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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 (<10 GHz) for the estimation of accumulated rainfall amounts. The integration of the bottom-up and top-down approaches has large potential for providing high accurate rainfall estimates exploiting their different and complementary nature. In this study, we perform a long-term (3 years) assessment of different satellite rainfall products exploiting the full range of microwave frequencies over Italy. Specifically, the integration of two top-down algorithms (CDRD, Cloud Dynamics and Radiation Database, and PNPR, Passive microwave Neural network Precipitation Retrieval) for estimating rainfall from conically and cross-track scanning radiometers, and one bottom-up algorithm (SM2RAIN) applied to the Advanced SCATterometer soil moisture product is carried out. The performances of the products, individually and merged together, are assessed at daily time scale. The integration of top-down and bottom-up approaches provides the highest performance both in terms of continuous and categorical scores (i.e., median correlation coefficient and root mean square error values equal to 0.71 and 6.62 mm, respectively). In such a combination, the limitations of the two approaches are compensated allowing a better estimation of ground accumulated rainfall through SM2RAIN while, overcoming the limitations of rainfall estimation for intense events during wet conditions through CDRD-PNPR product. The accuracy and the reliability of the merged product open new possibilities for their testing in hydrological applications, such as the monitoring and prediction of floods and droughts over large areas, including regions where ground-based measurements are sparse or not available. [ABSTRACT FROM AUTHOR]

This study shows how satellite-based passive and active microwave (MW) sensors can be used in conjunction with high-resolution Numerical Weather Prediction (NWP) simulations to provide insights of the precipitation structure of the tropical-like cyclone (TLC) Numa, which occurred on 15–19 November 2017. The goal of the paper is to characterize and monitor the precipitation at the different stages of its evolution from development to TLC phase, throughout the storm transition over the Mediterranean Sea. Observations by the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) and by the GPM constellation of MW radiometers are used, in conjunction with the Regional Atmospheric Modeling System (RAMS) simulations. The GPM-CO measurements are used to analyze the passive MW radiometric response to the microphysical structure of the storm, while the comparison between successive MW radiometer overpasses shows the evolution of Numa precipitation structure from its early development stage on the Ionian Sea into its TLC phase, as it persists over southern coast of Italy (Apulia region) for several hours. Measurements evidence stronger convective activity at the development phase compared to the TLC phase, when strengthening or weakening phases in the eye development, and the occurrence of warm rain processes in the areas surrounding the eye, are identified. The weak scattering and polarization signal at and above 89 GHz, the lack of scattering signal at 37 GHz, and the absence of electrical activity in correspondence of the rainbands during the TLC phase, indicate weak convection and the presence of supercooled cloud droplets at high levels. RAMS high-resolution simulations support what inferred from the observations, evidencing Numa TLC characteristics (closed circulation around a warm core, low vertical wind shear, intense surface winds, heavy precipitation), persisting for more than 24 h. Moreover, the implementation of DPR 3D reflectivity field in the RAMS data assimilation system shows a small (but non negligible) impact on the precipitation forecast over the sea up to a few hours after the DPR overpass. [ABSTRACT FROM AUTHOR]

