Comparisons of precipitation measurements by the Advanced Microwave Precipitation Radiometer and multiparameter radar

Includes bibliographical references.Multiparameter microwave radar measurements are based on dual-polarization and dual-frequency techniques and are well suited for microphysical inferences of complex precipitating clouds, since they depend upon the size, shape, composition, and orientation of a collection of discrete random scatterers. Passive microwave radiometer observations represent path integrated scattering and absorption phenomena of the same scatterers. The response of the upwelling brightness temperatures TB to the precipitation structure depends on the vertical distribution of the various hydrometeors and gases, and the surface features. As a result, combinations of both active and passive techniques contain great potential to markedly improve the longstanding issue of precipitation measurement from space. The NASA airborne Advanced Microwave Precipitation Radiometer (AMPR) and the National Center for Atmospheric Research (NCAR) CP-2 multiparameter radar were jointly operated during the 1991 Convection and Precipitation/Electrification experiment (CaPE) in central Florida. The AMPR is a four channel, high resolution, across-track scanning total power radiometer system using the identical multifrequency feedhorn as the widely utilized Special Sensor Microwave/Imager (SSM/I) satellite system. Surface and precipitation features are separable based on the TB behavior as a function of the AMPR channels. The radar observations are presented in a remapped format suitable for comparison with the multifrequency AMPR imagery. Striking resemblances are noted between the AMPR imagery and the radar reflectivity at successive heights, while vertical profiles of the CP-2 products along the nadir trace suggest a storm structure consistent with the viewed AMPR TB. Directly over the storm cores, the difference between the 37 and 85 GHz TB was noted to approach (and in some cases fall below) zero. Microwave radiative transfer computations show that this is theoretically possible for hail regions suspended aloft in the core of strong convective storms.This work was supported by the NASA Earth Science and Applications Division under Grant NAG8-890. The National Center for Atmospheric Research is sponsored by the National Science Foundation

The Oceanographic Multipurpose Software Environment (OMUSE v1.0)

In this paper we present the Oceanographic Multipurpose Software Environment (OMUSE). OMUSE aims to provide a homogeneous environment for existing or newly developed numerical ocean simulation codes, simplifying their use and deployment. In this way, numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales can be easily designed. Rapid development of simulation models is made possible through the creation of simple high-level scripts. The low-level core of the abstraction in OMUSE is designed to deploy these simulations efficiently on heterogeneous high-performance computing resources. Cross-verification of simulation models with different codes and numerical methods is facilitated by the unified interface that OMUSE provides. Reproducibility in numerical experiments is fostered by allowing complex numerical experiments to be expressed in portable scripts that conform to a common OMUSE interface. Here, we present the design of OMUSE as well as the modules and model components currently included, which range from a simple conceptual quasi-geostrophic solver to the global circulation model POP (Parallel Ocean Program). The uniform access to the codes' simulation state and the extensive automation of data transfer and conversion operations aids the implementation of model couplings. We discuss the types of couplings that can be implemented using OMUSE. We also present example applications that demonstrate the straightforward model initialization and the concurrent use of data analysis tools on a running model. We give examples of multiscale and multiphysics simulations by embedding a regional ocean model into a global ocean model and by coupling a surface wave propagation model with a coastal circulation model

Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar

We present a method to combine CryoSat-2 (CS2) radar altimeter and Sentinel-1 synthetic aperture radar (SAR) data to obtain sea ice thickness (SIT) estimates for the Barents and Kara seas. From the viewpoint of tactical navigation, along-track altimeter SIT estimates are sparse, and the goal of our study is to develop a method to interpolate altimeter SIT measurements between CS2 ground tracks. The SIT estimation method developed here is based on the interpolation of CS2 SIT utilizing SAR segmentation and segmentwise SAR texture features. The SIT results are compared to SIT data derived from the AARI ice charts; to ORAS5, PIOMAS and TOPAZ4 ocean-sea ice data assimilation system reanalyses; to combined CS2 and Soil Moisture and Ocean Salinity (SMOS) radiometer weekly SIT (CS2SMOS SIT) charts; and to the daily MODIS (Moderate Resolution Imaging Spectro-radiometer) SIT chart. We studied two approaches: CS2 directly interpolated to SAR segments and CS2 SIT interpolated to SAR segments with mapping of the CS2 SIT distributions to correspond to SIT distribution of the PIOMAS ice model. Our approaches yield larger spatial coverage and better accuracy compared to SIT estimates based on either CS2 or SAR data alone. The agreement with modelled SIT is better than with the CS2SMOS SIT. The average differences when compared to ice models and the AARI ice chart SIT were typically tens of centimetres, and there was a significant positive bias when compared to the AARI SIT (on average 27 cm) and a similar bias (24 cm) when compared to the CS2SMOS SIT. Our results are directly applicable to the future CRISTAL mission and Copernicus programme SAR missions.Peer reviewe

Real-Time Machine Learning for Quickest Detection

Safety-critical Cyber-Physical Systems (CPS) require real-time machine learning for control and decision making. One promising solution is to use deep learning to discover useful patterns for event detection from heterogeneous data. However, deep learning algorithms encounter challenges in CPS with assurability requirements: 1) Decision explainability, 2) Real-time and quickest event detection, and 3) Time-eficient incremental learning. To address these obstacles, I developed a real-time Machine Learning Framework for Quickest Detection (MLQD). To be specific, I first propose the zero-bias neural network, which removes decision bias and preferabilities from regular neural networks and provides an interpretable decision process. Second, I discover the latent space characteristic of the zero-bias neural network and the method to mathematically convert a Deep Neural Network (DNN) classifier into a performance-assured binary abnormality detector. In this way, I can seamlessly integrate the deep neural networks\u27 data processing capability with Quickest Detection (QD) and provide real-time sequential event detection paradigm. Thirdly, after discovering that a critical factor that impedes the incremental learning of neural networks is the concept interference (confusion) in latent space, and I prove that to minimize interference, the concept representation vectors (class fingerprints) within the latent space need to be organized orthogonally and I invent a new incremental learning strategy using the findings, I facilitate deep neural networks in the CPS to evolve eficiently without retraining. All my algorithms are evaluated on real-world applications, ADS-B (Automatic Dependent Surveillance Broadcasting) signal identification, and spoofing detection in the aviation communication system. Finally, I discuss the current trends in MLQD and conclude this dissertation by presenting the future research directions and applications. As a summary, the innovations of this dissertation are as follows: i) I propose the zerobias neural network, which provides transparent latent space characteristics, I apply it to solve the wireless device identification problem. ii) I discover and prove the orthogonal memory organization mechanism in artificial neural networks and apply this mechanism in time-efficient incremental learning. iii) I discover and mathematically prove the converging point theorem, with which we can predict the latent space topological characteristics and estimate the topological maturity of neural networks. iv) I bridge the gap between machine learning and quickest detection with assurable performance

Image based surface reflectance remapping for consistent and tool independent material appearence

Physically-based rendering in Computer Graphics requires the knowledge of material properties other than 3D shapes, textures and colors, in order to solve the rendering equation. A number of material models have been developed, since no model is currently able to reproduce the full range of available materials. Although only few material models have been widely adopted in current rendering systems, the lack of standardisation causes several issues in the 3D modelling workflow, leading to a heavy tool dependency of material appearance. In industry, final decisions about products are often based on a virtual prototype, a crucial step for the production pipeline, usually developed by a collaborations among several departments, which exchange data. Unfortunately, exchanged data often tends to differ from the original, when imported into a different application. As a result, delivering consistent visual results requires time, labour and computational cost. This thesis begins with an examination of the current state of the art in material appearance representation and capture, in order to identify a suitable strategy to tackle material appearance consistency. Automatic solutions to this problem are suggested in this work, accounting for the constraints of real-world scenarios, where the only available information is a reference rendering and the renderer used to obtain it, with no access to the implementation of the shaders. In particular, two image-based frameworks are proposed, working under these constraints. The first one, validated by means of perceptual studies, is aimed to the remapping of BRDF parameters and useful when the parameters used for the reference rendering are available. The second one provides consistent material appearance across different renderers, even when the parameters used for the reference are unknown. It allows the selection of an arbitrary reference rendering tool, and manipulates the output of other renderers in order to be consistent with the reference

