A Network of X-Band Meteorological Radars to Support the Motorway System (Campania Region Meteorological Radar Network Project)

he transport sector and road infrastructures are very sensitive to the issues connected to the atmospheric conditions. The latter constitute a source of relevant risk, especially for roads running in mountainous areas, where a wide spectrum of meteorological phenomena, such as rain showers, snow, hail, wind gusts and ice, threatens drivers’ safety. In such contexts, to face out critical situations it is essential to develop a monitoring system that is able to capillary surveil specific sectors or very small basins, providing real time information that may be crucial to preserve lives and assets. In this work, we present the results of the “Campania Region Meteorological Radar Network”, which is focused on the development of X-band radar-based meteorological products that can support highway traffic management and maintenance. The X-band measurements provided by two single-polarization systems, properly integrated with the observations supplied by disdrometers and conventional automatic weather stations, were involved in the following main tasks: (i) the development of a radar composite product; (ii) the devise of a probability of hail index; (iii) the real time discrimination of precipitation type (rain, mixed and snow); (iv) the development of a snowfall rate estimator. The performance of these products was assessed for two case studies, related to a relevant summer hailstorm (which occurred on 1 August 2020) and to a winter precipitation event (which occurred on 13 February 2021). In both cases, the X-band radar-based tools proved to be useful for the stakeholders involved in the management of highway traffic, providing a reliable characterization of precipitation events and of the fast-changing vertical structure of convective cells

On the role of local and large-scale atmospheric variability in snow cover duration: a case study of Montevergine Observatory (Southern Italy)

Snow cover plays an important role in Earth’s climate, hydrological and biological systems as well as in socio-economical dynamics, especially in mountain regions. The objective of this work is to provide the first evidence about snow cover variability in the Italian Southern Apennines and investigate the forcing mechanisms controlling it. To this purpose, we present a new historical long-term (from 1931 to 2008) series of snow cover duration data observed at Montevergine Observatory, a mountainous site located at 1280 m above sea level. From the analysis of this series, it emerged a strong interannual variability, an overall reduction over time of snow cover days until mid-1990s and a recovery in the last 10-years. We model snow cover duration employing a multiple linear regression, considering both local and large-scale climate factors as explanatory variables. Our findings show that snow cover duration appears to be primarily dependent on temperature, which exhibits a positive trend in the considered time interval. However, the interannual and decadal fluctuations of the examined parameter are also strongly modulated by two large-scale patterns, the Arctic Oscillation and the Eastern Mediterranean Pattern. In the last segment of the considered time interval, the increase in temperature is not consistent with the dominant patterns of large-scale indices, which proved to be more effective in capturing the recent rebound in snow cover duration. The results demonstrate that snow cover duration is linked to the global warming by a non-trivial relationship and that its behaviour, in specific periods, can be largely independent from rising temperature tendency, according to the prevailing phase of large-scale atmospheric patterns

Classification of daily heavy precipitation patterns and associated synoptic types in the Campania Region (southern Italy)

Using a 20-year (2002−2021) dataset of daily precipitation collected by 107 rain gauges in the period from October to May, this study introduces a classification of the main heavy precipitation spatial patterns for the Campania Region (southern Italy). To pursue this aim, we apply a cluster analysis on the most relevant principal modes extracted from a principal component analysis of the between-day correlation matrix. The characteristics of the identified patterns, as well as their interannual and monthly distribution, are presented and discussed. Moreover, using global and regional reanalysis products, we have determined the large-scale and mesoscale atmospheric circulation types associated with heavy precipitation patterns. The heavy precipitation episodes are generally triggered by an upper level trough approaching the southern Italy from west and promoting a very moist southwesterly flow. They have been clustered into six different patterns. The first four exhibit a rainfall amount distribution strongly connected with the orography of the investigated region. In such scenarios, the orographic lifting, the low-level wind convergence induced by the orography and the transport of moisture from local sources (the western and the southern Mediterranean) and from distant regions (the Atlantic and the Africa tropical areas) can be regarded as the primary forcing of heavy rainfall. In the other two patterns, the highest precipitation is generally observed in the coastal areas (Gulfs of Naples and Salerno) and in the northwestern side of the region (Caserta district), respectively. In such circumstances, the abundant precipitation is closely linked to convective activity over the Tyrrhenian Sea, which is sustained by a low-level convergence and, in the sixth pattern, by a moisture plume coming from the tropics. The results of this study provide new insights about the links between torrential precipitation spatial distribution and atmospheric circulation schemes in the southern Italy and promise to add a useful contribution for civil protection activities related to the management of environmental risks

