Seasonal Effect on Spatial and Temporal Consistency of the New GPM-Based IMERG-v5 and GSMaP-v7 Satellite Precipitation Estimates in Brazil’s Central Plateau Region

Abstract

This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0.1° spatial resolution and for a 0.25° grid for comparison with TRMM Multi-satellite Precipitation Analysis (TMPA). The assessment was made on an annual, monthly, and daily basis for both wet and dry seasons. Overall, IMERG presents the best annual and monthly results. In both time steps, IMERG’s precipitation estimations present bias with lower magnitudes and smaller root-mean-square error. However, GSMaP performs slightly better for the daily time step based on categorical and quantitative statistical analysis. Both IMERG and GSMaP estimates are seasonally influenced, with the highest difficulty in estimating precipitation occurring during the dry season. Additionally, the study indicates that GPM-based SPEs products are capable of continuing TRMM-based precipitation monitoring with similar or even better accuracy than obtained previously with the widely used TMPA product.

Assessment of IMERG-V06 Precipitation Product over Different Hydro-Climatic Regimes in the Tianshan Mountains, North-Western China

“…Compared to the daily and monthly estimates of 3B42V7, both IMERG products exhibited considerable enhancements in CC, RMSE, POD, and CSI. Such enhancements in GPM-based products were also reported in Singapore [35], Pakistan [11], Myanmar [40], Brazil [51], and China [39]. Improvements in the key performance measures of both IMERG products might be associated with improvements in the spatiotemporal resolution.…”

“…Previously, several studies have reported the performance of TRMM-and GPM-based precipitation products in different topographic and climatic conditions [7,11,31,34,51,[59][60][61][62]. Most of these studies concluded that the accuracy of precipitation retrieval algorithms used in the GPM and TRMM was strongly associated with the regional climatic system and topography.…”

“…The statistical measures used to assess the performance of SPPs were the correlation coefficient (CC), root mean square error (RMSE), BIAS, relative Bias (rBias), probability of detection (POD), false alarm ratio (FAR), success ratio (SR), and critical success index (CSI). Previously, several researchers have computed these measures to assess the performance of satellite-based products in different regions of the world [3,11,42,[50][51][52][53][54]. The ability of SPPs to track the spatial and temporal variabilities of precipitation over the study domain was also analyzed.…”

Performance Evaluation of Version 5 (V05) of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) over the Tianshan Mountains of China

“…The high quality of the meteorological gauging stations used in this study was already ensured by the National Meteorological Information Centre (NMIC) of China [40]. Moreover, several recent studies have successfully used the daily precipitation data of selected gauges in different hydro-climatological investigations [14,[51][52][53].…”

“…Such algorithms consider perceptible water and cloud-top temperature to derive precipitation information. Likewise, several algorithms have also been developed to convert the MW imageries, which are generally obtained from the passive microwave sensors onboard the Low-earth orbiting (LEO) satellites, to precipitation rates using the information of cloud profile and atmospheric constituents [14]. Precipitation information derived from IR (with high spatiotemporal resolution) and MW (with high precision and relation with rainfall) satellite imageries are often blended to provide improved information of precipitation [15].…”