The Dark Matter Contribution to Galactic Diffuse Gamma Ray Emission

Observations of diffuse Galactic gamma ray emission (DGE) by the Fermi Large Area Telescope (LAT) allow a detailed study of cosmic rays and the interstellar medium. However, diffuse emission models of the inner Galaxy underpredict the Fermi-LAT data at energies above a few GeV and hint at possible non-astrophysical sources including dark matter (DM) annihilations or decays. We present a study of the possible emission components from DM using the high-resolution Via Lactea II N-body simulation of a Milky Way-sized DM halo. We generate full-sky maps of DM annihilation and decay signals that include modeling of the adiabatic contraction of the host density profile, Sommerfeld enhanced DM annihilations, $p$-wave annihilations, and decaying DM. We compare our results with the DGE models produced by the Fermi-LAT team over different sky regions, including the Galactic center, high Galactic latitudes, and the Galactic anti-center. This work provides possible templates to fit the observational data that includes the contribution of the subhalo population to DM gamma-ray emission, with the significance depending on the annihilation/decay channels and the Galactic regions being considered.Comment: Published by PR

Spatial patterns in thunderstorm rainfall events and their coupling with watershed hydrological response

Weather radar systems provide detailed information on spatial rainfall patterns known to play a significant role in runoff generation processes. In the current study, we present an innovative approach to exploit spatial rainfall information of air mass thunderstorms and link it with a watershed hydrological model. Observed radar data are decomposed into sets of rain cells conceptualized as circular Gaussian elements and the associated rain cell parameters, namely, location, maximal intensity and decay factor, are input into a hydrological model. Rain cells were retrieved from radar data for several thunderstorms over southern Arizona. Spatial characteristics of the resulting rain fields were evaluated using data from a dense rain gauge network. For an extreme case study in a semi-arid watershed, rain cells were derived and fed as input into a hydrological model to compute runoff response. A major factor in this event was found to be a single intense rain cell (out of the five cells decomposed from the storm). The path of this cell near watershed tributaries and toward the outlet enhanced generation of high flow. Furthermore, sensitivity analysis to cell characteristics indicated that peak discharge could be a factor of two higher if the cell was initiated just a few kilometers aside. © 2005 Elsevier Ltd. All rights reserved

Registration and variability of side scan sonar imagery

Submitted in partial fulfillment of the requirements for the degree of Ocean Engineer at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution August 1988This thesis presents the results of several experiments performed on side scan sonar equipment and imagery with the aim of characterizing the acoustic variability of side scan sonar imagery and applying this information to image rectification and registration. A static test tank experiment is presented which analyzes the waveform, power spectral density, and temporal variability of the transmitted waveform. The results of a second static experiment conducted from the Woods Hole Oceanographic Institution Pier in Woods Hole, Massachusetts permit determination of the distribution and moments of intensity fluctuations of echoes from objects imaged in side scan sonograms. This experiment also characterizes temporal and spatial coherence of intensity fluctuations. A third experiment is presented in which a side scan sonar towfish images the bottom adjacent to the pier while running along an underwater track which reduces towfish instability. Imagery from this experiment is used to develop a rectification and registration algorithm for side scan sonat images. Preliminary image processing is described and examples presented, followed by favorable results for automated image rectification and registration.Massachusetts Commonwealth Centers of Excellence, Marine Imaging Systems, and The National Science Foundation for funding this researc