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

Content Provider	Springer Nature Link
Author	JOSHI, J C SRIVASTAVA, Sunita
Copyright Year	2017
Abstract	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.
Starting Page	1
Ending Page	12
Page Count	12
File Format	PDF
ISSN	02534126
Journal	Journal of Earth System Science
Volume Number	126
Issue Number	3
e-ISSN	0973774X
Language	English
Publisher	Springer India
Publisher Date	2017-03-29
Publisher Institution	Indian Academy of Sciences
Publisher Place	New Delhi
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Precipitation forecast forward algorithm Viterbi algorithm Baum–Welch algorithm Earth Sciences Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics)
Content Type	Text
Resource Type	Article
Subject	1900/1901

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