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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.

Snowfall detection and quantification are challenging tasks in the Earth system science field. Ground-based instruments have limited spatial coverage and are scarce or absent at high latitudes. Therefore, the development of satellite-based snowfall retrieval methods is necessary for the global monitoring of snowfall. Passive Microwave (PMW) sensors can be exploited for snowfall quantification purposes because their measurements in the high-frequency channels (> 80 GHz) respond to snowfall microphysics. However, the highly non-linear PMW multichannel response to snowfall, the weakness of snowfall signature and the contamination by the background surface emission/scattering signal make snowfall retrieval very difficult. This phenomenon is particularly evident at high latitudes, where light snowfall events in extremely cold and dry environmental conditions are predominant. ML techniques have been demonstrated to be very suitable to handle the complex PMW multichannel relationship to snowfall. Operational microwave sounders on near-polar orbit satellites such as the Advanced Technology Microwave Sounder (ATMS), and the European MetOp-SG Microwave Sounder in the future, offer a very good coverage at high latitudes. Moreover, their wide range of channel frequencies (from 23 GHz to 190 GHz), allows for the radiometric characterization of the surface at the time of the overpass along with the exploitation of the high-frequency channels for snowfall retrieval. The paper describes the High lAtitude sNow Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS), a new machine learning-based snowfall retrieval algorithm developed specifically for high latitude environmental conditions and based on the ATMS observations. HANDEL-ATMS is based on the use of an observational dataset in the training phase, where each ATMS multichannel observation is associated with coincident (in time and space) CloudSat Cloud Profiling Radar (CPR) vertical snow profile and surface snowfall rate. The main novelty of the approach is the radiometric characterization of the background surface (including snow covered land and sea ice) at the time of the overpass to derive multi-channel surface emissivities and clear-sky contribution to be used in the snowfall retrieval process. The snowfall retrieval is based on four different artificial neural networks for snow water path (SWP) and surface snowfall rate (SSR) detection and retrieval HANDEL-ATMS shows very good detection capabilities - POD = 0.83, FAR = 0.18, and HSS = 0.68 for the SSR detection module. Estimation error statistics show a good agreement with CPR snowfall products for SSR > 10−2 mm h−1 (RMSE 0.08 mm h−1, bias = 0,02 mm h−1). The analysis of the results for an independent CPR dataset and of selected snowfall events evidence the unique capability of HANDEL-ATMS to detect and estimate SWP and SSR also in presence of extreme cold and dry environmental conditions typical of high latitudes.

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.

Heavy precipitation systems that are typical in the Mediterranean area and often devastate the coastal regions, are described and analyzed here by exploiting active and passive microwave measurements and state-of-the-art precipitation products available in the Global Precipitation Measurement (GPM) mission era. The GPM is boosting its key role in integrating the established observational ground-based and satellite-borne tools not only for precipitation monitoring, but also for understanding and characterizing severe weather in the Mediterranean area. In this Chapter, we present three events that have recently challenged observational and forecasting capabilities, and caused damages at the ground. Making use of ground based and satellite-borne instruments, we address the problem of estimating precipitation of a small-scale and short-living intense thunderstorm, the capability to render the 3-D structure of a mesoscale organized convective system, and the key role of spaceborne microwave sensors in the characterization and monitoring of a tropical-like cyclone. To this end, we exploited satellite measurements probably beyond the role they have been designed for, showing few strategies to blend satellite data and products with conventional meteorological data, with the aim to increase the knowledge of severe systems in the Mediterranean area and to support operational forecasting activities in a climate change perspective.

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.