933 resultados para GOES (Meteorological satellite)
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A quantificação da precipitação é dificultada pela extrema aleatoriedade do fenômeno na natureza. Os métodos convencionais para mensuração da precipitação atuam no sentido de espacializar a precipitação mensurada pontualmente em postos pluviométricos para toda a área de interesse e, desta forma, uma rede com elevado número de postos bem distribuídos em toda a área de interesse é necessária para um resultado satisfatório. No entanto, é notória a escassez de postos pluviométricos e a má distribuição espacial dos poucos existentes, não somente no Brasil, mas em vastas áreas do globo. Neste contexto, as estimativas da precipitação com técnicas de sensoriamento remoto e geoprocessamento pretendem potencializar a utilização dos postos pluviométricos existentes através de uma espacialização baseada em critérios físicos. Além disto, o sensoriamento remoto é a ferramenta mais capaz para gerar estimativas de precipitação nos oceanos e nas vastas áreas continentais desprovidas de qualquer tipo de informação pluviométrica. Neste trabalho investigou-se o emprego de técnicas de sensoriamento remoto e geoprocessamento para estimativas de precipitação no sul do Brasil. Três algoritmos computadorizados foram testados, sendo utilizadas as imagens dos canais 1, 3 e 4 (visível, vapor d’água e infravermelho) do satélite GOES 8 (Geostacionary Operational Environmental Satellite – 8) fornecidas pelo Centro de Previsão de Tempo e Estudos Climáticos do Instituto Nacional de Pesquisas Espaciais. A área de estudo compreendeu todo o estado do Rio Grande do Sul, onde se utilizaram os dados pluviométricos diários derivados de 142 postos no ano de 1998. Os algoritmos citados buscam identificar as nuvens precipitáveis para construir modelos estatísticos que correlacionem as precipitações diária e decendial observadas em solo com determinadas características físicas das nuvens acumuladas durante o mesmo período de tempo e na mesma posição geográfica de cada pluviômetro considerado. Os critérios de decisão que norteiam os algoritmos foram baseados na temperatura do topo das nuvens (através do infravermelho termal), reflectância no canal visível, características de vizinhança e no plano de temperatura x gradiente de temperatura Os resultados obtidos pelos modelos estatísticos são expressos na forma de mapas de precipitação por intervalo de tempo que podem ser comparados com mapas de precipitação obtidas por meios convencionais.
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Meteorological satellite and radar data comparative analysis allows to correlate the precipitation structures observed in both images. Such analysis would make feasible the extension of the range of ground-based meteorological radars. In addition to the different spatial and temporal resolution of these images this comparative analysis presents difficulties due to the effects of rotation and distortion, besides the different formats, projections, and coordinate systems. This work employed an approach based on a Gaussian adaptive filter in order to compare such images. The statistical results obtained from the comparison of the images are matched to those produced by other methods.
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The United States National Ice Center (NIC) provides weekly ice analyses of the Arctic and Antarctic using information from ice reconnaissance, ship reports and high-resolution satellite imagery. In cloud-covered areas and regions lacking imagery, the higher-resolution sources are augmented by ice concentrations derived from Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMII) passive-microwave imagery. However, the SSMII-derived ice concentrations are limited by low resolution and uncertainties in thin-ice regions. Ongoing research at NIC is attempting to improve the utility of these SSMII products for operational sea-ice analyses. The refinements of operational algorithms may also aid future scientific studies. Here we discuss an evaluation of the standard operational ice-concentration algorithm, Cal/Val, with a possible alternative, a modified NASA Team algorithm. The modified algorithm compares favorably with CallVal and is a substantial improvement over the standard NASA Team algorithm in thin-ice regions that are of particular interest to operational activities.
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In this paper, nighttime light data are suggested as a proxy for spatial distribution of vehicles running in urban and nearby areas. Nighttime lights focus on human activities, in contrast to traditional Earth observing systems that focus on natural systems. It is the human activity being visible in the form of brightness of nocturnal lights. Two available nighttime lights dataset were used in this work. The first one was provided by the U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS), henceforth, DMSO-OLS. The second one is the NASA-NOAA Suomi National Polar-orbiting Polar-orbiting Partnership (NPP) satellite, henceforth, Suomi-NPP. To validate the new proposed methodology, hundreds of urban areas of South America were analyzed in a high degree of resolution. The results of this study showed that night-time lights are very well correlated with vehicle fleet, population, and impervious surfaces but with strong spatial variability. The results of this study suggest a better understanding of the human activities in the context of a vehicular-based city conception.
