987 resultados para Auxiliary data


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Projecte de recerca elaborat a partir d’una estada a la National Oceanography Centre of Southampton (NOCS), Gran Bretanya, entre maig i juliol del 2006. La possibilitat d’obtenir una estimació precissa de la salinitat marina (SSS) és important per a investigar i predir l’extensió del fenòmen del canvi climàtic. La missió Soil Moisture and Ocean Salinity (SMOS) va ser seleccionada per l’Agència Espacial Europea (ESA) per a obtenir mapes de salinitat de la superfície marina a escala global i amb un temps de revisita petit. Abans del llençament de SMOS es preveu l’anàlisi de la variabilitat horitzontal de la SSS i del potencial de les dades recuperades a partir de mesures de SMOS per a reproduir comportaments oceanogràfics coneguts. L’objectiu de tot plegat és emplenar el buit existent entre les fonts de dades d’entrada/auxiliars fiables i les eines desenvolupades per a simular i processar les dades adquirides segons la configuració de SMOS. El SMOS End-to-end Performance Simulator (SEPS) és un simulador adhoc desenvolupat per la Universitat Politècnica de Catalunya (UPC) per a generar dades segons la configuració de SMOS. Es va utilitzar dades d’entrada a SEPS procedents del projecte Ocean Circulation and Climate Advanced Modeling (OCCAM), utilitzat al NOCS, a diferents resolucions espacials. Modificant SEPS per a poder fer servir com a entrada les dades OCCAM es van obtenir dades de temperatura de brillantor simulades durant un mes amb diferents observacions ascendents que cobrien la zona seleccionada. Les tasques realitzades durant l’estada a NOCS tenien la finalitat de proporcionar una tècnica fiable per a realitzar la calibració externa i per tant cancel•lar el bias, una metodologia per a promitjar temporalment les diferents adquisicions durant les observacions ascendents, i determinar la millor configuració de la funció de cost abans d’explotar i investigar les posibiltats de les dades SEPS/OCCAM per a derivar la SSS recuperada amb patrons d’alta resolució.

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Auxiliary data include one file with alkenone-derived UK'37 data and sea surface temperatures (SST). On these data Figs. 7 and 8 of the manuscript are based. The SST are derived from UK'37 by using the transfer function: SST = 29.876 UK'37 - 1.334 of Conte et al. (2006). The data are against the ages (in A.D.) of samples derived from cores GT91-1 (39[deg]59'23"N, 17[deg]45'25"E), GT89-3 and GT90-3 (both 39[deg]45'43"N, 17[deg]53'55"E ). Also included are composite records for UK'37 and SST. For creating the composite records, GT-89-3 was taken as reference core. In the overlapping period the GT89-3 data seem in general lower than the GT91-1 data. To accommodate for this in the composite record, the average difference (0.0343 UK'37 units; equivalent to 1.023 [deg]C) was subtracted from the GT91-1 record. Hereafter, for each depth in the overlapping interval the respective values (UK'37 or SST) of GT89-3 and GT91-1 were averaged. We have also averaged with 16 additional alkenone measurements, from 1793 to 1851, performed in the GT90-3 core.

