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On 24-26 August 2022 Pakistan has suffered one of its most severe floods. The excessive monsoon rainfall throughout the summer, which was worsened by shorter bursts of extremely heavy rain that affected the regions of Sindh and Balochistan, directly contributed to the flooding. According to reports, Pakistan experienced more than three times its typical rainfall in August, making it the wettest month since 1961. The strong monsoon moist currents and thermal lows started from Arabian Sea penetrating into entire area of Balochistan and Sindh, and spread over upper and central parts of the country. The moist currents from the Bay of Bengal penetrating during the entire monsoon seasonfrom mid-June to September resulted in floods and land sliding across Pakistan, causing human casualties as well as widespread destruction of homes and infraructure. Floods cannot be entirely avoided, but their harmful effects can be significantly managed with careful planning and adequate preparation. The use of operational satellite precipitation products could facilitate prompt and accurate monitoring (and forecasting) as well as the implementation of impact-minimizing strategies, reducing vulnerability to floods.The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) application facility on Support to Operational Hydrology and Water Management (H SAF) (http://h-saf.eumetsat.int/) provides operational satellite products of important hydrological parameters, including snow cover and water equivalent, soil moisture, surface rain rate and cumulated rainfall. Among these, H SAF generates a near-real time (NRT) product providing instantaneous surface precipitation rate over the Meteosat Second Generation (MSG) Indian Ocean Data Coverage (IODC) every 15 minutes at 3-5 km spatial resolution (H SAF product ID H63). H63 is based on the rapid update blending technique combining passive microwave (PMW) precipitation rate estimates and IR measurements from MSG SEVIRI. On the other hand, since the beginning of March 2014, the NASA Global Precipitation Measurement (GPM) mission (https://gpm.nasa.gov/missions/GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) product provides quasi-global (60 N-60 S) precipitation rate estimates every 30 min at 0.1°x0.1° spatial resolution, combining information from the GPM constellation of PMW radiometers available over the majority of the Earth's surface with IR satellite measurements.This study proposes a comprehensive overview about the performance evaluation of IMERG (Early and Late run) and H63 products using rain gauges data in Pakistan, for the August 2022 Pakistan Flood event. Gauge-based rainfall data from the high-density climate station network of the Metrological Department Pakistan (PMD), are compared with NASA GPM IMERG rainfall products, and with H SAF and H63 rainfall product. Hourly and daily precipitation estimates are derived from the satellite products over specific regions entirely covered by 32 PMD stations. The goal of the study is to evaluate the performance of IMERG and H63 by means of a cross-comparison with rain gauges data (considered as the ground truth) using both statistical and graphical methods and analyze the results considering local environmental conditions. We also illustrate, through selected rainfall event cases and sub-regions, how insufficient coverage by PMW radiometers can lead to larger discrepancies in the IMERG and H63 estimates with respect to the ground measurements.

Drought events are projected to become more frequent and intense globally as a result of climate change. This weather hazard is particularly severe in the Horn of Africa, were it often produces damages to livestock, crop losses and food security emergencies. In order to reduce the severe impacts of this phenomenon and to promptly respond to humanitarian needs, it is essential to integrate the advances of the new generation satellite products to the current early warning systems. A study by West et al. (2019, 10.1016/j.rse.2019.111291) pointed-out how current drought monitoring systems require better spatial, temporal and spectral resolution to understand the complex nature of such events.For what concerns vegetation drought monitoring, current real-time systems (e.g. FEWS NET, GLDAS, NHyFAS) for emergency services do not take advantage of the benefits of geostationary satellite data and rather rely on Polar Operational Environmental Satellite data (POES) (Fensholt et al., 2011, 10.1016/j.jag.2011.05.009). Given their temporal resolution and considering that POES products present important data gaps problems due to the presence of clouds, they usually generate 10 or 16 days non-cloud contaminated vegetation composites. Consequently, this might affect timely response to natural hazards such droughts or floods. If on the one hand this data have been not fully accessible due to the requirements of considerable computational resources, the introduction of more powerful computers and distributed computing can narrow the gap.Precipitation deficits are a good indicator of meteorological drought. However, precise rainfall estimates are difficult to obtain given their large spatial variability. In Africa, convective storms can generate localized precipitation phenomena which can be difficult to measure. Additionally, weather stations data is limited and stations number have been decreasing. When using satellite products, studies on the African continent have shown how precipitation remote sensing products can present wet or dry biases (McNally et al., 2017, 10.1038/sdata.2017.12), which can in turn affect the outcomes of precipitation-derived meteorological indices (e.g. Standard Precipitation Index, SPI). Overall, the existence of different rain gauge, satellite or reanalysis products makes it non-trivial the identification of an optimal precipitation series (Le Coz & van de Giesen, 2020, 10.1175/JHM-D-18-0256.1) that can describe extreme events.The aim of this study is to show the benefits that the last generation of satellite products can offer to drought monitoring, in particular for those areas that lack reliable and dense in situ precipitation data. For this purpose, we will explore the relationships between meteorological and agricultural droughts for a subset of countries in the Greater Horn of Africa (Ethiopia, Somalia, Kenya), studying the relationships between vegetation health indexes (NDVI, VCI) and precipitation anomalies (e.g. SPI). The indexes related to the vegetation health have been calculated at daily time scale using the Meteosat SEVIRI radiometer, while the precipitation anomalies have been estimated using several precipitation products (e.g. rain gauge, satellite, reanalysis) at different short-term scales (30, 60, 90 and 180 days). A comprehensive intercomparison of the different precipitation products in the study area and their importance for the detection and forecast of agricultural droughts will be discussed.

