University of Arizona

About: University of Arizona is a education organization based out in Tucson, Arizona, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 63805 authors who have published 155998 publications receiving 6854915 citations. The organization is also known as: UA & U of A.

Curved Plasma Channel Generation Using Ultraintense Airy Beams

Abstract: Plasma channel generation (or filamentation) using ultraintense laser pulses in dielectric media has a wide spectrum of applications, ranging from remote sensing to terahertz generation to lightning control. So far, laser filamentation has been triggered with the use of ultrafast pulses with axially symmetric spatial beam profiles, thereby generating straight filaments. We report the experimental observation of curved plasma channels generated in air using femtosecond Airy beams. In this unusual propagation regime, the tightly confined main intensity feature of the axially nonsymmetric laser beam propagates along a bent trajectory, leaving a curved plasma channel behind. Secondary channels bifurcate from the primary bent channel at several locations along the beam path. The broadband radiation emanating from different longitudinal sections of the curved filament propagates along angularly resolved trajectories.

Precipitation estimation from remotely sensed information using artificial neural networks

Abstract: A system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of this system is an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information. The model was initially calibrated over the Japanese Islands using remotely sensed infrared data collected by the Geostationary Meteorological Satellite (GMS) and ground-based data collected by the Automated Meteorological Data Acquisition System (AMeDAS). The model was then validated for both the Japanese Islands (using GMS and AMeDAS data) and the Florida peninsula (using GOES-8 and NEXRAD data). An adaptive procedure is used to recursively update the network parameters when ground-based data are available. This feature dramatically improves the estimation performance in response to the diverse precipitation characteristics of different geographical regio...