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El objetivo del presente trabajo fue determinar la Evapotranspiración real (ETR) a nivel regional utilizando la información del satélite meteorológico NOAA-AVHRR y comparar los resultados obtenidos con los calculados a partir de un modelo de simulación de balance hídrico. Para la estimación de la ETR se analizaron 30 imágenes que abarcan el oasis Norte de Mendoza. Con la información de los canales C1 (Visible) y C2 (IRC) se obtuvo el índice verde normalizado (NDVI), a través del cual se siguió la evolución anual de la vegetación y con la correspondiente al Infrarrojo térmico (C4 y C5) se calculó la Temperatura de superficie (Ts) por el método Split - Windows Luego se vinculó la Ts calculada por teledetección con la temperatura del aire (Ta), para finalmente calcular la suma acumulada de las diferencias entre Ts y Ta, conocida como SDD (stress degree day) que permite estimar globalmente las características de stress hídrico a nivel regional. Conociendo (Ts-Ta) se estimó la ETR a partir de la radiación neta y de los coeficientes A y B que se estimaron según las características de la cobertura vegetal, aplicando una relación simplificada a partir del balance de energía, desarrollado por Jackson (1977) y Seguin (1983) según la ecuación: ETR = Rn + A -B ( Ts - Ta ) Posteriormente, se incluyó en los cálculos los valores de Emisividad y se hizo variar el coeficiente B de acuerdo a la ocupación del suelo en cada uno de los polígonos en que fue dividida el área de estudio. En la etapa final se compararon estadísticamente los datos de ETR estimados por los distintos métodos con los simulados por el modelo y se obtuvo como conclusión final que: la estimación de la ETR a nivel regional mediante datos satelitales, se adapta muy bien a la mayoría de los casos y es sencilla de calcular, por lo que la metodología desarrollada es fácilmente extrapolable a otros oasis de la región.
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Nighttime satellite imagery from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) has a unique capability to observe nocturnal light emissions from sources including cities, wild fires, and gas flares. Data from the DMSP OLS is used in a wide range of studies including mapping urban areas, estimating informal economies, and estimating urban populations. Given the extensive and increasing list of applications a repeatable method for assessing geolocation accuracy, performing inter-calibration, and defining the minimum detectable brightness would be beneficial. An array of portable lights was designed and taken to multiple field sites known to have no other light sources. The lights were operated during nighttime overpasses by the DMSP OLS and observed in the imagery. A first estimate of the minimum detectable brightness is presented based on the field experiments conducted. An assessment of the geolocation accuracy was performed by measuring the distance between the GPS measured location of the lights and the observed location in the imagery. A systematic shift was observed and the mean distance was measured at 2.9km. A method for in situ radiance calibration of the DMSP OLS using a ground based light source as an active target is presented. The wattage of light used by the active target strongly correlates with the signal measured by the DMSP OLS. This approach can be used to enhance our ability to make inter-temporal and inter-satellite comparisons of DMSP OLS imagery. Exploring the possibility of establishing a permanent active target for the calibration of nocturnal imaging systems is recommended. The methods used to assess the minimum detectable brightness, assess the geolocation accuracy, and build inter-calibration models lay the ground work for assessing the energy expended on light emitted into the sky at night. An estimate of the total energy consumed to light the night sky globally is presented.
River basin surveillance using remotely sensed data: a water resources information management system
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This thesis describes the development of an operational river basin water resources information management system. The river or drainage basin is the fundamental unit of the system; in both the modelling and prediction of hydrological processes, and in the monitoring of the effect of catchment management policies. A primary concern of the study is the collection of sufficient and sufficiently accurate information to model hydrological processes. Remote sensing, in combination with conventional point source measurement, can be a valuable source of information, but is often overlooked by hydrologists, due to the cost of acquisition and processing. This thesis describes a number of cost effective methods of acquiring remotely sensed imagery, from airborne video survey to real time ingestion of meteorological satellite data. Inexpensive micro-computer systems and peripherals are used throughout to process and manipulate the data. Spatial information systems provide a means of integrating these data with topographic and thematic cartographic data, and historical records. For the system to have any real potential the data must be stored in a readily accessible format and be easily manipulated within the database. The design of efficient man-machine interfaces and the use of software enginering methodologies are therefore included in this thesis as a major part of the design of the system. The use of low cost technologies, from micro-computers to video cameras, enables the introduction of water resources information management systems into developing countries where the potential benefits are greatest.
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This paper describes the techniques used to obtain sea surface temperature (SST) retrievals from the Geostationary Operational Environmental Satellite 12 (GOES-12) at the National Oceanic and Atmospheric Administration’s Office of Satellite Data Processing and Distribution. Previous SST retrieval techniques relying on channels at 11 and 12 μm are not applicable because GOES-12 lacks the latter channel. Cloud detection is performed using a Bayesian method exploiting fast-forward modeling of prior clear-sky radiances using numerical weather predictions. The basic retrieval algorithm used at nighttime is based on a linear combination of brightness temperatures at 3.9 and 11 μm. In comparison with traditional split window SSTs (using 11- and 12-μm channels), simulations show that this combination has maximum scatter when observing drier colder scenes, with a comparable overall performance. For daytime retrieval, the same algorithm is applied after estimating and removing the contribution to brightness temperature in the 3.9-μm channel from solar irradiance. The correction is based on radiative transfer simulations and comprises a parameterization for atmospheric scattering and a calculation of ocean surface reflected radiance. Potential use of the 13-μm channel for SST is shown in a simulation study: in conjunction with the 3.9-μm channel, it can reduce the retrieval error by 30%. Some validation results are shown while a companion paper by Maturi et al. shows a detailed analysis of the validation results for the operational algorithms described in this present article.