Snowfall detection and quantification are challenging tasks in the Earth science field. Ground-based instruments provide only punctual measurements, and therefore the development of satellite-based snowfall retrieval methods is necessary for the implementation of a global monitoring system of snowfall. In particular, Passive Microwave (PMW) radiometric measurements are used for snowfall remote sensing: the retrieval is based on the scattering effect of the snowflakes visible in the high-frequency channels (> 80 GHz) on the upwelling radiation. However, the detection is made difficult by the weakness of this signature and by the contamination by the background surface emission/scattering signal. This phenomenon is particularly evident at high latitudes, where the prevalence of very light snowfall events and the extremely cold and dry environmental conditions make snowfall retrieval very difficult. The exploitation of operational microwave sounders on near-polar orbits such as the Advanced Technology Microwave Sounder (ATMS) allows for a very good coverage at high latitudes. Moreover, the wide range of channels (from 22 GHz to 183 GHz), allows for a radiometric characterization of the surface at the time of the overpass.In this work the High lAtitude sNow Detection and rEtrieval aLgorithm for ATMS (HANDEL-ATMS), a new snowfall retrieval algorithm developed especially for high latitude environmental conditions and based on the ATMS observations, is described. The algorithm is based on the use of ATMS-CPR coincidence dataset, i. e. a dataset where each ATMS multichannel observation is associated with a vertical snow profile obtained by the CloudSat Cloud Profiling Radar (CPR) and therefore it is possible to analyze the relationship between the vertical precipitation structure and the PMW measurements in a direct way, without using simulated datasets.The algorithm is composed of three main modules. The first module, based on the PMW Empirical clod Surface Classification Algorithm (PESCA), exploits ATMS low-frequency channel observations to obtain the surface classification and radiometric characterization at the time of the overpass. The second module estimates a set of clear-sky simulated brightness temperatures (TBs) by exploiting the previously derived surface radiometric properties. The clear-sky TBs set is compared with the observed TBs to highlight the snowfall signature. The third module is composed of four neural networks, which have been tuned against the CPR snowfall products. These networks, which exploit the deviation of the ATMS TBs from the clear-sky simulated TBs and the PESCA surface classification flag as inputs, return as outputs a snowfall detection flag and the surface snowfall rate estimate. HANDEL-ATMS shows very good detection capabilities - POD = 0.83, FAR = 0.18 and HSS=0.68. Estimation error statistics show an overestimation of very light snowfall events, but a good agreement for more intense events with respect to CPR snowfall products. The analysis of the results for an independent ATMS-CPR coincidence dataset and of selected snowfall events evidence the capability of HANDEL-ATMS to well detect and estimate snowfall also in presence of extreme environmental conditions typical of higher latitudes – dry and cold atmosphere and snow-covered background surface.

Mediterranean hurricanes (Medicanes) are characterized by the presence of a quasi-cloud-free calm eye, spiral-like cloud bands, and strong winds around the vortex center. Typically, they reach a tropical-like cyclone (TLC) phase characterized by an axisymmetric warm core without frontal structures. Yet, some of them are not fully symmetrical, have a shallow warm-core structure, and a weak frontal activity. Finding a clear definition and potential classification of Medicanes based on their initiation and intensification processes, understanding the role of convection, and identifying the evolution to a TLC phase are all current research topics. In this study, passive microwave (PMW) measurements and products are used to characterize warm core (WC) and deep convection (DC) for six Medicanes that occurred between 2014 and 2021. A well-established methodology for tropical cyclones, based on PMW temperature sounding channels, is used to identify the WC while PMW diagnostic tools and products (e.g., cloud-top height (CTH) and ice water path (IWP)), combined with lightning data, are used for DC detection and characterization. The application of this methodology to Medicanes highlights the possibility to describe their WC depth, intensity, and symmetry and to identify the cyclone center. We also analyze to what extent the occurrence and characteristics of the WC are related to the Medicane’s intensity and DC development. The results show that Medicanes reaching full TLC phase are associated with deep and symmetric WCs, and that asymmetric DC features in the proximity of the center, and in higher CTH and IWP values, with scarce lighting activity. Medicanes that never develop to a fully TLC structure are associated with a shallower WC, weaker and more sparse DC activity, and lower CTHs and IWP values. Ultimately, this study illustrates the potential of PMW radiometry in providing insights into dynamic and thermodynamic processes associated with Medicanes’ WC characteristics and evolution to TLCs, thus contributing to the ongoing discussion about Medicanes’ definition.

Accurate precipitation estimates are paramount for the activities related to water management and risk assessment. Satellite-based rainfall estimates are generally obtained by an inversion of the atmospheric signals reflected or radiated by atmospheric hydrometeors, i.e., a “top-down” approach. The main drawback of this retrieval technique is related to the number of satellite overpasses, that may lead to a general underestimation of rainfall. To overcome these issues, recently, some studies have investigated the possibility to integrate the state-of-the-art rainfall products with rainfall estimates obtained by a consolidated “bottom-up” approach, SM2RAIN (Brocca et al., 2014) exploiting satellite soil moisture observations for obtaining accumulated rainfall estimates. The integration between top-down and bottom-up estimates can produce a more reliable rainfall product for hydrological applications, characterized by better estimation of rainfall amounts and timing. On this basis, the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) has started the development and the sharing of integrated datasets of top-down satellite-based precipitation and soil moisture-derived rainfall estimates. During the Continuous Development and Operational Phase (CDOP) 3 and 4, the H SAF consortium planned to develop and provide several integrated products to the users for hydrological applications, also by taking advantages of the next EPS-SG instruments with enhanced retrieval capabilities. In this study, the usefulness of integrated products for river discharge simulation is assessed in Italy. More in details, the integrated product between SM2RAIN-derived estimates and Passive Microwave auxiliary product H67 (H64), the gauge corrected version (H84) and the SM2RAIN-only derived product (H87), along with the parent products will be use to force a semidistributed rainfall-runoff model (MISDc) during the period 2016-2019. The obtained river discharge timeseries have been compared with observed ones in order to evaluate the skill of the investigated products showing confirming the added value of using an integrated rainfall product for hydrological applications. Moreover, the results will provide useful insight that will help in improving the integrated products. The analysis provided good results, confirming the added value of using an integrated rainfall product for hydrological applications, allowing to overcome some of the limitations of the state-of-the-art precipitation datasets.

Mediterranean hurricanes (Medicanes) are meso-scale cyclones typical of the Mediterranean area which during their lifetime may show some dynamical features with tropical cyclones: the presence of a quasi-cloud-free calm eye, spiral-like cloud bands elongated from the center, strong winds close to the vortex centre and a warm core. They are often associated to heavy rainfall and flooding, intense wind, and high waves and storm surge, and can be serious threats to human life and infrastructure. Recent studies highlighted that extra-tropical and tropical-like cyclone (TLC) characteristics can alternate or even coexist in the same cyclonic system, and that only in some cases strong diabatic forcing leads to tropical-like transition (i.e., purely barotropic structure) associated to shallow or deep warm core. In this study a comparative analysis among the Medicanes occurred during the Global Precipitation Measurement (GPM) era (i.e. since March 2014), is carried out. The goal is to extract common features from passive MW measurements to identify and characterize the transition to TLC phase during the Medicane evolution. Passive microwave measurements from the GPM constellation radiometers are used to characterize the precipitation structure and warm core properties throughout the Medicane evolution. In particular, the NASA/JAXA GPM Core Observatory (GPM-CO) active and passive microwave (MW) sensors are used in conjunction with ground-based LIghtning NETwork (LINET) measurements to analyse the rainband structure and infer microphysics processes and convection strength. On the other hand, MW temperature sounding channels available from AMSU-A and ATMS radiometers are used to identify the warm core and infer its properties (e.g., depth and symmetry) around the cyclone center. The most intense Medicane on record, named Ianos, which swept across the Ionian Sea between 14 and 18 September 2020, is anlaysed in detail. The GPM-CO Dual-frequency Precipitation Radar (DPR) overpass, available for the first time during a medicane TLC phase, provides key measurements and products to analyze the 3D precipitation structure in the rainbands, offering further evidence of the main precipitation microphysics processes inferred from the passive MW measurement analysis. Moreover, the GPM-CO overpasses highlight a significant change in deep convection features between Ianos development and mature phases, which explain the substantial drop in lightning activity during Ianos TLC phase. The study demonstrates the value of satellite MW measurements in the GPM era to provide evidence of Medicanes' transition to TLC phase and to characterize its precipitation structure and microphysics processes.

