Antarctic Bottom Water (AABW) supplies the lower limb of the global overturning circulation and ventilates the abyssal ocean. In recent decades, AABW has warmed, freshened and reduced in volume. Ross Sea Bottom Water (RSBW), the second largest source of AABW, has experienced the largest freshening. Here we use 23 years of summer measurements to document temporal variability in the salinity of the Ross Sea High Salinity Shelf Water (HSSW), a precursor to RSBW. HSSW salinity decreased between 1995 and 2014, consistent with freshening observed between 1958 and 2008. However, HSSW salinity rebounded sharply after 2014, with values in 2018 similar to those observed in the mid-late 1990s. Near-synchronous interannual fluctuations in salinity observed at five locations on the continental shelf suggest that upstream preconditioning and large-scale forcing influence HSSW salinity. The rate, magnitude and duration of the recent salinity increase are unusual in the context of the (sparse) observational record.

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).

It is widely known that precipitation is a key variable of the hydrological cycle that is strongly affected by recent climate changes. Therefore, there is a growing interest in research activities focused on alteration of rainfall regime, as it conditions the planning of countermeasures against flood and landslide hazards. The available literature about precipitation tendencies over Italian peninsula offers a limited number of studies about recent changes of extreme events and precipitation intensity. This work aims at adding a contribution to fill this research gap, investigating the changes in rainfall regime observed over the 2002–2021 period in the Campania region (southern Italy). To pursue this aim, a dataset including daily precipitation records collected at 107 stations was analysed both through 11 indices developed by the Expert Team on Climate Change Detection and Indices and through the Standardized Precipitation Index in order to detect signals of changes in extreme events and to assess tendencies towards drier or wetter conditions. The Theil‐Sen method and the Mann–Kendall non‐parametric test were employed to evaluate the trends and their statistical significance. The main results emerging from this work are (i) an increasing tendency in precipitation intensity and in the frequency of occurrence of heavy rainfall events in autumn, mainly in the northern part of the region and in the mountainous areas, (ii) an upward trend of the duration of the longest wet spell in the coastal areas and (iii) an increasing trend of dry spells in spring and in summer in the Gulf of Salerno.

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.

The aim of this study is to provide an evaluation of the impact of two largely used data assimilation techniques, namely three‐ and four‐dimensional variational data assimilation systems (3D‐Var and 4D‐Var), on the forecasting of heavy precipitation events using the Weather Research and Forecasting (WRF) model. For this purpose, two flash flood events in central Italy are analysed. The first occurred on September 14, 2012 during an Intensive Observation Period of the Hydrological cycle in the Mediterranean experiment (HyMeX) campaign, while the other occurred on May 3, 2018. Radial velocity and reflectivity acquired by C‐band weather radars at Mt. Midia (central Italy) and San Pietro Capofiume (northern Italy), as well as conventional observations (SYNOP and TEMP), are assimilated into the WRF model to simulate these damaging flash flood events. In order to evaluate the impact of the 3D‐Var and 4D‐Var assimilation systems on the estimation of short‐term quantitative precipitation forecasts, several experiments are carried out using conventional observations with and without radar data. Rainfall evaluation is performed by means of point‐by‐point and filtering methodologies. The results point to a positive impact of the 4D‐Var technique compared to results without assimilation and with 3D‐Var experiments. More specifically, the 4D‐Var system produces an increase of up to 22% in terms of the Fractions Skill Score compared to 3D‐Var for the first flash flood event, while an increase of about 5% is achieved for the second event. The use of a warm start initialization results in a considerable reduction in the spin‐up time and a significant improvement in the rainfall forecast, suggesting that the initial precipitation spin‐up problem still occurs when using 4D‐Var.