The Mediterranean Sea is a unique meteorological environment and a weather forecasting challenge, where severe weather events of different nature often originate and develop to hit coastal regions, causing major damages and casualties. Conventional ground-based instruments (e.g., raingauges and weather radars) are often inadequate to monitor events during their offshore development. On the other hand, in spite of their irregular time sampling, Low Earth Orbit satellites, carrying passive and active microwave (MW) sensors, can be fully exploited for the characterization and monitoring of heavy precipitation systems over this area. In this respect, the NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory (CO), equipped with the most advanced MW radiometer (GPM Microwave Imager, GMI) and the first spaceborne Dual-frequency Precipitation Radar (DPR), allows for the 3-D analysis of precipitation structures. Moreover the GPM constellation of MW radiometers ensures 1-3 hourly global coverage of precipitation. In this presentation, we show the GPM mission role in the characterization and monitoring of precipitation associated to extreme events, that hit Italy in the last years. Moreover, we test the capabilities and limitations of advanced satellite precipitation products in the GPM era for quantification and monitoring of precipitation associated with very intense and localized convective cells. To this aim, we analyze the flash flood occurred in Livorno, on the coast of Tuscany, Italy, in the night between 9 and 10 Sept. 2017. In this case, the precipitation products based on the GPM constellation of MW radiometers offer a wide spectrum of results, showing that better performances come from algorithms tailored for the specific region, while algorithms designed for global application are less sensitive to such small-scale thunderstorms. An analogous study is carried out for Numa, a Mediterranean tropical-like cyclone occurred on 16-18 November 2017 over southern Italy. GPM-CO measurements are used, in conjunction with LINET (LIghtning NETwork) data, to show how the rain-band structure evolves as the storm develops into its mature phase. High-resolution simulations were carried out with the Regional Atmospheric Modeling System (RAMS) to analyze the key mechanisms leading to the formation and evolution of Numa. We also show the impact of the assimilation of DPR reflectivity on the precipitation forecast. Long data records of reliable satellite precipitation-related measurements and products are becoming available in the GPM era. Here we make a first attempt to exploit them for a preliminary analysis of occurrence and intensity of extreme events in the Mediterranean, where they can be recognized as climate change signatures. This study evidences the need to fully exploit satellite data and products for studying and monitoring severe weather in the Mediterranean area, and to improve forecasting (and nowcasting) capabilities in this complex region. Thus efforts should be undertaken to develop satellite products tailored for these areas (working on algorithm calibration), and to provide the error structure of the products (working on validation). This would enable users to apply the most suitable product for a specific need, including hydrological applications, and for data assimilation of precipitation-related fields over the sea, where these systems often initiate. [ABSTRACT FROM AUTHOR]

Aims: To assess the extent of off‐label drug use and the occurrence of suspected adverse drug reactions (ADRs) among paediatric patients in Italian hospitals. Methods: We conducted a 2‐year prospective cohort study across 22 Italian hospital wards from September 2020 to September 2022. As part of the surveillance project, we performed a 6‐month retrieval of all reported ADRs and evaluated all drug prescriptions for their possible off‐label use. Following an educational project on pharmacovigilance addressed to healthcare professionals in participating wards, the same data collection was performed. Results: Among the 892 patients included in the study, 64% were admitted to paediatric wards and 36% to neonatal wards. Fifty per cent of all drugs prescribed were used off‐label and mainly concerned the administration of a different dose from the one authorized. In neonatal wards, off‐label prescriptions occurred slightly more often, with antibacterials being the most frequently used off‐label drugs. A total of 35 reports of suspected ADRs were collected, five before the educational project and 30 afterwards. Based on product licence, 10 of the total 35 reports concerned at least one off‐label drug use. Conclusions: The off‐label use of drugs in treating paediatric patients was extensive in Italian hospitals. Regulatory interventions are needed to promote the use of drugs based on the latest available literature and improve ADR reporting on children. Paediatric indications and dosages of the drugs most commonly used in children should be supported by appropriate ad hoc studies. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article about mechanical ventilation for transferred newborn infants with duct-dependent congenital heart disease.

Considering the clinical significance for myocarditis and pericarditis after immunization with mRNA COVID-19 vaccines, the present pharmacovigilance study aimed to describe these events reported with mRNA COVID-19 vaccines in the Vaccine Adverse Events Reporting System (VAERS). From 1990 to July 2021, the mRNA vaccines were the most common suspected vaccines related to suspected cases of myocarditis and/or pericarditis (myocarditis: N = 1,165; 64.0%; pericarditis: N = 743; 55.1%), followed by smallpox vaccines (myocarditis: N = 222; 12.2%; pericarditis: N = 200; 14.8%). We assessed all suspected cases through the case definition and classification of the Brighton Collaboration Group, and only definitive, probable, and possible cases were included in the analysis. Our findings suggested that myocarditis and pericarditis mostly involve young male, especially after the second dose with a brief time to onset. Nevertheless, this risk is lower (0.38/100,000 vaccinated people; 95% CI 0.36–0.40) than the risk of developing myocarditis after SARS-CoV-2 infection (1000–4000 per 100,000 people) and the risk of developing "common" viral myocarditis (1–10 per 100,000 people/year). Comparing with the smallpox vaccine, for which is already well known the association with myocarditis and pericarditis, our analysis showed a lower probability of reporting myocarditis (ROR 0.12, 95% CI 0.10–0.14) and pericarditis (ROR 0.06, 95% CI 0.05–0.08) following immunization with mRNA COVID-19 vaccines. [ABSTRACT FROM AUTHOR]

Abstract: We perform an optical characterization of UV laser ablated silicates (olivine, pyroxene), starting from their reflectance spectra in the 0.3–2.5 μm spectral range. The goal is to provide useful tools to model space weathering effects on surfaces of asteroids and TNOs (trans-neptunian objects). We determine that the reddening and darkening spectral trend is compatible with the Hapke''s space weathering model, using the optical constants of metallic iron in a silicate matrix. This result is supported by new magnetic susceptibility measurements on laser ablated orthopyroxene. We also investigate the potential contribution of formation of amorphous silicates in the process. Applying our results to silicate-rich surfaces in the Solar System, we investigate the possibility of a weathered olivine component on the surface of Centaur 5145 Pholus. Inclusion of this component slightly decreases the amount of complex organics and water ice from those previously estimated. Thus, the current Pholus spectrum is consistent with the presence of either unweathered or weathered olivine, or potentially both materials. [Copyright &y& Elsevier]

The NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory (CO) 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. Single-frequency (SF) (both Ku- and Ka-only) and double-frequency (DF) based products provide particle-size distribution (PSD) parameters, as well as precipitation rates. Background surface type, precipitation type and phase, and vertical extension of the storm are also provided. In this paper, an intercomparison between the SF and DF DPR outputs over the Mediterranean area during rainfall events in the first four years of 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. The vertical profiles and the near-surface values of the corrected reflectivity of the PSD parameters (mean mass-weighted diameter and normalized intercept) and of the rainfall rate have been analyzed. The data have been categorized for surface type (land and sea) and precipitation type (stratiform and convective). The results show a more marked difference between the DF and SF Ka-only based products than between DF and SF Ku-only based products. The feature is confirmed by the analysis of vertical profiles of the SF- and DF-based retrieved parameters. The statistical scores do not differ significantly between land and sea while they differ noticeably between stratiform and convective precipitation. [ABSTRACT FROM AUTHOR]

In this study, we present a new module for the Snow retrievaL ALgorithm fOr gMi (SLALOM) that retrieves surface snowfall rate using Global Precipitation Measurement (GPM) Microwave Imager measurements together with humidity and temperature vertical profiles. This module, named Surface Snowfall Rate Module, is tuned using colocated surface snowfall observations of the Cloud Profiling Radar onboard CloudSat. Using this new module, the SLALOM algorithm is able to predict surface snowfall rate with a relative bias of −13%, a root‐mean‐square error of 0.08 mm/hr, and a correlation coefficient of 0.7. Surface Snowfall Rate Module is then used to retrieve snowfall rate for three case studies and to provide a unique, 70°S to 70°N high‐resolution distribution of average surface snowfall rate from 2014 to 2017. This new product will be useful for surface precipitation analyses, global water budget estimation, and climatological analyses. Plain Language Summary: A comprehensive monitoring of Earth surface snowfall can only be reached using spaceborne instruments. However, estimating surface snowfall from space is challenging as the signal measured by space sensors is only indirectly related to surface snowfall rate. In this study, we developed a new algorithm that is able to estimate surface snowfall rate using observations from the Global Precipitation Measurement Microwave Imager together with humidity and temperature vertical profiles. We used this algorithm to provide a unique, 70°S to 70°N high‐resolution distribution of average surface snowfall rate from 2014 to 2017. This new product will be useful for surface precipitation analyses, global water budget estimation and climatological analyses. Key Points: We developed a surface snowfall rate retrieval algorithm for the Global Precipitation Measurement Microwave ImagerSurface snowfall rate is predicted with a relative bias of −13% and a correlation coefficient of 0.7 compare to CloudSat Cloud Profiling Radar observationsA unique, 70°S to 70°N high‐resolution distribution of average surface snowfall rate from 2014 to 2017 is provided [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60N/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 DTB) 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 DTB 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 DTB-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. [ABSTRACT FROM AUTHOR]

An important objective of the Global Precipitation Measurement (GPM) mission is the detection of falling snow, since it accounts for a significant fraction of precipitation in the mid-high latitudes. The GPM Core Observatory carries the first spaceborne Dual-frequency Precipitation Radar (DPR), designed with enhanced sensitivity to detect lighter liquid and solid precipitation. The primary goal of this study is to assess the DPR's ability to identify snowfall using near-coincident CloudSat Cloud Profiling Radar (CPR) observations and products as an independent reference dataset. CloudSat near global coverage and high sensitivity of the W-band CPR make it very suitable for snowfall-related research. While DPR/CPR radar sensitivity disparities contribute substantially to snowfall detection differences, this study also analyzes other factors such as precipitation phase discriminators that produce snowfall identification discrepancies. Results show that even if the occurrence of snowfall events correctly detected by DPR products is quite small compared to CPR (around 5–7%), the fraction of snowfall mass is not negligible (29–34%). A direct comparison of CPR and DPR reflectivities illustrates that DPR misdetection is worsened by a noise-reducing DPR algorithm component that corrects the measured reflectivity. This procedure eliminates the receiver noise and side lobe clutter effects, but also removes radar signals related to snowfall events that are associated with relatively low reflectivity values. In an effort to increase DPR signal fidelity associated with snowfall, this paper proposes a simple algorithm using matched DPR Ku/Ka radar reflectivities producing an increase of the fraction of snowfall mass detected by DPR up to 59%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The objective of this paper is to describe the development and evaluate the performance of a totally 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 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 two-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 general better estimate 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. [ABSTRACT FROM AUTHOR]

Gallerani M, Imberti D, Mari E, Marra A, Manfredini R. Risperidone and pulmonary embolism: a harmful association? Case series and review of the literature. Objective: Risperidone is an atypical antipsychotic drug used to treat a number of psychiatric diseases, such as schizophrenia, schizoaffective and bipolar disorders and irritability in children with autism. Moreover, it is also often administered for short-term treatment of persistent aggression in people with moderate-to-severe Alzheimer's dementia. A possible association between risperidone and venous thromboembolism (VTE) has been described. We intended to verify the dimension of the problem in our hospital setting. Methods: We considered all consecutive patients hospitalised in our Internal Medicine Department from January 2004 to December 2010, who were treated with risperidone and presented pulmonary embolism (PE). Results: Four cases of patients, apparently free from the well-known major risk factors for VTE (i.e. cancer, prolonged immobilisation, acute cardiac and respiratory failure, infections), who presented PE associated with risperidone therapy, were reported in details. Conclusions: A review of the available literature, discussing the possible different pathogenic reasons for this increased risk of VTE, is provided. [ABSTRACT FROM AUTHOR]

Background: : The risk of myocardial infarction, macular oedema and bone fractures associated with thiazolidinediones (TZDs) has been extensively investigated.Objective: : The aim of the study was to verify if the analysis of a large spontaneous reporting database could generate early signals on these adverse drug reactions (ADRs) associated with TZDs.Methods: : A case/non-case study, restricted to antidiabetic drugs, was performed on spontaneous reports of ADRs (2005-2008) in the US FDA Adverse Event Reporting System (AERS). The method was applied to TZDs, both as a drug class and as single agents. The reporting odds ratio (ROR) with 95% CI was calculated as a measure of disproportionality in the whole dataset and in a quarter-by-quarter analysis.Results: : TZD use was registered in 49 589 out of 301 950 drug-reaction pairs (16%), with significant disproportionality for myocardial infarction (ROR 4.71; 95% CI 4.40, 5.05), macular oedema (3.88; 2.79, 5.39) and bone fractures (1.73; 1.53, 1.96). Separate analysis of the two TZDs showed that only rosiglitazone was associated with myocardial infarction (7.86; 7.34, 8.34) and macular oedema (5.55; 3.94, 7.79), whereas pioglitazone was associated with multiple site fractures (2.00; 1.70, 2.35), in particular upper and lower limb and pelvic fractures. The quarter-by-quarter analysis identified disproportionality for myocardial infarction (3.13; 2.38, 4.10) and bone fractures since January-March 2005 (2.70; 1.04, 2.78).Conclusions: : The frequency of reporting of myocardial infarction, macular oedema and fractures was significantly higher for TZDs in comparison with other antidiabetic drugs, with large intraclass differences. Both myocardial infarction and bone fracture signals appeared before major publications on these safety issues. [ABSTRACT FROM AUTHOR]

