995 resultados para Climatic classification


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Summary The objective of this study was to develop a methodology capable of modeling the effect of viticultural climate on wine sensory characteristics. The climate was defined by the Géoviticulture Multicriteria Climatic Classification System (Tonietto and Carbonneau, 2004), based on the Heliothermal index (HI), Cool Night index (CI) and Dryness index (DI). The sensory wine description was made according with the methodology established by Zanus and Tonietto (2007). In this study we focused on the 5 principal wine producing regions of Brazil: Serra Gaúcha, Serra do Sudeste, Campanha (Meridional and Central), Planalto Catarinense and Vale do Submédio São Francisco. The results from Principal Component Analysis (PCA) show the HI and CI opposed to the DI. High HI values were associated to a lower perception of acidity, as well as to a lower perception of concentration (palate) and persistence by mouth. For the red wines, high HI values were positively associated with alcohol (palate), conversely to the DI index, which showed high values related to the perception of tanins and acidity. The higher the CI, the lower were the color intensity, tanins, concentration and persistence by mouth. It may be concluded that viticultural climate - expressed by the HI, CI and DI indexes ? adequately explained much of the sensory differences of the wines made in different regions. The methodology proposed and the enlargement of the database it will maybe open the possibility of modeling the part of wine sensory characteristics as dependent variables of the viticultural climate, as defined by the Géoviticulture MCC System. Keywords: viticultural climate, modeling, wine, tipicity.

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2007

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We have applied a number of objective statistical techniques to define homogeneous climatic regions for the Pacific Ocean, using COADS (Woodruff et al 1987) monthly sea surface temperature (SST) for 1950-1989 as the key variable. The basic data comprised all global 4°x4° latitude/longitude boxes with enough data available to yield reliable long-term means of monthly mean SST. An R-mode principal components analysis of these data, following a technique first used by Stidd (1967), yields information about harmonics of the annual cycles of SST. We used the spatial coefficients (one for each 4-degree box and eigenvector) as input to a K-means cluster analysis to classify the gridbox SST data into 34 global regions, in which 20 comprise the Pacific and Indian oceans. Seasonal time series were then produced for each of these regions. For comparison purposes, the variance spectrum of each regional anomaly time series was calculated. Most of the significant spectral peaks occur near the biennial (2.1-2.2 years) and ENSO (~3-6 years) time scales in the tropical regions. Decadal scale fluctuations are important in the mid-latitude ocean regions.

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This research assesses the potential impact of weekly weather variability on the incidence of cryptosporidiosis disease using time series zero-inflated Poisson (ZIP) and classification and regression tree (CART) models. Data on weather variables, notified cryptosporidiosis cases and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Both time series ZIP and CART models show a clear association between weather variables (maximum temperature, relative humidity, rainfall and wind speed) and cryptosporidiosis disease. The time series CART models indicated that, when weekly maximum temperature exceeded 31°C and relative humidity was less than 63%, the relative risk of cryptosporidiosis rose by 13.64 (expected morbidity: 39.4; 95% confidence interval: 30.9–47.9). These findings may have applications as a decision support tool in planning disease control and risk management programs for cryptosporidiosis disease.

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Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.

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Our review of paleoclimate information for New Zealand pertaining to the past 30,000 years has identified a general sequence of climatic events, spanning the onset of cold conditions marking the final phase of the Last Glaciation, through to the emergence to full interglacial conditions in the early Holocene. In order to facilitate more detailed assessments of climate variability and any leads or lags in the timing of climate changes across the region, a composite stratotype is proposed for New Zealand. The stratotype is based on terrestrial stratigraphic records and is intended to provide a standard reference for the intercomparison and evaluation of climate proxy records. We nominate a specific stratigraphic type record for each climatic event, using either natural exposure or drill core stratigraphic sections. Type records were selected on thebasis of having very good numerical age control and a clear proxy record. In all cases the main proxy of the type record is subfossil pollen. The type record for the period from ca 30 to ca 18 calendar kiloyears BP (cal. ka BP) is designated in lake-bed sediments from a small morainic kettle lake (Galway tarn) in western South Island. The Galway tarn type record spans a period of full glacial conditions (Last Glacial Coldest Period, LGCP) within the Otira Glaciation, and includes three cold stadials separated by two cool interstadials. The type record for the emergence from glacial conditions following the termination of the Last Glaciation (post-Termination amelioration) is in a core of lake sediments from a maar (Pukaki volcanic crater) in Auckland, northern North Island, and spans from ca 18 to 15.64±0.41 cal. ka BP. The type record for the Lateglacial period is an exposure of interbedded peat and mud at montane Kaipo bog, eastern North Island. In this high-resolution type record, an initial mild period was succeeded at 13.74±0.13 cal. ka BP by a cooler period, which after 12.55±0.14 cal. ka BP gave way to a progressive ascent to full interglacial conditions that were achieved by 11.88±0.18 cal. ka BP. Although a type section is not formally designated for the Holocene Interglacial (11.88±0.18 cal. ka BP to the present day), the sedimentary record of Lake Maratoto on the Waikato lowlands, northwestern North Island, is identified as a prospective type section pending the integration and updating of existing stratigraphic and proxy datasets, and age models. The type records are interconnected by one or more dated tephra layers, the ages of which are derived from Bayesian depositional modelling and OxCal-based calibrations using the IntCal09 dataset. Along with the type sections and the Lake Maratoto record, important, well-dated terrestrial reference records are provided for each climate event. Climate proxies from these reference records include pollen flora, stable isotopes from speleothems, beetle and chironomid fauna, and glacier moraines. The regional composite stratotype provides a benchmark against which to compare other records and proxies. Based on the composite stratotype, we provide an updated climate event stratigraphic classification for the New Zealand region. © 2013 Elsevier Ltd.

