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Remote Sens. 2018, 10(11), 1743; https://doi.org/10.3390/rs10111743

Assessment of Ground-Reference Data and Validation of the H-SAF Precipitation Products in Brazil

1
Weather Forecast Center and Climate Studies, National Institute for Space Research (CPTEC/INPE), São José dos Campos, SP 12227-010, Brazil
2
CETEMPS, University of L’Aquila, 67100 L’Aquila, Italy
3
Italian Civil Protection Department, 00189 Rome, Italy
4
Institute of Atmospheric Sciences and Climate (ISAC) National Research Council of Italy (CNR), 00133 Rome, Italy
5
Institute of Research for Development (IRD), 13572 Marseille, France
*
Author to whom correspondence should be addressed.
Received: 13 August 2018 / Revised: 18 October 2018 / Accepted: 24 October 2018 / Published: 5 November 2018
(This article belongs to the Special Issue Remote Sensing of Precipitation)
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Abstract

The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades; however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Paraíba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor. These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q 2 V u 15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms. Differently, for the Vale do Paraíba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values. View Full-Text
Keywords: rain gauges; radar; quality indexes; satellite rainfall retrievals; validation rain gauges; radar; quality indexes; satellite rainfall retrievals; validation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Martins Costa do Amaral, L.; Barbieri, S.; Vila, D.; Puca, S.; Vulpiani, G.; Panegrossi, G.; Biscaro, T.; Sanò, P.; Petracca, M.; Marra, A.C.; Gosset, M.; Dietrich, S. Assessment of Ground-Reference Data and Validation of the H-SAF Precipitation Products in Brazil. Remote Sens. 2018, 10, 1743.

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