The Mediterranean Basin is often hit by severe meteorological events, that can cause floods and flash floods. The intensity of these storms is both due to the presence of a warm sea, that contributes to feed the storm with high water vapor amounts, and to the complex orography of the region, which intensifies the precipitation over specific areas. The prediction of these events is very challenging, since different spatial and temporal scales are involved. Numerical Weather Prediction (NWP) models with a high spatial horizontal resolution are able to represent these kinds of events, but without a high precision in space, time and amount. For a better representation of extreme rainfall events, an important role can be played by the information given at the local scale to the NWP models by initial conditions. Data Assimilation (DA) can be a fundamental instrument to help NWP models to improve their prediction, through the production of better initial conditions. However, DA needs observational data, and there is a lack of meteorological data in open sea, where radar data are not available. In this context, satellite observational data are very interesting because they can provide data both over sea and over land. The AEROMET (AEROspatial data assimilation for METeorological weather prediction) project aims to study the satellite rain-rate assimilation in the Weather Research and Forecasting (WRF) model to improve the prediction of convective meteorological systems, with a particular focus to systems which originate over the sea. The assimilation method considers a certain rain-rate threshold, which is representative of convective precipitation, avoiding in this way to add an excessive water vapor amount to the model. In this work, we show the preliminary results of the AEROMET project. Examples are presented to show the feasibility of the method and statistics will be shown to quantify its impact on rainfall prediction. ACKNOWLEDGMENTS This work was done in the framework of the AEROMET project (A0375-2020-36588 - “Progetti di Gruppi di Ricerca 2020” LazioInnova - FESR Fondo Europeo di Sviluppo Regionale Programma Operativo regionale del Lazio).

Satellite-based precipitation estimates (SPEs) are generally validated using ground-based rain gauge or radar observations. However, in poorly instrumented regions, uncertainty in these references can lead to biased assessments of SPE accuracy. As a result, at regional or continental scales, an objective basis to evaluate SPEs is currently lacking. Here, we evaluate the potential for large-scale, spatially continuous evaluation of SPEs over land via the application of collocation-based techniques [i.e., triple collocation (TC) and quadruple collocation (QC) analyses]. Our collocation approach leverages the Soil Moisture to Rain (SM2RAIN) rainfall product, derived from the time series analysis of satellite-based soil moisture retrievals, in combination with independent rainfall datasets acquired from ground observations and climate reanalysis to validate four years of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) H23 daily rainfall product. Large-scale maps of the H23 correlation metric are generated using both TC and QC analyses. Results demonstrate that the SM2RAIN product is a uniquely valuable independent product for collocation analyses, because other available large-scale rainfall datasets are often based on overlapping data sources and algorithms. In particular, the availability of SM2RAIN facilitates the large-scale evaluation of SPE products like H23—even in areas that lack adequate ground-based observations to apply traditional validation approaches.

Mediterranean hurricanes (Medicanes) are meso-scale cyclones typical of the Mediterranean area which during their lifetime may some some dynamical features with tropical cyclones: the presence of a quasi-cloud-free calm eye, spiral-like cloud bands elongated from the center, strong winds close to the vortex center and a deep warm core. They are often associated to heavy rainfall and flooding, intense wind, and high waves and storm surge, and can be serious threats to human life and infrastructure. Recent studies highlighted that extra-tropical and tropical-like cyclone characteristics can alternate or even coexist in the same cyclonic system, and that only in some cases strong diabatic forcing leads to tropical-like transition (i.e., purely barotropic structure). In this study the satellite measurements from the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) active and passive microwave (MW) sensors are used to analyze the precipitation structure of the most intense Mediterranean Hurricane (Medicane) on record, named Ianos, which swept across the Ionian Sea between 14 and 18 September 2020. Two GPM-CO overpasses, available during Ianos development and tropical-like cyclone (mature) phase, are analyzed in detail. GPM Microwave Imager (GMI) measurements are used to carry out a comparative analysis of the medicane precipitation structure and microphysics processes between the two phases. The GPM-CO Dual-frequency Precipitation Radar (DPR) overpass, available for the first time during a medicane mature phase, provides key measurements and products to analyze the 3D precipitation structure in the rainbands, offering further evidence of the main precipitation microphysics processes inferred from the passive MW measurement analysis. Substantial difference in the rainband precipitation structure is observed, with deeper convection and stronger updraft features during development then at the mature phase, when also shallow precipitation/warm rain processes are observed in the inner region around the medicane eye. These features play a key role to explain the substantial drop in lightning activity during Ianos mature phase. Graupel-ice electrification process is inhibited due to the combined effect of strong horizontal wind and the observed limited growth of graupel. Starting from the detailed analysis of Ianos, a comparative study among the medicanes occurred during the GPM era is carried out. The goal is to extract common features from PMW measurements characterizing the different stage of medicanes’ evolution. The study demonstrates the value of the GPM-CO not only to characterize medicane precipitation structure and microphysics processes and convection strength with unprecedented detail, but also to provide evidence of tropical-like characteristics and of similarities with tropical cyclones for those medicanes undergoing tropical-like transition during their mature phase.

The NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory (CO) was launched on 27th February, 2014. It carries, for the first time, a Dual-frequency Precipitation Radar (DPR) designed to provide insights into the 3-D structure of precipitating clouds and rain intensity by using its Ka- and Ku-band frequencies. In addition to characterize the 3-D structure of precipitation, the DPR is used as calibrator for the GPM Microwave Imager (GMI). Single-frequency (SF) (both Ku- and Ka-only) and double-frequency (DF) based products provide, among the others, particle-size distribution (PSD) parameters (namely the mass-weighted mean diameter Dm and the normalized intercept parameter Nw), as well as precipitation rates. This book chapter focuses on reliability of the PSD parameters (limited to rainfall events, in this case we'll talk about of DSD - drop size distribution) over the Mediterranean area by taking as reference the DSD parameters estimated by ground-based radar measurement. Before of this, an inter-comparison between the SF and DF DPR outputs considering five years of data from GPM-CO mission is carried out. The goal is to investigate the reliability of SF-based products by assessing their quality compared to the DF-based ones, treated as a reference.

