. Snowfall detection and quantification are challenging tasks in the Earth system science field. Ground- 8 based instruments have limited spatial coverage and are scarce or absent at high latitudes. Therefore, the 9 development of satellite-based snowfall retrieval methods is necessary for the global monitoring of snowfall. 10 Passive Microwave (PMW) sensors can be exploited for snowfall quantification purposes because their 11 measurements in the high-frequency channels (> 80 GHz) respond to snowfall microphysics. However, the highly 12 non-linear PMW multichannel response to snowfall, the weakness of snowfall signature and the contamination by 13 the background surface emission/scattering signal make snowfall retrieval very difficult. This phenomenon is 14 particularly evident at high latitudes, where light snowfall events in extremely cold and dry environmental 15 conditions are predominant. ML techniques have been demonstrated to be very suitable to handle the complex 16 PMW multichannel relationship to snowfall. Operational microwave sounders on near-polar orbit satellites such 17 as the Advanced Technology Microwave Sounder (ATMS), and the European MetOp-SG Microwave Sounder in 18 the future, offer a very good coverage at high latitudes. Moreover, their wide range of channel frequencies (from 19 23 GHz to 190 GHz), allows for the radiometric characterization of the surface at the time of the overpass along 20 with the exploitation of the high-frequency channels for snowfall retrieval. The paper describes the High lAtitude 21 sNow Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS), a new machine learning-based 22 snowfall retrieval algorithm developed specifically for high latitude environmental conditions and based on the 23 ATMS observations.

Terrestrial gamma-ray flashes are brief submillisecond gamma-ray emissions, produced during thunderstorms and strictly correlated to lightning and atmospheric electric activity. Serendipitously discovered in 1994 by the Compton Gamma Ray Observatory, these elusive events have been further investigated by several missions and satellites devoted to high-energy astrophysics, such as RHESSI, AGILE and Fermi. Terrestrial gamma-ray flashes are thought to be bremsstrahlung gamma-rays, produced at the top of thunderclouds by avalanches of electrons accelerated within thunderstorm strong electric fields and abruptly braked in the atmosphere. Exhibiting energies ranging from few keV up to several tens of MeV, terrestrial gamma-ray flashes are the most energetic phenomenon naturally occurring on Earth and they can represent a severe risk for airplanes and aircraft transports, both for the crew and the on board electronics, that should be carefully investigated and understood. The AGILE (Astrorivelatore Gamma ad Immagini LEggero) satellite is an entirely Italian mission, launched in 2007 and still operational, aimed at investigating gamma-ray emissions from cosmic sources. The wide energy range and the unique submillisecond trigger logic of its on-board instruments, together with the narrow quasi-equatorial orbit of the spacecraft, make AGILE a very suitable instrument to detect and investigate terrestrial gamma-ray flashes. Recent improvements rose up the terrestrial gamma-ray flashes detection rate and lead to the observation, for the first time, of multiple events occurring within single thunderstorm processes.

On 5 September 2015 a violent hailstorm hit the Gulf and the city of Naples in Italy. The storm originated over the Tyrrhenian Sea dropping 7–10cm diameter hailstones along its path. During its mature phase, at 08:47 UTC, the hailstorm was captured by one overpass of the Global Precipitation Measurement mission Core Observatory (GPM-CO) embarking the GPM Microwave Imager (GMI) and the Ka/Ku-band Dual-frequency Precipitation Radar (DPR). In this paper, observations by both GMI and DPR are thoroughly analyzed in conjunction with other spaceborne and ground-based measurements, to show how the GPM-CO integrates established observational tools in monitoring, understanding, and characterizing severe weather. Rapid-scan MSG SEVIRI images show an extremely rapid development, with 10.8μm cloud-top temperatures dropping by 65K in 40min down to 198K. The LIghtning NETwork registered over 37,000 strokes in 5h, with intracloud positive stroke fraction increasing during the regeneration phases, when ground-based polarimetric radar and DPR support the presence of large graupel/hail particles. DPR Ku 40dBZ and 20dBZ echo top heights at 14km and 16km, respectively, indicate strong updraft and deep overshooting. GMI extremely low brightness temperatures (TBs) in correspondence of the convective core (158, 97, 67, and 87K at 18.7, 36.5, 89 and 166GHz) are compatible with the presence of massive ice particles. In two years of GPM global observations the storm ranks as fourth and first in terms of minimum 36.5 and 18.7GHz (V-pol) TBs, respectively. This study illustrates GPM-CO sensing capabilities for characterizing the structure of such severe hailstorm, while providing observational evidence of its intensity and rarity, both globally and over the Mediterranean area.

