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      Permanent link:
      https://www.ias.ac.in/article/fulltext/jess/126/01/0003

    • Keywords

       

      Temperature forecast; Hidden Markov Model optimisation; meteorological variables.

    • Abstract

       

      Maximum and minimum temperatures are used in avalanche forecasting models for snow avalanche hazard mitigation over Himalaya. The present work is a part of development of Hidden Markov Model (HMM) based avalanche forecasting system for Pir-Panjal and Great Himalayan mountain ranges of the Himalaya. In this work, HMMs have been developed for forecasting of maximum and minimum temperatures for Kanzalwan in Pir-Panjal range and Drass in Great Himalayan range with a lead time of two days. The HMMs have been developed using meteorological variables collected from these stations during the past 20 winters from 1992 to 2012. The meteorological variables have been used to define observations and states of the models and to compute model parameters (initial state, state transition and observation probabilities). The model parameters have been used in the Forward and the Viterbi algorithms to generate temperature forecasts. To improve the model forecasts, the model parameters have been optimised using Baum–Welch algorithm. The models have been compared with persistence forecast by root mean square errors (RMSE) analysis using independent data of two winters (2012–13, 2013–14). The HMM for maximum temperature has shown a 4–12% and 17–19% improvement in the forecast over persistence forecast, for day-1 and day-2, respectively. For minimum temperature, it has shown 6–38% and 5–12% improvement for day-1 and day-2, respectively.

    • Author Affiliations

       

      J C Joshi1 Tankeshwar Kumar2 Sunita Srivastava2 Divya Sachdeva1

      1. Snow and Avalanche Study Establishment, Sector 37-A, Chandigarh 160 036, India.
      2. Department of Physics, Panjab University, Chandigarh, India.

    • Dates

       
      Published
      6 February 2017
      Manuscript received
      29 December 2015
      Manuscript revised
      19 September 2016
      Accepted
      13 October 2016

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