On September 14th, 2020 a depression originated on the Libyan coasts generated a Mediterranean tropical-like cyclone (hereafter referred to as Medicane Ianos), which moved northward until it hit, with its northernmost cloud bands, the southern Italian coasts and finally bent towards Greece, where it made landfall on September 18th, 2020. Heavy precipitation and flash flooding were reported, associated to huge damages to railway, houses, and four casualties.The correct prediction, as much as possible, of the trajectory and the intensity of these events is fundamental to prevent risks to infrastructures, natural landscapes, and people. One of the ways to evaluate the performances of the numerical weather prediction models is the comparison with satellite measurements. In particular, the mature phase of Medicane Ianos, as predicted by the weather research and forecasting (WRF) model, has been compared, for the first time, with the Global Precipitation Measurement mission Core Observatory (GPM-CO) active and passive measurements. Different microphysics schemes were used in order to investigate which is the most suitable to achieve the best forecast of Medicane Ianos considering different parameters as depression localization, reflectivity, rainfall rate, integrated liquid and ice content. The results show that all the schemes identified the precipitation bands structure of the Medicane overestimating the vertical extent of the convective structures. At the same time, all the schemes predicted an excessive columnar ice water content if compared to the one estimated from satellite measurements. It has to be highlighted that the overestimation is marked on the western precipitation bands of the Medicane eye, while a better agreement is obtained for the northern bands. Similar results are obtained for columnar liquid water content, even if the quantitative estimation is closer to the GPM measurements. Finally, all the schemes located the Medicane circulation center further north-west of its actual position.

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku-and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are demonstrated to extend the capability of representing light rain and cold-season precipitation from DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and surface emissivity effects., J.G. acknowledges the efforts by Ian Adams (NASA Goddard Space Flight Center) for support of airborne data collection during the IMPACTS campaign, and support from NASA grant 80NSSC20K0087.

This paper describes a new Passive microwave Empirical cold Surface Classification Algorithm (PESCA) developed for snow cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, as several studies have highlighted the influence of snow cover radiative properties on the falling snow passive microwave signature. The developed methodology is based on the exploitation of the lower frequency channels (< 90 GHz), common to most microwave radiometers. The methodology applied to the conically scanning GMI and the cross-track scanning ATMS is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust for both sensors in dry conditions (TPW < 10 mm), and for mean surface elevation < 2500 m, independently of the cloud cover. The algorithm shows very good performance for cold temperatures (2 m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 K and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA [overall detection statistics: POD=0.98(0.92), FAR=0.01(0.08), HSS=0.72(0.69) for ATMS(GMI)]. Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometry, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes appear to be different also at high frequency (>90GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically and cross track scanning radiometers including the future operational EPS-SG mission microwave radiometers.

In this talk, surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s Core Observatory sensors and the CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product over the continental United States. The analysis spans a period between Nov. 2014 and Sept. 2020 and covers the following products: the Dual-Frequency Precipitation Radar product (2A.GPM.DPR) and its single frequency counterparts (2A.GPM.Ka, 2A.GPM.Ku); GPM Combined Radar Radiometer Algorithm (2B.GPM.DPRGMI.CORRA); the CloudSat Snow Profile product (2C-SNOW-PROFILE) and two passive microwave retrievals i.e. the Goddard PROFiling algorithm (2A.GPM.GMI.GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM). The 2C-SNOW product has the highest Heidke Skill Score (HSS=75%) for detecting snowfall among all the analysed products. SLALOM ranks the second (60%) while the Ka-band products falls at the end of the spectrum, with the HSS of 10% only. Low detection capabilities of the DPR products are a result of its low sensitivity. All the GPM retrievals underestimate not only the snow occurances but also snowfall volumes. Underestimation by a factor of two is present for all the GPM products compared to MRMS data. Large discrepancies (RMSE of 0.7 to 1.5 mm/h) between space-borne and ground-based snowfall rate estimates can be attributed to the complexity of ice scattering properties and differences in the algorithms' assumptions.

Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s Core Observatory sensors and the CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product over the continental United States during the period from November 2014 to September 2020. The analysis includes: the Dual-Frequency Precipitation Radar (DPR) retrieval and its single frequency counterparts, the GPM Combined Radar Radiometer Algorithm (CORRA), the CloudSat Snow Profile product (2C-SNOW-PROFILE) and two passive microwave retrievals, i.e., the Goddard PROFiling algorithm (GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM). The 2C-SNOW retrieval has the highest Heidke Skill Score (HSS) for detecting snowfall among the products analysed. SLALOM ranks second; it outperforms GPROF and the other GPM algorithms, all detecting only 30% of the snow events. Since SLALOM is trained with 2C-SNOW, it suggests that the optimal use of the information content in the GMI observations critically depends on the precipitation training dataset. All the retrievals underestimate snowfall rates by a factor of two compared to MRMS. Large discrepancies (RMSE of 0.7 to 1.5 mm h-1) between space-borne and ground-based snowfall rate estimates are attributed to the complexity of the ice scattering properties and to the limitations of the remote sensing systems: the DPR instrument has low sensitivity, while the radiometric measurements are affected by the confounding effects of the background surface emissivity and of the emission of supercooled liquid droplet layers.

This article describes the development of a machine learning (ML)-based algorithm for snowfall retrieval (Snow retrievaL ALgorithm fOr gpM–Cross Track, SLALOM-CT), exploiting ATMS radiometer measurements and using the CloudSat CPR snowfall products as references. During a preliminary analysis, different ML techniques (tree-based algorithms, shallow and convolutional neural networks—NNs) were intercompared. A large dataset (three years) of coincident observations from CPR and ATMS was used for training and testing the different techniques. The SLALOM-CT algorithm is based on four independent modules for the detection of snowfall and supercooled droplets, and for the estimation of snow water path and snowfall rate. Each module was designed by choosing the best-performing ML approach through model selection and optimization. While a convolutional NN was the most accurate for the snowfall detection module, a shallow NN was selected for all other modules. SLALOM-CT showed a high degree of consistency with CPR. Moreover, the results were almost independent of the background surface categorization and the observation angle. The reliability of the SLALOM-CT estimates was also highlighted by the good results obtained from a direct comparison with a reference algorithm (GPROF).

