La mesure des précipitations par satellite implique divers types d’instruments et de méthodes de restitution associées : instruments passifs dans l’infrarouge ou les micro-ondes, radars embarqués, fusion de ces diverses données, combinaison avec les données de pluviomètres au sol ou de modèles numériques. La question de la surveillance satellitaire de l’ensemble des composantes du cycle de l’eau terrestre clôt le chapitre.

Accurate estimation of precipitation at the global scale is of utmost importance. Even though satellite and reanalysis products are capable of providing high spatial-temporal resolution estimations at the global level, their uncertainties vary with regional characteristics, scales, and so on. The uncertainties among the estimates, in general, are much higher at the sub-daily scale compared to daily, monthly and annual scales. Therefore, quantifying these sub-daily estimations is of specific importance. In this context, this study seeks to explore the diurnal cycle of precipitation using all the currently available space-borne and reanalysis-based precipitation products with at least hourly resolution (IMERG, GSMaP, CMORPH, PERSIANN, ERA5) at the quasi-global scale (60◦N - 60◦S). The diurnal variability of precipitation is estimated using three parameters, namely, the precipitation amount, frequency, and intensity, all remapped at a common resolution of 0.25◦ and 1 h. All the estimates well represent the spatio-temporal variation across the globe. Nevertheless, considerable uncertainties exist in the estimates regarding the peak precipitation hour, as well as the diurnal mean precipitation amount, frequency, and intensity. In terms of diurnal mean precipitation, PERSIANN shows the lowest estimates compared to the other datasets, with the largest difference observed over the ocean rather than over land. As for diurnal frequency, ERA5 exhibits the highest disparity among the estimates, with a frequency twice as high as that of the other estimates. Furthermore, as expected being based on model reanalysis, ERA5 shows an early diurnal peak and the highest variability compared to the other datasets. Moreover, among the satellite estimates, IMERG, GSMaP, and CMORPH exhibit a similar pattern with a late afternoon peak over land and an early morning peak over the ocean.

Recent years have seen tremendous advancements in the field of extreme precipitation monitoring, particularly through the application of remote sensing technologies [...]

The Po Valley in northern Italy is a hotspot for tornadoes in Europe in spite of being surrounded by two mountain ridges: the Alps in the north and the Apennines in the southwest. The research focuses on the case study of 19 September 2021, when seven tornadoes (four of them rated as F2) developed in the Po Valley in a few hours. The event was analyzed using observations and numerical simulations with the convection-permitting Modello Locale in Hybrid Coordinates (MOLOCH) model. Observations show that during the event in the Po Valley, there were two surface boundaries that created a triple point: an out fl ow boundary generated by convection triggered in the Alpine foothills and a dryline generated by downslope winds from the Apennines, while warm and moist air advected westward from the Adriatic Sea east (ahead) of the boundaries. Tornadoes developed about 20 km northeast of the triple point. Numerical simulations with 500-m grid spacing suggest that the development of supercells and drylines in the Po Valley was sensitive to the elevation of the Apennines. Simulated vertical pro f iles show that the best combination of instability and wind shear for the development of tornadoes was attained within a narrow area located ahead of the dryline. A conceptual model for the development of tornadoes in the Po Valley is proposed, and the differences between tornado environments over a fl at terrain and over a region with complex terrain are discussed.

Estimating the frequency of extreme precipitation events, both locally and over extended areas, is key for developing risk reduction measures in present and future climates. Large areas of the world are characterized by sparse or absent rain-gauge networks, which poses significant challenges to the estimation of extreme events in many applications. Remote sensing and reanalysis datasets may contribute to filling some of these gaps, but their use meets some important obstacles: 1) remote sensing/reanalysis rainfall estimates are defined at coarse resolutions, thereby preventing direct validations against ground observations; 2) they usually span a ~20-year observation period, making it difficult to estimate the frequency of large extremes; 3) they suffer from significant uncertainties. Using the novel Metastatistical Extreme Value Distribution (MEVD) and a recent statistical downscaling technique, we compare ground and satellite-based/model estimates of rainfall to quantify the improvement achieved through downscaling in high-quantile quantification. We focus on ocean rainfall observations, which are rarely considered in validating global databases, from the Tao-Triton, Pirata, and Rama buoy networks. We quantify the estimation uncertainty for point extremes associated with the MSWEP rainfall dataset. We find that the MEVD-based extreme value downscaling approach generally improves point extreme estimates.

