242 research outputs found

    Detección automática de ascendentes intensas mediante imágenes satelitales y su relación con tiempo severo

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    Fil: Vidal, Luciano. Servicio Meteorológico Nacional. Dirección Nacional de Ciencia e Innovación en Productos y Servicios. Dirección de Productos de Modelación Ambiental y de Sensores Remotos; Argentina. Universidad de Buenos Aires.Numerosos estudios basados en datos de sensores remotos, especialmente de satélites, muestran que la frecuencia de convección húmeda profunda asociada con una variedad de fenómenos meteorológicos severos (ej., granizo grande, lluvias intensas, vientos dañinos, entre otros) es muy alta en la parte sur de Sudamérica, especialmente en Argentina. En presencia de tormentas severas, las imágenes satelitales generalmente presentan ciertas características o patrones espaciales muy útiles al momento de diagnosticar cuán probable es la ocurrencia de algún fenómeno severo como los mencionados, en el marco del proceso de elaboración de un alerta a muy corto plazo en una oficina operativa de pronóstico. Por ende, el desarrollo de algoritmos que ayuden a la detección automática de estos patrones ha sido foco de atención de numerosos investigadores alrededor del mundo. En particular, el grupo de investigación liderado por el Dr. Kristopher Bedka ha desarrollado un producto denominado "NASA LaRC Gridded Overshooting Cloud Top Detection", el cual, en el marco de un proyecto de colaboración con el Servicio Meteorológico Nacional de Argentina se tuvo acceso para realizar una primera evaluación en nuestra región para la estación cálida 2019-2020.Numerous studies based on remote sensing data, especially from satellites, show that the frequency of deep moist convection associated with a variety of severe weather events (e.g., large hail, heavy rain, damaging winds, etc.) is very high in the southern South America, especially in central and northern Argentina. In the presence of severe storms, satellite images generally present particular signatures or spatial patterns that are very useful when diagnosing how likely the occurrence of a severe phenomenon such as those mentioned is, within the framework of the process of preparing a very short-term alert in an operational forecasting office. Therefore, the development of algorithms that help to automatically detect these severe signatures has been the focus of attention of many researchers around the world. In particular, the research group led by Dr. Kristopher Bedka has developed a product called "NASA LaRC Gridded Overshooting Cloud Top Detection", which within the framework of a collaboration project with the National Meteorological Service of Argentina, was accessed to carry out a first evaluation in our region for the warm season 2019-2020

    Hazardous thunderstorm intensification over Lake Victoria

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    Weather extremes have harmful impacts on communities around Lake Victoria, where thousands of fishermen die every year because of intense night-time thunderstorms. Yet how these thunderstorms will evolve in a future warmer climate is still unknown. Here we show that Lake Victoria is projected to be a hotspot of future extreme precipitation intensification by using new satellite-based observations, a high-resolution climate projection for the African Great Lakes and coarser-scale ensemble projections. Land precipitation on the previous day exerts a control on night-time occurrence of extremes on the lake by enhancing atmospheric convergence (74%) and moisture availability (26%). The future increase in extremes over Lake Victoria is about twice as large relative to surrounding land under a high-emission scenario, as only over-lake moisture advection is high enough to sustain Clausius–Clapeyron scaling. Our results highlight a major hazard associated with climate change over East Africa and underline the need for high-resolution projections to assess local climate change

    I Went to the End of Time, and This is What I Found: A Look into the Making of a Solo Performance

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    abstract: I'll go to the end of time for you (and you don't even know my name) is an evening-length solo performance created and performed by Kristopher K.Q. Pourzal. It premiered November 8-10, 2013 in the Margaret Gisolo Dance Theatre of Arizona State University. The solo was the culmination (suspension, really) of a wild creative journey, the distillation of a process that initially involved several collaborators. Through a series of neurotically/erotically repetitive episodes of self-composed song, text, and dance, the work mines questions of the desire to be seen and the desire to feel alive. The conventions and constructs of the proscenium stage are both utilized and subverted in examining this platform as uniquely suited for revealing the nature of these experiences and their potential relationship. This document is primarily an account of the show's process--its before and after--and serves as a site of exploration, explanation, analysis, reflection, questioning, and ultimately furtherance of the practice-based research made manifest in the performances.Dissertation/ThesisM.F.A. Dance 201

    A Long-Term Overshooting Convective Cloud Top Detection Database over Australia Derived from MTSAT Japanese Advanced Meteorological Imager Infrared Observations

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    A 10-yr geostationary (GEO) overshooting cloud-top (OT) detection database using Multifunction Transport Satellite (MTSAT) Japanese Advanced Meteorological Imager (JAMI) observations has been developed over the Australian region. GEO satellite imagers collect spatially and temporally detailed observations of deep convection, providing insight into the development and evolution of hazardous storms, particularly where surface observations of hazardous storms and deep convection are sparse and ground-based radar or lightning sensor networks are limited. Hazardous storms often produce one or more OTs that indicate the location of strong updrafts where weather hazards are typically concentrated, which can cause substantial impacts on the ground such as hail, damaging winds, tornadoes, and lightning and to aviation such as turbulence and in-flight icing. The 10-yr OT database produced using an automated OT detection algorithm is demonstrated for analysis of storm frequency, diurnally, spatially, and seasonally relative to known features such as the Australian monsoon, expected regions of hazardous storms along the southeastern coastal regions of southern Queensland and New South Wales, and the preferential extratropical cyclone track along the Indian Ocean and southern Australian coast. A filter based on atmospheric instability, deep-layer wind shear, and freezing level was used to identify OTs that could have produced hail. The filtered OT database is used to generate a hail frequency estimate that identifies a region extending from north of Brisbane to Sydney and the GoldfieldsEsperance region of eastern Western Australia as the most hail-prone regions
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