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In the present investigation, an attempt is made to study late Quaternary foraminiferal and pteropod records of the shelf of northern Kerala and to evaluate their potentiality in paleocenographic and paleoclimatic reconstruction. The study gives details of sediment cores, general characteristics of foraminifera and pteropod species recorded from the examined samples and their systematic classification, spatial distribution of Recent foraminifera and pteropods and their response to varying bathymetry, nature of substrate, organic matter content in sediment and hydrography across the shelf. An attempt is also made to establish an integrated chronostratigraphy for the examined core sections. An effort is also made to identify microfaunal criteria useful in biostratigraphic division in shallow marine core sections. An attempt is made to infer various factors responsible for the change in microfaunal assemblage. Reconstruction of sea level changes during the last 36,000 years was attempted based on the pteropod record. The study reveals a bathymetric control on benthic/planktic (BF/PF) foraminiferal and pteropods/planktic foraminiferal (Pt/PF) abundance ratio. Bathymetric distribution pattern of BF/PF ratio is opposite to the (Pt/PF) ratio with decreasing trend of former from the shore across the shelf. Quantitative benthic foraminiferal record in the surficial sediments reveals a positive correlation between the diversity and bathymetry. R-mode cluster analysis performed on 30n significant Recent benthic foraminiferal, determines three major assemblage.

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Nowadays, the scientific and social significance of the research of climatic effects has become outstanding. In order to be able to predict the ecological effects of the global climate change, it is necessary to study monitoring databases of the past and explore connections. For the case study mentioned in the title, historical weather data series from the Hungarian Meteorological Service and Szaniszló Priszter’s monitoring data on the phenology of geophytes have been used. These data describe on which days the observed geophytes budded, were blooming and withered. In our research we have found that the classification of the observed years according to phenological events and the classification of those according to the frequency distribution of meteorological parameters show similar patterns, and the one variable group is suitable for explaining the pattern shown by the other one. Furthermore, our important result is that the dates of all three observed phenophases correlate significantly with the average of the daily temperature fluctuation in the given period. The second most often significant parameter is the number of frosty days, this also seem to be determinant for all phenophases. Usual approaches based on the temperature sum and the average temperature don’t seem to be really important in this respect. According to the results of the research, it has turned out that the phenology of geophytes can be well modelled with the linear combination of suitable meteorological parameters

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The paper catalogues the procedures and steps involved in agroclimatic classification. These vary from conventional descriptive methods to modern computer-based numerical techniques. There are three mutually independent numerical classification techniques, namely Ordination, Cluster analysis, and Minimum spanning tree; and under each technique there are several forms of grouping techniques existing. The vhoice of numerical classification procedure differs with the type of data set. In the case of numerical continuous data sets with booth positive and negative values, the simple and least controversial procedures are unweighted pair group method (UPGMA) and weighted pair group method (WPGMA) under clustering techniques with similarity measure obtained either from Gower metric or standardized Euclidean metric. Where the number of attributes are large, these could be reduced to fewer new attributes defined by the principal components or coordinates by ordination technique. The first few components or coodinates explain the maximum variance in the data matrix. These revided attributes are less affected by noise in the data set. It is possible to check misclassifications using minimum spanning tree.