by Sanchit Minocha and Imtiyaz A. Parvez
The Gorkha Earthquake that occurred on 25th April 2015 was a long anticipated, low angle thrust-faulting shallow event in Central Nepal that devastated the mountainous southern rim of the High Himalayan range. The earthquake was felt throughout central and eastern Nepal, much of the Ganges River plain in northern India, and northwestern Bangladesh, as well as in the southern parts of the Plateau of Tibet and western Bhutan. Two large aftershocks, with magnitudes 6.6 and 6.7, occurred in the region within one day of the main event, and several dozen smaller aftershocks occurred in the region during the succeeding days. In this study, we have analyzed the 350 aftershocks of the 2015 Gorkha Earthquake of Mw 7.8 to understand the spatial and temporal distribution of b-value and the fractal correlation dimension. The b-value is found to be 0.833 ± 0.035 from the Gutenberg-Richter relation by the least squares method and 0.95 ± 0.05 by the maximum likelihood method, indicating high stress bearing source zone. The spatial and temporal correlation dimension is estimated to be 1.07 ± 0.028 and 0.395 ± 0.0027 respectively. Spatial correlation dimension suggests a heterogeneous distribution of earthquake epicenters over a linear structure in space, while the temporal correlation dimension suggests clustering of aftershock activity in the time domain. The spatial variation of the b-value reveals that the b-value is high in the vicinity of the mainshock which is due to the sudden release of stress energy in the form of seismic waves. The spatial distribution of correlation dimension further confirms a linear source in the source zone as it varies from 0.8-1.0 in most of the region. We have also studied the temporal variation of b-value and correlation dimension that shows positive correlation for about first 15 days, then a negative correlation for next 45 days and after that, a positive correlation. The positive correlation suggests that the probability of large magnitude earthquakes decreases in response to increased fragmentation of the fault zone. The negative correlation means that there is a considerable probability of occurrences of large magnitude earthquakes, indicating stress release along the faults of a larger surface area . The correlation coefficient between b-value and the correlation dimension is estimated to be 0.26, which shows that there is no significant relation between them.
by Rekha Bharali Gogoi, Govindan Kutty, V. Rakesh & Arup Borogain
The impact of deploying a flow-dependent ensemble error covariance in Weather Research and Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation (DA) system is examined for short-range rainfall forecasts during an Indian summer monsoon season. The flow-dependent background error covariance (BEC) is generated using a 50-member ensemble, which is further updated using the ensemble transform Kalman filter (ETKF). Assimilation is performed using a Hybrid variational-ensemble (“Hybrid”) and traditional 3DVAR DA system during the 4 weeks of July 2013. The forecasted wind, temperature, and rainfall from the assimilation experiments are verified against corresponding observations. The results indicate that the flow-dependent ensemble background error covariance in 3DVAR has systematically improved the forecasted wind and temperature when compared to the traditional 3DVAR. Similarly, rainfall forecast skill is superior in the Hybrid experiments relative to that of 3DVAR. Convection-permitting resolution rainfall forecast is validated against 746 telemetric rain gauge observations over the state of Karnataka. The Hybrid experiments show higher quantitative precipitation forecast skill than 3DVAR, particularly towards the later stages of data assimilation cycling. Spatially, the 3DVAR experiment shows a dry bias over the upper peninsular regions and a slight wet bias over the central and the northern Indian regions, while the magnitude of such wet and dry biases is smaller in forecasts from Hybrid analysis. Additionally, the westerly wind over the peninsular Indian landmass analyzed by 3DVAR is considerably weaker than that analyzed by the Hybrid experiments. This is proposed as a possible reason for the reduced dry bias in rainfall forecasts over the Indian landmass in Hybrid versus 3DVAR experiments.