New improved Brazilian daily weather gridded data (1961–2020)

The demand for meteorological gridded datasets has increased within the last few years to inform studies such those in climate, weather, and agriculture. These studies require those data to be readily usable in standard formats with continuous spatial and temporal coverage. Since 2016, Brazil has a daily gridded meteorological data set with spatial resolution of 0.25° × 0.25° from January 1, 1980 to December 31, 2013 which was well received by the community. The main objective of this work is to improve the Brazilian meteorological data set. We do this by increasing the resolution of the minimum and maximum temperature (Tmax and Tmin) gridded interpolations from 0.25° × 0.25° to 0.1° × 0.1° by incorporating data on topographic relief, and increasing the time period covered (January 1, 1961–July 31, 2020). Besides Tmax and Tmin, we also gridded precipitation (pr), solar radiation (Rs), wind speed (u2), and relative humidity (RH) using observed data from 11,473 rain gauges and 1,252 weather stations. By means of ranked cross‐validation statistics, we selected the best interpolation among inverse distance weighting and angular distance weighting methods. We determined that interpolations for Tmax and Tmin are improved by using the elevation of a query point, that accounts for topographic relief, and a temperature lapse rate. Even though this new version has ≈25 years more data relative to the previous one, statistics from cross‐validation were similar. To allow researchers to assess the performance of the interpolation relative to station data in the area, we provide two types of gridded controls.

[1]  Henrique Boriolo Dias,et al.  Assessing the performance of two gridded weather data for sugarcane crop simulations with a process-based model in Center-South Brazil , 2021, International journal of biometeorology.

[2]  S. Motoike,et al.  Modeling oil palm crop for Brazilian climate conditions , 2021 .

[3]  L. Eloy,et al.  Water Appropriation on the Agricultural Frontier in Western Bahia and Its Contribution to Streamflow Reduction: Revisiting the Debate in the Brazilian Cerrado , 2021, Water.

[4]  Yuming Guo,et al.  Socioeconomic inequality in vulnerability to all-cause and cause-specific hospitalisation associated with temperature variability: a time-series study in 1814 Brazilian cities. , 2020, The Lancet. Planetary health.

[5]  V. Dubreuil,et al.  Assessment of Gridded CRU TS Data for Long-Term Climatic Water Balance Monitoring over the São Francisco Watershed, Brazil , 2020, Atmosphere.

[6]  F. Justino,et al.  Climatology and trends of downward shortwave radiation over Brazil , 2020 .

[7]  S. Hagemann,et al.  Performance evaluation of Eta/HadGEM2-ES and Eta/MIROC5 precipitation simulations over Brazil , 2020 .

[8]  Jaime Fern'andez del R'io,et al.  Array programming with NumPy , 2020, Nature.

[9]  A. Avila‐Diaz,et al.  Assessing current and future trends of climate extremes across Brazil based on reanalyses and earth system model projections , 2020, Climate Dynamics.

[10]  A. Avila‐Diaz,et al.  Extreme climate indices in Brazil: evaluation of downscaled earth system models at high horizontal resolution , 2020, Climate Dynamics.

[11]  P. Sentelhas,et al.  NASA/POWER and DailyGridded weather datasets—how good they are for estimating maize yields in Brazil? , 2019, International Journal of Biometeorology.

[12]  Nathan Lenssen,et al.  Improvements in the GISTEMP Uncertainty Model , 2019, Journal of Geophysical Research: Atmospheres.

[13]  P. Sentelhas,et al.  Soybean-maize succession in Brazil: Impacts of sowing dates on climate variability, yields and economic profitability , 2019, European Journal of Agronomy.

[14]  J. Arblaster,et al.  Geographic, Demographic, and Temporal Variations in the Association between Heat Exposure and Hospitalization in Brazil: A Nationwide Study between 2000 and 2015 , 2019, Environmental health perspectives.

[15]  W. Petersen,et al.  Grid box-level evaluation of IMERG over Brazil at various space and time scales , 2019, Atmospheric Research.

[16]  Carey W. King,et al.  Biofuel-water-land nexus in the last agricultural frontier region of the Brazilian Cerrado , 2018, Applied Energy.

[17]  Yuming Guo,et al.  Spatiotemporal and demographic variation in the association between temperature variability and hospitalizations in Brazil during 2000-2015: A nationwide time-series study. , 2018, Environment international.

[18]  P. Sentelhas,et al.  Solar Radiation Models and Gridded Databases to Fill Gaps in Weather Series and to Project Climate Change in Brazil , 2018, Advances in Meteorology.

[19]  M. H. Costa,et al.  Evolution of rain and photoperiod limitations on the soybean growing season in Brazil: The rise (and possible fall) of double-cropping systems , 2018 .

[20]  P. Teodoro,et al.  Occurrence of fire foci under different land uses in the State of Amazonas during the 2005 drought , 2018, Environment, Development and Sustainability.

[21]  P. Sentelhas,et al.  Assessment of different gridded weather data for soybean yield simulations in Brazil , 2018, Theoretical and Applied Climatology.

[22]  C. Schwierz,et al.  The influence of station density on climate data homogenization , 2017 .

[23]  Stephan Hoyer,et al.  xarray: N-D labeled arrays and datasets in Python , 2017 .

[24]  B. Scanlon,et al.  An update of Xavier, King and Scanlon (2016) daily precipitation gridded data set for the Brazil , 2017 .

[25]  Carey W. King,et al.  Daily gridded meteorological variables in Brazil (1980–2013) , 2016 .

[26]  Marko Wagner,et al.  Geostatistics For Environmental Scientists , 2016 .

[27]  Siu Kwan Lam,et al.  Numba: a LLVM-based Python JIT compiler , 2015, LLVM '15.

[28]  B. Scanlon,et al.  Performance evaluation of rainfall estimates by TRMM Multi‐satellite Precipitation Analysis 3B42V6 and V7 over Brazil , 2015 .

[29]  A. Ruane,et al.  Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation , 2015 .

[30]  S. Sorooshian,et al.  Early Examples from the Integrated Multi-Satellite Retrievals for GPM (IMERG) , 2014 .

[31]  Wei Wu,et al.  High-resolution spatial databases of monthly climate variables (1961–2010) over a complex terrain region in southwestern China , 2014, Theoretical and Applied Climatology.

[32]  A. Dégre,et al.  Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium , 2011 .

[33]  D. Gesch,et al.  Global multi-resolution terrain elevation data 2010 (GMTED2010) , 2011 .

[34]  Isaac Moradi,et al.  Quality control of global solar radiation using sunshine duration hours , 2009 .

[35]  C. Frei,et al.  Comparison of six methods for the interpolation of daily, European climate data , 2008 .

[36]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[37]  Scott M. Robeson,et al.  Daily Precipitation Grids for South America , 2006 .

[38]  P. Jones,et al.  Representing Twentieth-Century Space-Time Climate Variability. Part II: Development of 1901-96 Monthly Grids of Terrestrial Surface Climate , 2000 .

[39]  Christopher A. Fiebrich,et al.  Quality Assurance Procedures in the Oklahoma Mesonetwork , 2000 .

[40]  F. Désalmand.  Meteorology today : An introduction to weather, Climate, and the environment , 1998 .

[41]  D. Shepard.  A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.