Probabilistic seismic hazard assessment for some parts of the Indo-Gangetic plains,India     

Keshri  Chhotu Kumar  Mohanty  William Kumar  Ranjan  Pratul 《Natural Hazards》2020,103(1):815-843

Natural Hazards - Indo-Gangetic plains are seismically most vulnerable due to the proximity of adjacent great Himalayan earthquakes and thick alluvium deposits of the Ganga River system. As the...  相似文献   

Brain Storming Session on Surface and Ground Water Resources of Odisha : Status,Policy and Strategy.     

B. K. Mohanty  Subhajyoti Das 《Journal of the Geological Society of India》2018,91(1):125-126

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Settlement of Shallow Foundations on Cohesionless Soils Based on SPT Value Using Multi-Objective Feature Selection     

Ranajeet Mohanty  Sarat Kumar Das 《Geotechnical and Geological Engineering》2018,36(6):3499-3509

Accurate prediction of settlement for shallow footings on cohesionless soil is a complex geotechnical problem due to large uncertainties associated with soil. Prediction of the settlement of shallow footings on cohesionless soil is based on in situ tests as it is difficult to find out the properties of soil in the laboratory and standard penetration test (SPT) is the most often used in situ test. In data driven modelling, it is very difficult to choose the optimal input parameters, which will govern the model efficiency along with a better generalization. Feature subset selection involves minimization of both prediction error and the number of features, which are in general mutual conflicting objectives. In this study, a multi-objective optimization technique is used, where a non-dominated sorting genetic algorithm (NSGA II) is combined with a learning algorithm (neural network) to develop a prediction model based on SPT data based on the Pareto optimal front. Pareto optimal front gives the user freedom to choose a model in terms of accuracy and model complexity. It is also shown how NSGA II can be effectively applied to select the optimal parameters and besides minimizing the error rate. The developed model is compared with existing models in terms of different statistical criteria and found to be more efficient.  相似文献   

Development of extended WRF variational data assimilation system (WRFDA) for WRF non-hydrostatic mesoscale model     

Sujata Pattanayak  U C Mohanty 《Journal of Earth System Science》2018,127(4):48

The paper intends to present the development of the extended weather research forecasting data assimilation (WRFDA) system in the framework of the non-hydrostatic mesoscale model core of weather research forecasting system (WRF-NMM), as an imperative aspect of numerical modeling studies. Though originally the WRFDA provides improved initial conditions for advanced research WRF, we have successfully developed a unified WRFDA utility that can be used by the WRF-NMM core, as well. After critical evaluation, it has been strategized to develop a code to merge WRFDA framework and WRF-NMM output. In this paper, we have provided a few selected implementations and initial results through single observation test, and background error statistics like eigenvalues, eigenvector and length scale among others, which showcase the successful development of extended WRFDA code for WRF-NMM model. Furthermore, the extended WRFDA system is applied for the forecast of three severe cyclonic storms: Nargis (27 April–3 May 2008), Aila (23–26 May 2009) and Jal (4–8 November 2010) formed over the Bay of Bengal. Model results are compared and contrasted within the analysis fields and later on with high-resolution model forecasts. The mean initial position error is reduced by 33% with WRFDA as compared to GFS analysis. The vector displacement errors in track forecast are reduced by 33, 31, 30 and 20% to 24, 48, 72 and 96 hr forecasts respectively, in data assimilation experiments as compared to control run. The model diagnostics indicates successful implementation of WRFDA within the WRF-NMM system.  相似文献   

Ecological model analysis of respiratory health risk factors by ambient air pollution in Lucknow,the capital City of Uttar Pradesh,India     

Yadav  Ganesh  Singh  R. B.  Anand  Subhash  Pandey  B. W.  Mohanty  Ashutosh  Dash  Sushree Sangita 《GeoJournal》2021,87(4):469-483

Ambient air pollution, particularly in the urban environment of developing countries, has turned out to be a major health risk factor. We explore the compounded impact of age sensitivity, exposure, poverty, co-morbidity, etc., along with composite air pollution in determining morbidity and health burden of people in Lucknow, India. This cross-sectional study is confined to analyse respiratory health status across different socio-economic and geographic locations using n = 140 in-depth questionnaire method. We used mean daily ambient air pollution data of PM₁₀, PM_2.5, SO_2, and NO₂ for the 2008–2018 period. We used the ecological model framework to assess the risk at different hierarchical levels and compounded severity on a spatial scale. We also used Logistic regression model with log odds and odds ratio to analyze the association of risks outcomes with composite air pollution scores calculated using the principal component analysis method. There is a strong association of location-specific respiratory disease prevalence with an overall 32 percent prevalence. The prevalence of ecological model 1 (individual domain) is 4.3 percent, while ecological model 2 (community domain) has the highest prevalence at 32.4 percent. The logistic regression model shows that respiratory disease load is positively associated with age sensitivity (P < .001) and composite pollution level (P < .001). For another model with suffocation as the outcome variable, composite pollution level (P < .001) and exposure (P < .001) are positively associated. Optimum interventions are required at Ecological models 1, 2, and 3 levels for better respiratory health outcomes.