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Tässä työssä on esitetty väsyttävän kuormituksen mittaamiseen ja mittausdatan jälkikäsittelyyn sekä väsymismitoitukseen liittyviä menetelmiä. Menetelmien sovelluskohteena oli metsäkoneen kuormain, joka on väsyttävästi kuormitettu hitsattu rakenne. Teoriaosassa on kuvattu väsymisilmiötä ja väsymismitoitusmenetelmiä sekä kuormitusten tunnistamiseen ja mittausten jälkikäsittelyyn liittyviä menetelmiä. Yleisimmin käytettyjen väsymismitoitusmenetelmien rinnalle on esitetty luotettavuuteen perustuvaa väsymismitoitusmenetelmää. Kuormainten suunnittelussa on keveys- j a kestoikävaatimusten takia erityisen suuri merkitys väsymisen huomioimisella. Rakenteille on ominaista tietyt toiminnan kannalta välttämättömät hitsatut yksityiskohdat, jotka usein määräävät koko rakenteen kestoiän. Koska nämä ongelmakohdat pystytään useimmiten tunnistamaan jo suunnitteluvaiheessa, voidaan yksityiskohtien muotoilulla usein parantaa huomattavasti koko rakenteen kestoikää. Näiden yksityiskohtien optimointi on osittain mahdollista toteuttaa ilman kuormituskertymätietoa, mutta useimmiten kuormitusten tunnistaminen on edellytys parhaan ratkaisun löytymiselle. Tällöin toistaiseksi paras keino todellisen väsyttävän kuormituksen tunnistamiseksi on pitkäaikaiset kenttämittaukset. Kenttämittauksilla selvitetään rakenteeseen kohdistuvat kuormitukset venymäliuskojen avulla. Kuormitusten tunnistamisella on erityisen suuri merkitys kun halutaan määrittää rakenteen kestoikä. Väsyminen ja väsyttävä kuormitus ovat kuitenkin tilastollisia muuttujia j a yksittäiselle rakenteelle ei ole mahdollista määrittää tarkkaa k estoikää. Tilastollisia menetelmiä käyttäen on kuitenkin mahdollista määrittää rakenteen vaurioitumisriski. Laskettaessa vaurioitumisriskiä suurelle määrälle yksittäisiä rakenteita voidaan muodostaa tarkkojakin ennusteita mahdollisten vaurioiden lukumäärästä. Tällöin kuormituskertymätiedosta voi olla tavanomaisen suunnittelun lisäksi laajempaa hyötyä esimerkiksi takuukäsittelyssä. Tässä työssä on sovellettu esitettyjä teorioita käytännössä metsäkoneen harvesterin puomiston väsymistarkasteluun. Kyseisen rakenteen kuormituksia mitattiin kahden viikon aikana yhteensä 35 tuntia, jonka perusteella laskettiin väsyttävän kuormituksen tilastollinen jakauma esimerkkitapaukselle. Mittauksen perusteella ei voitu tehdä kuitenkaan johtopäätöksiä tuotteen koko elinkaaren kuormituksista eikä muiden samanlaisten tuotteiden kuormituksista, koska mitattu otos oli suhteellisen lyhyt ja rajoittui vain yhteen käyttäjään ja muutamaan käyttökohteeseen. Menetelmien testaamiseksi kyseinen otos oli kuitenkin riittävä. Kuormituskertymätietoa käytettiin hyväksi myös laatumääritysten muodostamisessaesimerkkitapaukselle. Murtumismekaniikkaan perustuvalla menetelmällä arvioitiinharvesteripilarin valun mahdollisten valuvirheiden suurin sallittu koko. Luotettavuuteen pohjautuvan mitoitusmenettelyn tarve näyttää olevanlisääntymässä, joten pitkäaikaisten kenttämittausten tehokas hyödyntäminen tulee olemaan keskeinen osa väsymismitoitusta lähitulevaisuudessa. Menetelmiä olisi mahdollista tehostaa yhdistämällä kuormituskertymään erilaisia kuormitusten suhteen riippuvia tunnettuja suureita kuten käsiteltävän puun halkaisija. Todellisettuotekohtaiset tilastolliset jakaumat kuormituksista voitaisiin muodostaa mahdollisesti tehokkaammin, jos esimerkiksi kuormitusten riippuvuus metsätyypistä pystyttäisiin ensin määrittämään.

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Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.

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In this thesis, a variety of available satellite data products have been made use of to bring out a synergistic analysis on the upwelling phenomenon in SEAS. Basic concepts of remote sensing, upwelling and linked oceanography topics have been dealt in this work .Auxiliary data products utilized in this study are described in chapter 2. The climatological monthly variability of the upwelling signatures are detailed under chapter 3. Chapter 4 presents the forcing factors that trigger the upwelling process in SEAS. Chapter 5 describes the oceanic response to the forcing factors with respect to the SST cooling and CHLA blooms. Chapter 6 presents the heat budget of the region and the variability of heat budget terms with respect to upwelling. Chapter 7 describes the inter-annual variability of upwelling intensity in SEAS and the influence of climatic events on upwelling.

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Orbital remote sensing has been used as a beneficial tool in improving the knowledge on oceanographic and hydrodynamic aspects in northern portion of the continental shelf of Rio Grande do Norte, offshore Potiguar Basin. Aspects such as geography, temporal and spatial resolution combined with a consistent methodology and provide a substantial economic advantage compared to traditional methods of in situ data collecting. Images of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's AQUA satellite were obtained to support systematic data collections related to the campaign of environmental monitoring and characterization of Potiguar Basin, held in May 2004. Images of Total Suspension Matter (TSM) and values of radiance standard were generated for the calculation of concentrations of total suspension matter (TSM), chlorophyll-a and sea surface temperature (SST). These data sets were used for statistical comparisons between measures in situ and satellite estimates looking validate algorithms or develop a comprehensive regional approach empirically. AQUA-MODIS images allowed the simultaneous comparison of two-dimensional water quality (total suspension matter), phytoplankton biomass (chlorophyll-a) variability and physical (temperature). For images of total suspension matter, the generated models showed a good correlation with the field data, allowing quantitative and qualitative analysis. The images of chlorophyll-a showed a consistent correlation with the in situ values of concentration. The algorithms adjusted for these images obtained a correlation coefficient fairly well with the data field in order that the sensor can be having an effect throughout the water column and not just the surface. This has led to a fit between the data of chlorophyll-the integration of the average sampling interval of the entire water column up to the level of the first optical depth, with the data generated from the images. This method resulted in higher values of chlorophyll concentration to greater depths, due to the fact that we are integrating more values of chlorophyll in the water column. Thus we can represent the biomass available in the water column. Images SST and SST measures in situ showed a mean difference DT (SST insitu - SST sat) around -0.14 ° C, considered low, making the results very good. The integration of total suspension matter, chlorophyll-a, the temperature of the sea surface (SST) and auxiliary data enabled the recognition of some of the main ways to fund the continental shelf. The main features highlighted were submerged canyons of rivers Apodi and Açu, some of the lines and beachrocks reefs, structural highs and the continental shelf break which occurs at depths around -60 m. The results confirmed the high potential for use of the AQUA-MODIS images to environmental monitoring of sea areas due to ease of detection of the field two-dimensional material in suspension on the sea surface, temperature and the concentration of chlorophyll-a

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Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea's optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea's special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model's mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003-2012 come with this paper as Supplementary materials.