On 2–3 October 2020, a heavy precipitation event severely affected northern Italy and in particular the western Alps, with rainfall amount exceeding 600 mm over 24 h. This event was associated with an upper-level trough over the western Mediterranean basin, a large-scale configuration typical of heavy precipitation phenomena on the southern side of the Alps, since it induces a northward transport of large amounts of moisture impinging on the orography. The present study shows that a relevant amount of moisture moved towards the Mediterranean basin in the form of an atmospheric river (AR), a long and narrow filament-shaped structure crossing the whole Atlantic Ocean, characterized in the present case by a maximum Integrated Vapour Transport exceeding 1000 kg m−1 s−1. Therefore, in addition to the local contribution from the Mediterranean Sea, a relevant amount of moisture moved from the Tropics towards the Mediterranean, feeding the precipitation systems. The presence of an AR represented a distinguishing aspect of the event, superimposed on the well-known dynamic-thermodynamic mechanisms of heavy precipitation over the Alps. High-resolution numerical simulations and diagnostic tools have been exploited to investigate in detail how the transport of water vapour associated with the AR has influenced the dynamics and favoured the severity of the heavy precipitation processes. The results disclose the role of the AR and add further details to the theoretical framework of heavy precipitation mechanisms in the Alpine area, improving our understanding of the complex interaction between large-scale flows and mesoscale dynamics during extreme precipitation episodes. Due to the relatively fast evolution of the synoptic disturbance, the typical mesoscale mechanisms would have led only to an ordinary intense rainfall event. The contribution of the AR turned the event into a devastating flood.

On the evening of November 12, 2019, an exceptional high tide hit the city of Venice and the central-southern area of its lagoon, damaging a large part of its historical center. The main cause of the event was a small warm-core mesoscale cyclone, which formed in the central Adriatic Sea and intensified during its northwestward movement. Simulations with different initialization times were carried out with the Weather Research and Forecasting (WRF) model, showing a strong sensitivity to the initial conditions, since the track (and strength) of the cyclone was determined by the exact position of an upper-level potential vorticity (PV) streamer. The factors responsible for the cyclone development are also investigated. The pre-existence of positive low-level cyclonic vorticity, associated with the convergence of the Sirocco and Bora winds in the Adriatic, made the environment favorable for cyclone development. Also, the interaction between the upper-level PV anomaly and the low-level baroclinicity, created by the advection of warm, humid air associated with the Sirocco, was responsible for the cyclone’s intensification, in a manner similar to a transitory (stable) baroclinic interaction at small horizontal scales. Conversely, convection and sea surface fluxes did not play a significant role, thus the warm-core feature appears mainly as a characteristic of the environment in which the cyclone developed rather than a consequence of diabatic processes. The cyclone does not fall into any of the existing categories for Adriatic cyclones.

