Jagdish Chandra Joshi

• PhD
• Scientist at Defence Research and Development Organisation

The availability of continuous weather data is essential in many applications such as the study of hydrology, glaciology, and modelling of extreme catastrophic events such as landslides, heavy precipitation, cloud burst and snow avalanches. Weather data are collected either manually or automatically, and due to variety of reasons, it becomes diffic...

High resolution meteorological fields have been found potentially useful for hydrological and agricultural applications and mitigation of hydro-meteorological hazards such as landslide and snow avalanches. These meteorological fields are being generated for Himalaya using Weather Research and Forecast (WRF) model with spatial resolution up to 3 km....

Avalanche forecasting is carried out using physical as well as statistical models. All these models have certain limitations associated with their mathematical formulation that enable them to perform variably with respect to forecast of an avalanche event and associated danger. To overcome limitations of each individual model, a multi-model decisio...

Artificial neural network (ANN)-based models have been developed for simulation of snowpack parameters—RAM hardness, shear strength, temperature, density, thickness and settlement of snowpack layers using manually observed weather data. The simulated snowpack parameters have been used for development of ANN for avalanche prediction. The complete sc...

Artificial neural network (ANN)-based models have been developed for simulation of snowpack parameters—RAM hardness, shear strength, temperature, density, thickness and settlement of snowpack layers using manually observed weather data. The simulated snowpack parameters have been used for development of ANN for avalanche prediction. The complete sc...

Snow, being a high temperature material, is thermodynamically very unstable. Snow crystals keep on changing their shape and size continuously to attain equilibrium state. The shape and size of snow crystals is determined by prevailing temperature and amount of moisture in the atmosphere. The change in shape and size of snow crystals under different...

Hidden Markov Model (HMM) has been developed for avalanche warning on 10 different road sectors in Pir-Panjal and Great Himalayan mountain ranges of North-West Himalaya. The model uses a data set of nine snow and meteorological variables—average air temperature, snow temperature index, snow drift index, snowfall in 24 h, snowfall in 48 h, snow wate...

North-West Himalayas are prone to snow avalanches during winter months (November to April). Excessive snowfall over many regions of the Himalaya during these months gives birth to hazardous snow avalanches that cause loss of precious lives of human beings and property worth millions every year. The avalanche threat also affects operational activiti...

A Hidden Markov Model (HMM) has been developed for prediction of quantitative snow fall 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 twenty winters from 1992-2012. There are six observations and six states of the m...

In this study development of avalanche forecasting models for three different mountain ranges (Pir-Panjal, Great Himalaya and Karakoram) of NorthWest Himalaya have been discussed. For Pir-Panjal and Great Himalayan ranges, Hidden Markov Model (HMM) based integrated avalanche forecasting system and for Karakoram range an ensemble system has been dev...

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 forecas...

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 s...

Frequent avalanche activity and poor route visibility due to bad weather make snow bound mountainous regions quite unsafe for travel during the winter months. Hence, there is a need for an accurate navigation device that can help in the safe movement of mountain travellers in avalanche prone areas. This paper presents the design and implementation...

In the present work meteorological observations-pressure, temperature and humidity of a station, Stage II, in Jammu & Kashmir (J&K) in Indian Western Himalaya are used for prediction of occurrence and non-occurrence of precipitation at that station using Hidden Markov Model. The model is developed with meteorological data of 15 winters (1992-2006)...

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...

Avalanche prediction is mainly done by conventional and statistical techniques and over Indian Himalayas it is predicted in terms of none, low, medium and high avalanche danger as well as occurrence or nonoccurrence of avalanche. In the present study, a quantitative range is calculated for each of the danger levels. Initially, normalized snow and m...

1. Snow climate of Indian Himalaya being very diverse, the characteristics of the snow pack are entirely different on different regions. The snow climatology (Sharma and Ganju, 2000) of the Himalaya reveal that the lower Himalayan zone is characterized by mild temperatures, heavy precipitation during winter and deep snow pack whereas middle Himalay...

Snow avalanche prediction needs exact future state of the snow pack as one of the inputs. Snow settlement affects the snow pack considerably and contributed both by fresh and standing snow. In the present study snow settlement in 24h is estimated for different ranges of fresh and standing snow at a representative observatory in both lower and middl...

Study of snow pack structure and properties has been done simultaneously on East, West, North, South aspects and level ground at Patsio (3800m-4200ma.s.l) bowl of Great Himalaya using snow pit observations and translucent profile of the snow pack in natural sun light. Resistance profile of the layered structure also has been taken using Ram penetra...

Conventional as well as numerical techniques are being widely used for the prediction of snow avalanches. The present approach combines both the techniques and delivers avalanche danger warning for 24 h in advance. Initially different levels of avalanche danger have been decided by observing fresh snow of 24 h and standing snow from a snow-meteorol...

Observations on a natural snow pack have revealed that there exist a substantial spatial variation in depth, density and layering profile of the snow pack on mountain slopes. The variation of snow mass on a mountain slope can be expressed well in terms of snow water equivalent (SWE), rather than the snow pack depth alone. In the present work, the S...

Chowkibal-Tangdhar axis of Kashmir region in India is a stretch of about 36 kms with 26 major avalanche sites. It falls in Pir Panjal range and crosses Nastachun pass cutting across Shamsabari Mountains, at an altitude of 3120m. Snow-meteorological data of 10 years recorded at two different altitude zones in the axis were statistically analyzed in...