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The spatial distribution of precipitation in Xinjiang, China, is extremely heterogeneous due to its complex subsurface, and microphysical studies of cloud precipitation over Xinjiang are severely constrained by observational means. The Global Precipitation Measurement (GPM) dual‐frequency precipitation radar (DPR) provides improved three‐dimensional observation data of strong and weak precipitation and performs effective observations in alpine mountain regions. Therefore, this study used GPM/DPR Level‐3 data from January 2015 to December 2022 to analyze the convective and stratiform precipitation characteristics over Xinjiang. The results showed that (1) the range of maximum precipitation reflectivity factors (PRF_max) near the ground and at different heights were larger in summer and maximum PRF_max values appeared in areas along the Altai, Tianshan, and Kunlun Mountains and plains on the respective mountain slopes. In summer, the PRF_max of stratiform (convective) precipitation nearsurface ranged from 24 to 34 dBZ (28–40 dBZ). (2) The two types of precipitation exhibited the highest storm top heights in summer, with convective precipitation having higher values than stratiform precipitation and mountainous areas having higher values than the surrounding basins due to the effects of topographic uplift. (3) The value domains of the PRF, particle mass‐weighted diameter (Dm), and particle number concentration for stratiform (convective) precipitation were more concentrated (wider), that is, PRF: 14–30 dBZ (12–40 dBZ), Dm: 0.9–1.4 mm (0.7–2.1 mm), and the particle concentration: 29–35 (24–39). The height of the center of the probability density of each variable was higher in the three major mountain ranges than in the two major basins owing to the influence of topography. PRF increased and then decreased with height. Convective raindrops were in an active state of high concentration and exhibited a large scale in the lower layers, whereas the particle spectral parameters of stratiform precipitation showed a more concentrated and symmetric distribution and insignificant changes with height. [ABSTRACT FROM AUTHOR]

In the future, as a result of global warming, it is possible that rainfall could become more intense and frequent. This could lead to more frequent triggering of damaging phenomena such as floods and landslides (named as a whole damaging hydrogeological events, DHE), and, consequently, to the increase of their impacts on territories, especially in regions where uncontrolled urban sprawl represents a factor that can exacerbate the problem. The analysis of a large quantity of information about both triggering rainfall and triggered phenomena can help to comprehend relationships between triggering precipitation and its related impacts. In this paper, to facilitate the investigation of the relationships between large and complex datasets concerning both rainfall and rainfall-related damage, we propose an index-based approach, illustrated by its application to the Calabria region (Southern Italy). In particular, this manuscript presents some results from a preliminary investigation aimed at assessing the "better" index to describe DHE. Five rainfall indices (RIs) were tested and five composite rainfall indices (CRIs), combinations of two or more RIs, are proposed. We calculated the RIs and the CRIs by means of 1,300,000 daily data registered in the observation period 1980–2020. The CRIs showed the best relationships with the data of damaging hydrogeological events (DHEs). Particularly, better results were obtained with landslides data than with floods data, perhaps due to the hydraulic characteristics of the Calabria rivers, affected by flash floods mainly influenced by very intense hourly rainfall events. [ABSTRACT FROM AUTHOR]

In the framework of the project "Integrated approach for the development across Europe of user oriented climate indicators for GFCS high-priority sectors: agriculture, disaster risk reduction, energy, health, water and tourism" (INDECIS 2017–2020), around 610K climate station-based observations were rescued over European regions for the main climate variables (maximum and minimum temperature, rainfall, sunshine duration and snow depth) along the 20th century at daily scale. Rescued data will constitute, together with other gathered regional datasets, the INDECIS-Raw-Dataset, which will expand current European data coverage contained in the European Climate Assessment & Dataset (ECA&D). An extensive examination of the ECA&D dataset was conducted to find spatial-temporal data gaps or stations with low percentage of daily data as prior candidates for data recovery in European regions. This exercise led us to focus our efforts on the Central European region and the Balkans. Digitizing was carried out by using a rigorous "key as you see" method, meaning that the digitizers type the values provided by data images, rather than using any coding system. Digitizers carefully cross-checked the typed values against original sources for the 10th, 20th and 30th day of each month to make sure that no days were skipped or repeated during the digitizing process. Monthly totals and statistical summaries were computed from transcribed data and were compared with monthly totals and summaries provided by data sources to check accuracy as preliminary quality control. The digitizing method and the quality control of the digitizing process applied in this study ensured an accurate data transcription according to the obtained statistics. [ABSTRACT FROM AUTHOR]

