Evaluation of high-resolution meteorological global data products using flux tower observations across Brazil

In the past decade, the scientific community has seen an increase in the number of global hydrometeorological products. This has been possible with efforts to push global hydrological and land surface modelling to hyper-resolution applications. As the resolution of these datasets increase, so does the need to compare their estimates against local in-situ measurements. This is particularly important for Brazil, whose large continental scale domain results in a wide range of climate and biomes. In this study, high-resolution (0.1-0.25 deg) global and regional meteorological datasets are compared against flux tower observations at 11 sites across Brazil (for periods between 1999-2010), covering Brazil’s main land cover types (tropical rainforest, woodland savanna, various croplands, and tropical dry forests) to assess the quality of four global reanalysis products [ERA5-Land, GLDAS2.0, GLDAS2.1, and MSWEPv2.2] and one regional gridded dataset developed from local interpolation of meteorological variables across the country [Brazilian National  Meteorological Database (referred here as Xavier)]. Whilst the only measured variable for MSWEP was precipitation, all other gridded datasets also included surface meteorological variables such as air temperature, wind speed, pressure, downward shortwave and longwave radiation, and specific humidity. Data products were evaluated for their ability to reproduce the daily and monthly meteorological observations at flux towers. A ranking system for data products was developed based on the mean squared error. To identify the possible causes for these errors further analysis was undertaken to determine the contributions of correlation, bias, and variation to the MSE. Results show that, for precipitation, MSWEP outperforms the other datasets at daily scales but at a monthly scale XAVIER performs best. For all other variables, ERA5-Land achieved the best ranking (smallest) errors at the daily scale and averaged the best rank for all variables at the monthly scale. GLDAS2.0 performed least well at both temporal scales, however the newer version (GLDAS2.1) was an improvement of its older version for almost every variable. Xavier wind speed and GLDAS2.0 solar radiation outperformed the other datasets at a monthly scale. The largest contribution to the MSE at the daily scale for all datasets and variables was the correlation contribution whilst at the monthly scale it was the bias contribution. ERA5-Land is recommended when using multiple hydro-meteorological variables to force land-surface models within Brazil.