Since their discovery, Terrestrial Gamma ray Flashes (TGFs) exhibited an evident correlation with thunderstorms and lightning activity. The fleeting nature of these events and the heavy absorption of gamma rays in the lowest atmospheric layers severely hamper the observation of this phenomenon, making us reveal just a small fraction of a probably much wider population. As each thunderstorm produces a large amount of lightning discharges during its lifetime, it is reasonable that even a large amount of TGFs are produced during the same event. However, detection of multiple TGFs coming from the same storm is difficult to perform, as it requires the constant monitoring of a spatially limited geographic region: this is not an easy task to perform for satellites on high‐inclination orbits that make them experience nonnegligible latitudinal shifts at each orbital passage over a certain region, preventing the monitoring of a limited geographic region throughout successive overpasses. In this perspective, the quasi‐equatorial (2.5°) orbit of the Astrorivelatore Gamma ad Immagini LEggero (AGILE) satellite ensures a minimal latitudinal shift when flying over the same region at successive passages, allowing for the follow‐up of thunderstorms in time. We exploit this feature of the AGILE satellite to search for multiple TGFs coming from the same geographic region and, in particular, from the same thunderstorm. We carry out this search on the AGILE TGF database (2009–2016), ending up with a sample of 79 systems producing more than one TGF, both during the same overpass and up to four overpasses after. Data acquired by geostationary meteorological satellites and cross correlation with radio sferics detected by World Wide Lightning Location Network are used to support this investigation. The AGILE satellite for the first time clearly establishes the multiple occurrences of TGFs from convective thunderstorms, both on timescales of minutes to several hours.

A multi-sensor analysis of convective precipitation events that occurred in central Italy in autumn 2012 during the HyMeX (Hydrological cycle in the Mediterranean experiment) Special Observation Period (SOP) 1.1 is presented. Various microphysical properties of liquid and solid hydrometeors are examined to assess their relationship with lightning activity. The instrumentation used consisted of a C-band dual-polarization weather radar, a 2-D video disdrometer, and the LINET lightning network. Results of T-matrix simulation for graupel were used to (i) tune a fuzzy logic hydrometeor classification algorithm based on Liu and Chandrasekar (2000) for the detection of graupel from C-band dual-polarization radar measurements and (ii) to retrieve graupel ice water content. Graupel mass from radar measurements was related to lightning activity. Three significant case studies were analyzed and linear relations between the total mass of graupel and number of LINET strokes were found with different slopes depending on the nature of the convective event (such as updraft strength and freezing level height) and the radar observational geometry. A high coefficient of determination (R-2 = 0.856) and a slope in agreement with satellite measurements and model results for one of the case studies (15 October 2012) were found. Results confirm that one of the key features in the electrical charging of convective clouds is the ice content, although it is not the only one. Parameters of the gamma raindrop size distribution measured by a 2-D video disdrometer revealed the transition from a convective to a stratiform regime. The raindrop size spectra measured by a 2-D video disdrometer were used to partition rain into stratiform and convective classes. These results are further analyzed in relation to radar measurements and to the number of strokes. Lightning activity was not always recorded when the precipitation regime was classified as convective rain. High statistical scores were found for relationships relating lightning activity to graupel aloft.

Terrestrial gamma ray flashes (TGFs) are brief (approximately hundreds of microseconds) intense gamma ray emissions coming from Earth's atmosphere (∼15 km above sea level), correlated with thunderstorms and atmospheric electric activity. Since their unexpected discovery in the early 1990s by the Burst And Transient Source Experiment/Compton Gamma Ray Observatory, TGFs have been further investigated by several satellites devoted to high‐energy astrophysics. The Astrorivelatore Gamma ad Immagini LEggero (AGILE) mission turned out to be particularly suitable to detect these events, due to a very wide energy range (up to 100 MeV), an optimized triggering system, and a unique low‐inclination near‐equatorial orbit (2.5°). We describe a detection system, developed for the AGILE satellite, whose aim is to provide real‐time meteorological information on each detected TGF. We take advantage of data acquired by geostationary satellites to promptly identify the associated storm and follow its evolution in space and time, in order to study its previous onset and development. Data from Low‐Earth Orbit meteorological satellites, such as the Global Precipitation Mission, as well as ground measurements from lightning detection networks, can be integrated in the pipeline. This system allows us a prompt characterization of the ground meteorological conditions at TGF time which will provide instrument‐independent trigger validation, fill in a database for subsequent statistical analysis, and eventually, on a longer term perspective, serve as a real‐time alert service open to the community.

