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.

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Abstract. This work aims to provide a comparison between three dimensional and four dimensional variational data assimilation methods (3D-Var and 4D-Var) for a heavy rainfall case in central Italy. To evaluate the impact of the assimilation of reflectivity and radial velocity acquired from Monte Midia Doppler radar into the Weather Research Forecasting (WRF) model, the quantitative precipitation forecast (QPF) is used.The two methods are compared for a heavy rainfall event that occurred in central Italy on 14 September 2012 during the first Special Observation Period (SOP1) of the HyMeX (HYdrological cycle in Mediterranean EXperiment) campaign. This event, characterized by a deep low pressure system over the Tyrrhenian Sea, produced flash floods over the Marche and Abruzzo regions, where rainfall maxima reached more than 150 mm 24 h −1 .To identify the best QPF, nine experiments are performed using 3D-Var and 4D-Var data assimilation techniques. All simulations are compared in terms of rainfall forecast and precipitation measured by the gauges through three statistical indicators: probability of detection (POD), critical success index (CSI) and false alarm ratio (FAR). The assimilation of conventional observations with 4D-Var method improves the QPF compared to 3D-Var. In addition, the use of radar measurements in 4D-Var simulations enhances the performances of statistical scores for higher rainfall thresholds.

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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.

Abstract. In recent years, extreme events related to cooling and heating have taken high resonance, motivating the scientific community to carry out an intensive research activity, aimed to detect their variability and frequency. In this work, we have investigated about the frequency, the duration, the severity and the intensity of heat and cold waves in a Southern Italy high-altitude region, by analysing the climatological time series collected in Montevergine observatory. Following the guidelines provided by CLIVAR project (Climate and Ocean Variability, Predictability and Change), we have adopted indicators based on percentiles and duration to define a heat wave and cold event.Main results have highlighted a strong and significant positive trend in the last 40 years in heat waves frequency, severity and intensity. On the contrary, in recent decades, cold wave events have exhibited a significant and positive trend only in intensity. Moreover, through the usage of two Wavelet Analysis tools, the Cross Wavelet Transform and the Wavelet Coherence, we have investigated about the connections between the extreme temperature events occurred in Montevergine and the large-scale atmospheric patterns. The heat wave events have exhibited relevant relationships with the Western European Zonal Circulation and the North Atlantic Oscillation, whereas the variability of cold wave events have shown linkages with the Eastern Mediterranean Pattern and the North Sea Caspian Pattern. In addition, the main features of synoptic patterns that have caused summer heat waves and winter cold waves in Montevergine site are presented.

Abstract. Here we present the rescue of sub-daily meteorological observations collected from 1884 to 1963 at Montevergine Observatory, located in the Southern Apennines in Italy. The recovered dataset consists of 3-daily observations of the following atmospheric variables: dry-bulb temperature, wet-bulb temperature, water vapour pressure, relative humidity, atmospheric pressure, cloud type, cloud cover, rainfall, snowfall and precipitation type. The data, originally available only as paper-based records, have been digitized following the World Meteorological Organization standard practices. After a cross-check, the digitized data went through three different automatic quality control tests: the gross error test, which verifies whether the data are within acceptable range limits; the tolerance test, which flags whether values are above or below monthly climatological limits that are defined in accordance with a probability distribution model specific to each variable; and the temporal coherency test, which checks the rate of change and flags unrealistic jumps in consecutive values. The result of this process is the publication of a new historical dataset that includes, for the first time, digitized and quality-controlled sub-daily meteorological observations collected since the late 19th century in the Mediterranean region north of the 37th parallel. These data are critical to enhancing and complementing previously rescued sub-daily historical datasets – which are currently limited to atmospheric pressure observations only – in the central and northern Mediterranean regions. Furthermore, the Montevergine Observatory (MVOBS) dataset can enrich the understanding of high-altitude weather and climate variability, and it contributes to the improvement of the accuracy of reanalysis products prior the 1950s. Data are available on the NOAA's National Centers for Environmental Information (NCEI) public repository and are associated with a DOI: https://doi.org/10.25921/cx3g-rj98 (Capozzi et al., 2019).

This work proposes a multi-parameter method for the detection of cloud-to-ground stroke rate (SR CG ) associated to convective cells, based on the measurements of a low-cost single-polarization X-band weather radar. To train and test our procedure, we built up a multi-year dataset, collecting 1575 radar reflectivity volumes that were acquired in the pilot study area of Naples metropolitan environment matched with the LIghtning NETwork (LINET) strokes and meteorological in-situ data. Three radar-based variables are extracted simultaneously for each rain cell and properly merged together, using "ad hoc" classification methods, to produce an estimation of the expected lightning activity for each rain cell. These variables, proxies of mixed-phase particles and ice amount into a convective cell, are combined into a single label to cluster the SR CG into two categories: SR CG = 0 (no production of strokes) or SR CG > 0 (stroke production), respectively. Overall, the main results are comparable with those that were obtained from more advanced radar systems, showing a Critical Success Index of 0.53, an Equitable Threat Score of 0.34, a Frequency Bias Index of 1.00, a Heidke Skill Score of 0.42, a Hanssen-Kuiper Skill Score of 0.42, and an area under the curve of probability of detection as a function of false alarm rate (usually referred as ROC curve) equal to 0.78. The developed technique, although with some limitations, outperforms those based on the use of single stroke proxy parameters. stroke discharges with a spatial resolution as high as up to 100 m and with a detection efficiency up to 95%, although these performances depend on the network density and the type of sensors.The real-time surveillance of stroke occurrence can also rely on weather radar measurements, which are able to track and characterize the three-dimensional (3D) structure of rain cells, thus allowing for identifying the developing cycle of cells and the areas much prone to stroke activity, even before the occurrence of the first lightning event (e.g., [5]). Therefore, the set-up of a reliable, affordable, and accessible radar-based stroke detection system, complementary to traditional ground-based stroke networks, can be very useful for risk prevention and for safety of human life, goods and services. In addition, a stand-alone radar-based stroke detection system could cover those areas where data from lightning networks are not freely accessible or where their detection efficiency levels are not constant over large domains, due to the irregular distribution of lightning sensors.This work is aimed at proposing a new algorithm for the radar-based detection of stroke activity based on a multi-variable approach.To explain our approach, it is useful to briefly summarize the atmosphere electrification mechanisms and the radar-based approaches so far proposed in the state of the art literature. Electrification mechanisms in thunderstorms are explained following the widely accepted Non-Inductive Charging (NIC) theory, whose evidences have been supported...