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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.

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.

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 ground-based-radar-derived raindrop size distribution (DSD) parameters—mass-weighted drop diameter Dmass and normalized intercept parameter NW—are the sole resource for direct validation of the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission Core Observatory satellite-based retrieved DSD. Both Dmass and NW are obtained from radar-measured reflectivity ZH and differential reflectivity ZDR through empirical relationships. This study uses existing relationships that were determined for the GPM ground validation (GV) program and directly compares the NASA S-band polarimetric radar (NPOL) observables of ZH and ZDR and derived Dmass and NW with those calculated by two-dimensional video disdrometer (2DVD). The joint NPOL and 2DVD datasets were acquired during three GPM GV field campaigns conducted in eastern Iowa, southern Appalachia, and western Washington State. The comparative study quantifies the level of agreement for ZH, ZDR, Dmass, and log(NW) at an optimum distance (15–40 km) from the radar as well as at distances greater than 60 km from radar and over mountainous terrain. Interestingly, roughly 10%–15% of the NPOL ZH–ZDR pairs were well outside the envelope of 2DVD-estimated ZH–ZDR pairs. The exclusion of these pairs improved the comparisons noticeably.

The National Aeronautics and Space Administration Global Precipitation Measurement (GPM) mission ground validation program uses dual-polarization radar moments to estimate raindrop size distribution (DSD) parameters, the mass-weighted mean drop diameter Dmass, and normalized intercept parameter NW, to validate the GPM Core Observatory–derived DSD parameters. The disdrometer-based Dmass and NW are derived through empirical relationships between Dmass and differential reflectivity ZDR, and between NW, reflectivity ZH, and Dmass. This study employs large datasets collected from two-dimensional video disdrometers (2DVD) during six different field studies to derive the requisite empirical relationships. The uncertainty of the derived Dmass(ZDR) relationship is evaluated through comparisons of 2DVD-calculated and ZDR-estimated Dmass, where ZDR is calculated directly from 2DVD observations. Similarly, the uncertainty of the NW(ZH, Dmass) relationship is evaluated through 2DVD-calculated and Dmass and ZH-estimated NW, where Dmass and ZH are directly calculated from 2DVD observations. This study also presents the sensitivity of Dmass(ZDR) relationships to climate regime and to disdrometer type after developing three additional Dmass(ZDR) relationships from second-generation Particle Size Velocity (PARSIVEL2) disdrometer (P2) observations collected in the Pacific Northwest, in Iowa, and at Kwajalein Atoll in the tropical Pacific Ocean. The application of P2-derived Dmass(ZDR) relationship based on precipitation in the northwestern United States to P2 observations collected over the tropical ocean resulted in the highest error among comparisons of the three datasets.

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.

In this article, we report the first investigation over time of the atmospheric conditions around terrestrial gamma-ray flash (TGF) occurrences, using GPS sensors in combination with geostationary satellite observations and ERA5 reanalysis data. The goal is to understand which characteristics are favorable to the development of these events and to investigate if any precursor signals can be expected. A total of 9 TGFs, occurring at a distance lower than 45 km from a GPS sensor, were analyzed and two of them are shown here as an example analysis. Moreover, the lightning activity, collected by the World Wide Lightning Location Network (WWLLN), was used in order to identify any links and correlations with TGF occurrence and precipitable water vapor (PWV) trends. The combined use of GPS and the stroke rate trends identified, for all cases, a recurring pattern in which an increase in PWV is observed on a timescale of about two hours before the TGF occurrence that can be placed within the lightning peak. The temporal relation between the PWV trend and TGF occurrence is strictly related to the position of GPS sensors in relation to TGF coordinates. The life cycle of these storms observed by geostationary sensors described TGF-producing clouds as intense with a wide range of extensions and, in all cases, the TGF is located at the edge of the convective cell. Furthermore, the satellite data provide an added value in associating the GPS water vapor trend to the convective cell generating the TGF. The investigation with ERA5 reanalysis data showed that TGFs mainly occur in convective environments with unexceptional values with respect to the monthly average value of parameters measured at the same location. Moreover, the analysis showed the strong potential of the use of GPS data for the troposphere characterization in areas with complex territorial morphologies. This study provides indications on the dynamics of con-vective systems linked to TGFs and will certainly help refine our understanding of their production, as well as highlighting a potential approach through the use of GPS data to explore the lightning activity trend and TGF occurrences. publishedVersion

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.

