967 resultados para satellite data processing


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There is described data processing at the flaw detector with combined multisectional eddy-current transducer and heterofrequency magnetic field. The application of this method for detecting flaws in rods and pipes under the conditions of significant transverse displacements is described.

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We present a comparative analysis of satellite derived climatologies in the Cape Verde region (CV). In order to establish chlorophyll a variability, in relation to other oceanographic phenomena, a set of, relatively long (from five to eight years), time series of chlorophyll a, sea surface temperature, wind and geostrophic currents, were ensembled for the Eastern Central Atlantic (ECA). We studied seasonal and inter-annual variability of phytoplankton concentration, in relation to the rest of the variables, with a special focus in CV. We compared the situation within the archipelago with those of the surrounding marine environments, such as the North West African Upwelling (NWAU), North Atlantic Subtropical Gyre (NASTG), North Equatorial Counter Current (NECC) and Guinea Dome (GD). At the seasonal scale, CV region behaves partly as the surrounding areas, nevertheless, some autochthonous features were also found. The maximum peak of the pigment having a positive correlation with temperature is found at the end of the year for all the points in the archipelago; a less remarkable rise with negative correlation is also detected in February for points CV2 and CV4. This is behavior that none of the surrounding environments have shown. This enrichment was found to be preceded by a drastic drop in wind intensity (SW Monsoon) during summer months. The inter-annual analysis shows a tendency for decreasing of the chlorophyll a concentration.

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We present a comparative analysis of satellite derived climatologies in the Cape Verde region (CV). In order to establish chlorophyll a variability, in relation to other oceanographic phenomena, a set of, relatively long (from five to eight years), time series of chlorophyll a, sea surface temperature, wind and geostrophic currents, were ensembled for the Eastern Central Atlantic (ECA). We studied seasonal and inter-annual variability of phytoplankton concentration, in relation to the rest of the variables, with a special focus in CV. We compared the situation within the archipelago with those of the surrounding marine environments, such as the North West African Upwelling (NWAU), North Atlantic Subtropical Gyre (NASTG), North Equatorial Counter Current (NECC) and Guinea Dome (GD). At the seasonal scale, CV region behaves partly as the surrounding areas, nevertheless, some autochthonous features were also found. The maximum peak of the pigment having a positive correlation with temperature is found at the end of the year for all the points in the archipelago; a less remarkable rise with negative correlation is also detected in February for points CV2 and CV4. This is behavior that none of the surrounding environments have shown. This enrichment was found to be preceded by a drastic drop in wind intensity (SW Monsoon) during summer months. The inter-annual analysis shows a tendency for decreasing of the chlorophyll a concentration.

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In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

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Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

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The present study brings out the influence of transport dynamics on the aerosol distribution over the Indian region at a few selected geographically distinct locations. Over the Bay of Bengal the dominant pathway of aerosol transport during the pre-monsoon period is through higher altitudes (~ 3 km); directed from the Indian main land. In contrast, the aerosol pathways over the Arabian Sea during the same period are quite complex. They are directed from geographically different environments around the ocean through different altitudes. However in general, the day-to-day variability of AOD at both these regions is significantly influenced by the features of atmospheric circulation especially, the wind convergence at higher altitudes (around 3 km). Over the Ganga Basin during the winter period, the wind convergence at lower altitudes (< I km) govems the shon term variations in AOD, while the mean AOD distribution at this location is mainly governed by the local anthropogenic sources.

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A combination of satellite data, reanalysis products and climate models are combined to monitor changes in water vapour, clear-sky radiative cooling of the atmosphere and precipitation over the period 1979-2006. Climate models are able to simulate observed increases in column integrated water vapour (CWV) with surface temperature (Ts) over the ocean. Changes in the observing system lead to spurious variability in water vapour and clear-sky longwave radiation in reanalysis products. Nevertheless all products considered exhibit a robust increase in clear-sky longwave radiative cooling from the atmosphere to the surface; clear-sky longwave radiative cooling of the atmosphere is found to increase with Ts at the rate of ~4 Wm-2 K-1 over tropical ocean regions of mean descending vertical motion. Precipitation (P) is tightly coupled to atmospheric radiative cooling rates and this implies an increase in P with warming at a slower rate than the observed increases in CWV. Since convective precipitation depends on moisture convergence, the above implies enhanced precipitation over convective regions and reduced precipitation over convectively suppressed regimes. To quantify this response, observed and simulated changes in precipitation rate are analysed separately over regions of mean ascending and descending vertical motion over the tropics. The observed response is found to be substantially larger than the model simulations and climate change projections. It is currently not clear whether this is due to deficiencies in model parametrizations or errors in satellite retrievals.

