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.
Highlights
The knowledge about the distribution of water around the globe is an aspect of extreme relevance for the management of natural resources
We present the results of the evaluation of rain gauge measurements and the performance radar-based rainfall algorithms for one case study of Vale do Paraíba and Manaus, respectively
This work analyzed the data collected by mobile X-band polarimetric radars during two campaigns of the CHUVA project, both in the Vale do Paraíba and Manaus regions
Summary
The knowledge about the distribution of water around the globe is an aspect of extreme relevance for the management of natural resources. The precipitation is, within the hydrological cycle, unanimously recognized as a central component, regulating the energy balance through the interactions of water vapor and clouds, where redistribution of latent heat occurs in the atmosphere. A detailed characterization about precipitation, its formation processes and its life cycle is essential to improve the quality of weather and climate forecasts and to help decision-makers in their resolutions to be taken in areas affected by the rain. The quantification of precipitation, in terms of frequency and intensity, is performed by rain gauge and meteorological radars [single and double polarization] at ground. Satellite-based precipitation estimates fill these gaps and complement the rainfall observation system
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