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

Leandro Salles,Henrique Roig,Diogo Olivetti,Welber Ferreira,Tati Almeida,Frédéric Satgé

doi:10.3390/w11040668

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.

Highlights

Precipitation is a key component of the water cycle, which is facing unprecedented changes related to both climate change and human growth population
(12 March 2014 to 29 November 2016), it was necessary to aggregate Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) values from their initial 0.1◦ grid to TRMM Multi-satellite Precipitation Analysis (TMPA)’s 0.25◦ grid scale [21]. Both IMERG and GSMaP were resampled from their 0.1◦ grid resolution into a 0.05◦ resolution dataset, using the nearest neighbor method
The 0.10◦ IMERG and GSMaP grids were used to assess the impacts of the spatial resolution improvement provided by Global Precipitation Measurement (GPM) datasets, while the other grids were used to compare GPM-based with Tropical Rainfall Measurement Mission (TRMM)-based satellite precipitation estimates (SPEs) products

Summary

Introduction

Precipitation is a key component of the water cycle, which is facing unprecedented changes related to both climate change and human growth population. Climate change redistributes precipitation’s seasonality [1] and intensity [2], threatening the six-fold increase in water extraction observed in the 20th century in response to the increasing world population, food needs, and economics level (vision, water council, 2000). In this context, The World Meteorological Organization (WMO) defines precipitation as an Essential Climate Variable (ECV) to understand and adapt to these ongoing changes. It could be even more marked over tropical regions due to high spatial and temporal precipitation variability

Objectives

Methods

Results

Discussion

Conclusion