Real-time storm detection and weather forecast activation through data mining and events processing   总被引：1，自引：0，他引：1  

Xiang Li  Beth Plale  Nithya Vijayakumar  Rahul Ramachandran  Sara Graves  Helen Conover 《Earth Science Informatics》2008,1(2):49-57

Each year across the USA, destructive weather events disrupt transportation and commerce, resulting in both loss of lives and property. Mitigating the impacts of such severe events requires innovative new software tools and cyberinfrastructure through which scientists can monitor data for specific severe weather events such as thunderstorms and launch focused modeling computations for prediction and forecasts of these evolving weather events. Bringing about a paradigm shift in meteorology research and education through advances in cyberinfrastructure is one of the key research objectives of the Linked Environments for Atmospheric Discovery (LEAD) project, a large-scale, interdisciplinary NSF funded project spanning ten institutions. In this paper we address the challenges of making cyberinfrastructure frameworks responsive to real-time conditions in the physical environment driven by the use cases in mesoscale meteorology. The contribution of the research is two-fold: on the cyberinfrastructure side, we propose a model for bridging between the physical environment and e-Science¹ workflow systems, specifically through events processing systems, and provide a proof of concept implementation of that model in the context of the LEAD cyberinfrastructure. On the algorithmic side, we propose efficient stream mining algorithms that can be carried out on a continuous basis in real time over large volumes of observational data. ¹ e-Science is used to describe computationally intensive science that is typically carried out in highly distributed network  相似文献   

Climate change projections for the Texas High Plains and Rolling Plains     

Modala  Naga Raghuveer  Ale  Srinivasulu  Goldberg  Daniel W.  Olivares  Miriam  Munster  Clyde L.  Rajan  Nithya  Feagin  Rusty A. 《Theoretical and Applied Climatology》2017,129(1-2):263-280

Potential changes in future climate in the Texas Plains region were investigated in the context of agriculture by analyzing three climate model projections under the A2 climate scenario (medium–high emission scenario). Spatially downscaled historic (1971–2000) and future (2041–2070) climate datasets (rainfall and temperature) were downloaded from the North American Regional Climate Change Assessment Program (NARCCAP). Climate variables predicted by three regional climate models (RCMs) namely the Regional Climate Model Version3–Geophysical Fluid Dynamics Laboratory (RCM3-GFDL), Regional Climate Model Version3–Third Generation Coupled Global Climate Model (RCM3-CGCM3), and Canadian Regional Climate Model–Community Climate System Model (CRCM-CCSM) were evaluated in this study. Gaussian and Gamma distribution mapping techniques were employed to remove the bias in temperature and rainfall data, respectively. Both the minimum and maximum temperatures across the study region in the future showed an upward trend, with the temperatures increasing in the range of 1.9 to 2.9 °C and 2.0 to 3.2 °C, respectively. All three climate models predicted a decline in rainfall within a range of 30 to 127 mm in majority of counties across the study region. In addition, they predicted an increase in the intensity of extreme rainfall events in the future. The frost-free season as predicted by the three models showed an increase by 2.6–3.4 weeks across the region, and the number of frost days declined by 17.9 to 30 %. Overall, these projections indicate considerable changes to the climate in the Texas Plains region in the future, and these changes could potentially impact agriculture in this region.

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Assessment and characterization of heavy metal resistance in Palk Bay sediment bacteria     

Nithya C  Gnanalakshmi B  Pandian SK 《Marine environmental research》2011,71(4):283-294

The present study aimed at characterizing the heavy metal resistance and assessing the resistance pattern to multiple heavy metals (300 mmol L?1) by Palk Bay sediment bacteria. From 46 isolates, 24 isolates showed resistance to more than eight heavy metals. Among the 24 isolates S8-06 (Bacillus arsenicus), S8-10 (Bacillus pumilus), S8-14 (B. arsenicus), S6-01 (Bacillus indicus), S6-04 (Bacillus clausii), SS-06 (Planococcus maritimus) and SS-08 (Staphylococcus pasteuri) exhibited high resistance against arsenic, mercury, cobalt, cadmium, lead and selenium. Plasmid curing confirmed that the heavy metal resistance in S8-10 is chromosomal borne. Upon treatment with the heavy metals, the strain S8-10 showed many morphological and physiological changes as shown by SEM, FTIR and AAS analysis. S8-10 removed 47% of cadmium and 96% of lead from the growth medium. The study suggests that sediment bacteria can be biological indicators of heavy metal contamination.  相似文献   

Parameter estimation of SWAT and quantification of consequent confidence bands of model simulations   总被引：1，自引：0，他引：1  

Nithya Krishnan  Cibin Raj  I. Chaubey  K. P. Sudheer 《Environmental Earth Sciences》2018,77(12):470

Soil and Water Assessment Tool (SWAT) is a river basin scale model widely used to study the impact of land management practices in large, complex watersheds. Even though model output uncertainties are generally recognized to affect watershed management decisions, those uncertainties are largely ignored in model applications. The uncertainties of SWAT simulations are quantified using various methods, but simultaneous attempt to calibrate a model so as to reduce the uncertainty are seldom done. This study aims to use an uncertainty reduction procedure that helps calibrate the SWAT model. The shuffled complex evolutionary metropolis algorithm for uncertainty analysis is employed for this purpose, and is demonstrated using the data from the St. Joseph River basin, USA. The values of the performance indices, the r² and the Nash–Sutcliffe efficiency (NSE) for the simulations during calibration period was found to be 0.81 (same for r² and NSE) and 0.79 for validation period indicating a good simulation by the model. The results also indicate that the algorithm helps reduce the uncertainty (percentage of coverage?=?62% and average width?=?19.2 m³/s), and also identifies the plausible range of parameters that simulate the processes with less uncertainty. The confidence bands of simulations are obtained that can be employed in making uncertainty-based decisions on watershed management practices.  相似文献