Unique spaceborne measurements of the three-dimensional structure of convective clouds producing terrestrial gamma ray flashes (TGFs) were performed using both active and passive microwave sensors on board the Global Precipitation Measurement (GPM)-Core Observatory satellite, finding coherent features for nine TGF-producing storms. The delineation of cloud structure using the radar reflectivity factor shows convective cells with significant vertical development and thick layers with high ice content. Compared to other cumulonimbus clouds in the tropics, the TGFs counterparts have higher reflectivity values above 3 and 8 km altitude showing in all cases a cumulonimbus tower and the TGFs locations are very close, or coincident, to these high Z columns, where reflectivity exceeds 50 dBz. Using the GPM Microwave Imager radiometer, most thunderstorms show a very strong depression of polarization corrected temperature (PCT) at channel 89 GHz, indicating a strong scattering signal by ice in the upper cloud layers. At channel 166 GHZ, the difference between vertical and horizontal brightness temperature signal always returns positive values, from 0.2 up to 13.7 K indicating a complex structure with randomly/vertically oriented ice particles. The PCT was used to characterize the analyzed storms in terms of hydrometeor types, confirming in 7/9 cases a high likelihood of hail/graupel presence. To perform analysis on the TGFs parent flashes, radio atmospherics data from the Earth Networks Total Lightning Network lightning network were used. Waveform data indicate that all cases are intra-cloud events and TGFs typically take place during the peak of flash rate production. Finally, the analysis of the most intense event is shown.

Since early March 2014, the NASA/JAXA Global Precipitation Measurement Core- Observatory (GPM-CO) satellite has allowed analysis of precipitation systems around the globe, thanks to the capabilities of the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR). In this work, we demonstrate how GPM-CO measurements obtained from 4 years of observations over the Mediterranean area can be used as an extremely effective tool to study the main climatological characteristics of the most intense Mediterranean storm structures. DPR and GMI-based Precipitation Features (PFs) parameters are used as proxies of the vertical structure and microphysical properties of these events, and their statistical distribution is analyzed to identify extremes. The analysis of annual, seasonal and geographical distribution of the identified deep convective systems highlights substantial differences in their diurnal cycle and in the distribution between land-sea and summer-winter. There is a general shift of the convective systems from the south (mostly over the sea) in the cold season, to the north (mostly over land) in the warm season. The analysis shows also that the inferred convective intensity is not always related to heavy precipitation. Known DPR and GMI-based criteria were adopted to identify overshooting top events and potential hailstorms, identify extreme deep convection signatures, like those observed for tropical and subtropical systems, and the most intense occur mostly over the sea. Although the analysis is limited to four years, the results show that the GPM-CO offers unprecedented measurements to identify and characterize extreme weather events in the Mediterranean region, with unique potentials for future long-term climatology and interannual variability analysis.

In this article we report the first investigation over time of the atmospheric conditions around TGFs occurrence, using GPS sensors in combination with geostationary satellite observations and ERA5 reanalyses data. The goal is to understand which characteristics are favourable to the development of these events and to investigate if any precursor signals can be expected. A total of 9 TGFs, occurred at a distance lower than 45 km from a GPS sensor, were analysed and two of them are shown here as an example analysis. Moreover, the lightning activity, collected by the World Wide Lightning Location Network (WWLLN) was used in order to identify any links and correlations with TGF occurrence and PWV trends. The combined use of GPS and the stroke rate trends identified, for all cases, a recurred pattern in which an increase of PWV is observed on a timescale of about two hours before the TGF occurrence that can be placed within the lightning peak. The temporal relation between the PWV trend and TGF occurrence is strictly related to the position of GPS sensors in relation to TGF coordinates. The life cycle of these storms observed by geostationary sensors, described TGFs producing clouds as intense with a wide range of extensions and, in all cases, the TGF is located at the edge of the convective cell. Furthermore, the satellite data give an added value in associating the GPS water vapor trend to the convective cell generating the TGF. The investigation with ERA5 reanalyses data showed that TGFs mainly occur in convective environment with not exceptional values with respect to the monthly average value of parameters measured in the same location. Moreover the analysis showed the strong potential of the use of GPS data for the troposphere characterization in areas with complex territorial morphology. This study provided indications on the dynamics of convective systems linked to TGFs and will certainly help refine our understanding on their production highlights a potential approach through the use of GPS data to explore the lightning activity trend and the TGFs occurrence.