Within the development of passive microwave precipitation retrieval techniques, and, inparticular, of snowfall detection and retrieval techniques, the possibility to characterize thefrozen background surface (snowcover and sea ice conditions) at the time of the overpassappears to be a relevant task. As demonstrated by many recent studies (e.g., Tabkiri et al.,2019, Ebtehaj and Kummerow 2017, Panegrossi et al., 2017), the microwave signalrelated to snowfall is strongly influenced by the surface conditions, and the response of theobserved brightness temperatures to the presence and intensity of snowfall depends oncomplex interconnections between environmental conditions (surface temperature, watervapor content, snow water path, cloud depth, presence of supercooled droplets) and thedifferent surface conditions (wet or dry snow cover, sea ice concentration and type, etc.).The use of surface classification climatological datasets results inadequate for the purposebecause of the extreme variability of the frozen surface conditions. It is thereforenecessary to be able to identify the background surface condition as close as possible (inspace and time) to that of the observation. The conically scanning GPM Microwave Imager(GMI) and cross-track the Advanced Technology Microwave Sounder (ATMS) are the mostadvanced currently available microwave radiometers. They are both equipped withchannels at several different frequencies that can be exploited both for the identification ofthe frozen surface conditions and for snowfall detection and retrieval at the time of theoverpass over a precipitation event (i.e., Rysman et al., 2018). Moreover, they can beused to analyze the potentials of future radiometers with similar characteristics such as theEPS-SG Microwave Sounder (MWS) and Microwave Imager (MWI), which represent thefuture in terms of European operational radiometers that can be exploited for precipitationretrieval at all latitudes (including the Polar Regions). In the last years we have developedtwo frozen surface classification schemes based on the use of GMI and ATMS lowfrequency channels (from 10 GHz up to 36 GHz) and on ancillary near-surfacetemperature and columnar water vapor data (obtained from ECMWF global ERA5reanalysis). The algorithm is able to identify 9 classes of soil including different type ofsnow and sea ice. The results of such classification have been compared with otherproducts, such as the NASA-GPROF soil type classification, and with snowcover and seaice global datasets (such as GMASI- Autosnow, and SNODAS from NOAA, and ECMWFERA5). In particular, the comparison with SNODAS over Northern America region showsthat the probability of detection of snow-covered surfaces varies between 86% - 98%(79%-95%) for GMI (ATMS) with a relatively small false alarm ratio (10%-30%). Theanalysis evidenced the main factors limiting the detection capability, such as the moisturecontent, the presence of orography, the snow cover beam filling and the snow depth.

Within the Copernicus Climate Change Service (C3S), the Climate Data Store (CDS) built by ECMWF will provide open and free access to global and regional products of Essential Climate Variables (ECV) based on satellite observations spanning several decades, amongst other things. Given its significance in the Earth system and particularly for human life, the ECV precipitation will be of major interest for users of the CDS.C3S strives to include as many established, high-quality data sets as possible in the CDS. However, it also intends to offer new products dedicated for first-hand publication in the CDS. One of these products is a climate data record based on merging satellite observations of daily and monthly precipitation by both passive microwave (MW) sounders (AMSU-B/MHS) and imagers (SSMI/SSMIS) on a 1°x1° spatial grid in order to improve spatiotemporal satellite coverage of the globe.The MW sounder observations will be obtained using, as input data, the FIDUCEO Fundamental Climate data Record (FCDR) for AMSU-B/MHS in a new global algorithm developed specifically for the project based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR; Sanò et al., 2015), adapted for climate applications (PNPR-CLIM). The algorithm consists of two Artificial Neural Network-based modules, one for precipitation detection, and one for precipitation rate estimate, trained on a global observational database built from Global Precipitation Measurement-Core Observatory (GPM-CO) measurements. The MW imager observations by SSM/I and SSMIS will be adopted from the Hamburg Ocean Atmosphere Fluxes and Parameters from Satellite data (HOAPS; Andersson et al., 2017), based on the CM SAF SSM/I and SSMIS FCDR (Fennig et al., 2017). The Level 2 precipitation rate estimates from MW sounders and imagers are combined through a newly developed merging module to obtain Level 3 daily and monthly precipitation and generate the 18-year precipitation CDR (2000-2017).Here, we present the status of the Level 2 product’s development. We carry out a Level-2 comparison and present first results of the merged Level-3 precipitation fields. Based on this, we assess the product’s expected plausibility, coverage, and the added value of merging the MW sounder and imager observations.ReferencesAnderssonet al., 2017, DOI:10.5676/EUM_SAF_CM/HOAPS/V002Fennig, et al., 2017, DOI:10.5676/EUM_SAF_CM/FCDR_MWI/V003Sanò, P., et al., 2015, DOI: 10.5194/amt-8-837-2015

Snowfall remote sensing is becoming an increasingly popular topic within both the scientific community and operational services. Studies focused on snow retrievals are important because snow represents a reservoir of fresh water and its quantification is a crucial task to thoroughly understanding the hydrological cycle. In addition, snow-cover plays a key role in the climate system, modifying the global energy budget because of its high albedo. In addition, snowfalls often represent a hazard to several public services (e.g. transportations, energy providers) as well as properties (e.g. roof loading) but also an opportunity (e.g. for hydropower).Passive microwave observations provided by currently operating spaceborne radiometers (e.g. Advanced Technology Microwave Sounder (ATMS), the Global Precipitation Measurement (GPM) Microwave Imager (GMI)) are a unique source of global information on the occurrence and the quantity of snowfall. However, because of the weaker and more complex signatures of snow at microwave frequencies [1] compared to those from rainfall, the retrieval schemes used by such instruments are still not fully optimised for snowfall detection and estimation, and subject to large errors. The ESA-funded RAINCAST project aims, among other tasks, at the verification of passive microwave snowfall products with the goal of fostering and defining new retrieval algorithms and mission concepts specifically optimised for snowfall quantification.In this study we show a comparative analysis between passive microwave snowfall rate estimates and high quality ground-based radar snowfall measurements to quantify the actual strengths and limitations in state-of-the-art passive microwave snowfall products. In particular, the performance of the Goddard profiling algorithm version 5A (GPROF V5A) and of a recently developed snowfall retrieval algorithm for GMI named SLALOM [2, 3] are investigated. The differences between GPROF and SLALOM are explored in relation to the environmental conditions (including the presence of supercooled droplets aloft that tend to mask the typical snowfall signature) where the snowfall retrievals are likely less accurate. In addition, ATMS snowfall products are analysed as well for selected case studies to evidence the potential and limitations of the different snowfall products in relation to the algorithm’s design (e.g., GPROF vs. SLALOM) and sensor characteristics (GMI and ATMS). Then quantitative assessments for all products are discussed by exploiting one year of ground reference radar network data over Northern U.S. and Canada provided by the Multi-Radar/Multi-sensor System (MRMS) product, available at 1x1 km horizontal regularresolution and 2 min time sampling, and providing gauge adjusted surface precipitation rate together with the indication of its phase.Our analysis confirms results from recent work on the same topic [e.g., 4], although a long term large scale analysis that quantify passive microwave retrieval is not found in the past literature. This work is particularly relevant not only for the quantification of the limitations of the current snowfall retrieval algorithms, but also to give recommendations for algorithm development for upcoming satellite missions (e.g. EPS-SG MWS, MWI/ICI), and for future satellite mission concepts.REFERENCES[1] Liu, G. et al, 2008. doi: 10.1029/2007JD009766.[2] Rysman, J.-F. et al., 2018. doi: 10.3390/rs10081278[3] Rysman J.-F., et al., 2019. doi:10.1029/2019GL084576,[4] Von Lerber, et al. doi: /10.1175/JAMC-D-17-0176.1