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Satellite-based rainfall monitoring is widely used for climatological studies because of its full global coverage but it is also of great importance for operational purposes especially in areas such as Africa where there is a lack of ground-based rainfall data. Satellite rainfall estimates have enormous potential benefits as input to hydrological and agricultural models because of their real time availability, low cost and full spatial coverage. One issue that needs to be addressed is the uncertainty on these estimates. This is particularly important in assessing the likely errors on the output from non-linear models (rainfall-runoff or crop yield) which make use of the rainfall estimates, aggregated over an area, as input. Correct assessment of the uncertainty on the rainfall is non-trivial as it must take account of • the difference in spatial support of the satellite information and independent data used for calibration • uncertainties on the independent calibration data • the non-Gaussian distribution of rainfall amount • the spatial intermittency of rainfall • the spatial correlation of the rainfall field This paper describes a method for estimating the uncertainty on satellite-based rainfall values taking account of these factors. The method involves firstly a stochastic calibration which completely describes the probability of rainfall occurrence and the pdf of rainfall amount for a given satellite value, and secondly the generation of ensemble of rainfall fields based on the stochastic calibration but with the correct spatial correlation structure within each ensemble member. This is achieved by the use of geostatistical sequential simulation. The ensemble generated in this way may be used to estimate uncertainty at larger spatial scales. A case study of daily rainfall monitoring in the Gambia, west Africa for the purpose of crop yield forecasting is presented to illustrate the method.

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Current changes in the tropical hydrological cycle, including water vapour and precipitation, are presented over the period 1979-2008 based on a diverse suite of observational datasets and atmosphere-only climate models. Models capture the observed variability in tropical moisture while reanalyses cannot. Observed variability in precipitation is highly dependent upon the satellite instruments employed and only cursory agreement with model simulations, primarily relating to the interannual variability associated with the El Niño Southern Oscillation. All datasets display a positive relationship between precipitation and surface temperature but with a large spread. The tendency for wet, ascending regions to become wetter at the expense of dry, descending regimes is in general reproduced. Finally, the frequency of extreme precipitation is shown to rise with warming in the observations and for the model ensemble mean but with large spread in the model simulations. The influence of the Earth’s radiative energy balance in relation to changes in the tropical water cycle are discussed

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We use the third perihelion pass by the Ulysses spacecraft to illustrate and investigate the “flux excess” effect, whereby open solar flux estimates from spacecraft increase with increasing heliocentric distance. We analyze the potential effects of small-scale structure in the heliospheric field (giving fluctuations in the radial component on timescales smaller than 1 h) and kinematic time-of-flight effects of longitudinal structure in the solar wind flow. We show that the flux excess is explained by neither very small-scale structure (timescales < 1 h) nor by the kinematic “bunching effect” on spacecraft sampling. The observed flux excesses is, however, well explained by the kinematic effect of larger-scale (>1 day) solar wind speed variations on the frozen-in heliospheric field. We show that averaging over an interval T (that is long enough to eliminate structure originating in the heliosphere yet small enough to avoid cancelling opposite polarity radial field that originates from genuine sector structure in the coronal source field) is only an approximately valid way of allowing for these effects and does not adequately explain or account for differences between the streamer belt and the polar coronal holes.

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We investigate the “flux excess” effect, whereby open solar flux estimates from spacecraft increase with increasing heliocentric distance. We analyze the kinematic effect on these open solar flux estimates of large-scale longitudinal structure in the solar wind flow, with particular emphasis on correcting estimates made using data from near-Earth satellites. We show that scatter, but no net bias, is introduced by the kinematic “bunching effect” on sampling and that this is true for both compression and rarefaction regions. The observed flux excesses, as a function of heliocentric distance, are shown to be consistent with open solar flux estimates from solar magnetograms made using the potential field source surface method and are well explained by the kinematic effect of solar wind speed variations on the frozen-in heliospheric field. Applying this kinematic correction to the Omni-2 interplanetary data set shows that the open solar flux at solar minimum fell from an annual mean of 3.82 × 1016 Wb in 1987 to close to half that value (1.98 × 1016 Wb) in 2007, making the fall in the minimum value over the last two solar cycles considerably faster than the rise inferred from geomagnetic activity observations over four solar cycles in the first half of the 20th century.

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Satellite measurements and numerical forecast model reanalysis data are used to compute an updated estimate of the cloud radiative effect on the global multi-annual mean radiative energy budget of the atmosphere and surface. The cloud radiative cooling effect through reflection of shortwave radiation dominates over the longwave heating effect, resulting in a net cooling of the climate system of –21 Wm-2. The shortwave radiative effect of cloud is primarily manifest as a reduction in the solar radiation absorbed at the surface of -53 Wm-2. Clouds impact longwave radiation by heating the moist tropical atmosphere (up to around 40 Wm-2 for global annual means) while enhancing the radiative cooling of the atmosphere over other regions, in particular higher latitudes and sub-tropical marine stratocumulus regimes. While clouds act to cool the climate system during the daytime, the cloud greenhouse effect heats the climate system at night. The influence of cloud radiative effect on determining cloud feedbacks and changes in the water cycle are discussed.