Hidden Markov Model for quantitative prediction of snowfall and analysis of hazardous snowfall events over Indian Himalaya

A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There...

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Bibliographic Details
Published in:Journal of Earth System Science 2017-04, Vol.126 (3), p.33, Article 33
Main Authors: JOSHI, J C, TANKESHWAR, K, SRIVASTAVA, Sunita
Format: Article
Language:English
Subjects:
Algorithms
Earth and Environmental Science
Earth Sciences
Error analysis
Error correction
Forecasting skill
Markov analysis
Markov chains
Mathematical models
Personal computers
Predictions
Risk assessment
Root-mean-square errors
Snow
Snowfall
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
Weather forecasting
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Summary:A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six observations and six states of the model. The most probable observation and state sequence has been computed using Forward and Viterbi algorithms, respectively. Baum–Welch algorithm has been used for optimizing the model parameters. The model has been validated for two winters (2012–2013 and 2013–2014) by computing root mean square error (RMSE), accuracy measures such as percent correct (PC), critical success index (CSI) and Heidke skill score (HSS). The RMSE of the model has also been calculated using leave-one-out cross-validation method. Snowfall predicted by the model during hazardous snowfall events in different parts of the Himalaya matches well with the observed one. The HSS of the model for all the stations implies that the optimized model has better forecasting skill than random forecast for both the days. The RMSE of the optimized model has also been found smaller than the persistence forecast and standard deviation for both the days.
ISSN:0253-4126
0973-774X
DOI:10.1007/s12040-017-0810-6