Next Article in Journal
Validation and Assessment of Multi-GNSS Real-Time Precise Point Positioning in Simulated Kinematic Mode Using IGS Real-Time Service
Previous Article in Journal
Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering
Previous Article in Special Issue
Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables
Open AccessArticle

Using Satellite Error Modeling to Improve GPM-Level 3 Rainfall Estimates over the Central Amazon Region

1
Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, SP 12227-010, Brazil
2
Sid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA
*
Author to whom correspondence should be addressed.
Current address: Géosciences Environnement Toulouse (GET), Centre National de la Recherche Scientifique, Toulouse 31055, France.
Remote Sens. 2018, 10(2), 336; https://doi.org/10.3390/rs10020336
Received: 29 December 2017 / Revised: 8 February 2018 / Accepted: 21 February 2018 / Published: 23 February 2018
This study aims to assess the characteristics and uncertainty of Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Level 3 rainfall estimates and to improve those estimates using an error model over the central Amazon region. The S-band Amazon Protection National System (SIPAM) radar is used as reference and the Precipitation Uncertainties for Satellite Hydrology (PUSH) framework is adopted to characterize uncertainties associated with the satellite precipitation product. PUSH is calibrated and validated for the study region and takes into account factors like seasonality and surface type (i.e., land and river). Results demonstrated that the PUSH model is suitable for characterizing errors in the IMERG algorithm when compared with S-band SIPAM radar estimates. PUSH could efficiently predict the satellite rainfall error distribution in terms of spatial and intensity distribution. However, an underestimation (overestimation) of light satellite rain rates was observed during the dry (wet) period, mainly over rivers. Although the estimated error showed a lower standard deviation than the observed error, the correlation between satellite and radar rainfall was high and the systematic error was well captured along the Negro, Solimões, and Amazon rivers, especially during the wet season. View Full-Text
Keywords: global precipitation measurement; IMERG; PUSH; error model; validation; Amazon global precipitation measurement; IMERG; PUSH; error model; validation; Amazon
Show Figures

Graphical abstract

MDPI and ACS Style

Oliveira, R.; Maggioni, V.; Vila, D.; Porcacchia, L. Using Satellite Error Modeling to Improve GPM-Level 3 Rainfall Estimates over the Central Amazon Region. Remote Sens. 2018, 10, 336.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop