Sebastián M. Torres

Bio: Sebastián M. Torres is an academic researcher from Cooperative Institute for Mesoscale Meteorological Studies. The author has contributed to research in topics: Radar & Weather radar. The author has an hindex of 17, co-authored 77 publications receiving 845 citations. Previous affiliations of Sebastián M. Torres include University of Oklahoma & National Oceanic and Atmospheric Administration.

High-Temporal-Resolution Capabilities of the National Weather Radar Testbed Phased-Array Radar

Abstract: Since 2007 the advancement of the National Weather Radar Testbed Phased-Array Radar (NWRT PAR) hardware and software capabilities has been supporting the implementation of high-temporal-resolution (∼1 min) sampling. To achieve the increase in computational power and data archiving needs required for high-temporal-resolution sampling, the signal processor was upgraded to a scalable, Linux-based cluster with a distributed computing architecture. The development of electronic adaptive scanning, which can reduce update times by focusing data collection on significant weather, became possible through functionality added to the radar control interface and real-time controller. Signal processing techniques were implemented to address data quality issues, such as artifact removal and range-and-velocity ambiguity mitigation, absent from the NWRT PAR at its installation. The hardware and software advancements described above have made possible the development of conventional and electronic scanning capabil...

Design, Implementation, and Demonstration of a Staggered PRT Algorithm for the WSR-88D

Abstract: This paper describes the implementation of the staggered pulse repetition time (PRT) technique on NOAA's research and development WSR-88D in Norman, Oklahoma. The prototype algorithm incorporates a novel rule for the correct assignment of Doppler mean velocity that is needed to accommodate arbitrary stagger ratios. Description of the rule, consideration of errors, and choice of appropriate stagger ratios are presented. The staggered PRT algorithm is integrated with the standard processing on the WSR-88D, some details of which are included in the paper. A simple ground clutter canceller removes the pure complex time series mean (DC) component from autocovariance estimates; censoring of overlaid echoes and thresholding are equivalent to those used on the WSR-88D. Further, a cursory verification of statistical errors indicates good agreement with theoretical expectations. Although the staggered PRT algorithm operates in real time, it was advantageous to collect several events of staggered PRT time s...

Whitening in Range to Improve Weather Radar Spectral Moment Estimates. Part I: Formulation and Simulation

Abstract: A method for estimation of spectral moments on pulsed weather radars is presented. This scheme operates on oversampled echoes in range; that is, samples of in-phase and quadrature-phase components are collected at a rate several times larger than the reciprocal of the transmitted pulse length. The spectral moments are estimated by suitably combining weighted averages of these oversampled signals in range with usual processing of samples (spaced at the pulse repetition time) at a fixed range location. The weights in range are derived from a whitening transformation; hence, the oversampled signals become uncorrelated and, consequently, the variance of the estimates decreases significantly. Because the estimate errors are inversely proportional to the volume scanning times, it follows that storms can be surveyed much faster than is possible with current processing methods, or equivalently, for the current volume scanning time, accuracy of the estimates can be greatly improved. This significant improvement is achievable at large signal-to-noise ratios.

Ground Clutter Canceling with a Regression Filter

Abstract: This paper explores ground clutter filtering with a class of cancelers that use regression. Regression filters perform this task in a simple manner, resulting in similar or better performance than the fifth-order elliptic filter implemented in the WSR-88D. Assuming a slowly varying clutter signal, a suitable projection of the composite signal is used to notch a band of frequencies at either side of zero Doppler frequency. The complexity of this procedure is reduced by using a set of orthogonal polynomials. The frequency response of the resulting filter is related to the number of samples in each input block and the maximum order of approximating polynomials. Through simulations, it is demonstrated that the suppression characteristic of this filter is better than that of step-initialized infinite impulse response filters, whereby transients degrade the theoretical frequency response. The performance of regression filters is tested with an actual weather signal, and their efficiency in ground clutt...

Multiple fully adaptive notch filter design based on allpass sections

Abstract: We develop a canonical, adaptive cascade-structure IIR notch filter to detect and track multiple time-varying frequencies in additive white Gaussian noise. The algorithm uses allpass frequency transformation filters and a truncated gradient. Simulations indicate that our algorithm is computationally simple, converges rapidly, and has good frequency resolution.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Signal analysis

Abstract: dix A. Even if you don’t choose to memorize them this system aids in reference and retreival of important formulas. The book was compiled from notes developed during eight years of teaching a graduate course on the subject and was used as a text. Thus it has been student tested. Appendix F contains a number of problems, grouped to be used on a chapter by chapter basis The problems are designed to illustrate practical applications of the text material. The parts of this book of most interest and value to the EMC engineer will be the chapters on Thermal Noise, Antennas, Propagation and Transmission Lines, and Reflection and Refraction. This is not to downpade the chapters on Statistics and Its Applications, Signal Processing and Detection, and Some System Characteristics which also contain much potentially useful materials. Additional plus values for the book include a list of 40 references, a table of symbols used throughout the book, and a subject index. Some readers may find the condensed type and close line spacing hard to read. It was apparently set up by typewriter using an elite type face with single line spacing. When reduced down to a 6 by 9 5 inch size page it is too crowded for easy reading. In spite of this shortcoming your reviewer recommends this book as a worthwhile reference in this field of interest.