In Mediterranean area (MA), floods are, after droughts, the most dangerous meteorological hazard causing severe damages of manmade infrastructures, produce large loss of lives, and adversely affect the opportunities for socio-economic development of MA countries. MA is characterized by a relatively high number of flash floods (FFs) and floods occurring in small catchments normally triggered by deep convective systems associated with intense precipitation with a limited areal extent and high initial wetness conditions. For these reasons, the understating of the role of rainfall and soil moisture as well as the techniques available for their monitoring is particularly important. In this chapter, we review the most important studies carried out on floods in MA, which focuses on (1) the importance of the rainfall and soil moisture in the triggering and evolution of these events; (2) the monitoring techniques and models available to estimate these two variables along with their operational use for their prediction.

Difficulties in the representation of rainfall variability using ground-based sensors over mountainous areas necessitate the use of high-resolution satellite precipitation data sets from combined passive microwave (PMW) and geostationary infrared observations. The accuracy of these data sets depends greatly on the uncertainty characteristics of the PMW sensor retrievals and sampling frequency. In this paper, we evaluate the retrieval error characteristics from different PMW sensors over mountainous terrain using high temporal and spatial resolution ground-based radar (GR) reference rainfall data sets from two field experiments: one in the mid-Atlantic East Coast of the United States and the second in the Northeastern Italian Alps. We extracted matchups of PMW/GR rainfall based on a matching methodology that identifies GR volume scans coincident with PMW overpasses, and scales GR parameters to the satellite products’ nominal spatial resolution. Different PMW precipitation retrieval algorithms are evaluated for four PMW sensors. The error analysis shows varying error characteristics across different PMW retrievals. Moreover, the magnitude-dependent systematic error, going from overestimation or weak underestimation of light precipitation to mainly underestimation of heavier precipitation, shows weak covariation with the ground reference. Detection capabilities for all sensors increase markedly with an increasing reference rate and convective occurrence because of the more pronounced ice-scattering signal associated with deeper convection and high rain rates.

A new precipitation retrieval algorithm for the AMSR2 is described. The algorithm is based on the cloud dynamics and radiation database (CDRD) Bayesian approach and represents an evolution of the previous version applied to SSMIS observations, and used operationally within the EUMETSAT H-SAF program. This new product presents as main innovation the use of a very large database entirely empirical, derived from coincident radar and radiometer observations from the NASA/JAXA GPM-CO launched on February 28, 2014. The other new aspects are: 1) a new rain-/no-rain screening approach; 2) use of EOF and CCA for dimensionality reduction; 3) use of new ancillary variables to categorize the database and mitigate the problem of non-uniqueness of the retrieval solution; and 4) development and implementations of modules for computation time minimization. A verification study for case studies over Italy and for coincident AMSR2/GPM-CO observations over the MSG full disk area has been carried out. Results show remarkable AMSR2 capabilities for RR retrieval over ocean (for RR > 0.1 mm/h), good capabilities over vegetated land (for RR > 1 mm/h), while for coastal areas the results are less certain. Comparisons with NASA GPM products, and with ground-based radar data, show that the new CDRD for AMSR2 is able to depict very well the areas of high precipitation over all surface types. The algorithm is also able to handle an extremely large observational database available from GPM-CO and to provide rainfall estimate with minimum latency, making it suitable for NRT hydrological and operational applications.

The accurate estimation of rainfall from remote sensing is of paramount importance for many applications as, for instance, the mitigation of natural hazards like floods, droughts, and landslides. Traditionally, microwave observations in the frequency between 10 and 183 GHz are used for estimating rainfall based on the direct interaction of radiation with the hydrometeors within precipitating clouds in a so-called top-down approach. Recently, a bottom-up approach was proposed that uses satellite soil moisture products derived from microwave observations (

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.

The assessment of the actual observational capabilities of snowfall by spaceborne microwave radiometers is crucial to develop and improve precipitation retrieval algorithms. Exploiting coincident spaceborne active and passive microwave sensor datasets can effectively enhance our understanding of high-frequency microwave channels sensitivity to snowfall. This study illustrates the results of the analysis of matched Global Precipitation Measurement (GPM) Microwave Imager (GMI) and CloudSat Cloud Profiling Radar (CPR) snowfall observations (mainly found at latitudes between 55° and 65°) providing insights on GMI multi-frequency signals associated with different snowfall types. These findings are used to develop a new algorithm to retrieve snow water path associated to surface snowfall from GMI multichannel measurements.

Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) is a EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) program, designed to deliver satellite products of hydrological interest (precipitation, soil moisture and snow parameters) over the European and Mediterranean region to research and operations users worldwide. Six satellite precipitation algorithms and concomitant precipitation products are the responsibility of various agencies in Italy. Two of these algorithms have been designed for maximum accuracy by restricting their inputs to measurements from conical and cross-track scanning passive microwave (PMW) radiometers mounted on various low Earth orbiting satellites. They have been developed at the Italian National Research Council/Institute of Atmospheric Sciences and Climate in Rome (CNR/ISAC-Rome), and are providing operational retrievals of surface rain rate and its phase properties. Each of these algorithms is physically based, however, the first of these, referred to as the Cloud Dynamics and Radiation Database (CDRD) algorithm, uses a Bayesian-based solution solver, while the second, referred to as the PMW Neural-net Precipitation Retrieval (PNPR) algorithm, uses a neural network-based solution solver. Herein we first provide an overview of the two initial EU research and applications programs that motivated their initial development, EuroTRMM and EURAINSAT (European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale), and the current H-SAF program that provides the framework for their operational use and continued development. We stress the relevance of the CDRD and PNPR algorithms and their precipitation products in helping secure the goals of H-SAF's scientific and operations agenda, the former helpful as a secondary calibration reference to other algorithms in H-SAF's complete mix of algorithms. Descriptions of the algorithms' designs are provided including a few examples of their performance. This aspect of the development of the two algorithms is placed in the context of what we refer to as the TRMM era, which is the era denoting the active and ongoing period of the Tropical Rainfall Measuring Mission (TRMM) that helped inspire their original development. In 2015, the ISAC-Rome precipitation algorithms will undergo a transformation beginning with the upcoming Global Precipitation Measurement (GPM) mission, particularly the GPM Core Satellite technologies. A few years afterward, the first pair of imaging and sounding Meteosat Third Generation (MTG) satellites will be launched, providing additional technological advances. Various of the opportunities presented by the GPM Core and MTG satellites for improving the current CDRD and PNPR precipitation retrieval algorithms, as well as extending their product capability, are discussed.

