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 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.     

The major lightning regions and associated casualties over India

Natural Hazards - Lightning, a climate-related highly localized natural phenomenon, claims lives and damage properties. These losses could only be reduced by the identification of active seasons...     

Rapid flash flood calamity in Chamoli,Uttarakhand region during Feb 2021: an analysis based on satellite data

Natural Hazards - The present study investigates the accelerating factors for extreme flash flood at Chamoli district of Uttarakhand on 7 February 2021. The Sentinel-2A and 2B satellite data have...     

Liquefaction behavior of Indo-Gangetic region using novel metaheuristic optimization algorithms coupled with artificial neural network

Natural Hazards - The present research aims to co-relate the plasticity and liquefaction response of soil as well as its significance in defining liquefaction probability. To accomplish this,...     

Physical and Mechanical Characterisation of Metadolerite

Geotechnical and Geological Engineering - The present work aims to understand the stress–strain response of an igneous rock, metadolerite, typically found in dyke form, under high loading...     

Aerosol particle behavior during Dust Storm and Diwali over an urban location in north western India

In this study, the aerosol behavior during two contradictory events, i.e., dust storm (DS) and Diwali (DW) has been studied over Jaipur. The aerosol particle number concentration shows distinct features between DS and DW events. The total ANC was found minimum during DS while maximum during DW. The 24 h mean of total ANC was 9.15 × 10⁴ (±7.71 × 10⁴) and 5.38 × 10⁵ (±3.73 × 10⁵ particles/l) during DS and DW, respectively. The total ANC increases from 7.78 × 10⁴ to 5.32 × 10⁵ and 3.52 × 10⁵ to 1.70 × 10⁶ particles/l in 24 h during DS and DW, respectively. In DS, the ANC in coarse mode (2 < particle diameter < 20 μm) is significantly high while in DW, the ANC in fine mode (0.3 < particle diameter < 2 μm) exhibits higher concentration. During dust episode, a significant change in ANC (3.0 × 10³ to 1.12 × 10⁵ particles/l) was observed for the particle of size range in 2.0–20 μm with a slight increase in particles number concentration (7.48 × 10⁴ to 4.20 × 10⁵ particles/l) in 0.3–2.0 μm range is also observed. During DS, the fine and coarse mode particles increased 4.61 and 36.44 times while during DW, it increased 3.83 and 0.95 times, respectively. The relatively high particle levels during DW are attributed for two reasons: local emissions due to burning of fire crackers and meteorological conditions, i.e., low wind speeds and low mixing-layer heights lead to relatively high particle concentrations.

     

Consolidation of a poroelastic half-space with anisotropic permeability and compressible constituents by axisymmetric surface loading

The fully coupled Biot quasi-static theory of linear poroelasticity is used to study the consolidation of a poroelastic half-space caused by axisymmetric surface loads. The fluid and solid constituents of the poroelastic medium are compressible and its permeability in the vertical direction is different from its permeability in the horizontal direction. An analytical solution of the governing equations is obtained by taking the displacements and the pore pressure as the basic state variables and using a combination of the Laplace and Hankel transforms. The problem of an axisymmetric normal load is discussed in detail. An explicit analytical solution is obtained for normal disc loading. Detailed numerical computations reveal that the anisotropy in permeability as well as the compressibilities of the fluid and solid constituents of the poroelastic medium have significant effects on the consolidation of the half-space. The anisotropy in permeability may accelerate the consolidation process and may lead to a dilution in the theoretical prediction of the Mandel-Cryer effect. The compressibility of the solid constituents may also accelerate the consolidation process.