In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring.

This article belongs to the Special Issue High Resolution Active Optical Remote Sensing Observations of Aerosols, Clouds and Aerosol-Cloud Interactions and Their Implication to Climate., Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency, This research was funded by the Italian Research Council (CNR) Short Term Mobility Program. The NASA Micro-Pulse Lidar Network is supported by the NASA Earth Observing System (S. Platnick) and Radiation Sciences Program (H. Maring).

One of the main goals of the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission is to retrieve parameters of the raindrop size distribution (DSD) globally. As a standard product of the Dual-Frequency Precipitation Radar (DPR) on board the GPM Core Observatory satellite, the mass-weighted mean diameter Dm and the normalized intercept parameter Nw are estimated in three dimensions at the resolution of the radar. These are two parameters of the three-parameter gamma model DSD adopted by the GPM algorithms. This study investigates the accuracy of the Dm retrieval through a comparative study of C-band ground radars (GRs) and GPM products over Italy. The reliability of the ground reference is tested by using two different approaches to estimate Dm. The results show good agreement between the ground-based and spaceborne-derived Dm, with an absolute bias being generally lower than 0.5 mm over land in stratiform precipitation for the DPR algorithm and the combined DPR–GMI algorithm. For the DPR–GMI algorithm, the good agreement extends to convective precipitation as well. Estimates of Dm from the DPR high-sensitivity (HS) Ka-band data show slightly worse results. A sensitivity study indicates that the accuracy of the Dm estimation is independent of the height above surface (not shown) and the distance from the ground radar. On the other hand, a nonuniform precipitation pattern (interpreted both as high variability and as a patchy spatial distribution) within the DPR footprint is usually associated with a significant error in the DPR-derived estimate of Dm.

Both numerical modeling and laboratory experiments document the possibility of a raindrop size distribution (DSD) to evolve to an equilibrium stage (EDSD), where all the principal processes occur at steady rates. The aim of this work is to observe the temporal behavior of the DSD and to directly investigate the conditions favorable to the onset of the EDSD in natural rain. We exploited a large disdrometer dataset collected in the framework of the Ground Validation activities related to the NASA Global Precipitation Measurement mission. More than 200,000 one-minute data of two-dimensional video disdrometer (2DVD) are collected over USA to represent a wide range of precipitation types. The original data are averaged over 2 min and an automatic algorithm is used on a selected subset to identify samples with EDSD. Results show that the EDSD occurs mainly in convective events and lasts for very short time intervals (2 to 4 min). It is more frequent for rain rate between 20 and 40 mm h − 1 and it mostly occurs during sharp increase of precipitation rates.

The spatial variability of parameters of the raindrop size distribution and its derivatives is investigated through a field study where collocated Particle Size and Velocity (Parsivel2) and two-dimensional video disdrometers were operated at six sites at Wallops Flight Facility, Virginia, from December 2013 to March 2014. The three-parameter exponential function was employed to determine the spatial variability across the study domain where the maximum separation distance was 2.3 km. The nugget parameter of the exponential function was set to 0.99 and the correlation distance d0 and shape parameter s0 were retrieved by minimizing the root-mean-square error, after fitting it to the correlations of physical parameters. Fits were very good for almost all 15 physical parameters. The retrieved d0 and s0 were about 4.5 km and 1.1, respectively, for rain rate (RR) when all 12 disdrometers were reporting rainfall with a rain-rate threshold of 0.1 mm h−1 for 1-min averages. The d0 decreased noticeably when one or more disdrometers were required to report rain. The d0 was considerably different for a number of parameters (e.g., mass-weighted diameter) but was about the same for the other parameters (e.g., RR) when rainfall threshold was reset to 12 and 18 dBZ for Ka- and Ku-band reflectivity, respectively, following the expected Global Precipitation Measurement mission’s spaceborne radar minimum detectable signals. The reduction of the database through elimination of a site did not alter d0 as long as the fit was adequate. The correlations of 5-min rain accumulations were lower when disdrometer observations were simulated for a rain gauge at different bucket sizes.

