Assessment of ground-reference data and validation of the H-SAF precipitation products in Brazil

2018

Article référencé dans le Web of Science WOS:000451733800074

do Amaral L. M. C., Barbieri S., Vila D., Puca S., Vulpiani G., Panegrossi G., Biscaro T., Sano P., Petracca M., Marra A. C., Gosset Marielle, Dietrich S.

Remote Sensing, 2018, 10 (11), p. art. 1743 [24 p.] ISSN 2072-4292

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 Paraiba 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-q2Vu15) 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 Paraiba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values.

Sciences du milieu [021] ; Hydrologie [062] ; Télédétection [126]

BRESIL

Fonds IRD [F B010074766]

fdi:010074766