The purpose of this study is to describe a new algorithm based on a neural network approach (Passive microwave Neural network Precipitation Retrieval – PNPR) for precipitation rate estimation from AMSU/MHS observations, and to provide examples of its performance for specific case studies over the European/Mediterranean area. The algorithm optimally exploits the different characteristics of Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) channels, and their combinations, including the brightness temperature (TB) differences of the 183.31 channels, with the goal of having a single neural network for different types of background surfaces (vegetated land, snow-covered surface, coast and ocean). The training of the neural network is based on the use of a cloud-radiation database, built from cloud-resolving model simulations coupled to a radiative transfer model, representative of the European and Mediterranean Basin precipitation climatology. The algorithm provides also the phase of the precipitation and a pixel-based confidence index for the evaluation of the reliability of the retrieval. Applied to different weather conditions in Europe, the algorithm shows good performance both in the identification of precipitation areas and in the retrieval of precipitation, which is particularly valuable over the extremely variable environmental and meteorological conditions of the region. The PNPR is particularly efficient in (1) screening and retrieval of precipitation over different background surfaces; (2) identification and retrieval of heavy rain for convective events; and (3) identification of precipitation over a cold/iced background, with increased uncertainties affecting light precipitation. In this paper, examples of good agreement of precipitation pattern and intensity with ground-based data (radar and rain gauges) are provided for four different case studies. The algorithm has been developed in order to be easily tailored to new radiometers as they become available (such as the cross-track scanning Suomi National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS)), and it is suitable for operational use as it is computationally very efficient. PNPR has been recently extended for applications to the regions of Africa and the South Atlantic, and an extended validation over these regions (using 2 yr of data acquired by the Tropical Rainfall Measuring Mission precipitation radar for comparison) is the subject of a paper in preparation. The PNPR is currently used operationally within the EUMETSAT Hydrology Satellite Application Facility (H-SAF) to provide instantaneous precipitation from passive microwave cross-track scanning radiometers. It undergoes routinely thorough extensive validation over Europe carried out by the H-SAF Precipitation Products Validation Team.

In this study, precipitation retrievals exploiting the available overpasses of passive microwave (PMW) cross-track and conically scanning radiometers in the GPM era are used to monitor the evolution of heavy precipitation systems occurred during the fall 2014 in Italy. Two different physically-based retrieval algorithms (CDRD for SSMIS and PNPR for AMSU/MHS and ATMS) are used in conjunction with official NASA/JAXA GPM instantaneous precipitation products (for AMSR-2 and GMI). The comparison with dual-polarization radar observations at ground evidences the ability of the different sensors to identify different precipitation areas and regimes. This is particularly relevant in presence of complex orography, often found in proximity of coastal areas for the analyzed cases. Analysis of the accuracy and consistency of the retrievals is carried out taking into account the different spatial resolution and viewing geometry of the different radiometers and the different approaches used for the precipitation retrieval.

A novel algorithm for the detection of precipitation is described and tested. The algorithm is applicable to any modern passive microwave radiometer on board polar orbiting satellites independent of the observation geometry and channel frequency assortment. The algorithm is based on the application of canonical correlation analysis and on the definition of a threshold to be applied to the resulting linear combination of the brightness temperatures in all available channels. The algorithm has been developed using a 2-year data set of co-located Special Sensor Microwave Imager/Sounder (SSMIS) and Tropical Rainfall Measuring Mission precipitation radar (TRMM-PR) measurements and Advanced Microwave Sounding Unit (AMSU) Microwave Humidity Sounder and TRMM-PR measurements. This data set was partitioned into four classes depending on the background surface emissivity (vegetated land, arid land, ocean, and coast) with the same procedure applied for each surface class. In this paper we describe the procedure and evaluate the results in comparison with many well-known algorithms for the detection of precipitation.The algorithm shows a small rate of false alarms and superior detection capability; it can efficiently detect (probability of detection between 0.55 and 0.71) minimum rain rate varying from 0.14 mm h(-1) (AMSU over ocean) to 0.41 (SSMIS over coast) with the remarkable result of 0.25 mm h(-1) over arid land surfaces.

