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Because of the ongoing climate change, the frequency of extreme rainfall events at the global scale is expected to increase, resulting in higher social and economic impacts. Thus, improving the forecast accuracy and the risk communication is a fundamental goal to limit social and economic damages. Both Numerical Weather Prediction (NWP) and radar-based nowcasting systems still have open issues, mainly in terms of precipitation correct time/space localization predictability and rapid forecast accuracy decay, respectively. Trying to overcome these issues, this work aims to present a nowcasting system combining an NWP model (WRF), using a 3 h rapid update cycling 3DVAR assimilation of radar reflectivity data, with the radar-based nowcasting system PhaSt through a blending technique. Moreover, an innovative post-processing algorithm named SWING (Score-Weighted Improved NowcastinG) has been developed in order to take into account the timely and spatial uncertainty in the convective field simulation. The overarching goal is to pave the way for an easy and automatic communication of the heavy rainfall warning derived by the nowcasting procedure. The results obtained applying the SWING algorithm over a case study of 22 days in the fall 2019 season suggest that the algorithm could improve the predictive capability of a traditional deterministic nowcasting forecast system, keeping a useful forecast timing and thus integrating the current forecast procedures. Eventually, the main advantage of the SWING algorithm is also its very high versatility, since it could be used with any meteorological model also in a multi-model forecast approach. [ABSTRACT FROM AUTHOR]

A Multivariate Neural Network (MNN) algorithm is proposed for the reconstruction of significant wave height time series, without any increase of the error of the MNN output with the number of number of modeled data. The algorithm uses a weight error function during the learning phase, to improve the modeling of the higher significant wave height. The ability of the MNN to reconstruct sea storms is tested by applying the equivalent triangle storm model. Finally an application to the NOAA buoys moored off California shows a good performance of the MNN algorithm, both during sea storms and calm time periods. [ABSTRACT FROM AUTHOR]

In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring. [ABSTRACT FROM AUTHOR]