Calabria (Southern Italy) is one of the Italian regions most affected by desertification phenomena. This study presents a detailed analysis of the sensitivity to desertification of an economically important agricultural area in the province of Crotone. The Environmentally Sensitive Areas methodology, developed during the European Union MEDALUS project, has been applied by means of a consistent set of pedological, vegetation, climatic and management data of the study area. Analysis of 15 biophysical and social-economic indicators and an evaluation of 4 Quality Indices allowed the classification of the study area into potential, fragile and critical areas in relation to desertification. The main result is a pronounced sensitivity to desertification of the area: about 46% of the land emerges as being already affected by degradation phenomena, falling within the worst class (‘critical’), while about 39% of the area belongs to the ‘fragile’ class. [ABSTRACT FROM PUBLISHER]

The seasonal precipitation (SP) trend and its sensitivity to teleconnection patterns over the East Mediterranean (EM) region remain inconsistent. Based on rainfall records during 1974–2016 at seven meteorological stations in the Gaza region, this study aims to (1) analyze the observed SP trend over the Gaza region, and (2) examine the SP sensitivity to climate indices. Pearson and Spearman correlations between climate indices and SP in the current and following years were calculated, and the seasonal period (particular month) with the highest correlation was identified. Results show that the climate indices, with greater impact on SP over the Gaza region in the autumn and spring, were in the order; El Niño-Southern Oscillation (ENSO) > East Atlantic/Western Russia (EAWR) > North Atlantic Oscillation (NAO) > Arctic Oscillation (AO). The indices' impact was minimal in the winter precipitation. ENSO types' correlations (Southern Oscillation Index-SOI and Niño 3.4) were moderate and significant at α = 0.05. Rainfall at most stations positively correlates with AO and EAWR in spring and autumn. During the study period, warm phases of ENSO (i.e., El Niño) intensified autumn precipitation. Simultaneously with warm phases of EAWR or AO, more influence on autumn precipitation is exerted. Cold phases of ENSO (i.e., La Niña) have an adverse impact compared to El Niño. EAWR co-variation was evident only with the ENSO. Regarding AO, a non-meaningful action was noticed during the neutral phases of ENSO and EAWR. The findings of this study help understand and predict the seasonal trend of precipitation over the Gaza region. This is essential to set up climate change mitigation and adaptation strategies in the EM region. [ABSTRACT FROM AUTHOR]

A non-homogeneous Poisson model was proposed to analyze the sequences of dry spells below prefixed thresholds as an upgrade of a stochastic procedure previously used to describe long periods of no rainfall. Its application concerned the daily precipitation series in a 60-year time span at four rain gauges (Calabria, southern Italy), aiming at testing the different behaviors of the dry spells below prefixed thresholds in two paired periods (1951–1980 and 1981–2010). A simulation analysis performed through a Monte Carlo approach assessed the statistical significance of the variation of the mean values of dry spells observed at an annual scale in the two 30-year periods. The results evidenced that the dry spells durations increased passing from the first 30-year period to the second one for all the thresholds analyzed. For instance, for the Cassano station, an increase of about 10% of the maximum dry spell duration was detected for a threshold of 5 mm. Moreover, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 were lower than the corresponding ones evaluated with the data generated for the previous 30-year period. Specifically, the difference between the two 30-year periods in terms of the return period of long dry spells occurrence increased with the growing thresholds. As an example, for the Cosenza rain gauge with a threshold of 1 mm, the return period for a dry spell length of 70 days decreased from 20 years (in 1951–1980) to about 10 years (in 1981–2010), while for a threshold of 5 mm, the return period for the dry spell lengths of 120 days decreases from 70 years to about 20 years. These results show a higher probability of the occurrence of long dry spells in the more recent period than in the past. [ABSTRACT FROM AUTHOR]

