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A new stable backward iterative technique to correct for path attenuation and differential attenuation is presented here. The technique named, neural network iterative polarimetric precipitation estimator by radar (NIPPER), is based on a polarimetric model used to train an embedded neural network, constrained by the measurement of the differential phase along the rain path. Simulations are used to investigate the efficiency, accuracy, and the robustness of the proposed technique. The precipitation is characterized with respect to raindrop size, shape, and orientation distribution. The performance of NIPPER is evaluated by using simulated radar volumes scan generated from S-band radar measurements. A sensitivity analysis is performed in order to evaluate the expected errors of NIPPER. These evaluations show relatively better performance and robustness of the attenuation correction process when compared with currently available techniques. [ABSTRACT FROM AUTHOR]

A large data set of raindrop size distribution (RSD) measurements collected with the Joss-Waldvogel disdrometer (JWD) and the 2-D video disdrometer (2DVD) in the U.K., Greece, Japan, and theU.S. are analyzed and modeled. This work extends a previous effort devoted to the exploitation of U.K. data and the design of a stochastic procedure to randomly generate synthetic RSD intermittent time series. This study seeks to: 1) explore the differences of RSD-derived moments for distinct hydroclimate regions, ranging from tropics to subtropics and mid and northern latitudes; 2) compare the governing parameters of the normalized gamma RSD for both stratiform and convective events and perform a sensitivity analysis by using different best fitting techniques; 3) exploit the time-correlation structure of the estimated RSD parameters as the input of a vector autoregressive stationary model used to simulate time series (or horizontal profiles) of RSDs and, consequently, its moments as the rain rate and concentration; and 4) characterize the distribution of the inter-rain duration and rain duration to design a semi-Markov chain to represent the intermittency feature of the rainfall process in a climatological framework. This climatological analysis and the related stochastic RSD generation model may find useful applications within both hydrometeorology and radio propagation. [ABSTRACT FROM AUTHOR]

Hydrometeorological and radio propagation applications can benefit from the capability to model the time evolution of raindrop size distribution (RSD). A new stochastic vector autoregressive semi-Markov model is proposed to randomly synthesize (generate) the temporal series of the three driving parameters of a normalized Gamma RSD. Rainfall intermittence is reproduced through a discrete semi-Markov process, modeled from disdrometer measurements using two-state analytical statistics of rain and dry period duration. The overall model is set up by means of a large set of disdrometer measurements, collected from 2003 to 2005 at Chilbolton, U.K. The driving parameters of the retrieved RSD are estimated using three approaches: the Gamma moment method and the 1-D and 3-D maximum-likelihood methods. Interestingly, these methodologies lead to quite different results, particularly when one is interested in evaluating RSD higher order moments such as the rain rate. The accuracy of the proposed RSD time-series generation technique is evaluated against available disdrometer measurements, providing excellent statistical scores. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]