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Daily Time Series (daily + time_series)
Selected AbstractsThe meta-analysis of the Italian studies on short-term effects of air pollution (MISA): old and new issues on the interpretation of the statistical evidencesENVIRONMETRICS, Issue 3 2007Pierantonio Bellini Abstract The second meta-analysis of the Italian studies on short-term health effects of air pollution, known as MISA-2, was based on daily time series of indicators of both pollution and of health outcomes. It covered 15 cities during 1996,2002 for a total population of approximately nine millions. Health outcomes included mortality for natural causes, for respiratory diseases and for cardiovascular conditions, as well as hospital admissions for respiratory, cardiac and cerebrovascular diseases. Pollutants considered in univariate analyses were sulphur dioxide (SO2), nitrogen oxide (NO2), carbon monoxide (CO), suspended particulate matter (SPM) measuring less than 10,µm diameter (PM10) and ozone (O3, limited to the summer period). Results, including risk estimates, have been largely confirmatory of findings obtained in previous large meta-analytic studies carried out in North America and in Europe. A full report in Italian is available. The present contribution summarises the results of MISA-2 and addresses three major issues regarding their interpretation: robustness of the causal inferential process, the role of specific air pollutants and the reliability of risk estimates. The former issue is stressed according to Bradford Hill's criteria and the conclusion is reached that at least for the association of air pollution with an increase in mortality the evidence for causality is strong. Assessing the role of each air pollutant is problematic: there is some evidence that the effects of PM10 are partly confounded by other pollutants, but PM10 may not be the best indicator of the role of air SPM (routine measures of PM2.5 have not been introduced in Italy). As for risk estimates, the per cent increase in risk of mortality for unit increase in PM10 concentration, measured in MISA-2, is remarkably similar to estimates in other studies and there is indication for linearity of the dose,response relationship. Copyright © 2007 John Wiley & Sons, Ltd. [source] Simulating pan-Arctic runoff with a macro-scale terrestrial water balance modelHYDROLOGICAL PROCESSES, Issue 13 2003Michael A. Rawlins Abstract A terrestrial hydrological model, developed to simulate the high-latitude water cycle, is described, along with comparisons with observed data across the pan-Arctic drainage basin. Gridded fields of plant rooting depth, soil characteristics (texture, organic content), vegetation, and daily time series of precipitation and air temperature provide the primary inputs used to derive simulated runoff at a grid resolution of 25 km across the pan-Arctic. The pan-Arctic water balance model (P/WBM) includes a simple scheme for simulating daily changes in soil frozen and liquid water amounts, with the thaw,freeze model (TFM) driven by air temperature, modelled soil moisture content, and physiographic data. Climate time series (precipitation and air temperature) are from the National Centers for Environmental Prediction (NCEP) reanalysis project for the period 1980,2001. P/WBM-generated maximum summer active-layer thickness estimates differ from a set of observed data by an average of 12 cm at 27 sites in Alaska, with many of the differences within the variability (1,) seen in field samples. Simulated long-term annual runoffs are in the range 100 to 400 mm year,1. The highest runoffs are found across northeastern Canada, southern Alaska, and Norway, and lower estimates are noted along the highest latitudes of the terrestrial Arctic in North America and Asia. Good agreement exists between simulated and observed long-term seasonal (winter, spring, summer,fall) runoff to the ten Arctic sea basins (r = 0·84). Model water budgets are most sensitive to changes in precipitation and air temperature, whereas less affect is noted when other model parameters are altered. Increasing daily precipitation by 25% amplifies annual runoff by 50 to 80% for the largest Arctic drainage basins. Ignoring soil ice by eliminating the TFM sub-model leads to runoffs that are 7 to 27% lower than the control run. The results of these model sensitivity experiments, along with other uncertainties in both observed validation data and model inputs, emphasize the need to develop improved spatial data sets of key geophysical quantities (particularly climate time series) to estimate terrestrial Arctic hydrological budgets better. Copyright © 2003 John Wiley & Sons, Ltd. [source] Trends in daily precipitation and temperature extremes across western Germany in the second half of the 20th centuryINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 9 2005Yeshewatesfa Hundecha Abstract The evolution of daily extreme precipitation and temperature from 1958 to 2001 was investigated within the German side of the Rhine basin. Trends of a set of extreme precipitation and temperature indices defined on daily time series of precipitation and temperature were calculated at 611 precipitation and 232 temperature stations located within the study area and their corresponding significances were tested using the non-parametric Kendall- tau test. The results obtained indicated that both the daily minimum and maximum extreme temperatures have increased over the investigation period, with the degree of change showing seasonal variability. On an annual basis, the change in the daily minimum extreme temperature was found to be greater than that of the daily maximum extreme temperature. The daily extreme heavy precipitation has shown increasing trends both in magnitude and frequency of occurrence in all seasons except summer, where it showed the opposite trend. The station values of the daily precipitation were also interpolated on a regular grid of 5 km × 5 km so that the changes in the indices could be investigated on areal precipitation by aggregating the interpolated precipitation