Review of the Different Sources of Uncertainty in Single Polarization Radar-Based Estimates of Rainfall

Abstract: It is well acknowledged that there are large uncertainties associated with radar-based estimates of rainfall. Numerous sources of these errors are due to parameter estimation, the observational system and measurement principles, and not fully understood physical processes. Propagation of these uncertainties through all models for which radar-rainfall are used as input (e.g., hydrologic models) or as initial conditions (e.g., weather forecasting models) is necessary to enhance the understanding and interpretation of the obtained results. The aim of this paper is to provide an extensive literature review of the principal sources of error affecting single polarization radar-based rainfall estimates. These include radar miscalibration, attenuation, ground clutter and anomalous propagation, beam blockage, variability of the Z–R relation, range degradation, vertical variability of the precipitation system, vertical air motion and precipitation drift, and temporal sampling errors. Finally, the authors report some recent results from empirically-based modeling of the total radar-rainfall uncertainties. The bibliography comprises over 200 peer reviewed journal articles.

Resampling Methods for Dependent Data

Abstract: can be formulated in terms of basis functions and discuss the difŽ culties in posterior simulations due to interdependence of these functions. Such interdependence affects the efŽ ciency of the sampling algorithms to draw from posterior distribution of the tree structures. Different strategies that are suggested in the literature for dealing with the sampling problem are discussed, and a detailed example using binary classiŽ cation is presented to illustrate the Bayesian analysis. To alleviate the problems that arise in tree models, Chapter 7 introduces partition models. The partition models can be considered a generalization of tree models, and they allow for sampling from the posterior distributions of the tree structure. As noted by the authors, these models suffer from lack of interpretability in high dimensions. The authors present one-dimensional partition models whose analysis provides a general framework for the changepoint problems. Multidimensional partition models, where partitions are deŽ ned by Dirichlet tessellations, are considered, and Bayesian inference for classiŽ cation problems is discussed. Disease mapping models are presented as an application of partition models for spatial problems. Chapter 8 is a short chapter describing Bayesian nearest-neighbor modeling. The nearest-neighbor classiŽ cation algorithm, which is used commonly in pattern recognition, is given a probabilistic formulation by introducing a parameter that controls the degree of association between the neighboring classes. In Chapter 9 the authors generalize the single-response case to multipleresponse models where the observed response is a collection of values. The chapter focuses on regression models and does not consider classiŽ cation models such as the multivariate probit models. Multivariate Bayesian regression framework is introduced where basis functions can be determined from the data using methods of Chapter 3. A generalization is considered using seemingly unrelated regression (SUR) models and their Bayesian analysis is presented. Prior speciŽ cation for basis function matrix are discussed, and computational details of the MCMC methods are given for both models. The book has a comprehensive bibliography, and each chapter (except Chap. 1) has a section on further reading. There are two appendixes at the end. Appendix B gives a summary of posterior inference results that are helpful for some of the development in the text. In summary, this book presents recent developments in Bayesian nonlinear modeling and provides a complete treatment of regression and classiŽ cation problems by emphasizing a data-driven approach in determining appropriate models. Its in-depth coverage of implementation issues and detailed discussion of pros and cons of different modeling strategies make it attractive for many researchers. It deŽ nitely makes my list of recommended texts in Bayesian statistics.

Agile-Beam Phased Array Radar for Weather Observations

Abstract: Weather radars with conventional antenna cannot provide desired volume scan updates at intervals of one minute or less, which is essential for significant improvement in warning lead time of impending storm hazards. The agile-beam multimission phased array radar (MPAR) discussed herein is one potential candidate that can provide faster scanning. It also offers a unique potential for multipurpose use to not only sample weather, but support air traffic needs and track noncooperative airplanes, thus making it an affordable option. After introducing the basic idea behind electronic beam steering, the needs for frequent observations of convective weather are explained. Then, advantages of the phased array radar (PAR) for weather monitoring and improving data quality are examined. To explore and develop weather-related applications of the PAR, a National Weather Radar Testbed (NWRT) has been established in Norman, Oklahoma. The NWRT's main purpose is to address the advanced capabilities anticipated within the n...