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High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on plant functional types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts on biogeochemistry and biophysics.

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On 20 March 2015, a total solar eclipse occurred over Ny-Ålesund (78.9° N, 11.9° E), Svalbard, in the high Arctic. It was the first time that the surface radiation components during the totality of a solar eclipse were measured by a Baseline Surface Radiation Network (BSRN) station. With the Ny-Ålesund long-term radiation data set as background (available at doi:10.1594/PANGAEA.150000), we present here the peculiarities of the radiation components and basic meteorology observed during the eclipse event. The supplementary data set contains the basic BSRN radiation and surface meteorological data in 1 min resolution for March 2015, and is available at doi:10.1594/PANGAEA.854326. The eclipse radiation data will be a useful auxiliary data set for further studies on micrometeorological surface-atmosphere exchange processes in the Svalbard environment, and may serve as a test case for radiative transfer studies.

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This dataset contains the result of a joint least squares inversion of GRACE and altimetry data. The results are evaluated in terms of sea level change for the global ocean as well as dedicated areas. In addition, some auxiliary data is provided to enable reproducibility of the results in Rietbroek et al. 2016, and a google Earth kmz file is provided which visualizes the trends derived from the inversion results.

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The BSRN Toolbox is a software package supplied by the WRMC and is freely available to all station scientists and data users. The main features of the package include a download manager for Station- to-Archive files, a tool to convert files into human readable TAB-separated ASCII-tables (similar to those output by the PANGAEA database), and a tool to check data sets for violations of the "BSRN Global Network recommended QC tests, V2.0" quality criteria. The latter tool creates quality codes, one per measured value, indicating if the data are "physically possible," "extremely rare," or if "intercomparison limits are exceeded." In addition, auxiliary data such as solar zenith angle or global calculated from diffuse and direct can be output. All output from the QC tool can be visualized using PanPlot (doi:10.1594/PANGAEA.816201).

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Marine mammals exploit the efficiency of sound propagation in the marine environment for essential activities like communication and navigation. For this reason, passive acoustics has particularly high potential for marine mammal studies, especially those aimed at population management and conservation. Despite the rapid realization of this potential through a growing number of studies, much crucial information remains unknown or poorly understood. This research attempts to address two key knowledge gaps, using the well-studied bottlenose dolphin (Tursiops truncatus) as a model species, and underwater acoustic recordings collected on four fixed autonomous sensors deployed at multiple locations in Sarasota Bay, Florida, between September 2012 and August 2013. Underwater noise can hinder dolphin communication. The ability of these animals to overcome this obstacle was examined using recorded noise and dolphin whistles. I found that bottlenose dolphins are able to compensate for increased noise in their environment using a wide range of strategies employed in a singular fashion or in various combinations, depending on the frequency content of the noise, noise source, and time of day. These strategies include modifying whistle frequency characteristics, increasing whistle duration, and increasing whistle redundancy. Recordings were also used to evaluate the performance of six recently developed passive acoustic abundance estimation methods, by comparing their results to the true abundance of animals, obtained via a census conducted within the same area and time period. The methods employed were broadly divided into two categories – those involving direct counts of animals, and those involving counts of cues (signature whistles). The animal-based methods were traditional capture-recapture, spatially explicit capture-recapture (SECR), and an approach that blends the “snapshot” method and mark-recapture distance sampling, referred to here as (SMRDS). The cue-based methods were conventional distance sampling (CDS), an acoustic modeling approach involving the use of the passive sonar equation, and SECR. In the latter approach, detection probability was modelled as a function of sound transmission loss, rather than the Euclidean distance typically used. Of these methods, while SMRDS produced the most accurate estimate, SECR demonstrated the greatest potential for broad applicability to other species and locations, with minimal to no auxiliary data, such as distance from sound source to detector(s), which is often difficult to obtain. This was especially true when this method was compared to traditional capture-recapture results, which greatly underestimated abundance, despite attempts to account for major unmodelled heterogeneity. Furthermore, the incorporation of non-Euclidean distance significantly improved model accuracy. The acoustic modelling approach performed similarly to CDS, but both methods also strongly underestimated abundance. In particular, CDS proved to be inefficient. This approach requires at least 3 sensors for localization at a single point. It was also difficult to obtain accurate distances, and the sample size was greatly reduced by the failure to detect some whistles on all three recorders. As a result, this approach is not recommended for marine mammal abundance estimation when few recorders are available, or in high sound attenuation environments with relatively low sample sizes. It is hoped that these results lead to more informed management decisions, and therefore, more effective species conservation.