Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

Full text
Author(s):
Oliveira, Romulo [1, 2, 3] ; Maggioni, Viviana [2] ; Vila, Daniel [1] ; Porcacchia, Leonardo [2]
Total Authors: 4
Affiliation:
[1] INPE, CPTEC, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[2] George Mason Univ, Sid & Reva Dewberry Dept Civil Environm & Infrast, Fairfax, VA 22030 - USA
[3] CNRS, GET, F-31055 Toulouse - France
Total Affiliations: 3
Document type: Journal article
Source: REMOTE SENSING; v. 10, n. 2 FEB 2018.
Web of Science Citations: 5
Abstract

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, Solimoes, and Amazon rivers, especially during the wet season. (AU)

FAPESP's process: 09/15235-8 - Cloud processes of the main precipitation systems in Brazil: a contribution to cloud resolving modeling and to the GPM (Global Precipitation Measurement)
Grantee:Luiz Augusto Toledo Machado
Support type: Research Projects - Thematic Grants