This study shows the application of a total lightning data assimilation technique to the RAMS (Regional Atmospheric Modeling System) forecast. The method, which can be used at high horizontal resolution, helps to initiate convection whenever flashes are observed by adding water vapour to the model grid column. The water vapour is added as a function of the flash rate, local temperature and graupel mixing ratio. The methodology is set-up to improve the short-term (3 h) precipitation forecast and can be used in real-time forecasting applications. However, results are also presented for the daily precipitation for comparison with other studies. The methodology is applied to twenty cases occurred in fall 2012, that were characterized by widespread convection and lightning activity. For these cases a detailed dataset of hourly precipitation containing thousands of raingauges over Italy, which is the target of this study, is available through the HyMeX (HYdrological cycle in the Mediterranean Experiment) initiative. This dataset gives the unique opportunity to verify the precipitation forecast at the short range (3 h) and over a wide area (Italy). Results for the 27 October case study show how the methodology works and its positive impact on the 3 h precipitation forecast. In particular, the model represents better the convection over the sea using the lightning data assimilation and, when convection is advected over the land, the precipitation forecast improves over the land. It is also shown that the precise location of the convection by lightning data assimilation, improves the precipitation forecast at fine scales (meso-β). The application of the methodology to twenty cases gives a statistically robust evaluation of the impact of the total lightning data assimilation on the model performance. Results show an improvement of all statistical scores, with the exception of the Bias. The Probability of Detection (POD) increases by 3–5 % for the 3 h forecast and by more than 5 % for daily precipitation, depending on the precipitation threshold considered. Score differences between simulations with or without data assimilation are significant at 95 % level for most scores and thresholds considered, showing the positive and statistically robust impact of the lightning data assimilation on the precipitation forecast.

The objective of this paper is to describe the development and evaluate the performance of a completely new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track Advanced Technology Microwave Sounder (ATMS) radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation-sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered. The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area the statistical analysis was carried out for a 2-year (2013–2014) dataset of coincident observations over a regular grid at 0.5° × 0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h−1 over vegetated land and 0.71 mm h−1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with generally better estimation of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC > 0.90, ME < −0.22 mm h−1, RMSE < 2.75 mm h−1 and FSE% < 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications.

The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60°N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 ∆TB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 ∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg·m−2, IWP > 0.24 kg·m−2 over land, and SIC > 57%, TPW > 5.1 kg·m−2 over sea). The complex combined 166 ∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms.

An intercomparison of microwave multiple scattering radiative transfer codes used in generating databases for satellite rainfall retrieval algorithms has been carried out to ensure that differences obtained from retrieval techniques do not originate from the underlying radiative transfer code employed for the forward modeling. A set of profiles containing liquid water and ice contents of cloud and rain water as well as snow, graupel and pristine ice were distributed to the participants together with a black box routine providing Mie single scattering, atmospheric background absorption and surface emissivity. Simulations were to be carried out for nadir and off-nadir (53.1/spl deg/) observation angles at frequencies between 10 and 85 GHz. Among the radiative transfer models were two-stream, multiple stream and Monte Carlo models. The results showed that there were two major sources of differences between the codes. 1) If surface reflection/emission was considered isotropic, simulated brightness temperatures were significantly higher than for specular reflection and this effect was most pronounced at nadir observation and over ocean-type surfaces. 2) Flux-type models including delta-scaling could partially compensate for the errors introduced by the two-stream approximation. Largest discrepancies occurred at high frequencies where atmospheric scattering is most pronounced and at nadir observation. If the same surface boundary conditions, the same multiple-stream resolution and the same scaling procedures are used, the models were very close to each other with discrepancies below 1 K.

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 19435 (16231) 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.

Precipitation estimation from passive microwave radiometry based on physically based profile retrieval algorithms must be aided by a microphysical generator providing structure information on the lower portions of the cloud, consistent with the upper-cloud structures that are sensed. One of the sources for this information is mesoscale model simulations involving explicit or parameterized microphysics. Such microphysical information can be then associated to brightness temperature signatures by using radiative transfer models, forming what are referred to as cloud‐radiation databases. In this study cloud‐radiation databases from three different storm simulations involving two different mesoscale models run at cloud scales are developed and analyzed. Each database relates a set of microphysical profile realizations describing the space‐time properties of a given precipitating storm to multifrequency brightness temperatures associated to a measuring radiometer. In calculating the multifrequency signatures associated with the individual microphysical profiles over model space‐time, the authors form what are called brightness temperature model manifolds. Their dimensionality is determined by the number of frequencies carried by the measuring radiometer. By then forming an analogous measurement manifold based on the actual radiometer observations, the radiative consistency between the model representation of a rain cloud and the measured representation are compared. In the analysis, the authors explore how various microphysical, macrophysical, and environmental factors affect the nature of the model manifolds, and how these factors produce or mitigate mismatch between the measurement and model manifolds. Various methods are examined that can be used to eliminate such mismatch. The various cloud‐radiation databases are also used with a simplified profile retrieval algorithm to examine the sensitivity of the retrieved hydrometeor profiles and surface rainrates to the different microphysical, macrophysical, and environmental factors of the simulated storms. The results emphasize the need for physical retrieval algorithms to account for a number of these factors, thus preventing biased interpretation of the rain properties of precipitating storms, and minimizing rms uncertainties in the retrieved quantities.

The Special Observation Period (SOP1), part of the HyMeX campaign (Hydrological cycle in the Mediterranean Experiments, 5 September–6 November 2012), was dedicated to heavy precipitation events and flash floods in the western Mediterranean, and three Italian hydro-meteorological monitoring sites were identified: Liguria–Tuscany, northeastern Italy and central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models, including hydrological models and marine models, allowed an unprecedented monitoring and analysis of high-impact weather events around the Italian hydro-meteorological sites. This activity has seen strong collaboration between the Italian scientific and operational communities. In this paper an overview of the Italian organization during SOP1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in northeastern Italy, IOP13 (15–16 October 2012) in central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems, including the hydrological impacts. The usefulness of having different weather forecast operational chains characterized by different numerical weather prediction models and/or different model set up or initial conditions is finally shown for one of the events (IOP19).

