Daily gridded meteorological variables in Brazil (1980–2013). (8th October 2015)

• Record Type: Journal Article
• Authors: Xavier, Alexandre C.; King, Carey W.; Scanlon, Bridget R.
• Is Part Of: International journal of climatology. Volume 36:Number 6(2016)
• Page Start: 2644
• Page End: 2659
• Publication Date: 2015-10-08
• Subjects: Brazil -- interpolation -- precipitation -- evapotranspiration -- meteorological variables -- data set -- gridded data; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.4518
• Languages: English
• ISSNs: 0899-8418

Abstract

ABSTRACT: Basic meteorological data are essential for evaluating impacts of spatiotemporal variability in climate forcing on hydrology and agroecosystems. The objective of this work was to develop high‐resolution grids (0.25 ∘ × 0.25 ∘ ) of daily precipitation, evapotranspiration, and the five climate variables generally required to estimate evapostranspiration for Brazil. These five variables are maximum and minimum temperature, solar radiation, relative humidity, and wind speed. We tested six different interpolation schemes to create the grids for these variables. The data were obtained from 3625 rain gauge and 735 weather stations for period of 1980–2013. We used a cross‐validation approach that compares point observed data to point interpolated estimates to select the best interpolation scheme for each climate variable. We also present the performance of the best interpolation for each climate variable at daily timescales and for river basins. The inverse distance weighting and angular distance weighting methods produced the best results. Performance of all methods was poorer prior to 1995 because of fewer stations and available data. The performance of the interpolation varies for different seasons for almost all variables. Forecasting capability was tested for precipitation only and performed adequately for the system state (wet or dry). Variations in the interpolation schemes across river basins are primarily attributed to differences in gauge or station network density. This freely available gridded meteorological data set significantly advances the availability of climate data in Brazil.