Modelling the Eddy Pattern in the harbour of Zeebrugge

Hydrodynamic

Generating global products of LAI and FPAR from SNPP-VIIRS data: theoretical background and implementation

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation have been successfully generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data since early 2000. As the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard, the Suomi National Polar-orbiting Partnership (SNPP) has inherited the scientific role of MODIS, and the development of a continuous, consistent, and well-characterized VIIRS LAI/FPAR data set is critical to continue the MODIS time series. In this paper, we build the radiative transfer-based VIIRS-specific lookup tables by achieving minimal difference with the MODIS data set and maximal spatial coverage of retrievals from the main algorithm. The theory of spectral invariants provides the configurable physical parameters, i.e., single scattering albedos (SSAs) that are optimized for VIIRS-specific characteristics. The effort finds a set of smaller red-band SSA and larger near-infraredband SSA for VIIRS compared with the MODIS heritage. The VIIRS LAI/FPAR is evaluated through comparisons with one year of MODIS product in terms of both spatial and temporal patterns. Further validation efforts are still necessary to ensure the product quality. Current results, however, imbue confidence in the VIIRS data set and suggest that the efforts described here meet the goal of achieving the operationally consistent multisensor LAI/FPAR data sets. Moreover, the strategies of parametric adjustment and LAI/FPAR evaluation applied to SNPP-VIIRS can also be employed to the subsequent Joint Polar Satellite System VIIRS or other instruments.Accepted manuscrip

Assessment of the Contribution of Traffic Emissions to the Mobile Vehicle Measured PM2.5 Concentration by Means of WRF-CMAQ Simulations

INE/AUTC 12.0

DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic

Feasibility of Imaging Tissue Electrical Conductivity by Switching Field Gradients with MRI.

Tissue conductivity is a biophysical marker of tissue structure and physiology. Present methods of measuring tissue conductivity are limited. Electrical impedance tomography, and magnetic resonance electrical impedance tomography rely on passing external current through the object being imaged, which prevents its use in most human imaging. Recently, the RF field used for MR excitation has been used to non-invasively measure tissue conductivity. This technique is promising, but conductivity at higher frequencies is less sensitive to tissue structure. Measuring tissue conductivity non-invasively at low frequencies remains elusive. It has been proposed that eddy currents generated during the rise and decay of gradient pulses could act as a current source to map low-frequency conductivity. This work centers on a gradient echo pulse sequence that uses large gradients prior to excitation to create eddy currents. The electric and magnetic fields during a gradient pulse are simulated by a finite-difference time-domain simulation. The sequence is also tested with a phantom and an animal MRI scanner equipped with gradients of high gradient strengths and slew rate. The simulation demonstrates that eddy currents in materials with conductivity similar to biological tissue decay with a half-life on the order of nanoseconds and any eddy currents generated prior to excitation decay completely before influencing the RF signal. Gradient-induced eddy currents can influence phase accumulation after excitation but the effect is too small to image. The animal scanner images show no measurable phase accumulation. Measuring low-frequency conductivity by gradient-induced eddy currents is presently unfeasible

Hot Spine Loops and the Nature of a Late-Phase Solar Flare

The fan-spine magnetic topology is believed to be responsible for many curious features in solar explosive events. A spine field line links distinct flux domains, but direct observation of such feature has been rare. Here we report a unique event observed by the Solar Dynamic Observatory where a set of hot coronal loops (over 10 MK) connected to a quasi-circular chromospheric ribbon at one end and a remote brightening at the other. Magnetic field extrapolation suggests these loops are partly tracer of the evolving spine field line. Continuous slipping- and null-point-type reconnections were likely at work, energizing the loop plasma and transferring magnetic flux within and across the fan quasi-separatrix layer. We argue that the initial reconnection is of the "breakout" type, which then transitioned to a more violent flare reconnection with an eruption from the fan dome. Significant magnetic field changes are expected and indeed ensued. This event also features an extreme-ultraviolet (EUV) late phase, i.e. a delayed secondary emission peak in warm EUV lines (about 2-7 MK). We show that this peak comes from the cooling of large post-reconnection loops beside and above the compact fan, a direct product of eruption in such topological settings. The long cooling time of the large arcades contributes to the long delay; additional heating may also be required. Our result demonstrates the critical nature of cross-scale magnetic coupling - topological change in a sub-system may lead to explosions on a much larger scale.Comment: Accepted for publication in ApJ. Animations linked from pd

Modeling Covariate Effects in Group Independent Component Analysis with Applications to Functional Magnetic Resonance Imaging

Independent component analysis (ICA) is a powerful computational tool for separating independent source signals from their linear mixtures. ICA has been widely applied in neuroimaging studies to identify and characterize underlying brain functional networks. An important goal in such studies is to assess the effects of subjects' clinical and demographic covariates on the spatial distributions of the functional networks. Currently, covariate effects are not incorporated in existing group ICA decomposition methods. Hence, they can only be evaluated through ad-hoc approaches which may not be accurate in many cases. In this paper, we propose a hierarchical covariate ICA model that provides a formal statistical framework for estimating and testing covariate effects in ICA decomposition. A maximum likelihood method is proposed for estimating the covariate ICA model. We develop two expectation-maximization (EM) algorithms to obtain maximum likelihood estimates. The first is an exact EM algorithm, which has analytically tractable E-step and M-step. Additionally, we propose a subspace-based approximate EM, which can significantly reduce computational time while still retain high model-fitting accuracy. Furthermore, to test covariate effects on the functional networks, we develop a voxel-wise approximate inference procedure which eliminates the needs of computationally expensive covariance estimation. The performance of the proposed methods is evaluated via simulation studies. The application is illustrated through an fMRI study of Zen meditation.Comment: 36 pages, 5 figure