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Abstract:In the last few years, the number of worldwide operational X-band weather radars has rapidly been growing, thanks to an established technology that offers reliability, high performance, and reduced efforts and costs for installation and maintenance, with respect to the more widespread C-and S-band systems. X-band radars are particularly suitable for nowcasting activities, as those operated by the LaMMA (Laboratory of Monitoring and Environmental Modelling for the sustainable development) Consortium in the framework of its institutional duties of operational meteorological surveillance. In fact, they have the capability to monitor precipitation, resolving very local scales, with good spatial and temporal details, although with a reduced scanning range. The Consortium has recently installed a small network of X-band weather radars that partially overlaps and completes the existing national radar network over the north Tyrrhenian area. This paper describes the implementation of this regional network, detailing the aspects related with the radar signal processing chain that provides the final reflectivity composite, starting from the acquisition of the signal power data. The network performances are then qualitatively assessed for three case studies characterised by different precipitation regimes and different seasons. Results are satisfactory especially during intense precipitations, particularly regarding what concerns their spatial and temporal characterisation.

Abstract. The estimation of chlorophyll concentration in marine waters is fundamental for a number of scientific and practical purposes. Standard ocean color algorithms applicable to moderate resolution imaging spectroradiometer (MODIS) imagery, such as OC3M and MedOC3, are known to overestimate chlorophyll concentration ([CHL]) in Mediterranean oligotrophic waters. The performances of these algorithms are currently evaluated together with two relatively new algorithms, OC5 and SAM_LT, which make use of more of the spectral information of MODIS data. This evaluation exercise has been carried out using in situ data collected in the North Tyrrhenian and Ligurian Seas during three recent oceanographic campaigns. The four algorithms perform differently in Case 1 and Case 2 waters defined following global and local classification criteria. In particular, the mentioned [CHL] overestimation of OC3M and MedOC3 is not evident for typical Case 1 waters; this overestimation is instead significant in intermediate and Case 2 waters. OC5 and SAM_LT are less sensitive to this problem, and are generally more accurate in Case 2 waters. These results are finally interpreted and discussed in light of a possible operational utilization of the [CHL] estimation methods.

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The high relevance of satellites for collecting information regarding precipitation at global scale implies the need of a continuous validation of satellite products to ensure good data quality over time and to provide feedback for updating and improving retrieval algorithms. However, validating satellite products using measurements collected by sensors at ground is still a challenging task. To date, the Dual-frequency Precipitation Radar (DPR) aboard the Core Satellite of the Global Precipitation Measurement (GPM) mission is the only active sensor able to provide, at global scale, vertical profiles of rainfall rate, radar reflectivity, and Drop Size Distribution (DSD) parameters from space. In this study, we compare near surface GPM retrievals with long time series of measurements collected by seven laser disdrometers in Italy since the launch of the GPM mission. The comparison shows limited differences in the performances of the different GPM algorithms, be they dual- or single-frequency, although in most cases, the dual-frequency algorithms present the better performances. Furthermore, the agreement between satellite and ground-based estimates depends on the considered precipitation variable. The agreement is very promising for rain rate, reflectivity factor, and the mass-weighted mean diameter (Dm), while the satellite retrievals need to be improved for the normalized gamma DSD intercept parameter (Nw).

In this paper, we present a research project named NEFO-CAST, that targets a very-short-term forecasting platform with high accuracy and small-scale spatial resolution. The innovative solution lies in adopting a new generation of interactive satellite terminals, called SmartLNB, that serves both as a weather sensor and the transceiver for the forecasting platform. Throughout the paper, we highlight the main features of the system, including the advantages compared to state-of-the-art solutions, the expected results, and the market perspectives.

In this work we propose a technique for 15-minutes cumulative rainfall mapping, applied over Tuscany, using Italian weather radar networks together with the regional rain gauge network.In order to assess the accuracy of the radar-based rainfall estimates, we have compared them with spatial coincident rain gauge measurements. Precipitation at ground is our target observable: rain gauge measurements of such parameter have a so small error that we consider it negligible (especially if compared from what retrievable from radars).In order to make comparable the observations given from these two types of sensors, we have collected cumulative rainfall over areas a few tens of kilometres wide. The method used to spatialise rain gauges data has been the Ordinary Block Kriging. In this case the comparison results have shown a good correlation between the cumulative rainfall obtained from the rain gauges and those obtained by the radar measurements.Such results are encouraging in the perspective of using the radar observations for near real time cumulative rainfall nowcasting purposes.In addition the joint use of satellite instruments as SEVIRI sensors on board of MSG-3 satellite can add relevant information on the nature, spatial distribution and temporal evolution of cloudiness over the area under study. For this issue we will analyse several MSG-3 channel images, which are related to cloud physical characteristics or ground features in case of clear sky.