Precipitation Diurnal Cycle Assessment of Satellite-Based Estimates over Brazil

João Maria De Sousa Afonso,Manoel Alonso Gan,Daniel Alejandro Vila,Rayana Santos Araujo Palharini,Naurinete De Jesus Da Costa Barreto,Joao Henry Huamán Chinchay,David Pareja Quispe

doi:10.3390/rs12142339

Abstract

The main objective of this study is to assess the ability of several high-resolution satellite-based precipitation estimates to represent the Precipitation Diurnal Cycle (PDC) over Brazil during the 2014–2018 period, after the launch of the Global Precipitation Measurement satellite (GPM). The selected algorithms are the Global Satellite Mapping of Precipitation (GSMaP), The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data from different national and regional networks were used as the reference dataset after going through rigid quality control tests. All datasets were interpolated to a common 0.1° × 0.1° grid every 3 h for comparison. After a hierarchical cluster analysis, seven regions with different PDC characteristics (amplitude and phase) were selected for this study. The main results of this research could be summarized as follow: (i) Those regions where thermal heating produce deep convective clouds, the PDC is better represented by all algorithms (in term of amplitude and phase) than those regions driven by shallow convection or low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms the rest of the algorithms with lower bias and less dispersion. In this case, the gauge-adjusted version improves the satellite-only retrievals of the same algorithm suggesting that daily gauge-analysis is useful to reduce the bias in a sub-daily scale; (iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F)) overestimates rainfall for almost all times and all the regions, while the satellite-only version provide better results than the final version; (iv) CMORPH has the better performance for a transitional regime between a coastal land-sea breeze and a continental amazonian regime. Further research should be performed to understand how shallow clouds processes and convective/stratiform classification is performed in each algorithm to improve the representativity of diurnal cycle.

Highlights

Precipitation, and its time and space distributions, is of paramount importance to any country, in particular for those of continental size such as Brazil
The Integrated Multi-satellitE Retrievals for GPM (IMERG) (IMERG-F and IMERG-L) and Global Satellite Mapping of Precipitation (GSMaP) (GSMaP-G and GSMaP-Motion Vector Kalman (MVK)) products are available for a grid with the same resolution above, but CMORPH data are given in an irregular grid of 0.08◦ × 0.07◦ (Table 2)
While CMORPH and IMERG suite overestimate the precipitation for all times, GSMAP suite presents the lowest values for almost all times with the best performance for GSMaP-G

Summary

Introduction

Precipitation, and its time and space distributions, is of paramount importance to any country, in particular for those of continental size such as Brazil. Rain gauge data are always required in almost all areas of activities: water resources management (with emphasis on potable water), agriculture, energy generation by hydroelectric power plants, just to mention some. The largest part of Amazon rain forest, which is located over Brazilian territory, plays an important role in the global precipitation and energy budgets [2]. The meteorological and climate multi-scale processes (mainly at the highest spatial and temporal scales) over these unpopulated regions have not been well understood, due to the absence or a poor distribution of surface measurements, especially, in complex terrain and remote forest areas. Satellite-based precipitation estimates (SPE) could be an excellent complement to conventional data in those regions where other measurement systems are difficult to implement and maintain [3], but they need to be validated, in most cases regionally, to understand the uncertainties associated with the physical processes associated with each rainfall regime

Objectives

Methods

Results

Conclusion