Error investigation of rain retrievals from disdrometer data using triple colocation

Assessing the uncertainty of precipitation measurements is a challenging problem because precipitation estimates are inevitably influenced by various errors and environmental conditions. A way to characterize the error structure of coincident measurements is to use the triple colocation (TC) statistical method. Unlike more typical approaches, where measures are compared in pairs and one of the two is assumed error-free, TC has the enviable advantage to succeed in characterizing the uncertainties of co-located measurements being compared to each other, without requiring the knowledge of the true value which is often unknown. However, TC requires to have at least three co-located measuring systems and the compliance with several initial assumptions. In this work, for the first time, TC is applied to in-situ measurements of rain precipitation acquired by three co-located devices: a weighing rain gauge, a laser disdrometer and a bidimensional video disdrometer. Both parametric and nonparametric formulations of TC are implemented to derive the rainfall product precision associated with the three devices. While the parametric TC technique requires tighter constraints and explicit assumptions which may be violated causing some artifacts, the nonparametric formulation is more flexible and requires less strict constrains. For this reason, a comparison between the two TC formulations is also presented to investigate the impact of TC constrains and their possible violations. The results are obtained using a statistically robust dataset spanning a 1.5 year period collected in Switzerland and presented in terms of traditional metrics. According to triple colocation analysis, the two disdrometers outperform the classical weighing rain gauge and they have similar measurement error structure regardless of the integration time intervals

Historical snowfall precipitation data in the Apennine Mountains, Italy

This database includes a large collection of quality-controlled and homogenized historical snow records measured in the 1951-2001 period in the Central and Southern Apennine Mountains (Italy). Such data have been manually digitized from the Hydrological Yearbooks of the Italian National Hydrological and Mareographic Service (hereafter, NHMS), the institution that managed the hydro-meteorological data collection in Italy from 1917 to 2002. More specifically, the rescued dataset includes the monthly observations of three different variables: · The snow cover duration (SCD), which is defined as total number of days in a given month with snow depth on the ground >=1 cm. This variable is available for 110 stations between 288 and 1430 m above the sea level (ASL). · The number of days with snowfall (NDS), which is total number of days in a given month on which the accumulated snowfall (i.e. the amount of fresh snow with respect to the previous observations) is at least 1 cm. This variable is available for 114 stations between 288 and 1430 m ASL. · The height of new snow (HN), which is defined as the monthly amount of fresh snow (expressed in cm). The monthly value is intended as the sum of daily HN data observed in a determined month. This variable is available for 120 stations between 288 and 1750 m ASL. Note that for HN variable, the data availability is restricted to the period 1971-2001. The considered dataset has been subjected to an accurate quality control consisting of several statistical tests: the gross error test, which flags the data that are above or below acceptable physical limits, the consistency test, which involves an inter-variable check, and the tolerance test, which is focused on the outlier detection. In addition, the homogeneity of the rescued time series has been checked using Climatol method (Guijarro, 2018). The latter is based on the Standard Normal Homogeneity Test (Alexandersson, 1986) for the identification of the breaks and on a linear regression approach for the adjustments (Easterling and Peterson, 1995). Climatol has been also employed for the filling of missing values. The database is structured into three different folders (one for each variable). In a determined folder, the user finds two files, one containing the main information regarding the available stations (code, station name, latitude and longitude (in decimal degrees) and altitude ASL (in m)), the other one the monthly time series for the considered variable. Note that the original data sources of this database, the Hydrological Yearbooks of the NHMS, are freely accessible in printed version (i.e. as scanned images in portable document format) through the Italian Institute for Environmental Protection and Research (ISPRA) website (http://www.bio.isprambiente.it/annalipdf). Additional information about the data rescue processing can be found in the preprint “Historical snowfall measurements in the Central and Southern Apennine Mountains: climatology, variability and trend”, open for discussion in The Cryosphere journal (https://doi.org/10.5194/egusphere-2024-1056)