The use of validated gridded data in assessment procedures is of fundamental importance, as it allows to calculate trends and changes not only at locations equipped with meteorological stations, but to whole regions. Using validated data can help in developing a more precise risk assessment especially in regions with a complex orography and sub-regional scale microclimates. In this study, we have used the CHIRPS dataset, an observation-validated satellite dataset, to assess extreme precipitation in the eight alert areas of Calabria (southern Italy). CHIRPS is a quasi-global gridded rainfall dataset, spanning 50°S-50°N at all longitudes, incorporating 0.05° resolution satellite imagery with in-situ station data starting in 1981 (Funk et al., 2015).The Calabria region has been selected for this assessment study, as it is a challenging region with complex orography, and with a high number of rain stations, used in previous gridded data validation studies. The CHIRPS dataset was validated for Calabria for the 1981-2010 time period by means of the registrations of 79 rain gauges of the Multi-Risk Functional Centre of the Regional Agency for Environmental Protection. Metrics used were the mean error and standard deviation (Caroletti and Coscarelli, 2019). The alert areas were defined according to the Directive "Sistema di Allertamento regionale per il rischio meteoidrogeologico ed idraulico in Calabria" (Resolution of Regione Calabria Government n. 535 of November15th, 2017). Four areas are located on the western side of the region, and four on the eastern side.We investigated the changes in the number of extreme events and the values of extreme precipitation in each area in the 1981-2017 time period. We considered extreme all precipitation events that occurred over the 99th percentile threshold for the study period.By using 5-years running means of precipitation at each satellite data cell, CHIRPS showed an increase in the magnitude of extreme events for seven of the eight alert areas. The increase is especially strong in four areas (the three southernmost eastern alert areas and the Lamezia Terme area in the west).The number of extreme precipitation events, which also shows a growing trend, in contrast with the decrease of precipitation days overall, can explain the slight growing trend in yearly precipitation for these same areas over the 1981-2017 time period. [ABSTRACT FROM AUTHOR]

Validation studies are of major importance in science, and climate data are no exception to the rule. Metrics are the common tools used to assess data accuracy; it is generally not possible to define generalized metrics, but they must be tailored on the specific use of the evaluated dataset, on the variable used and on the regions of interest.Calabria (southern Italy) is a challenging area for rainfall studies, because of its complex orography and high vulnerability to climate-change due to its position at the southernmost tip of Italy and in the center of the Mediterranean basin. In this study, we have validated the skills in reproducing rainfall over Calabria of the CHIRPS satellite data, of the E-OBS dataset and of 14 GCM-RCM combined models belonging to the ENSEMBLES project set.Rainfall data have been validated for the 1951-2010 time period by means of the registrations of 79 rain gauges of the Multi-Risk Functional Centre of the Regional Agency for Environmental Protection (Regione Calabria). Metrics used were the mean error and standard deviation. The results have shown that the skills of E-OBS, of most ENSEMBLES models, and of CHIRPS (whose data are avilable only for the 1981-2010 validation period) are very similar. RCMs based on the ECHAM5 Global Climate Model showed slightly better results than both reanalysis and satellite data, both in mean error and standard deviation error. There was no appreciable change in performance for any of the evaluated datasets between the 1951-1980 and the 1981-2010 time periods.The whole validation-and-assessment procedure, used in this work, is general and easily applicable to any other region where ground data and gridded data are available. The procedure could be applied to develop a more precise risk assessment in those regions with a complex orography and where there is an inadequate amount of representative stations. This validation procedure could be a useful tool in support of policy makers and scientists in decisions related to precipitation risks. [ABSTRACT FROM AUTHOR]