Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil

The spatial and temporal distribution of precipitation is great importance for the rain-fed agricultural production socioeconomics Mato Grosso (MT), Brazil. MT has a sparse network ground rain gauges that limits effective use information sustainable water resources in region. Several gridded products from remote sensing reanalysis land surface models are currently available can enhance such information. However, these at different resolutions which add some challenges to stakeholders (users) identify their appropriateness specific applications (e.g., irrigation requirements, length growing season, drought monitoring). Thus, it necessary provide an assessment reliability estimates. objective this work was compare regional estimates over as provided by Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis Research Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Precipitation (GPM), Climatology Project (GPCP) with ground-based measurements. comparison conducted 2000–2018 period eleven weather stations covered climate zones using daily, monthly, annual resolutions. used Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, Wilks methods. results showed GPM GLDAS did not differ significantly measured precipitation. TRMM slightly overestimated daily about 4.7% but show significant difference on monthly scales when compared local GPCP underestimated 7.1%. MERRA 22.9% average. In general, all satisfactorily estimated precipitation, most them precipitation; however, they low accuracy estimating TRMM, GPM, GPCP, had highest performance, high low, while lowest performance. findings study be support decision-making process region application related management, sustainability agriculture production, management.