Levetiracetam is a pyrrolidine derivate that is approved for the treatment of seizures. It has a favourable pharmacokinetic profile, no clearly established pharmacological interactions, good tolerability and offers the possibility of rapid treatment. We describe a case of pancytopenia and multiple infections observed in the context of levetiracetam treatment. A 65-year-old woman who underwent surgical removal of a meningioma developed progressive pancytopenia complicated by pneumonia and multiple liver abscesses after starting levetiracetam therapy. Bone marrow aspiration and biopsy confirmed the diagnosis. A gradual spontaneous recovery of normal haematopoiesis followed discontinuation of levetiracetam. [ABSTRACT FROM AUTHOR]

State-of-the-art satellite rainfall products are often the only way for measuring precipitation in remote areas of the world. These products are mainly based on the exploitation of microwave (MW) radiometers on board LEO satellites (top-down approach). However, it is well known that they may fail in properly reproducing the amount of precipitation reaching the ground, which is of paramount importance for hydrological applications (natural hazards forecast and mitigation, water management). Currently, one of the major issues impacting the quality of the information retrieved from space is related to the estimation of light precipitation that causes a general underestimation of the total amount of rainfall. This issue is particularly important over land due to the uncertainty and spatial variability of surface emissivity. With the purpose of improving the accuracy of satellite rainfall products, some approaches using satellite soil moisture (SM) data were recently developed (bottom-up approach). In 2013 a new method for estimating rainfall using satellite SM observations, called SM2RAIN, has been proposed. The method is based on the inversion of the soil water balance equation, i.e. it estimates rainfall by using the variation in time of the amount of water stored into to the soil, thus considering it "as a natural rain gauge". SM2RAIN has been applied both at local and global scale with ground- and satellite-based SM data as input with satisfactory results in terms of rainfall estimation. Moreover, previous studies found that the correction of rainfall estimates through SM2RAIN provides improvement in flood modeling. However SM-only based rainfall estimates suffer a number of issues such as the inability to estimate precipitation when the soil is fully saturated, when it is vegetated or snow-covered, besides not providing any precipitation data over the sea. Thus a fully global satellite rainfall product, able to integrate rainfall estimates derived from top-down and bottom-up approaches, has been demonstrated to be highly beneficial for increasing the accuracy of satellite precipitation estimates.Through the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF), the SM2RAIN approach together with the MW-based rainfall product H23 are the scientific background for the development of the "H64 precipitation-soil moisture integrated product". H23 provides daily precipitation estimates by combining MW precipitation rate estimates derived from conical (H01) and cross-track (H02) scanning radiometers. SM estimates obtained through H16 and H101 H SAF products are used to estimate rainfall through SM2RAIN, and then merged with H23 precipitation estimates through a simple nudging scheme. H64 has been designed to combine optimally the two different approaches in order to provide a more reliable rainfall product, that can be used in an operational framework. Preliminary results show that H64 can efficiently reproduce the observed rainfall during a 4-year period, providing a useful and reliable source of information over scarcely instrumented areas. This first assessment provides very useful insights about the development of a near-real time rainfall product based on SM2RAIN and MW-based top-down precipitation products. [ABSTRACT FROM AUTHOR]

