily precipitation in the Punjab region. Hydrol Process 25(23):3575–3598

    Google Scholar 

  • Saha S, van den Dool HM (1988) A measure of the practical limit of predictability. Mon Weather Rev 116(12):2522–2526

    Google Scholar 

  • Shank DB, Hoogenboom G, Mcclendon RW (2008) Dew point temperature prediction using artificial neural networks. J Appl Meteorol Climatol 47(6):1757–1769

    Google Scholar 

  • Sharma SS, Ganju A (2000) Complexities of avalanche forecasting in Western Himalaya - an overview. Cold Reg Sci Tech 31(2):95–102

    Google Scholar 

  • Shekhar MS, Kumar MS, Joshi P, 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 

  • Shrestha DL, Robertson DE, Wang QJ, Pagano TC, Hapuarachchi HAP (2013) Evaluation of numerical weather prediction model precipitation forecasts for short-term stream flow forecasting purpose. Hydrol EarthSys Sci 17(5):1913–1931

    Google Scholar 

  • Sievers O, Fraedrich K, Raible CC (2000) Self-adapting analog ensemble predictions of tropical cyclone tracks. Weather Forecast 15(5):623–629

    Google Scholar 

  • Sikder S, Hossain F (2016) Assessment of the weather research and forecasting model generalized parameterization schemes for advancement of precipitation forecasting in monsoon-driven river basins. J Adv Model Earth Sys 8(3):1210–1228

    Google Scholar 

  • Singh D, Kumar A (2020) Analog ensemble (AE) systems for real time quantitative precipitation forecasts (QPFs) for different forecast lead times at local scale over the north-west Himalaya (NWH). Meteorol Atmos Phy, India. https://doi.org/10.1007/s00703-020-00763-0

    Book  Google Scholar 

  • Singh D, Dimri AP, 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(4):491–496

    Google Scholar 

  • Singh D, Srinivasan K, Ganju A, Snehmani, (2010) Comparative study of performance of different weather forecast models at specific sites in northwest Himalaya in India. Meteorol Atmos Phy 107(3):137–147

    Google Scholar 

  • Singh D, Kumar A, Shekhar MS (2019) Quality of local scale surface weather analogs over the north-westHimalaya (NWH). J Earth Syst Sci, India. https://doi.org/10.1007/s12040-019-1216-4

    Book  Google Scholar 

  • Srinivasan K, Kumar A, Verma J, Ganju A (2010) Statistical downscaling of MM5 model output to better assess avalanche threats. Ann Glacio 51(54):14–18

    Google Scholar 

  • Terzago S, Hardenberg JV, Palazzi E, Provenzale A (2014) Snowpack changes in the Hindu Kush-Karakoram Himalaya from CMIP5 Global Climate Models. J Hydrometeorol 15(6):2293–2313

    Google Scholar 

  • Tiwari PR, Kar SC, Mohanty UC, Dey S, Sinha P, Raju PVS, Shekhar MS (2014) Dynamical downscaling approach for winter time seasonal scale simulation over the Western Himalayas. Acta Geophys 62(4):930–952

    Google Scholar 

  • Toth Z (1989) Long-range weather forecasting using an analog approach. J Clim 2(6):594–607

    Google Scholar 

  • van den Dool HM (1989) A new look at weather forecasting through analogues. Mon Weather Rev 117(10):2230–2247

    Google Scholar 

  • Vijaya Kumar TSV, Krishnamurti TN (2006) High resolution numerical weather prediction over the Indian subcontinent. J Earth Syst Sci 115:529–555

    Google Scholar 

  • Wagenbrenner NS, Forthofer JM, Lamb BK, Shannon KS, Butler BW (2016) Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja. Atmos Chem Phys 16(8):5229–5241

    Google Scholar 

  • Wilks DS (2006) Statistical Methods in the Atmospheric Sciences. Academic Press, 30 Corporate Drives, Suite 400, Burlington, MA 01803, USA

  • Winstral A, Jonas T, Helbig N (2017) Statistical downscaling of gridded wind speed data using local topography. J Hydrometeorol 18:335–348

    Google Scholar 

  • Woodcock F (1980) On use of analogues to improve regression forecasts. Mon Weather Rev 108(3):292–297

    Google Scholar 

  • Xavier PK, Goswami BN (2007) An analog method for real-time forecasting of Summer Monsoon subseasonal variability. Mon Weather Rev 135(12):4149–4160

    Google Scholar 

  • Young KC (1994) A multivariate chain model for simulating climatic parameters from daily data. J Appl Meteorol 33(6):661–671

    Google Scholar 

  • Zhang H, Pu Z, Zhang X (2013) Examination of errors in near-surface temperature and wind from WRF numerical simulations in regions of complex terrain. Weather Forecast 28(3):893–914

    Google Scholar 

  • Zorita E, Stroch HV (1999) The Analog method as a simple statistical downscaling technique: comparison with more complicated methods. J Clim 12(8):2474–2489

    Google Scholar 

  • Zorita E, Hughes JP, Lettemaier DP, Von Storch H (1995) Stochastic characterizationof regional circulation patterns for climate model diagnosis and estimation of local scale precipitation. J Clim 8(5):1023–1042

    Google Scholar 

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Acknowledgements

This study was not possible without the free data made available by the Center for Snow and Avalanche Studies, Colorado, U.S.A (https://snowstudies.org/csas-archival-data/) and the data source is acknowledged. Positive criticism, insightful comments and invaluable guidance by the anonymous reviewers helped to improve the quality of manuscript and reviewers are acknowledged. Sincere efforts of the Scientists and Technical Staff of the DGRE (formerly SASE) for collecting data in the remote locations of the North-West Himalaya (NWH), India, are acknowledged.

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  1. Defence Geoinformatics Research Establishment (DGRE), Defence Research and Development Organisation, Him Parisar, Sector 37 A, Chandigarh, 160036, India

    Dan Singh

  2. Department of Physics, National Institute of Technology Kurukshetra, Kurukshetra, 136119, India

    Ashavani Kumar

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Appendices

Appendix

Appendix 'A'

The Pearson Correlation Coefficients (CCs) between the AE system (MMX, MNX and ME) or CM model (CM) values for d0 and their corresponding observations on d0, d1, d2 and d3 in the test period for variables. The CCs are calculated at each station and shown using box plots for six stations in the NWH and three stations in the SBB.

figure a

Appendix 'B'

The observed temporal mean values (OB), the AE system [MMX, MNX, ME and individual analog days (ad)] or CM model temporal mean values of variables for d0 for the test period. The temporal mean values of variables are computed at each station and shown using box plots for six stations in the NWH and three stations in the SBB.

figure b

Appendix 'C'

The observed standard deviations (OB), standard deviations of the AE system [MMX, MNX, ME and individual analog days (ad)] and CM model values of variables for d0 for the test period. The standard deviations of variables are computed at each station and shown using box plots for six stations in the NWH and three stations in the SBB.

figure c

Appendix 'D'

The Root Mean Square Differences (RMSDs) of the AE system [MMX, MNX, ME and individual analog days (ad)] and CM model (CM) values of variables for d0 from their corresponding observations on d0, d1, d2, and d3 for the test period. The RMSDs are calculated at each station for every variable and shown using box plots for six stations in the NWH and three stations in the SBB.

figure d

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Singh, D., Kumar, A. Quality of Local Scale Surface Weather Analogs in Two Climatologically and Geographically Distinct Mountainous Regions. Meteorol Atmos Phys 134, 23 (2022). https://doi.org/10.1007/s00703-022-00861-1

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