Polynyas are dynamic stretches of open water surrounded by ice. They typically occur in remote regions of the Arctic and Antarctic, thus remote sensing is essential for monitoring their dynamics. On regional scales, daily passive microwave radiometers provide useful information about their extent because of their independence from cloud coverage and daylight; nonetheless, their coarse resolution often does not allow an accurate discrimination between sea ice and open water. Despite its sensitivity to the presence of clouds, thermal infrared (TIR) Moderate Resolution Imaging Spectroradiometer (MODIS) provides higher-resolution information (typically 1 km) at large swath widths, several times per day, proving to be useful for the retrieval of the size of polynyas. In this study, we deal with Aqua satellite MODIS observations of a frequently occurring coastal polynya in the Terra Nova Bay (TNB), Ross Sea (Antarctica). The potential of a new methodology for estimating the variability of this polynya through MODIS TIR during the 2010 and 2011 freezing season (April to October) is presented and discussed. The polynya is observed in more than 1600 radiance scenes, after a preliminary filter evaluates and discards cloudy and fog-contaminated scenes. This reduces the useful MODIS swaths to about 50% of the available acquisitions, but a revisit time of less than 24 h is kept for about 90% of the study period. As expected, results show a high interannual variability with an opening/closing fluctuation clearly depending on the regime of the katabatic winds recorded by the automatic weather stations Rita and Eneide along the TNB coast. Retrievals are also validated through a comparison with a set of 196 co-located high-resolution ENVISAT ASAR images. Although our estimations slightly underestimate the ASAR derived extents, a good agreement is found, the linear correlation reaching 0.75 and the average relative error being about 6%. Finally, a sensitivity test on the applied thermal thresholds supports the effectiveness of our setting.

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 (D_m), while the satellite retrievals need to be improved for the normalized gamma DSD intercept parameter (N_w).

Extreme weather events bear a significant impact on coastal human activities and on the related economy. Forecasting and hindcasting the action of sea storms on piers, coastal structures and beaches is an important tool to mitigate their effects. To this end, with particular regard to low coasts and beaches, we have developed a computational model chain based partly on open-access models and partly on an ad-hoc-developed numerical calculator to evaluate beach wave run-up levels and flooding. The offshore wave simulations are carried out with a version of the WaveWatch III model, implemented by CCMMMA (Campania Centre for Marine and Atmospheric Monitoring and Modelling – University of Naples Parthenope), validated with remote-sensing data. The waves thus computed are in turn used as initial conditions for the run-up calculations, carried out with various empirical formulations; the results were finally validated by a set of specially conceived video-camera-based experiments on a micro-tidal beach located on the Ligurian Sea. Statistical parameters are provided on the agreement between the computed and observed values. It appears that, while the system is a useful tool to properly simulate beach flooding during a storm, empirical run-up formulas, when used in a coastal vulnerability context, have to be carefully chosen, applied and managed, particularly on gravel beaches.

The prediction of the formation, spacing and location of rip currents is a scientific challenge that can be achieved by means of different complementary methods. In this paper the analysis of numerical and experimental data, including RPAS (remotely piloted aircraft systems) observations, allowed us to detect the presence of rip currents and rip channels at the mouth of Sele River, in the Gulf of Salerno, southern Italy. The dataset used to analyze these phenomena consisted of two different bathymetric surveys, a detailed sediment analysis and a set of high-resolution wave numerical simulations, completed with Google Earth^TM images and RPAS observations. The grain size trend analysis and the numerical simulations allowed us to identify the rip current occurrence, forced by topographically constrained channels incised on the seabed, which were compared with observations.

Destructive marine storms bring large waves and unusually high surges of water to coastal areas, resulting in significant damages and economic loss. This study analyses the characteristics of a destructive marine storm on the strongly inhabited coastal area of Gulf of Naples, along the Italian coasts of the Tyrrhenian Sea. This is highly vulnerable to marine storms due to the accelerated relative sea level rise trend and the increased anthropogenic impact on the coastal area. The marine storm, which occurred on 28 December 2020, was analyzed through an unstructured wind–wave coupled model that takes into account the main marine weather components of the coastal setup. The model, validated with in situ data, allowed the establishment of threshold values for the most significant marine and atmospheric parameters (i.e., wind intensity and duration) beyond which an event can produce destructive effects. Finally, a first assessment of the return period of this event was evaluated using local press reports on damage to urban furniture and port infrastructures.

Nowadays, laser disdrometers constitute a very appealing tool for measuring surface precipitation properties, by virtue of their capability to estimate not only the rainfall amount and intensity, but also the number, the size and the velocity of falling drops. However, disdrometric measures are affected by various sources of error being some of them related to environmental conditions. This work presents an assessment of Thies Clima laser disdrometer performance with a focus on the relationship between wind and the accuracy of the disdrometer output products. The 10-min average rainfall rate and total rainfall accumulation obtained by the disdrometer are systematically compared with the collocated measures of a standard tipping bucket rain gauge, the FAK010AA sensor, in terms of familiar statistical scores. A total of 42 rainy events, collected in a mountainous site of Southern Italy (Montevergine observatory), are used to support our analysis. The results show that the introduction of a new adaptive filtering in the disdrometric data processing can reduce the impact of sampling errors due to strong winds and heavy rain conditions. From a quantitative perspective, the novel filtering procedure improves by 8% the precipitation estimates with respect to the standard approach widely used in the literature. A deeper examination revealed that the signature of wind speed on raw velocity-diameter spectrographs gradually emerges with the rise of wind strength, thus causing a progressive increase of the wrongly allocated hydrometeors (which reaches 70% for wind speed greater than 8 m s⁻¹). With the aid of reference rain-gauge rainfall data, we designed a second simple methodology that makes use of a correction factor to mitigate the wind-induced bias in disdrometric rainfall estimates. The resulting correction factor could be applied as an alternative to the adaptive filtering suggested by this study and may be of practical use when dealing with disdrometric data processing.