Modelling for Science, for a better future - some recent outcomes
Probabilistic earthquake hazard assessment for Peninsular India
by Ashish, C. Lindholm, I. A. Parvez and D. Kühn
In this paper, a new probabilistic seismic hazard assessment (PSHA) is presented for Peninsular India. The PSHA has been performed using three different recurrence models: a classical seismic zonation model, a fault model, and a grid model. The development of a grid model based on a non-parameterized recurrence model using an adaptation of the Kernel-based method that has not been applied to this region before. The results obtained from the three models have been combined in a logic tree structure in order to investigate the impact of different weights of the models. Three suitable attenuation relations have been considered in terms of spectral acceleration for the stable continental crust as well as for the active crust within the Gujarat region. While Peninsular India has experienced large earthquakes, e.g., Latur and Jabalpur, it represents in general a stable continental region with little earthquake activity, as also confirmed in our hazard results. On the other hand, our study demonstrates that both the Gujarat and the Koyna regions are exposed to a high seismic hazard. The peak ground acceleration for 10 % exceedance in 50 years observed in Koyna is 0.4 g and in the Kutch region of Gujarat up to 0.3 g. With respect to spectral acceleration at 1 Hz, estimated ground motion amplitudes are higher in Gujarat than in the Koyna region due to the higher frequency of occurrence of larger earthquakes. We discuss the higher PGA levels for Koyna compared Gujarat and do not accept them uncritically.
An Assessment of Optimality of Observations in High-resolution Weather Forecasting
by Prashant Goswami and V. Rakesh
Data assimilation is a critical component for short-range weather forecasting; a number of algorithms have been developed and applied for assimilation of different kind of observations. However, an important but less explored question is the (optimal) amount of observation for maximum improvement in forecasts through data assimilation. Because the meteorological fields at different spatial and temporal resolutions are not necessarily mutually independent, indefinite increase in resolution of observations may be ineffective; thus data optimality in this sense can be defined as the maximum resolution of observation beyond which no appreciable improvement occurs due to assimilation of data. Based on forecasts of seven events over a complex terrain (urban location, Delhi) with different combinations of observations, we show that improvement in forecast skill does not saturate even with assimilation of observations a few kilometers (<10 km) apart. The improvement due to assimilation of data from each of the profilers is appreciable; however, the impact was generally the highest for assimilation of data from all the four profilers. Applicable strategies for observation system design over high-impact areas are discussed.
Impact of data assimilation on high-resolution rainfall forecasts: A spatial, seasonal, and category analysis
by Rakesh V and Prashant Goswami
In a limited area model (LAM), the impact of data assimilation is likely to depend on the background state through lateral boundary forcing; this may introduce certain seasonality in the impact of data assimilation on rainfall forecasting. It is also likely that the impact of data assimilation on forecasts will have certain spatial variability. Finally, owing to the convective nature of rainfall and the roles of parameterization scheme, the impact of data assimilation may depend on the category (intensity) of rainfall. Here these aspects for rainfall forecasts at high resolution were examined. Using a LAM (An advanced version of Weather Research and Forecasting Model), we have carried out twin simulations with and without data assimilation; the simulations without data assimilation are used as the benchmark for assessing the impact of data assimilation. Analysis of simulations for 40 sample days distributed over the years 2012–2014 over Karnataka (southern state in India) is carried out to estimate impact of data assimilation. Various statistical measures show that data assimilation improved the rainfall prediction in most cases; however, there is also strong seasonality and location dependence in impact of data assimilation. Our results also show that improvement due to data assimilation is higher/lower for lower/higher rainfall categories. Analysis shows that the cases where the initial states with data assimilation depart strongly from the first guess generally result in less or even negative impact. It is pointed out that the results have important implications in design of observation system and assessment of impact of forecasts.
- Evaluation of high resolution rainfall forecasts over Karnataka for the 2011 southwest and northeast monsoon seasons
- Propagation of torsional surface waves in a double porous layer lying over a Gibson half space
- Propagation of Torsional surface waves in an inhomogeneous anisotropic fluid saturated porous layered half space under initial stress with varying properties
- Wave propagation across the imperfectly bonded interface between cracked elastic solid and porous solid saturated with two immiscible viscous fluids
- Prediction of Indian rainfall during the summer monsoon season on the basis of links with equatorial Pacific and Indian Ocean climate indices