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The distribution and intensity of a bloom of the toxic cyanobacterium, Microcystis aeruginosa, in western Lake Erie was characterized using a combination of satellite ocean-color imagery, field data, and meteorological observations. The bloom was first identified by satellite on 14 August 2008 and persisted for more than 2 months. The distribution and intensity of the bloom was estimated using a satellite algorithm that is sensitive to near-surface concentrations of M. aeruginosa. Increases in both area and intensity were most pronounced for wind stress less than 0.05 Pa. Area increased while intensity did not change for wind stresses of 0.05–0.1 Pa, and both decreased for wind stress greater than 0.1 Pa. The recovery in intensity at the surface after strong wind events indicated that high wind stress mixed the bloom through the water column and that it returned to the surface once mixing stopped. This interaction is consistent with the understanding of the buoyancy of these blooms. Cloud cover (reduced light) may have a weak influence on intensity during calm conditions. While water temperature remained greater than 15°C, the bloom intensified if there were calm conditions. For water temperature less than 15°C, the bloom subsided under similar conditions. As a result, wind stress needs to be considered when interpreting satellite imagery of these blooms.
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As part of its Data User Element programme, the European Space Agency funded the GlobMODEL project which aimed at investigating the scientific, technical, and organizational issues associated with the use and exploitation of remotely-sensed observations, particularly from new sounders. A pilot study was performed as a "demonstrator" of the GlobMODEL idea, based on the use of new data, with a strong European heritage, not yet assimilated operationally. Two parallel assimilation experiments were performed, using either total column ozone or ozone profiles retrieved at the Royal Netherlands Meteorological Institute (KNMI) from the Ozone Monitoring Instrument (OMI). In both cases, the impact of assimilating OMI data in addition to the total ozone columns from the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) on the European Centre for Medium Range Weather Forecasts (ECMWF) ozone analyses was assessed by means of independent measurements. We found that the impact of OMI total columns is mainly limited to the region between 20 and 80 hPa, and is particularly important at high latitudes in the Southern hemisphere where the stratospheric ozone transport and chemical depletion are generally difficult to model with accuracy. Furthermore, the assimilation experiments carried out in this work suggest that OMI DOAS (Differential Optical Absorption Spectroscopy) total ozone columns are on average larger than SCIAMACHY total columns by up to 3 DU, while OMI total columns derived from OMI ozone profiles are on average about 8 DU larger than SCIAMACHY total columns. At the same time, the demonstrator brought to light a number of issues related to the assimilation of atmospheric composition profiles, such as the shortcomings arising when the vertical resolution of the instrument is not properly accounted for in the assimilation. The GlobMODEL demonstrator accelerated scientific and operational utilization of new observations and its results - prompted ECMWF to start the operational assimilation of OMI total column ozone data.
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"Replaces NOAA/PA 73021"--P. [8]
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"TER-157-0003."
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The Taita Hills in southeastern Kenya form the northernmost part of Africa’s Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor. Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable reflectance throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular reflectance field measurements were taken and where horizontal visibility meteorological data concurrent with image acquisition were available. The proposed historical empirical line method (HELM) for absolute atmospheric correction was found to be the only applied technique that could derive surface reflectance factor within an RMSE of < 0.02 ps in the SPOT visible and near-infrared bands; an accuracy level identified as a benchmark for successful atmospheric correction. A multi-scale segmentation/object relationship modelling (MSS/ORM) approach was applied to map LULC in the Taita Hills from the multi-temporal SPOT imagery. This object-based procedure was shown to derive significant improvements over a uni-scale maximum-likelihood technique. The derived LULC data was used in combination with low cost GIS geospatial layers describing elevation, rainfall and soil type, to model degradation in the Taita Hills in the form of potential soil loss, utilizing the simple universal soil loss equation (USLE). Furthermore, human population distribution and abundance were modelled with satisfactory results using only SPOT and GIS derived data and non-Gaussian predictive modelling techniques. The SPOT derived LULC data was found to be unnecessary as a predictor because the first and second order image texture measurements had greater power to explain variation in dwelling unit occurrence and abundance. The ability of the procedures to be implemented locally in the developing world using low-cost or freely available data and software was considered. The techniques discussed in this thesis are considered equally applicable to other medium- and high-resolution optical satellite imagery, as well the utilized SPOT data.