Millimitere (mm) and sub-millimiter (sub-mm) radiometer observation of the atmosphere from space is an appealing topic given the variety of information obtainable. The exploitation of window frequencies and various gaseous absorption bands at 50/60, 118, 183 allow for a better representation of tropospheric temperature profiles, water vapor and cloud liquid contents, as well as for hail detection, and to some extent, rainfall and snowfall estimates. These observations have shown tangible impacts on numerical weather prediction and data assimilation, climate benchmarking, hydrometeorology, extreme weather nowcasting, and civil protection. Further benefits for ice cloud retrievals are expected from observations at higher frequency, such as 243 and 664 GHz channels foreseen in the upcoming EUMETSAT Polar System-Secon Generation (EPS-SG) Ice cloud imager (ICI) sensor [1] , [2] . The increase in frequency, and consequently the reduction in wavelength, from mm to sub-mm also gives the technological advantage of reduced size of the overall system, maintaining performances unchanged, thus making it easier to implement constellation of radiometers with the glaring benefit of incrementing the repetition time of the satellite overpasses. A precursor on this topic was proposed by Prof. Marzano in 2009 [3] with the FLOwer constellation of MM-wave RADiometers (FLORAD) mission. The FLORAD concept consisted in tree small satellites (<500 kg each) in a pseudo-stationary orbit (also termed as resonant or floreal orbit) to have a repetition rate of 1 hour over the Mediterranean area with a cross-track scanner sensor named FLOMIS (FLORAD microwave imager-sounder) with channels ranging from 90 to 230 GHz. Two evolutions of FLORAD were proposed later, adding radio occultation [4] or cloud radar [5] . Ten years later, technological progress allowed the deployment of a proof-of-concept radiometer on a cubesat (1.23 kg), named TEMPEST-D [6] , as well as TROPICS [7] , a six-radiometer constellation (5.34 kg each). These missions exploit satellites that are orders of magnitude smaller and cheaper than traditional satellites operated by federal agencies, revolutionizing the next-generations of Earth-observations [8] . In Europe, the ESA/EUMETSAT prototype satellite of the Arctic Weather Satellite (AWS) mission has been recently approved. The AWS Microwave Radiometer (MWR) is a 19 channel cross-track scanning radiometer consisting of a rotating antenna focusing the incoming radiation onto four feedhorns (one for each group of channels) and four receivers, covering the frequency range 50–325 GHz. The AWS will be the forerunner of the potential EPS-Sterna mission, a constellation of small (120 kg) polar-orbiting satellites based on the AWS, each carrying a single microwave radiometer providing frequent coverage of the Earth and full coverage of the polar zones with no gaps. The EPS-Sterna would complement the MetOp series as well as the US NOAA’s Joint Polar Satellite System by providing more frequent observations mainly for temperature and humidity sounding but also for improving precipitation monitoring at high latitudes.

Precipitation frequency analysis based on satellite products is still limited by estimation errors and by the use of statistical methods inadequate for these products. However, when it comes to poorly gauged areas of the world, satellite products can be a vital source of information. We present here a new method to derive satellite-based estimates of extreme precipitation quantiles with long return period in poorly gauged areas. The method relies on the identification of relations between statistics of the satellite estimation error and errors in the parameters of a non-asymptotic extreme value distribution. We show an application of the method in three areas with diverse climatic conditions in Austria and in the South-eastern Mediterranean, showcasing results for different scenarios of rain gauge density. We find that simple linear relations can explain 35-90% of the variance in the error of the parameters of the non-asymptotic extreme value distribution. Using these relations, we derive estimates of extreme return levels with drastically reduced bias and dispersion with respect to the ones directly obtained from the satellite estimates.

Why is the Po Valley a hot spot in Europe for tornadoes? In this study the authors propose an explanation to this issue, studying a tornado outbreak that affected the Po Valley on 19 September 2021. During that event seven tornadoes (four of them ranked as F2 according to the Fujita scale) developed between Lombardia and Emilia-Romagna regions in a few hours. Although tornadoes are not rare in Italy, so many tornadoes in such a short time are an unusual event. The case study was analysed by means of observations and numerical simulations obtained with the convection permitting MOLOCH model. Observations showed that during the event there were two low-level boundaries in the Po Valley: a cold front coming from the Alps and a dry line generated by the downslope winds from the Apennines. These two boundaries created a triple point, like those observed during tornado outbreaks in the US MidWest, but on a smaller scale. Observations proved a strong correlation between tornado developments and low-level boundaries. Numerical simulations with 500 m grid spacing showed that a warm and moist air tongue from the Adriatic Sea played a fundamental role in generating the supercells, causing an advection of vorticity and favouring instability conditions. Moreover, through numerical experiments, it has been proved that this moist air tongue was sensitive to the Froude number of the south-westerly flow from the Apennines: the greater the Froude number, the further north and narrower was the tongue of air, with impacts on the development of supercells. Along the cold front large amounts of streamwise vorticity were generated by the buoyancy gradient. Furthermore, the dry line played a key role in the generation of tornadoes, creating locally large amounts of instability and strong wind veering near the surface: kinematic and windshear parameters were comparable to those observed in US-tornado events only along a narrow path near the dry line. Comparing these results with previous papers, the presence of thermal boundaries and dry lines represents a typical pattern during tornado-events in the region. Then, in conclusion, a conceptual model also useful for forecasting applications is proposed for the development of tornadoes in the Po Valley, which explains why tornadoes are relatively common in Northern Italy.

