Jagdish Chandra Joshi
Articles written in Journal of Earth System Science
Volume 119 Issue 5 October 2010 pp 597-602
Temperature and fresh snow are essential inputs in an avalanche forecasting model.Without these parameters,prediction of avalanche occurrence for a region would be very difficult.In the complex terrain of Himalaya,nonavailability of snow and meteorological data of the remote locations during snow storms in the winter is a common occurrence.In view of this persistent problem present study estimates maximum temperature,minimum temperature,ambient temperature and precipitation intensity on different regions of Indian western Himalaya by using similar parameters of the neighbouring regions.The location at which parameters are required and its neighbouring locations should all fall in the same snow climatic zone.Initial step to estimate the parameters at a location,is to shift the parameters of neighbouring regions at a reference height corresponding to the altitude of the location at which parameters are to be estimated.The parameters at this reference height are then spatially interpolated by using Barnes objective analysis.The parameters estimated on different locations are compared with the observed one and the Root Mean Square Errors (RMSE)of the observed and estimated values of the parameters are discussed for the winters of 2007 –2008.
Volume 123 Issue 8 December 2014 pp 1771-1779
A numerical avalanche prediction scheme using Hidden Markov Model (HMM) has been developed for Chowkibal–Tangdhar road axis in J&K, India. The model forecast is in the form of different levels of avalanche danger (no, low, medium, and high) with a lead time of two days. Snow and meteorological data (maximum temperature, minimum temperature, fresh snow, fresh snow duration, standing snow) of past 12 winters (1992–2008) have been used to derive the model input variables (average temperature, fresh snow in 24 hrs, snow fall intensity, standing snow, Snow Temperature Index (STI) of the top layer, and STI of buried layer). As in HMMs, there are two sequences: a state sequence and a state dependent observation sequence; in the present model, different levels of avalanche danger are considered as different states of the model and Avalanche Activity Index (AAI) of a day, derived from the model input variables, as an observation. Validation of the model with independent data of two winters (2008–2009, 2009–2010) gives 80% accuracy for both day-1 and day-2. Comparison of various forecasting quality measures and Heidke Skill Score of the HMM and the NN model indicate better forecasting skill of the HMM.
Volume 128 | Issue 8
Click here for Editorial Note on CAP Mode