Climatological data, national summary.

Mode of access: Internet.

Climatological data.

Mode of access: Internet.

SPARC Data Initiative: A comparison of ozone climatologies from international satellite limb sounders

A comprehensive quality assessment of the ozone products from 18 limb-viewing satellite instruments is provided by means of a detailed intercomparison. The ozone climatologies in form of monthly zonal mean time series covering the upper troposphere to lower mesosphere are obtained from LIMS, SAGE I/II/III, UARS-MLS, HALOE, POAM II/III, SMR, OSIRIS, MIPAS, GOMOS, SCIAMACHY, ACE-FTS, ACE-MAESTRO, Aura-MLS, HIRDLS, and SMILES within 1978–2010. The intercomparisons focus on mean biases of annual zonal mean fields, interannual variability, and seasonal cycles. Additionally, the physical consistency of the data is tested through diagnostics of the quasi-biennial oscillation and Antarctic ozone hole. The comprehensive evaluations reveal that the uncertainty in our knowledge of the atmospheric ozone mean state is smallest in the tropical and midlatitude middle stratosphere with a 1σ multi-instrument spread of less than ±5%. While the overall agreement among the climatological data sets is very good for large parts of the stratosphere, individual discrepancies have been identified, including unrealistic month-to-month fluctuations, large biases in particular atmospheric regions, or inconsistencies in the seasonal cycle. Notable differences between the data sets exist in the tropical lower stratosphere (with a spread of ±30%) and at high latitudes (±15%). In particular, large relative differences are identified in the Antarctic during the time of the ozone hole, with a spread between the monthly zonal mean fields of ±50%. The evaluations provide guidance on what data sets are the most reliable for applications such as studies of ozone variability, model-measurement comparisons, detection of long-term trends, and data-merging activities.

The SPARC Data Initiative: comparisons of CFC-11, CFC-12, HF and SF6 climatologies from international satellite limb sounders

A quality assessment of the CFC-11 (CCl3F), CFC-12 (CCl2F2), HF, and SF6 products from limb-viewing satellite instruments is provided by means of a detailed intercomparison. The climatologies in the form of monthly zonal mean time series are obtained from HALOE, MIPAS, ACE-FTS, and HIRDLS within the time period 1991–2010. The intercomparisons focus on the mean biases of the monthly and annual zonal mean fields and aim to identify their vertical, latitudinal and temporal structure. The CFC evaluations (based on MIPAS, ACE-FTS and HIRDLS) reveal that the uncertainty in our knowledge of the atmospheric CFC-11 and CFC-12 mean state, as given by satellite data sets, is smallest in the tropics and mid-latitudes at altitudes below 50 and 20 hPa, respectively, with a 1σ multi-instrument spread of up to ±5 %. For HF, the situation is reversed. The two available data sets (HALOE and ACE-FTS) agree well above 100 hPa, with a spread in this region of ±5 to ±10 %, while at altitudes below 100 hPa the HF annual mean state is less well known, with a spread ±30 % and larger. The atmospheric SF6 annual mean states derived from two satellite data sets (MIPAS and ACE-FTS) show only very small differences with a spread of less than ±5 % and often below ±2.5 %. While the overall agreement among the climatological data sets is very good for large parts of the upper troposphere and lower stratosphere (CFCs, SF6) or middle stratosphere (HF), individual discrepancies have been identified. Pronounced deviations between the instrument climatologies exist for particular atmospheric regions which differ from gas to gas. Notable features are differently shaped isopleths in the subtropics, deviations in the vertical gradients in the lower stratosphere and in the meridional gradients in the upper troposphere, and inconsistencies in the seasonal cycle. Additionally, long-term drifts between the instruments have been identified for the CFC-11 and CFC-12 time series. The evaluations as a whole provide guidance on what data sets are the most reliable for applications such as studies of atmospheric transport and variability, model–measurement comparisons and detection of long-term trends. The data sets will be publicly available from the SPARC Data Centre and through PANGAEA (doi:10.1594/PANGAEA.849223).

Storm data /

Latest issue consulted: Vol. 50, no. 12 (Dec. 2008).

Soil profile internal drainage for a central pivot fertigated coffee crop

Coffee cultivation via central-pivot fertigation can lead to fertilizer losses by soil profile internal drainage when water application is excessive and soils have low water retention and cation adsorption capacities. This study analyses the deep water losses from the top 1 m sandy soil layer of east Bahia, Brazil, cultivated with coffee at a high technology level (central-pivot fertigation), using above normal N fertilizer rates. The deep drainage (Q) estimation is made through the application of a climatologic water balance (CWB) program having as input direct measures of irrigation and rainfall, climatological data from weather stations, and measured soil water retention characteristics. The aim of the study is to contribute to the understanding of the hydric regime of coffee crops managed by central-pivot irrigation, analyzing three scenarios (Sc): i) rainfall only, ii) rainfall and irrigation full year, and iii) rainfall and irrigation dry season only. Annual Q values for the 2008/2009 agricultural year were: Sc i = 811.5 mm; Sc ii = 1010.5 mm; and Sc iii = 873.1 mm, so that the irrigation interruption in the wet season reduced Q by 15.7%, without the appearance of water deficit periods. Results show that the use of the CWB program is a convenient tool for the evaluation of Q under the cited conditions.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Mathematical model to estimate of the deterioration of wooden poles in contact with soil used in rural areas