A new method for studying hailstorms from space offers more consistent and more complete views of how and where hail forms, and how climate change might influence hail’s impacts in the future.

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.

Snow is the main positive component of surface mass balance in Antarctica. Therefore, accurate snow measurements of snowfall play a crucial role in characterizing the Antarctic ice sheet's variability and its impact on the sea-level rise. The remote sensing of precipitation and in situ measurements are, in general, challenging tasks and even more difficult in an environment like Antarctica. Radar profilers are increasingly used in Antarctic research stations to highlight snowfall processes through vertical reflectivity profiles and improve the quantitative precipitation estimation, also exploiting the synergy with surface measurements. This work summarizes the field campaign experience at the Italian Antarctic station "Mario Zucchelli," analyzing the vertical profiles of reflectivity collected by a Micro Rain Radar (MRR), set with a vertical resolution of 35 m and a temporal resolution of 1 min. Such an MRR set up allowed us to use a trustworthy range gate just 105 m above the ground, thus avoiding contamination of clutter. Factors influencing the behavior of vertical profiles are analyzed, emphasizing the sublimation process and its implications on surface snowfall estimation at the ground.

The measurement of precipitation (rainfall and snowfall) across the globe is important for a variety of scientific and social applications. Knowledge of the occurrence and amount of precipitation, together with its distribution and changes are crucial for improving our understanding of the global energy and water cycle, as well as for monitoring water resource availability and for hydrological modeling to help alleviate flood and drought impacts. The use of conventional instruments (gauge or radar) to map global precipitation is essentially limited to land areas and thus satellite observations must be used to provide estimates of global precipitation. Many satellite sensors operating over the last 50 years have provided data for a range of techniques, algorithms and schemes developed to generate quantitative precipitation estimates. Current satellite-based precipitation products can provide estimates at up to 4 km every 30 minutes. This chapter outlines the basis of satellite precipitation estimation, the satellites and sensors used, and the range of techniques and schemes used to generate the precipitation products.

Extreme precipitation heavily affects society and economy in Africa because it triggers natural hazards and contributes large amounts of freshwater. Understanding past changes in extreme precipitation could help us improve our projections of extremes, thus reducing the vulnerability of the region to climate change. Here, we combine high-resolution satellite data (1981–2019) with a novel non-asymptotic statistical approach, which explicitly separates intensity and occurrence of the process. We investigate past changes in extreme daily precipitation amounts relevant to engineering and risk management. Significant (α=0.05) positive and negative trends in annual maximum daily precipitation are reported in ∼20 % of Africa both at the local scales (0.05°) and mesoscales (1°). Our statistical model is able to explain ∼90% of their variance, and performs well (72% explained variance) even when annual maxima are explicitly censored from the parameter estimation. This suggests possible applications in situations in which the observed extremes are not quantitatively trusted. We present results at the continental scale, as well as for six areas characterized by different climatic characteristics and forcing mechanisms underlying the ongoing changes. In general, we can attribute most of the observed trends to changes in the tail heaviness of the intensity distribution (25% of explained variance, 38% at the mesoscale), while changes in the average number of wet days only explain 4% (12%) of the variance. Low-probability extremes always exhibit faster trend rates than annual maxima (∼44% faster, in median, for the case of 100-year events), implying that changes in infrastructure design values are likely underestimated by approaches based on trend analyses of annual maxima: flexible change-permitting models are needed. No systematic difference between local and mesoscales is reported, with locally-varying impacts on the areal reduction factors used to transform return levels across scales.