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 (i.e., viewing angle, horizontal resolution, channel frequencies) of the cross-track and conically scanning radiometers used. The two algorithms use dynamical/meteorological/environmental variables as ancillary information to characterize the observed event, and mitigate the ambiguity of the cloud microphysical structures (and 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 within the H-SAF program, where the rainfall estimates are compared against radar observations and rain gauge network measurements for several precipitation events in Europe, characterized by different environmental, meteorological, dynamical conditions, and by different precipitation regimes. We will 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 the consistency of the retrievals from close in time overpasses of the cross-track and conically scanning radiometers for the same event will be discussed. Consistency, besides accuracy of the retrievals, is necessary in order to be able to fully exploit all current of future cross-track and conically scanning radiometer overpasses for monitoring precipitation, and to be able to use them in conjunction with IR GEO observations in blending/morphing resolution enhancing techniques for nowcasting and/or hydrological applications.

A verification study is conducted for the Cloud Dynamics and Radiation Database (CDRD) rainfall algorithm over the southern Mediterranean Basin (25–36° N / 25° W–45° E), based on TRMM Microwave Imager (TMI) passive microwave (PMW) brightness temperature (TB) measurements. The CDRD's required synthetic database is generated from a high resolution, nonhydrostatic cloud resolving model equipped with a precision RTE model. This dual modeling system is used to simulate numerous and precipitating storms over Europe and across the Mediterranean Basin as the database's foundation. Extracted from the simulations for the database are the a priori knowledge variables involving many thousands of synthetic precipitating microphysical-meteorological profiles and their concomitant synthetic multi-frequency PMW TBs. This information provides the probability framework for the CDRD algorithm's Bayesian inversion process and thus the means to relate measured satellite TBs to the most probable rainfall solutions. As the newest algorithm feature, the CDRD scheme makes use of meteorological parameters (tags) of observed precipitation systems, obtained from global NWP model synthesized initialization data, for both precipitation regime recognition and Bayesian retrieval constraints. These two steps effectively reduce non-uniqueness problems in obtaining retrieval solutions. Comparison of TMI-based CDRD retrievals to coincident TRMM Precipitation Radar (PR) retrievals, used as ground truth, provides the means to assess the algorithm's performance and the twofold use of meteorological tag information for control and constraint. PR-retrieved cloud property parameters (e.g., freezing level) are also compared to NWP model tags to further verify algorithm design performance. The verification procedure utilizes a full year (2010) of TMI and PR measurements, indicating CDRD over-ocean and over-land results are within 0.10 and 0.17 mm hr-1 of the PR means, with accompanying 1.24 and 2.94 mm hr-1 rms errors. The CDRD algorithm is also compared to the TRMM TMI facility algorithm (2al2-v7), exhibiting slight improvement over water (in correlation coefficient) and significantly improvement over land. Error statistics indicate CDRD vis-a-vis 2al2 over-land improvements of 78% and 14% in regards to mean error and rms error, respectively.

The EUMETSAT Satellite Application Facility on support to Operational Hydrology and Water Management (H-SAF) was established by the EUMETSAT Council on July 3, 2005 and started activity on September 1, 2005. The development programme duration was 5 years, ending on August 31, 2010. Soon after, the first phase of a follow-on Continuous Operations-Development Programme (CDOP-1) started with a 18-month period of duration. A following second phase (CDOP-2) has started on March 1, 2012 to provide long-term perspective (five years) to the initiative. The Italian Meteorological Service serves as Host Institute on behalf of 12 European member countries. H-SAF products include precipitation, soil moisture and snow parameters. Some products are based only on satellite observations (OBS), while other products are based on the assimilation of satellite measurements/products into numerical models (ASS). In addition to products development and generation, H-SAF includes a product validation programme and a hydrological validation programme. The Italian Civil Protection Department (DPC) coordinates the validation programme. Here, we will focus on the OBS precipitation products that are based on algorithms developed by CNR-ISAC in collaboration with the international community. While some of these products are being generated during the CDOP-2 phase, other products were first generated during the H-SAF development programme, then were under continuous development during CDOP-1 in order to improve their performance, and are now operationally generated at the Italian Centro Nazionale di Meteorologia e Climatologia Aeronautica (CNMCA), which is responsible of operational product generation and dissemination. Specifically, we will present and discuss the algorithms on which all these precipitation products are based. We will also discuss the activities that have been planned for CDOP-2, in order to enhance and improve algorithms and processing schemes and extend them to satellites that will be operational in the 2012-2017 timeframe - with special emphasis on the GEO Meteosat Third Generation (MTG) satellite which is scheduled to be launched by EUMETSAT in 2016, and on the LEO Core Observatory of the Global Precipitation Measurement (GPM) mission which will launched by NASA and JAXA in 2013.

Precipitation occurring over the Mediterranean basin is typically unusually difficult to measure due to variability resulting from the irregular terrain. Radar based precipitation measurements also are compromised by the terrain, which contaminates reflectivity with excessive ground cover and blocks the radar beam at low elevation angles. That same terrain also is instrumental in providing for the localization of rain producing storms that results in long term and flash flooding situations, especially in the fall

Rainfall retrieval estimation algorithms, based on passive and active microwave sensor data, are applied to along-track nadir-looking observations of a cyclone over ocean that occurred on February 8, 1993 during TOGA-COARE. The estimated rainfall rates derived from the radiometer data are compared with those obtained from ARMAR radar. Results in terms of reflectivity profiles and upwelling brightness temperatures, reconstructed from the estimated cloud structures, are discussed.

In the perspective of flood hazard multisensor monitoring, the event of 4–6 November, 1994 over Northern Italy is described and analyzed in this paper using traditional ground‐based rainfall observations and remote sensing techniques. Satellite imagery is used with different objectives: the interpretation of SAR measurements allows the identification of flooded areas, Meteosat infrared images show high intensity rain areas and SSM/I passive microwave data provide estimates of the rain‐rates. The IFA‐SAP algorithm, a profile based retrieval technique for estimating rainfall rates and precipitating cloud parameters from spacebome multifrequency microwave radiometers, has been used in the latter case. The use of multisensor observations considerably supplements conventional monitoring systems which prove inadequate for many hydrological purposes. The present paper is written in the spirit of encouraging a posteriori analyses of observed heavy rainfall events so as to explore the potential of a real‐time oper...