In São Paulo State, mainly in rural areas, the utilization of wooden poles is observed for different purposes. In this context, wood in contact with the ground presents faster deterioration, which is generally associated to environmental factors and, especially to the presence of fungi and insects. With the use of mathematical models, the useful life of wooden structures can be predicted by obtaining "climatic indexes" to indicate, comparatively among the areas studied, which have more or less tendency to fungi and insects attacks. In this work, by using climatological data of several cities at São Paulo State, a simplified mathematical model was obtained to measure the aggressiveness of the wood in contact with the soil.

Estimating Lorenz's reference state in an ocean with a nonlinear equation of state for seawater

The study of the mechanical energy budget of the oceans using Lorenz available potential energy (APE) theory is based on knowledge of the adiabatically re-arranged Lorenz reference state of minimum potential energy. The compressible and nonlinear character of the equation of state for seawater has been thought to cause the reference state to be ill-defined, casting doubt on the usefulness of APE theory for investigating ocean energetics under realistic conditions. Using a method based on the volume frequency distribution of parcels as a function of temperature and salinity in the context of the seawater Boussinesq approximation, which we illustrate using climatological data, we show that compressibility effects are in fact minor. The reference state can be regarded as a well defined one-dimensional function of depth, which forms a surface in temperature, salinity and density space between the surface and the bottom of the ocean. For a very small proportion of water masses, this surface can be multivalued and water parcels can have up to two statically stable levels in the reference density profile, of which the shallowest is energetically more accessible. Classifying parcels from the surface to the bottom gives a different reference density profile than classifying in the opposite direction. However, this difference is negligible. We show that the reference state obtained by standard sorting methods is equivalent, though computationally more expensive, to the volume frequency distribution approach. The approach we present can be applied systematically and in a computationally efficient manner to investigate the APE budget of the ocean circulation using models or climatological data.

Extended time weather forecasts contributes to agricultural productivity estimates

Weather conditions in critical periods of the vegetative crop development influence crop productivity, thus being a basic parameter for crop forecast. Reliable extended period weather forecasts may contribute to improve the estimation of agricultural productivity. The production of soybean plays an important role in the Brazilian economy, because this country is ranked among the largest producers of soybeans in the world. This culture can be significantly affected by water conditions, depending on the intensity of water deficit. This work explores the role of extended period weather forecasts for estimating soybean productivity in the southern part of Brazil, Passo Fundo, and Londrina (State of Rio Grande do Sul and Parana, respectively) in the 2005/2006 harvest. The goal was to investigate the possible contribution of precipitation forecasts as a substitute for the use of climatological data on crop forecasts. The results suggest that the use of meteorological forecasts generate more reliable productivity estimates during the growth period than those generated only through climatological information.

Uncertainties In Future Projections Of Extreme Rainfall: The Role Of Climate Model, Emission Scenario And Randomness

Climate change has resulted in substantial variations in annual extreme rainfall quantiles in different durations and return periods. Predicting the future changes in extreme rainfall quantiles is essential for various water resources design, assessment, and decision making purposes. Current Predictions of future rainfall extremes, however, exhibit large uncertainties. According to extreme value theory, rainfall extremes are rather random variables, with changing distributions around different return periods; therefore there are uncertainties even under current climate conditions. Regarding future condition, our large-scale knowledge is obtained using global climate models, forced with certain emission scenarios. There are widely known deficiencies with climate models, particularly with respect to precipitation projections. There is also recognition of the limitations of emission scenarios in representing the future global change. Apart from these large-scale uncertainties, the downscaling methods also add uncertainty into estimates of future extreme rainfall when they convert the larger-scale projections into local scale. The aim of this research is to address these uncertainties in future projections of extreme rainfall of different durations and return periods. We plugged 3 emission scenarios with 2 global climate models and used LARS-WG, a well-known weather generator, to stochastically downscale daily climate models’ projections for the city of Saskatoon, Canada, by 2100. The downscaled projections were further disaggregated into hourly resolution using our new stochastic and non-parametric rainfall disaggregator. The extreme rainfall quantiles can be consequently identified for different durations (1-hour, 2-hour, 4-hour, 6-hour, 12-hour, 18-hour and 24-hour) and return periods (2-year, 10-year, 25-year, 50-year, 100-year) using Generalized Extreme Value (GEV) distribution. By providing multiple realizations of future rainfall, we attempt to measure the extent of total predictive uncertainty, which is contributed by climate models, emission scenarios, and downscaling/disaggregation procedures. The results show different proportions of these contributors in different durations and return periods.

