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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]

With the advance of remote sensing technologies, meteorological satellites have become an alternative in the process of monitoring and measuring meteorological variables, both spatially and temporally. The present study brings some additional elements to the existent validations of satellite-based precipitation estimates from CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station) all around the world, by analyzing its monthly product in the period 1981–2019 over the central region of the state of São Paulo, Brazil. There are significant variations over time in the number of rain gauges used by CHIRPS at the region, and the product quality has been evaluated under these conditions. Initially, the general relationship between satellite estimates and surface rainfall data is assessed using the linear adjustment and error analysis in both temporal and spatial perspectives, followed by a trend analysis using Laplace test. Results show an average decrease of 20% in R2 values when gauges were not used as anchor/reference stations by CHIRPS; the same behavior is observed for the other metrics. The monthly map analysis, besides the evident impact of the use or not of the gauges as reference stations, showed a better performance of CHIRPS (in terms of R2) during the dry period (April to August) than for the wet period (October to March), especially when anchor stations were not available. On the other hand, CHIRPS tends to underestimate (overestimate) low (high) rain rate events. Finally, despite the changes in product over time, monthly trends showed, in general, the same pattern of variability in rainfall over 38 years and a prevalence toward the reduction of rainfall. In summary, CHIRPS product seems a reasonable alternative for regions that lack historical rainfall information, but a careful analysis on the product diagnosis should be made when temporal analysis is conducted. [ABSTRACT FROM AUTHOR]