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Tracking Galaxy Evolution Through Low-Frequency Radio Continuum Observations using SKA and Citizen-Science Research using Multi-Wavelength Data     

Ananda Hota  C. Konar  C. S. Stalin  Sravani Vaddi  Pradeepta K. Mohanty  Pratik Dabhade  Sai Arun Dharmik Bhoga  Megha Rajoria  Sagar Sethi 《Journal of Astrophysics and Astronomy》2016,37(4):41

We present a brief review of progress in the understanding of general spiral and elliptical galaxies, through merger, star formation and AGN activities. With reference to case studies performed with the GMRT, we highlight the unique aspects of studying galaxies in the radio wavelengths where powerful quasars and bright radio galaxies are traditionally the dominating subjects. Though AGN or quasar activity is extremely energetic, it is extremely short-lived. This justify focussing on transitional galaxies to find relic-evidences of the immediate past AGN-feedback which decide the future course of evolution of a galaxy. Relic radio lobes can be best detected in low frequency observations with the GMRT, LOFAR and in future SKA. The age of these relic radio plasma can be as old as a few hundred Myr. There is a huge gap between this and what is found in optical bands. The very first relic-evidences of a past quasar activity (Hanny’s Voorwerp) was discovered in 2007 by a Galaxy Zoo citizen-scientist, a school teacher, in the optical bands. This relic is around a few tens of thousand years old. More discoveries needed to match these time-scales with star formation time-scales in AGN host galaxies to better understand black hole galaxy co-evolution process via feedback-driven quenching of star formation. It is now well-accepted that discovery and characterization of such faint fuzzy relic features can be more efficiently done by human eye than a machine. Radio interferometry images are more complicated than optical and need the citizen-scientists to be trained. RAD@home, the only Indian citizen-science research project in astronomy, analysing TIFR GMRT Sky Survey (TGSS) 150 MHz data and observing from the Giant Meterwave Radio Telescope (GMRT), was launched in April 2013. Unique, zero-infrastructure zero-funded design of RAD@home as a collaboratory of 69 trained e-astronomers is briefly described. Some of the new-found objects like episodic radio galaxies, radio-jet and companion galaxy interaction, radio galaxy bent by motion of the intra-filament medium in a Mpc-scale galaxy filament etc. are briefly presented as demonstration of its potential. Citizen-science has not only opened up a new way for astronomy research but also possibly the only promising way to extract maximum science out of the Big Data in the SKA-era. This possibly can convert the Big Data problem into a prospect. Citizen-science can contribute to the knowledge creation in never-seen-before speed and in approach. As it is based on internet, it can provide an equal opportunity of academic-growth to people even in the under-developed regions where we always need to put our optical and radio telescopes. This can liberate the research-activity of city-based research-institutes out of the four brick walls and alleviate various socio-economic and geo-political constraints on growth of citizens educated in undergraduate-level science but located in remote areas.  相似文献   

Erratum to: Internal waves over the shelf in the western Bay of Bengal: a case study     

Madhu Joshi  Ambarukhana Devendra Rao  Sachiko Mohanty  Himansu Kesari Pradhan  Vadlamani S. N. Murty  Koneru Venkata Siva Rama Prasad 《Ocean Dynamics》2017,67(1):163-164

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Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model   总被引：3，自引：1，他引：2  