Signals of change in the Campania region rainfall regime: An analysis of extreme precipitation indices (2002–2021)

Abstract 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

Rain Motion Vectors Analysis From the Radar Network in Italy

In-cloud motion vector retrieval is of great interest in several atmospheric science research fields. Short time extrapolation of radar data (precipitation nowcasting), assimilation into numerical weather prediction models, study of atmospheric circulation, as well as reference scenarios for future satellite missions, are glaring example where the knowledge of in-cloud motion vectors can play a relevant role. In this work, a dataset of nearly one-year and half of measurements collected by ground-based weather radars over the Italian peninsula, is used to perform the reconstruction of horizontal in-cloud rain motion vectors (RMV) using both optical flow-based solutions from literature and an innovative extension of the multiple Doppler solution that make use of mosaicked Doppler radar data. The outcomes of the techniques implemented are evaluated in terms of reference Doppler measurements, reanalysis wind fields from ERA5 and evaluating the impact of the RMVs in a semi-lagrangian precipitation nowcasting framework. To the author knowledge, this is the first attempt in quantitatively evaluating RMVs. Results show that the use of Doppler information enhances the dynamic range of the retrieved RMV intensity with respect to optical flow-based solutions giving a better agreement with the ERA5 too. In terms of precipitation nowcasting, the use of Doppler-driven RMV does not give significant improvements due to gradients shown by RMV intensity when constrained with the measured Doppler

Influence of Wind-Induced Effects on Laser Disdrometer Measurements: Analysis and Compensation Strategies

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

Validation of gpm rainfall and drop size distribution products through disdrometers in italy

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)

Database of the Italian disdrometer network (V02)

<p>The database includes 1-minute records of Drop Size Distribution (DSD) collected by the disdrometer network along the Italian peninsula. The disdrometers belong to seven Italian institutions that in 2021 decided to bring together their know-how, experience, and instruments for measuring DSDs giving birth to the Italian Group of Disdrometry (in Italian it reads: Gruppo Italiano Disdrometria, GID, <a href="https://www.gid-net.it/">https://www.gid-net.it/</a>). The database covers a period of 9 years (from 2012 to 2021) although not all the disdrometers have measurements for the whole period. In order to obtain uniform and high quality DSD, the same processing has been adopted to all the disdrometer raw data.</p><p>The processing along with the characteristics of the Italian disdrometer network and of the relative DSD database have been presented in the data paper "Database of the Italian disdrometer network" pubbliched on June 2023 in the Journal "Earth System Science Data" (DOI: https://doi.org/10.5194/essd-15-2417-2023). </p><p>The DSDs of the database have been obtained following the V02 of the GID processing. DSDs obtained with V01 of the GID processing are available here https://doi.org/10.5281/zenodo.6875801 (Adirosi et al., 2022). The difference among the two version of the GID processing is the diameter-fall velocity relation considered for the DSD computation. Version 01 of the GID algorithm uses the Atlas et al. (1973; <i>Rev. Geophys.</i>) fall velocity at sea level for all the GID sites, while V02 modified the terminal fall velocity relation to take into account the lower air density at the altitude of the disdrometer installation site. It should be noted that most of the GID disdrometers are located at low altitudes (h < 400 m above sea level) where the differences between the fall velocity at sea level and the one at site height can be considered negligible (i.e., less than 2%).  </p><p>Most of the data have been also used for a validation study of the precipitation products of the Dual-frequency Precipitation Radar (DPR) aboard the Core Satellite of the Global Precipitation Measurement (GPM) mission. The results of the validation study are reported in the paper "Validation of GPM Rainfall and Drop Size Distribution Products through Disdrometers in Italy" published on May 2021 in the Journal "Remote Sensing" (DOI: https://doi.org/10.3390/rs13112081).</p&gt