Analysis of the IMERG-GPM Precipitation Product Analysis in Brazilian Midwestern Basins Considering Different Time and Spatial Scales

Abstract

Precipitation products derived from satellites have emerged as a promising approach for obtaining precipitation estimates, enabling accurate long-term observations and describing the water cycle dynamics from a global scale to a local scale. The quality of these products has improved significantly in the last decades, especially with the emergence of TRMM missions and its successor GPM. The objective of this study was to evaluate the daily, monthly and annual precipitation estimates provided by IMERG version 05 of the GPM, with the data observed by the rainfall stations of the Brazilian Agency of Water and Sanitation (ANA) in the basins of the Brazilian midwest. In order to compare the data, the spatialization of the data of the rainfall stations was performed by means of the ordinary kriging technique, interpolating the data for grids of 0.1° × 0.1° that correspond to the specialized grids of the GPM satellite. The data were evaluated quantitatively by means of statistical metrics. The GPM satellite precipitation product performed relatively well on a daily scale for regions with smooth topography, and was able to describe the rainfall regime on larger time scales, regardless of the terrain conditions. However, the satellite retrievals were unable to reproduce rainfall extremes in virtually all situations, which may limit their application in frequency analyses.

Evaluating the Performance and Applicability of Satellite Precipitation Products over the Rio Grande–San Juan Basin in Northeast Mexico

“…The ranking of errors, mostly between 10 and 100 mm, is consistent in this study following previous monthly analyses [2,17,56]. However, these are deemed insignificant, as the monthly precipitation average is at least 300 mm.…”

“…Published studies [2,19,24,56,57] have found that IMERG and CMORPH correlations are influenced by elevation. Consistent with these findings, our results indicate that correlations above 0.7 are observed at elevations between 200 and 600 m a.s.l.…”

Comparison of Methods for Filling Daily and Monthly Rainfall Missing Data: Statistical Models or Imputation of Satellite Retrievals?

“…However, as the satellite estimates are corrected prior to imputation, lower values of RMSE and higher levels of linear correlation were obtained by [36], which again suggests that a bias correction procedure might improve rainfall estimation. As for the IMERG-GPM product, a tendency of overestimation was observed for most target sites, which is in agreement with the results of [34] for the study region. The seasonal patterns of the observed rainfall were, to some extent, captured in the Córrego do Ouro, the Quirinópolis, and the Montividiu gauging stations, but were grossly misrepresented in the Cachoeira de Goiás (such lack of fit was also observed for the daily time scale).…”

“…We note that both sites are located in regions with weak topographic gradients, which should, at least to some extent, exclude the influence of terrain complexity in the relatively poor performance of the satellite product [33]. However, some influence of long-term climate conditions and of the variability of rainfall extremes [34,45] is apparently perceived in these sites. It is also worth noting that the GPM product performed similarly, in terms of MAE (4.1 mm, on average), to the MLR models (4.0 mm, on average).…”

“…On the other hand, due to the limitations of the retrieval algorithms, satellite estimates are frequently biased, even after time-averaging [32], which might hinder their utilization without pre-processing or bias correction [33]. Yet, the levels of bias may considerably vary for distinct intervals of rainfall amounts (e.g., [34]) and, at least for some ranges, the satellite retrievals may reasonably agree with ground-based information. This fact has prompted the research question as to whether utilizing satellite rainfall estimates for filling missing data in rainfall gauging stations enclosed by a satellite pixel comprises a more effective and accurate alternative than using data from nearby sites.…”