P. V. S. Raju  Jayaraman Potty  U. C. Mohanty 《Meteorology and Atmospheric Physics》2011,113(3-4):125-137

Comprehensive sensitivity analyses on physical parameterization schemes of Weather Research Forecast (WRF-ARW core) model have been carried out for the prediction of track and intensity of tropical cyclones by taking the example of cyclone Nargis, which formed over the Bay of Bengal and hit Myanmar on 02 May 2008, causing widespread damages in terms of human and economic losses. The model performances are also evaluated with different initial conditions of 12?h intervals starting from the cyclogenesis to the near landfall time. The initial and boundary conditions for all the model simulations are drawn from the global operational analysis and forecast products of National Center for Environmental Prediction (NCEP-GFS) available for the public at 1° lon/lat resolution. The results of the sensitivity analyses indicate that a combination of non-local parabolic type exchange coefficient PBL scheme of Yonsei University (YSU), deep and shallow convection scheme with mass flux approach for cumulus parameterization (Kain-Fritsch), and NCEP operational cloud microphysics scheme with diagnostic mixed phase processes (Ferrier), predicts better track and intensity as compared against the Joint Typhoon Warning Center (JTWC) estimates. Further, the final choice of the physical parameterization schemes selected from the above sensitivity experiments is used for model integration with different initial conditions. The results reveal that the cyclone track, intensity and time of landfall are well simulated by the model with an average intensity error of about 8?hPa, maximum wind error of 12?m?s^?1and track error of 77?km. The simulations also show that the landfall time error and intensity error are decreasing with delayed initial condition, suggesting that the model forecast is more dependable when the cyclone approaches the coast. The distribution and intensity of rainfall are also well simulated by the model and comparable with the TRMM estimates.  相似文献   

Monthly prediction of rainfall over India and its homogeneous zones during monsoon season: a supervised principal component regression approach on general circulation model products     

Archana Nair  U. C. Mohanty  Nachiketa Acharya 《Theoretical and Applied Climatology》2013,111(1-2):327-339

A supervised principal component regression (SPCR) technique has been employed on general circulation model (GCM) products for developing a monthly scale deterministic forecast of summer monsoon rainfall (June–July–August–September) for different homogeneous zones and India as a whole. The time series of the monthly observed rainfall as the predictand variable has been used from India Meteorological Department gridded (1°?×?1°) rainfall data. Lead 0 (forecast initialized in the same month) monthly products from GCMs are used as predictors. The sources of these GCMs are International Research Institute for Climate and Society, Columbia University, National Center for Environmental Prediction, and Japan Agency for Marine Earth Science and Technology. The performance of SPCR technique is judged against simple ensemble mean of GCMs (EM) and it is found that over almost all the zones the SPCR model gives better skill than EM in June, August, and September months of monsoon. The SPCR technique is able to capture the year to year observed rainfall variability in terms of sign as well as the magnitude. The independent forecasts of 2007 and 2008 are also analyzed for different monsoon months (Jun–Sep) in homogeneous zones and country. Here, 1982–2006 have been considered as development year or training period. Results of the study suggest that the SPCR model is able to catch the observational rainfall over India as a whole in June, August, and September in 2007 and June, July, and August in 2008.  相似文献   

Seasonal prediction of Indian summer monsoon in NCEP coupled and uncoupled model     

Hemantkumar S. Chaudhari  Samir Pokhrel  Sachiko Mohanty  Subodh K. Saha 《Theoretical and Applied Climatology》2013,114(3-4):459-477

This study has identified probable factors that govern ISMR predictability. Furthermore, extensive analysis has been performed to evaluate factors leading to the predictability aspect of Indian Summer Monsoon Rainfall (ISMR) using uncoupled and coupled version of National Centers for Environmental Prediction Coupled Forecast System (CFS). It has been found that the coupled version (CFS) has outperformed the uncoupled version [Global Forecast System (GFS)] of the model in terms of prediction of rainfall over Indian land points. Even the spatial distribution of rainfall is much better represented in the CFS as compared to that of GFS. Even though these model skills are inadequate for the reliable forecasting of monsoon, it imparts the capacious knowledge about the model fidelity. The mean monsoon features and its evolution in terms of rainfall and large-scale circulation along with the zonal and meridional shear of winds, which govern the strength of the monsoon, are relatively closer to the observation in the CFS as compared to the GFS. Furthermore, sea surface temperature–rainfall relation is fairly realistic and intense in the coupled version of the model (CFS). It is found that the CFS is able to capture El Niño Southern Oscillation ISMR (ENSO-ISMR) teleconnections much strongly as compared to GFS; however, in the case of Indian Ocean Dipole ISMR teleconnections, GFS has the larger say. Coupled models have to be fine-tuned for the prediction of the transition of El Niño as well as the strength of the mature phase has to be improved. Thus, to sum up, CFS tends to have better predictive skill on account of following three factors: (a) better ability to replicate mean features, (b) comparatively better representation of air–sea interactions, and (c) much better portrayal of ENSO-ISMR teleconnections. This study clearly brings out that coupled model is the only way forward for improving the ISMR prediction skill. However, coupled model’s spurious representation of SST variability and mean model bias are detrimental in seasonal prediction.  相似文献