Toward improved hydrologic prediction with reduced uncertainty using sequential multi-model combination

The contemporary usage of hydrologic models has been to rely on a single model to perform the simulation and predictions. Despite the tremendous progress, efforts and investment put into developing more hydrologic models, there is no convincing claim that any particular model in existence is superior to other models for various applications and under all circumstances. This results to reducing the size of the plausible model space and often leads to predictions that may well-represent some phenomena or events at the expenses of others. Assessment of predictive uncertainty based on a single model is subject to statistical bias and most likely underestimation of uncertainty. This endorses the implementation of multi-model methods for more accurate estimation of uncertainty in hydrologic prediction. In this study, we present two methods for the combination of multiple model predictors using Bayesian Model Averaging (BMA) and Sequential Bayesian Model Combination (SBMC). Both methods are statistical schemes to infer a combined probabilistic prediction that possess more reliability and skill than the original model members produced by several competing models. This paper discusses the features of both methods and explains how the limitation of BMA can be overcome by SBMC. Three hydrologic models are considered and it is shown that multi-model combination can result in higher prediction accuracy than individual models. © 2008 ASCE

Engineering estimates for supersonic flutter of curved shell segments

Engineering estimates for supersonic flutter of curved shell panel

Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting

In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measure are used to quantify the quality of ensemble prediction. Copyright 2006 by the American Geophysical Union

Clustering of Entanglement Points in Highly Strained Polymer Melts

Polymer melts undergoing large deformation by uniaxial elongation are studied by molecular dynamics simulations of bead-spring chains in melts. Applying a primitive path analysis to strongly deformed polymer melts, the role of topological constrains in highly entangled polymer melts is investigated and quantified. We show that the over-all, large scale conformations of the primitive paths (PPs) of stretched chains follow affine deformation while the number and the distribution of entanglement points along the PPs do not. Right after deformation, PPs of chains retract in both directions parallel and perpendicular to the elongation. Upon further relaxation we observe a long-lived clustering of entanglement points. Together with the delayed relaxation time this leads to a metastable inhomogeneous distribution of topological constraints in the melts.Comment: 28 pages, 14 figure

Anisotropic superconducting properties of aligned Sm0.95_{0.95}La0.05_{0.05}FeAsO0.85_{0.85}F0.15_{0.15} microcrystalline powder

The Sm$_{0.95}$La$_{0.05}$FeAsO$_{0.85}$F$_{0.15}$ compound is a quasi-2D layered superconductor with a superconducting transition temperature T$_c$ = 52 K. Due to the Fe spin-orbital related anisotropic exchange coupling (antiferromagnetic or ferromagnetic fluctuation), the tetragonal microcrystalline powder can be aligned at room temperature using the field-rotation method where the tetragonal $\it{ab}$-plane is parallel to the aligned magnetic field B$_{a}$ and $\it{c}$-axis along the rotation axis. Anisotropic superconducting properties with anisotropic diamagnetic ratio $\chi_{c}/\chi_{ab}\sim$ 2.4 + 0.6 was observed from low field susceptibility $\chi$(T) and magnetization M(B$_{a}$). The anisotropic low-field phase diagram with the variation of lower critical field gives a zero-temperature penetration depth $\lambda_{c}$(0) = 280 nm and $\lambda_{ab}$(0) = 120 nm. The magnetic fluctuation used for powder alignment at 300 K may be related with the pairing mechanism of superconductivity at lower temperature.Comment: 4 pages, 6 figure

Anomaly Matching in Gauge Theories at Finite Matter Density

We investigate the application of 't Hooft's anomaly matching conditions to gauge theories at finite matter density. We show that the matching conditions constrain the low-energy quasiparticle spectrum associated with possible realizations of global symmetries.Comment: 11 pages, 1 figure, LaTeX. Section C is corrected and added reference

A VLSI pipeline design of a fast prime factor DFT on a finite field

A conventional prime factor discrete Fourier transform (DFT) algorithm is used to realize a discrete Fourier-like transform on the finite field, GF(q sub n). A pipeline structure is used to implement this prime factor DFT over GF(q sub n). This algorithm is developed to compute cyclic convolutions of complex numbers and to decode Reed-Solomon codes. Such a pipeline fast prime factor DFT algorithm over GF(q sub n) is regular, simple, expandable, and naturally suitable for VLSI implementation. An example illustrating the pipeline aspect of a 30-point transform over GF(q sub n) is presented