Abstract. Mass accumulation is a key geophysical parameter in understanding the Antarctic climate and its role in the global system. The local mass variation is driven by a number of different mechanisms: the deposition of snow and ice crystals on the surface from the atmosphere is generally modified by strong surface winds and variations in temperature and humidity at the ground, making it difficult to measure directly the accumulation by a sparse network of ground based instruments. Moreover, the low cloud total water/ice content and the varying radiative properties of the ground pose problems in the retrieval of precipitation from passive space-borne sensors at all frequencies. Finally, numerical models, despite their high spatial and temporal resolution, show discordant results and are difficult to be validated using ground-based measurements. A significant improvement in the knowledge of the atmospheric contribution to the mass balance over Antarctica is possible by using active space-borne instruments, such as the Cloud Profiling Radar (CPR) on board the low earth orbit CloudSat satellite, launched in 2006 and still operating. The radar measures the vertical profile of reflectivity at 94 GHz (sensitive to small ice particles) providing narrow vertical cross-sections of clouds along the satellite track. The aim of this work is to show that, after accounting for the characteristics of precipitation and the effect of surface on reflectivity in Antarctica, the CPR can retrieve snowfall rates on a single event temporal scale. Furthermore, the CPR, despite its limited temporal and spatial sampling capabilities, also effectively observes the annual snowfall cycle in this region. Two years of CloudSat data over Antarctica are analyzed and converted in water equivalent snowfall rate. Two different approaches for precipitation estimates are considered in this work. The results are analyzed in terms of annual and monthly averages, as well as in terms of instantaneous values. The derived snowfall maps are compared with ERA-Interim reanalysis and with in situ measurements, showing overall agreement. The effects of coastlines in enhancing precipitation rates and cloud precipitation efficiency are recognized. A significant seasonal signal also affects the averaged spatial extent of snowfall patterns. A comparison with snow accumulation ground measurements of single snowfall events shows consistency with the CPR retrievals: all the retrieved snowfall episodes correspond to an increase of snow accumulation at the ground, while several episodes of increase of snow stack height are not related to significant retrieved snowfall rate, likely indicating the local contribution of blowing snow. The results show that CPR can be a valuable source of snowfall rate data in Antarctica that can be used at different temporal scales, providing support to the sparse network of ground-based instruments both for numerical model validation and climatological studies.

The development phase (DP) of the EUMETSAT Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF) led to the design and implementation of several precipitation products, after 5 yr (2005–2010) of activity. Presently, five precipitation estimation algorithms based on data from passive microwave and infrared sensors, on board geostationary and sun-synchronous platforms, function in operational mode at the H-SAF hosting institute to provide near real-time precipitation products at different spatial and temporal resolutions. In order to evaluate the precipitation product accuracy, a validation activity has been established since the beginning of the project. A Precipitation Product Validation Group (PPVG) works in parallel with the development of the estimation algorithms with two aims: to provide the algorithm developers with indications to refine algorithms and products, and to evaluate the error structure to be associated with the operational products. In this paper, the framework of the PPVG is presented: (a) the characteristics of the ground reference data available to H-SAF (i.e. radar and rain gauge networks), (b) the agreed upon validation strategy settled among the eight European countries participating in the PPVG, and (c) the steps of the validation procedures. The quality of the reference data is discussed, and the efforts for its improvement are outlined, with special emphasis on the definition of a ground radar quality map and on the implementation of a suitable rain gauge interpolation algorithm. The work done during the H-SAF development phase has led the PPVG to converge into a common validation procedure among the members, taking advantage of the experience acquired by each one of them in the validation of H-SAF products. The methodology is presented here, indicating the main steps of the validation procedure (ground data quality control, spatial interpolation, up-scaling of radar data vs. satellite grid, statistical score evaluation, case study analysis). Finally, an overview of the results is presented, focusing on the monthly statistical indicators, referred to the satellite product performances over different seasons and areas.