to any desired scale. This enables assessment of the hydrological consequences of the changes in the extreme precipitation. Although the spatial pattern remained more or less similar with that of the point-scale trends for all indices, the average trend magnitude showed an increase with the scale of the area on which precipitation was aggregated. Copyright © 2005 Royal Meteorological Society [source] Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank,JOURNAL OF FORECASTING, Issue 3 2009Alberto Cabrero Abstract The main focus of this paper is to model the daily series of banknotes in circulation. The series of banknotes in circulation displays very marked seasonal patterns. To the best of our knowledge the empirical performance of two competing approaches to model seasonality in daily time series, namely the ARIMA-based approach and the Structural Time Series approach, has never been put to the test. The application presented in this paper provides valid intuition on the merits of each approach. The forecasting performance of the models is also assessed in the context of their impact on the liquidity management of the Eurosystem.,,Copyright © 2008 John Wiley & Sons, Ltd. [source] Model choice in time series studies of air pollution and mortalityJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 2 2006Roger D. Peng Summary., Multicity time series studies of particulate matter and mortality and morbidity have provided evidence that daily variation in air pollution levels is associated with daily variation in mortality counts. These findings served as key epidemiological evidence for the recent review of the US national ambient air quality standards for particulate matter. As a result, methodological issues concerning time series analysis of the relationship between air pollution and health have attracted the attention of the scientific community and critics have raised concerns about the adequacy of current model formulations. Time series data on pollution and mortality are generally analysed by using log-linear, Poisson regression models for overdispersed counts with the daily number of deaths as outcome, the (possibly lagged) daily level of pollution as a linear predictor and smooth functions of weather variables and calendar time used to adjust for time-varying confounders. Investigators around the world have used different approaches to adjust for confounding, making it difficult to compare results across studies. To date, the statistical properties of these different approaches have not been comprehensively compared. To address these issues, we quantify and characterize model uncertainty and model choice in adjusting for seasonal and long-term trends in time series models of air pollution and mortality. First, we conduct a simulation study to compare and describe the properties of statistical methods that are commonly used for confounding adjustment. We generate data under several confounding scenarios and systematically compare the performance of the various methods with respect to the mean-squared error of the estimated air pollution coefficient. We find that the bias in the estimates generally decreases with more aggressive smoothing and that model selection methods which optimize prediction may not be suitable for obtaining an estimate with small bias. Second, we apply and compare the modelling approaches with the National Morbidity, Mortality, and Air Pollution Study database which comprises daily time series of several pollutants, weather variables and mortality counts covering the period 1987,2000 for the largest 100 cities in the USA. When applying these approaches to adjusting for seasonal and long-term trends we find that the Study's estimates for the national average effect of PM10 at lag 1 on mortality vary over approximately a twofold range, with 95% posterior intervals always excluding zero risk. [source] Environmental controls and patterns of cumulative radial increment of evergreen tree species in montane, temperate rainforests of Chiloé Island, southern ChileAUSTRAL ECOLOGY, Issue 3 2009CECILIA A. PÉREZ Abstract We investigated the local environmental controls on daily fluctuations of cumulative radial increment and cambial hydration of three dominant, evergreen tree species from montane, Coastal rainforests of Chiloé Island, Chile (42° 22, S). During 2 years (1997,1998 and 1998,1999) we recorded hourly cumulative radial increments using electronic band dendrometers in the long-lived conifer Fitzroya cupressoides (Cupressaceae), the evergreen broad-leaved Nothofagus nitida (Nothofagaceae), and the narrow-leaved conifer Podocarpus nubigena (Podocarpaceae). We also measured soil and cambial tissue hydration using capacitance sensors, together with air and soil temperature and rainfall during the period of the study. In addition, we collected cores of these tree species to evaluate how dendrometer measurements reflect annual tree ring width. One-year long daily time series of cumulative radial increments suggests that radial growth of Fitzroya cupressoides was initiated slowly in early spring, with a maximum in early summer. Multiple regressions showed positive relations between daily precipitation and radial index (i.e. the difference in cumulative radial increment of two consecutive days) in the three species. According to path analysis there was a significant direct effect of changes in tree hydration on radial index of the three focal species. In emergent, pioneer species such as Nothofagus and Fitzroya, radial index was negatively affected by changes in maximum air temperature and photosynthetically active radiation, probably because of high evapotranspiration demand on warm sunny days. The shade-tolerant species Podocarpus nubigena was positively affected by photosynthetically active radiation. Our diel scale findings support the use of tree ring widths for reconstructing past climate in these southern temperate forests and provide evidence that rainforest trees may be highly sensitive to future declines in rainfall and temperature increases during summer. [source] | ||||||||||