The development of precipitation retrieval techniques, as well as the quality assessment of satellite-based global precipitation estimates, can now benefit from the availability of unique cloud and precipitation observations by the two spaceborne radars: the Dual-frequency Precipitation Radar (DPR) on board the NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory, and the NASA CloudSat Cloud Profiling Radar (CPR). The multi-year, quasi-global, and complementary DPR and CPR measurements offer a unique and extensive resource to analyze spaceborne microwave radiometer precipitation observational capabilities. This can be particularly useful in remote areas and/or where ground-based observations are sparse or not available and in conditions and regimes where passive MW precipitation retrieval is more challenging. A recent study (Panegrossi et al., 2017) illustrated the potential of the use of these observational datasets to improve snowfall detection and retrieval techniques for MW radiometers at higher latitudes. Recently, a new algorithm for snowfall detection and retrieval for the Global Precipitation Measurement (GPM) Microwave Imager (GMI) (SLALOM, Snow retrievaL ALgorithm fOr gMi) has been developed (Rysman et al., 2018). The algorithm is tuned and evaluated against coincident observations of the CPR, and enables the detection of supercooled water and the retrieval of snow water path (SWP). From a forward-looking perspective we are currently investigating the use of cross-track scanning radiometers, in particular the Advanced Technology MicroWave Sounder (ATMS), for snowfall detection and retrieval. It is worth noticing that ATMS sensor is currently carried by near-polar operational LEO satellites, and therefore it could be used for investigating the higher latitudes. In particular, the potential for snowfall detection of the several high-frequency channels, i.e. the five channels in the 183.3 GHz water vapor absorption band, is being investigated. These channels are very sensitive to snowfall and are less affected by the background surface. The results of a sensitivity study based on the analysis of snowfall events for which triple coincidences of CPR,ATMS, and GMI observations are available is presented. The events analyzed have been extracted from the 3B-CSATGPM dataset (Turk, 2018). A comparison of the different response of ATMS and GMI to the same snowfall event is carried out. In particular, the TBs measured by both radiometers and different channel combinations have been compared considering snowfall profiles obtained by CPR and environmental conditions (i.e., moisture and temperature profiles, and presence of supercooled droplets). Some intense snowfall events has been taken into account, such as an event that took place in Eastern Siberia in 2014/04/30, an event which took place on the Greenland coast 2014/09/21, and an event that took place in the Andes region in 2016/06/03.These studies pave the way towards the definition of future missions for snowfall global monitoring by combining a core satellite equipped with a multi-frequency radar in synergy with current and future MW radiometers (such as the EPS-SG Microwave Imager and Microwave Sounder). [ABSTRACT FROM AUTHOR]

This work proposes a multi-parameter method for the detection of cloud-to-ground stroke rate (SRCG) associated to convective cells, based on the measurements of a low-cost single-polarization X-band weather radar. To train and test our procedure, we built up a multi-year dataset, collecting 1575 radar reflectivity volumes that were acquired in the pilot study area of Naples metropolitan environment matched with the LIghtning NETwork (LINET) strokes and meteorological in-situ data. Three radar-based variables are extracted simultaneously for each rain cell and properly merged together, using "ad hoc" classification methods, to produce an estimation of the expected lightning activity for each rain cell. These variables, proxies of mixed-phase particles and ice amount into a convective cell, are combined into a single label to cluster the SRCG into two categories: SRCG = 0 (no production of strokes) or SRCG > 0 (stroke production), respectively. Overall, the main results are comparable with those that were obtained from more advanced radar systems, showing a Critical Success Index of 0.53, an Equitable Threat Score of 0.34, a Frequency Bias Index of 1.00, a Heidke Skill Score of 0.42, a Hanssen-Kuiper Skill Score of 0.42, and an area under the curve of probability of detection as a function of false alarm rate (usually referred as ROC curve) equal to 0.78. The developed technique, although with some limitations, outperforms those based on the use of single stroke proxy parameters. [ABSTRACT FROM AUTHOR]

The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades; however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Paraíba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor. These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q 2 V u 15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms. Differently, for the Vale do Paraíba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values. [ABSTRACT FROM AUTHOR]

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]