This paper shows the results of a tailored version of a previously published methodology, designed to simulate lightning activity, implemented into the Regional Atmospheric Modeling System (RAMS).The method gives the flash density at the resolution of the RAMS grid scale allowing for a detailed analysis of the evolution of simulated lightning activity.The system is applied in detail to two case studies occurred over the Lazio Region, in Central Italy. Simulations are compared with the lightning activity detected by the LINET network. The cases refer to two thunderstorms of different intensity which occurred, respectively, on 20 October 2011 and on 15 October 2012.The number of flashes simulated (observed) over Lazio is 19 435 (16 231) for the first case and 7012 (4820) for the second case, and the model correctly reproduces the larger number of flashes that characterized the 20 October 2011 event compared to the 15 October 2012 event.There are, however, errors in timing and positioning of the convection, whose magnitude depends on the case study, which mirrors in timing and positioning errors of the lightning distribution. For the 20 October 2011 case study, spatial errors are of the order of a few tens of kilometres and the timing of the event is correctly simulated. For the 15 October 2012 case study, the spatial error in the positioning of the convection is of the order of 100 km and the event has a longer duration in the simulation than in the reality.To assess objectively the performance of the methodology, standard scores are presented for four additional case studies. Scores show the ability of the methodology to simulate the daily lightning activity for different spatial scales and for two different minimum thresholds of flash number density. The performance decreases at finer spatial scales and for higher thresholds.The comparison of simulated and observed lighting activity is an immediate and powerful tool to assess the model ability to reproduce the intensity and the evolution of the convection. This shows the importance of using computationally efficient lightning schemes, such as the one described in this paper, in forecast models.

In this study a one-dimensional numerical cloud electrification model, called the Explicit Microphysics Thunderstorm Model (EMTM), is used to find quantitative relationships between the simulated electrical activity and microphysical properties in convective clouds. The model, based on an explicit microphysics scheme coupled to an ice–ice noninductive electrification scheme, allows us to interpret the connection of cloud microphysical structure with charge density distribution within the cloud, and to study the full evolution of the lightning activity (intracloud and cloud-to-ground) in relation to different environmental conditions. Thus, we apply the model to a series of different case studies over continental Europe and the Mediterranean region. We first compare, for selected case studies, the simulated lightning activity with the data provided by the ground-based Lightning Detection Network (LINET) in order to verify the reliability of the model and its limitations, and to assess its ability to reproduce electrical activity consistent with the observations. Then, using all simulations, we find a correlation between some key microphysical properties and cloud electrification, and derive quantitative relationships relating simulated flash rates to minimum thresholds of graupel mass content and updrafts. Finally, we provide outlooks on the use of such relationships and comments on the future development of this study.

In the first two parts of this study we have presented a performance analysis of our new Cloud Dynamics and Radiation Database (CDRD) satellite precipitation retrieval algorithm on various convective and stratiform rainfall case studies verified with precision radar ground truth data, and an exposition of the algorithm's detailed design in conjunction with a proof-of-concept analysis vis-a-vis its theoretical underpinnings. In this third part of the study, we present the underlying analysis used to identify what we refer to as the optimal metrological and geophysical tags, which are the optimally effective atmospheric and geographic parameters that are used to refine the selection of candidate microphysical profiles used for the Bayesian retrieval. These tags enable extending beyond the conventional Cloud Radiation Database (CRD) algorithm by invoking meteorological-geophysical guidance, drawn from a simulated database, which affect and are in congruence with the observed precipitation states. This is guidance beyond the restrictive control provided by only simulated radiative transfer equation (RTE) model-derived database brightness temperature (TB) vector proximity information in seeking to relate physically consistent precipitation profile solutions to individual satellite-observed TB vectors. The first two parts of the study have rigorously demonstrated that the optimal tags effectively mitigate against solution ambiguity, where use of only a CRD framework (TB guidance only) leads to pervasive non-uniqueness problems in finding rainfall solutions. Alternatively, a CDRD framework (TB + tag guidance) mitigates against non-uniqueness problems through improved constraints. It remains to show how these optimal tags are identified. By use of three statistical analysis procedures applied to a database from 120 North American atmospheric simulations of precipitating storms (independent of the 60 simulations for the European-Mediterranean basin region used in the Parts 1 and 2 studies), we examine 25 separate dynamical-thermodynamical-hydrological (DST) and geophysical parameters for their relationships to rainfall variables - specifically, surface rain rate and columnar liquid/ice/total water paths of precipitating hydrometeors. The analysis identifies seven optimal parameter tags which exceed all others in the strengths of their correlations to the precipitation variables but also have observational counterparts in the operational global forecast model outputs. The seven optimal tags are (1 and 2) vertical velocities at 700 and 500 hPa; (3) equivalent potential temperature at surface; (4) convective available potential energy; (5) moisture flux 50 hPa above surface; (6) freezing level height; and (7) terrain height, i.e., surface height.

