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The traditional approach to characterize extreme rainfall events is based on using past observed data to fit a probability distribution, which is used to associate a certain probability of occurrence to a rainfall magnitude, under a stationary assumption. Observed series, however, may exhibit trends and abrupt changes, resulting from the natural hydro climatic variability and the climate change context, which are not usually considered in the stationary modelling procedure and which may affect the characterization of extreme events. In this paper, it was evaluated the presence of these temporal changes in rainfall series and their impact to estimate design quantiles, using rainfall data of 317 stations, located in São Paulo State, Brazil. The results showed that extreme rainfall events are changing in the state. Low-return period events (up to 10 years) are intensifying. Moderate- and high-return period events, on the other hand, did not present a clear spatial pattern. Trends and abrupt changes may significantly influence the estimation of design quantiles. The presence of the former in rainfall series exhibited more influence on low-return period events, showing, however, no clear relationship with high-return period design quantiles. The presence of the latter seems to influence the characterization process, affecting estimated quantiles of all evaluated return periods. Such findings highlight the need to consider the presence of trends and abrupt changes and their influence on design quantiles for a more reliable characterization. [ABSTRACT FROM AUTHOR]

This paper constructs probability density distributions of direct normal, diffuse horizontal, and global horizontal irradiance for 1-min clearness indexes kb, kd, and kt, respectively. Previous studies on the clearness index, conditioned to the optical air mass, are analyzed and distributions of these three indexes, using data from two solarimetric stations in Brazil, are presented. The pattern found in probability density distributions, in general, matches the patterns found in the literature. The 50th percentile (P50) of kb values from Sao Paulo city (a location with high levels of pollution) is 0.22, and from the countryside region of the state of Sao Paulo (a location with expected low levels of pollution), it is 0.55. Regarding kt values, P50 is 0.57 and 0.72 for these two regions, respectively. We conclude that different atmospheric conditions result in a different dependence relation between probability density distribution and air mass increases. These conditions include not only cloud formation, but also pollution levels and the influence of relative humidity. These specific results endorse the need for local radiation measurements to the characterization of few-minute average of transmittance indexes. [ABSTRACT FROM AUTHOR]

The study of climatic variables in large scales with surface meteorological stations is limited due to the low density of these stations in many regions, possible sources of errors related to missing data, and uncertainties about the calibration sensors. Global gridded data (GD) systems can minimize these problems. Thus, studies that validate GDs with "ground truth" are important for several applications such as climate change. The objective of this study was to compare long series of surface data with 10-day estimates of average air temperature (T) and precipitation (P) using data from the European Center for Medium-Range Weather Forecast (ECMWF) and the National Aeronautics and Space Administration (NASA) for important agricultural locations in the states of Minas Gerais and São Paulo in Brazil. Despite the different spatial resolutions between ECMWF and NASA, the purpose of this paper was to evaluate the two data sources as they are readily available. The GD performance was evaluated by linear regression analysis. Analyses were performed for each meteorological variable for entire years and separated by seasons. The estimates of T from both ECMWF and NASA systems were accurate with the minimum Willmott concordance index (d) and RMSEp of 0.86, 0.37 °C, respectively, and precision with R2 0.61. The estimates of P had a minimum R2, d, and RMSEp of 0.48, 0.79, 2.15 °C respectively. The decreasing orders of (R2) were autumn > winter > spring > summer for T and winter > autumn > spring > summer for P, varying from 0.93 to 0.61 for T and from 0.77 to 0.48 for P. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of pluviometric precipitation time series obtained from 45 data collection points, with daily rain measurements (in mm), from 45 counties within the nine climatic regions of the state of São Paulo, mostly for the period of 1950–1997. São Paulo State (Brazil) is situated between the inter-tropical climatic zone, dominated by tropical and equatorial masses, and the sub-tropical climatic zone controlled by tropical and polar air masses. Due to this location the State can be divided into three climatic zones: south-east and south-west, which are permanently humid, and north with a well defined dry period. Rescaled range analysis, or R/S analysis, has been used to investigate the scale properties of the time series. We found two clear values for the Hurst exponent, one for a period of 10 to 200 days (H1), and another for a period above 210 days (H2). The value of H1 lies between 0.6 to 0.7 for all climatic zones indicating characteristics of persistence. However, H2 is (i) close to 0.5 for the littoral area, showing random characteristics, (ii) close to 0.6 for mountain areas, indicating persistence characteristics, and (iii) close to 0.4 for the plane area, irrespective of whether the climate is dry or humid, and shows anti-persistence characteristics. [ABSTRACT FROM AUTHOR]