The Multi-sensor Approach for Satellite Hail Advection (MASHA) is a new satellite hybrid technique conceived for the real time detection and advection of hail clouds. MASHA is based on a machine learning algorithm able to identify hail clouds from satellite measurements and predict the evolution of hail-bearing systems every 5 min. The machine Learning techniques represent a valuable tool to address this problem. In particular, the use of deep learning model allows to automatically combine low level data and providing accurate predictions. Operationally, MASHA combines the strengths of the MWCC-H method to detect hail through the whole GPM constellation (Laviola et al., 2020a-b) with the high temporal rate of the Meteosat Rapid Scan Service (MSG-RSS). The novelty of this approach is offering the unprecedented possibility to advect hail-bearing systems in real-time and at very high spatial resolution. This opens the way to the operational applications of MASHA method by offering an unprecedented support to the nowcasting of hailstorms and to regional numerical weather predictions. Recent applications experimented the ingestion of lightning strikes and radar hail indices in order to improve the reconstruction of hail fields when the GPM-C overpasses are missing. The result is a near-real time, more consistent, high-resolution hail map.ReferencesLaviola S., V. Levizzani, R. R. Ferraro, and J. Beauchamp: Hailstorm Detection by Satellite Microwave Radiometers. Remote Sens. 2020a, 12(4), 621; https://doi.org/10.3390/rs12040621Laviola S., G. Monte, V. Levizzani, R. R. Ferraro, and J. Beauchamp: A new method for hail detection from the GPM constellation. A prospective for a global hailstorm climatology. Remote Sens. 2020b, 12(21), 3553; https://doi.org/10.3390/rs12213553

Quantitative estimation of snowfall using radar is a challenging task that is usually accomplished using relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR), typically expressed as power-law (Ze = a × SRb) whose parameters are obtained from long-term measurements. Unfortunately, the changeability of microphysical and scattering characteristics of snowflakes makes them highly variable. The proposed method takes advantage of the estimation of the snowflake microphysical characteristics and develops six Ze-SR relationships depending on particle habit. A classification of particles is obtained by comparing co-located Micro Rain Radar and Parsivel disdrometer observations coupled with a DDA backscattering model in terms of radar reflectivity and is used to select the appropriate Ze-SR relationship. The method was tested using ground-based instruments installed at the Italian Antarctic Station Mario Zucchelli, in the framework of the projects APP (Antarctic Precipitation Properties), MALOX (MAss LOst in wind fluX), and IAMCO (Italian Antarctic Meteo-Climatological Observatory), funded by the Italian National Antarctic Program (PNRA). The Micro Rain Radar was set at the highest vertical resolution (35 m) so that the first trusted range gate was at only 105-m height, close enough to the ground level to be compared with disdrometer particle size distributions. We analyzed data from 52 precipitation days of the 2018–2019 and 2019–2020 summers for a total of 23,566 snowfall minutes. Disdrometer data were corrected from the influence of wind by assigning a reliability weight to each Parsivel bin based on simultaneous disdrometer, MRR, and wind measurements. This method preserves more precipitation data than the more widely used censoring methods that eliminate data collected when wind speed exceeds a given threshold: since strong winds are often associated with significant snow events, censoring methods tend to discard inportant precipitation measurements. The consistency of disdrometer and radar measurements is tested for six snow categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine) in terms of radar reflectivity matched in a 10-min time frame. The related Ze-SR relationship of the selected snow category is used to calculate the cumulated snowfall amount. The comparisons of Ze from disdrometer and MRR at the 105-m height show good agreement, even for nonwind-corrected disdrometere data, although agreement significantly improves if wind-correction is applied. Of the precipitation minutes, we classified 75% of them as aggregate, with a significant percentage of dendrites. Only 5,830 out of 23,566 falling particles showed pristine characteristics. We estimated 84.6 mm w.e. of accumulated snowfall for the 52 events. Such estimates were compared with measurements from a weighing pluviometer available for 32 out of the 52 considered days. Estimation using variable Ze-SR relationships results in a better agreement with the pluviometer (64 mm w.e. vs. 66.5 mm w.e.) with respect to estimates from fixed Ze-SR relationships found in the literature. Results show that combining MRR and disdrometer is undoubtedly valuable for snowfall estimations. In fact, the significant uncertainties in snowfall radar estimates related to the variability of snow microphysical features can be mitigated by using variable Ze-SR relationships.