A Report on implementation of operational Global and Indian Ocean HYCOM at INCOIS

A state of the art operational forecasting system with data assimilation (DA) is established at INCOIS, which is the first of it's kind in the country. The Indian Ocean model is the highest resolution operational system with DA available for the basin compared to any operational agency in the world. The core of the system is a 1/16th eddy resolving Indian ocean Hybrid Coordinate Model (HYCOM), nested to a 1/4th Global HYCOM which provides lateral boundary conditions to the high-resolution model. The system uses data assimilation scheme based on Tentral Statistical Interpolation (T-SIS) scheme. A five-year hindcast for the period 2012 to 2016 has been carried out using both setups. This report presents a detailed evaluation of both global and Indian ocean models in comparison with observations and two other established systems, NRL HYCOM and GODAS from INCOIS. The five-year hindcast results show that both Indian Ocean and global model simulated SST, SSS, SLA, currents and vertical structure of the ocean favourably when compared with observations and other models. Bias, RMSD, correlation and skill score compared to observations from each of the four models for selected parameters are evaluated as part of this exercise. Sea-level and currents, show a notable better performance for the new setups at INCOIS over NRL-HYCOM and INCOIS-GODA

An assessment of the Indian Ocean mean state and seasonal cycle in a suite of interannual CORE-II simulations

We present an analysis of annual and seasonal mean characteristics of the Indian Ocean circulation and water masses from 16 global ocean–sea-ice model simulations that follow the Coordinated Ocean-ice Reference Experiments (CORE) interannual protocol (CORE-II). All simulations show a similar large-scale tropical current system, but with differences in the Equatorial Undercurrent. Most CORE-II models simulate the structure of the Cross Equatorial Cell (CEC) in the Indian Ocean. We uncover a previously unidentified secondary pathway of northward cross-equatorial transport along 75 °E, thus complementing the pathway near the Somali Coast. This secondary pathway is most prominent in the models which represent topography realistically, thus suggesting a need for realistic bathymetry in climate models. When probing the water mass structure in the upper ocean, we find that the salinity profiles are closer to observations in geopotential (level) models than in isopycnal models. More generally, we find that biases are model dependent, thus suggesting a grouping into model lineage, formulation of the surface boundary, vertical coordinate and surface salinity restoring. Refinement in model horizontal resolution (one degree versus degree) does not significantly improve simulations, though there are some marginal improvements in the salinity and barrier layer results. The results in turn suggest that a focus on improving physical parameterizations (e.g. boundary layer processes) may offer more near-term advances in Indian Ocean simulations than refined grid resolution

AFM plough YBCO microbridges: substrate effects

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PBCO/YBCO bilayer growth and optimization for the fabrication of buffered step-edge Josephson junctions

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Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection

Abstract Object detection is a critical and complex problem in computer vision, and deep neural networks have significantly enhanced their performance in the last decade. There are two primary types of object detectors: two stage and one stage. Two-stage detectors use a complex architecture to select regions for detection, while one-stage detectors can detect all potential regions in a single shot. When evaluating the effectiveness of an object detector, both detection accuracy and inference speed are essential considerations. Two-stage detectors usually outperform one-stage detectors in terms of detection accuracy. However, YOLO and its predecessor architectures have substantially improved detection accuracy. In some scenarios, the speed at which YOLO detectors produce inferences is more critical than detection accuracy. This study explores the performance metrics, regression formulations, and single-stage object detectors for YOLO detectors. Additionally, it briefly discusses various YOLO variations, including their design, performance, and use cases

Nano-plough constrictions on thin YBCO films using AFM nano-lithography

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Improved ocean analysis for the Indian Ocean

The National Centers for Environmental Prediction (NCEP) and the Indian National Centre for Ocean Information Services (INCOIS) produce global ocean analyses based on the Global Ocean Data Assimilation System (GODAS). This system uses a state of the art ocean general circulation model named moduler ocean model (MOM) and the 3D-Variational (3DVar) data assimilation technique. In this study we have evaluated the INCOIS-GODAS operational analysis products with an upgrade of the physical model from MOM4p0d to MOM4p1. Two experiments were performed with same atmospheric forcing fields:(i) using MOM4p0d (GODASp0), and (ii) using MOM4p1 (GODASp1). Observed temperature and salinity profiles were assimilated in both experiments. Validation with independent observations show improvement of sea surface temperature(SST), sea surface salinity (SSS) and surface currents in the new analysis GODASp1 as compared to the old analysis GODASp0

RF impedance studies in Ni nanocrystalline powders

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