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The impacts of hailstorms on human beings and structures and the associated high economic costs have raised significant interest in studying storm mechanisms and climatology, thus producing a substantial amount of literature in the field. To contribute to this effort, we have explored the hail frequency in the Mediterranean basin during the last two decades (1999–2021) on the basis of hail occurrences derived from the observations of the microwave radiometers on board satellites of the Global Precipitation Measurement Constellation (GPM-C) from 2014 (date of GPM Core Observatory launch) onwards and merging multiple other satellite platforms prior to 2014. According to the MWCC-H method, two hail event categories (hail and super hail) are identified, and their spatiotemporal distributions are evaluated to identify the hail development areas in the Mediterranean and the corresponding monthly climatology of hail occurrences. Our results show that the northern sectors of the domain (France, Alpine Region, Po Valley, and Central-Eastern Europe) tend to be hit by hailstorms from June to August, while the central sectors (from Spain to Turkey) are more affected as autumn approaches. The trend analysis shows that the mean number of hail events over the entire domain tends to substantially increase, showing a higher increment during 2010–2021 than during 1999–2010. This behavior was particularly enhanced over Southern Italy and the Balkans. Our findings point to the existence of "sub-hotspots", i.e., Mediterranean regions most susceptible to hail events and thus possibly more vulnerable to climate change effects. [ABSTRACT FROM AUTHOR]

Snowfall detection and measurement represent highly difficult problems in modern hydrometeorology. Ground measurements are complicated due to detection technology limitations, snow drift and accumulation issues, and error definition. The snowfall detection from space is in turn affected by all detection limitations that characterize the measurement of rainfall with the addition of several complications, such as the indirect character of remote sensing precipitation estimation, the presence of frozen or snow-covered terrain, and the unknown vertical distribution of hydrometeors in the cloud column. Several methods for the retrieval of snowfall intensity from satellite have been proposed in recent times using passive and active sensors. No satisfactory answer to the general problem of quantitative snowfall intensity determination has been found to date, but several studies contribute to delineate a working framework for the future operational retrieval algorithms. [ABSTRACT FROM AUTHOR]

During recent decades East Africa (EA) and Southern Africa (SA) have experienced an intensification of hydrological hazards, such as floods and droughts, which have dramatically affected the population, making these areas two of the regions of the African continent most vulnerable to these hazards. Thus, precipitation monitoring and the evaluation of its variability have become fundamentally important actions through the analysis of long-term data records. In particular, satellite-based precipitation products are often used because they counterbalance the sparsity of the rain gauge networks which often characterize these areas. The aim of this work is to compare and contrast the capabilities of three daily satellite-based products in EA and SA from 1983 to 2017. The selected products are two daily rainfall datasets based on high-resolution thermal infrared observations, TAMSAT version 3 and CHIRPS, and a relatively new global product, MSWEP version 2.2, which merges satellite-based, rain gauge and re-analysis precipitation data. The datasets have been directly intercompared, avoiding the traditional rain gauge validation. This is done by means of pairwise comparison statistics at 0.25° spatial resolution and daily time scale to assess rain–detection and quantitative estimate capabilities. Monthly climatology and spatial distribution of seasonality are analyzed as well. The time evolution of the statistical indexes has been evaluated in order to analyze the stability of the rain detection and estimation performances. Considerable agreement among the precipitation products emerged from the analysis, in spite of the differences occurring in specific situations over complex terrain, such as mountainous and coastal regions and deserts. Moreover, the temporal evolution of the statistical indices has demonstrated that the agreement between the products improved over time, with more stable capabilities in identifying precipitating days and estimating daily precipitation starting in the second half of the 1990s. [ABSTRACT FROM AUTHOR]

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties' influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm's performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions. [ABSTRACT FROM AUTHOR]

The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle's acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management. [ABSTRACT FROM AUTHOR]