A climatologia aplicada ao ambiente construído : uma contribuição à caracterização climática de Porto Alegre/RS

Com caráter informativo, inicia-se apresentando a relação entre a climatologia e o ambiente construído, em seus campos especificos de aplicação (urbanismo, projeto de edificações, execução de obras, e manutenção e uso das construções), considerando-se os aspectos históricos, econômico-ecológicos e os ligados à súde e ao conforto do usuário, abrangidos por tal relação. A importância da disponibilidade de informações climatológicas é então comentada, bem como diversas metodalagias de análise e representação de dados climáticos. Com caráter aplicativo, apresenta-se uma contribuigão à caracterização climática da cidade de Porto Alegre, RS. São analisadas os valores médios horários-mensais doa principais parâmetros climáticos disponíveis (temperatura, umidade, velocidade e direção do vento, e nebulosidade), propondo-se uma representação gráfica sintética alternativa para os mesmos. A análise é complementada pala execução de dois procedimentos técnicos: avaliação do conforto térmico, com identificação do período critico de verão, e projeto de dispositivo de sombreamento de aberturas retangulares. Embora esteja em foco o caso específico de Porto Alegre, tais metodologias encontram-se descritas de modo a serem aplicadas a qualquer outra localidade, procurando-se ampliar a utilidade prática do presente estudo.

A gestão da qualidade nos cursos de graduação a distância da UFRN/SEDIS : a percepção dos discentes

The management of water resources in the river basin level, as it defines the Law nº 9433/97, requires the effective knowledge of the processes of hydrological basin, resulting from studies based on consistent series of hydrological data that reflect the characteristics of the basin. In this context, the objective of this work was to develop the modeling of catchment basin of the river Jundiaí - RN and carry out the study of attenuation of a flood of the dam Tabatinga, by means of a monitoring project of hydrological data and climatology of the basin, with a view to promoting the development of research activities by applying methodologies unified and appropriate for the assessment of hydrological studies in the transition region of the semiarid and the forest zone on the coast of Rio Grande do Norte. For the study of the hydrological characteristics of the basin was conducted the automatic design of the basin of the river Jundiaí, with the aid of programs of geoprocessing, was adopted a hydrological model daily, the NRCS, which is a model determined and concentrated. For the use of this model was necessary to determine some parameters that are used in this model, as the Curve Number. Having in mind that this is the first study that is being conducted in the basin with the employment of this model, it was made sensitivity analysis of the results of this model from the adoption of different values of CN, situated within a range appropriate to the conditions of use, occupation and the nature of the soil of this basin. As the objective of this study was also developing a simulation model of the operation of the Tabatinga dam and with this flood control caused in the city of Macaíba, it was developed a mathematical model of fluid balance, developed to be used in Microsoft Excel. The simulation was conducted in two phases: the first step was promoted the water balance daily that allowed the analysis of the sensitivity of the model in relation to the volume of waiting, as well as the determination of the period of greatest discharges daily averages. From this point, it was assumed for the second stage, which was in the determination of the hydrograph of discharges effluent slots, that was determined by means of the fluid balance time, on the basis of the discharges effluents generated by a mathematical equation whose parameters were adjusted according to the hydrograph daily. Through the analyzes it was realized that the dam Tabatinga only has how to carry out the attenuation of floods through the regularization of the volume of waiting, with this there is a loss of approximately 56.5% on storage capacity of this dam, because for causing the attenuation effect of filled the shell of this dam has to remain more than 5m below the level of the sill, representing at least 50.582.927m3. The results obtained with the modeling represents a first step in the direction of improving the level of hydrological information about the behavior of the basins of the semiarid. In order to monitor quantitatively the hydrographic basin of the river Jundiaí will be necessary to install a rain gauge register, next to the Tabatinga dam and a pressure transducer, for regular measurements of flow in the reservoir of the dam. The climatological data will be collected in full automatic weather station installed in Agricultural School Jundiaí

Épocas de plantio de milho em função das deficiências hídricas no solo em Cambará-PR

The aim of this study was to try the forecast of corn (Zea mays L.) sowing dates, the understanding of the quantitative effect of water deficits on that crop and crop yield decrease on a basis of a climatic model of water deficit forecast. This model was applied at Cambara (lat. 23 degrees 00'S, long. 50 circle 02'WGr, altitude 450 m), PR, Brazil. The model estimates yield decrease, in relation to potential values, as a function of the sowing dates which determine flowering and grain filling dates, highly critical times in relation to water deficit. The estimates were done from expected values of water deficit, at the 80% probability level and accumulated degrees-days, using several climatological data. Results show that the first ten days of November are the best corn sowing date under dry or irrigated conditions. Under these same conditions, the worst time showed to be August. Estimates of total needs of supplementar irrigation get values of 126 e 226 mm, respectively to the corn sowed at the first ten days of November and August.

Avaliação da eficiência de sistema fito-pedológico (Wetlands) na depuração de efluentes domésticos gerados em pequena comunidade

Pós-graduação em Agronomia (Energia na Agricultura) - FCA