Global monitoring of the precipitation requires the full exploitation of all overpasses of present and future satellites carrying cross-track and conically scanning passive microwave (PMW) radiometers. Therefore, it is essential to achieve consistency and accuracy of passive microwave precipitation retrievals from the different sensors orbiting around the globe. Within the EUMETSAT H-SAF program (Satellite Application Facility on Support to Operational Hydrology and Water Management, http://hsaf.meteoam.it) we have developed two different passive microwave precipitation retrieval algorithms, one based on a physically-based Bayesian approach for conically scanning radiometers (i.e., SSMIS), and the other one based on Neural Network approach for cross-track scanning radiometers (i.e., AMSU-A/MHS). The two algorithms are based on the same physical foundation, i.e., same cloud-radiation model simulations to be used as a priori information in the Bayesian solver and as training dataset in the neural network approach. They also use similar procedures for screening of non-precipitating pixels, identification of frozen background surface, presence of snowfall, and determination of a pixel based quality index of the surface precipitation retrievals. These procedures are calibrated according to the different characteristics of the radiometers used. The two algorithms use dynamical/meteorological/environmental variables as ancillary information to characterize the observed event, and mitigate the ambiguity of the rainfall rates at the ground associated to any given set of measured multichannel brightness temperatures. A verification study of the latest versions of the two algorithms has been carried out, where the PMW rainfall estimates are compared against radar observations and raingauge network measurements for several precipitation events in Europe, characterized by different environmental, meteorological, dynamical conditions, and by different precipitation regimes. In this paper we present the main results of this study, discussing strengths and potentials of the two algorithms in relation to the different characteristics of the observed events. In addition we describe the efforts made to achieve consistency of the retrievals from close in time overpasses of the cross-track and conically scanning radiometers for the same event, foreseeing potential improvements in nowcasting and/or hydrological applications.

Abstract. The EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) was established by the EUMETSAT Council on 3 July 2005, starting activity on 1 September 2005. The Italian Meteorological Service serves as Leading Entity on behalf of twelve European member countries. H-SAF products include precipitation, soil moisture and snow parameters. Some products are based only on satellite observations, while other products are based on the assimilation of satellite measurements/products into numerical models. In addition to product development and generation, H-SAF includes a product validation program and a hydrological validation program that are coordinated, respectively, by the Italian Department of Civil Protection and by the Polish Institute of Meteorology and Water Management. The National Center of Aeronautical Meteorology and Climatology (CNMCA) of the Italian Air Force is responsible for operational product generation and dissemination. In this paper we describe the H-SAF precipitation algorithms and products, which have been developed by the Italian Institute of Atmospheric Sciences and Climate (in collaboration with the international community) and by CNMCA during the Development Phase (DP, 2005–2010) and the first Continuous Development and Operations Phase (CDOP-1, 2010–2012). The precipitation products are based on passive microwave measurements obtained from radiometers onboard different sun-synchronous low-Earth-orbiting satellites (especially, the SSM/I and SSMIS radiometers onboard DMSP satellites and the AMSU-A + AMSU-B/MHS radiometer suites onboard EPS-MetOp and NOAA-POES satellites), as well as on combined infrared/passive microwave measurements in which the passive microwave precipitation estimates are used in conjunction with SEVIRI images from the geostationary MSG satellite. Moreover, the H-SAF product generation and dissemination chain and independent product validation activities are described. Also, the H-SAF program and its associated activities that currently are being carried out or are planned to be performed within the second CDOP phase (CDOP-2, 2012–2017) are presented in some detail. Insofar as CDOP-2 is concerned, it is emphasized that all algorithms and processing schemes will be improved and enhanced so as to extend them to satellites that will be operational within this decade – particularly the geostationary Meteosat Third Generation satellites and the low-Earth-orbiting Core Observatory of the international Global Precipitation Measurement mission. Finally, the role of H-SAF within the international science and operations community is explained.