Influence of binary fraction on the fragmentation of young massive clusters—a Monte Carlo simulation     

Tanuka Chattopadhyay  Abisa Sinha  Asis Kumar Chattopadhyay 《Astrophysics and Space Science》2016,361(4):120

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Power spectrum analysis of atmospheric ozone during the summer     

J. Chattopadhyay  H. S. Rathor 《Pure and Applied Geophysics》1972,95(1):186-193

Summary This paper describes a study of the fluctuations in total atmospheric ozone amount as measured with a Dobson Spectrophotometer during the summer season over three north Indian stations, using the technique of power spectrum analysis. In all 19 spectra have been constructed. The long term trend was removed by applying a high pass filter. The main conclusions are: (1) The nature of the spectrum at a station generally differs from year to year. (2) Spectra of different stations during the same year, are generally different. (3) From the significance study of the spectral peaks, most of the spectra revealed the presence of two types of periodicities. One of these is in the range of 10–17 days while the other is found to have a range of 4.5–8.3 days. (4) It has been suggested that the former periodicity may be closely linked to the index cycle, while the latter may be related to the long waves in the Westerlies.  相似文献   

A neurocomputing approach to predict monsoon rainfall in monthly scale using SST anomaly as a predictor   总被引：3，自引：0，他引：3  

Nachiketa Acharya  Surajit Chattopadhyay  Makarand A. Kulkarni  Uma C. Mohanty 《Acta Geophysica》2012,60(1):260-279

A relationship between summer monsoon rainfall and sea surface temperature anomalies was investigated with the aim of predicting the monthly scale rainfall during the summer monsoon period over a section (80°–90°E, 14°–24°N) of eastern India that depends heavily upon the rainfall during the summer monsoon months for its agricultural practices. The association between area-averaged rainfall of June over the study zone and global sea surface temperature (SST) anomalies for the period 1982–2008 was examined and the variability of rainfall in monthly scale was calculated. With a view to significant variability in the rainfall in the monthly scale, it was decided to implement the artificial neural network (ANN) for forecasting the monthly scale rainfall using the SST anomalies as a predictor. Finally, the potential of ANN in this prediction has been assessed.  相似文献   

A study of generalized second law of thermodynamics in modified f(R) Horava–Lifshitz gravity     

Surajit Chattopadhyay  Rahul Ghosh 《Astrophysics and Space Science》2012,341(2):669-674

This work investigates the validity of the generalized second law of thermodynamics in modified f(R) Horava–Lifshitz gravity proposed by Chaichian et al. (Class. Quantum Grav. 27: 185021, 2010), which is invariant under foliation-preserving diffeomorphisms. It has been observed that the equation of state parameter behaves like quintessence (w>−1). We study the thermodynamics of the apparent, event and particle horizons in this modified gravity. We observe that under this gravity, the time derivative of total entropy stays at positive level and hence the generalized second law is validated.  相似文献   

Announcements     

Somsubhra Chattopadhyay  Manoj K. Jha 《水文科学杂志》2013,58(3):495-496

ABSTRACT

Global climate variations are expected to cause serious challenges to water resources planning and management, including an increase in sea level, abrupt changes in rainfall patterns and changes in ecosystems. This study evaluates impacts of mid-century climate variability as projected by climate models in the Haw River watershed, which contributes significantly to Jordan Lake, a major source of drinking water supply in central North Carolina, USA. The watershed-based hydrological model, Soil and Water Assessment Tool (SWAT), was successfully calibrated with very good to excellent performance. Projected precipitation and temperature information for 2040–2069 from four dynamically downscaled regional climate models (RCMs) was used to force the SWAT modeling set-up of the watershed. On a long-term basis, a 38% decrease in the precipitation in early fall is expected while spring months are expected to receive 30% higher precipitation compared to the baseline condition (1980–2009). Water yield was found to increase in spring months, with a maximum of 74% increase on average. Summer months are expected to have on average 8% higher evapotranspiration (ET) than the baseline. Analysis of the change in average monthly streamflow at the watershed outlet (which leads to Lake Jordan) shows that there might be, on average, an 80% increase in streamflow in spring months (February, March, April and May), with the greatest increase (107%) in May. In general, simulation results indicated that the hydrological response of the watershed is very sensitive to the potential variation in climate (precipitation and temperature), with precipitation being one of the decisive factors in water yield increase.