In this paper we illustrate a new, simple and complementary ground-based methodology to retrieve the vertically resolved atmospheric precipitation intensity through a synergy between measurements from the National Aeronautics and Space Administration (NASA) Micropulse Lidar network (MPLNET), an analytical model solution and ground-based disdrometer measurements. The presented results are obtained at two mid-latitude MPLNET permanent observational sites, located respectively at NASA Goddard Space Flight Center, USA, and at the Universitat Politècnica de Catalunya, Barcelona, Spain. The methodology is suitable to be applied to existing and/or future lidar/ceilometer networks with the main objective of either providing near-real time (3h latency) rainfall intensity measurements and/or to validate satellite missions, especially for critical light precipitation (

In this paper, we illustrate a new, simple and complementary ground-based methodology to retrieve the vertically resolved atmospheric precipitation intensity through a synergy between measurements from the National Aeronautics and Space Administration (NASA) Micropulse Lidar network (MPLNET), an analytical model solution and ground-based disdrometer measurements. The presented results are obtained at two mid-latitude MPLNET permanent observational sites, located respectively at NASA Goddard Space Flight Center, USA, and at the Universitat Politècnica de Catalunya, Barcelona, Spain. The methodology is suitable to be applied to existing and/or future lidar/ceilometer networks with the main objective of either providing near real-time (3 h latency) rainfall intensity measurements and/or to validate satellite missions, especially for critical light precipitation (

The NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory (CO) carries, for the first time, a dual-frequency Precipitation Radar (DPR). The DPR provides insights into the three-dimensional structure of precipitating clouds and intensity and is currently considered the main calibration instrument for radiometer derived global precipitation estimates. The DPR employs three scanning modes: Matched Scan (MS), Normal Scan (NS) and High Sensitivity scan (HS). The Ka- and Ku-band radar (MS) footprints of the inner swath consist of 25 angle bins with a vertical resolution of 125 m while the Ku-band radar (NS) covers the full swath with 49 angle bins with the same vertical resolution. The HS Ka-band radar footprints are interlaced with matched Ku/Ka-band footprints and consist of 24 angle bins with range sampling at 250 m. The swath widths of Ka- and Ku-band radars are 120 km and 245 km, respectively, while the diameter of both Ka- and Ku-band footprints at nadir is 5 km about. While the precipitation retrieval algorithms of the Ka- and Ku-band radar Matched (MS) footprints provide a double-frequency based output, the Ku-band Normal Scan (NS) and Ka-band High Sensitivity scan (HS) precipitation estimates for same footprints, are single-frequency based. The goal of the present works is to carry out an intercomparison of the single- and double-frequency DPR algorithms by considering the respective outputs over the Mediterranean area during rain events. A number of DPR related variables have been considered (i.e. measured and corrected reflectivity, surface rainfall rate, mean mass diameter, normalized intercept parameter, liquid and ice water content vertical profiles) to perform the analysis. The data have been categorized for surface type (land and sea) and precipitation type (stratiform and convective). The results show the differences in the performance of the single- and double-frequency algorithms as function of the surface and precipitation type. The vertical profiles of the single- and double-frequency based retrieved parameters are compared in order to assess their variability along the precipitating column.

The Italian radar network is currently composed by 20 C-band and 2 X-band systems, managed by 11 administra- tions, able to cover most of the country. The product generation at national level is carried out by the Department of Civil Protection (DPC) that currently manages 7 C-band and 2 X-band systems, all with dual-polarization capa- bility. The national mosaic provides an estimation of the precipitation intensity with a five minutes time resolution over a equi-spaced 1 km grid. A Quality Index (QI) map is associated to the rainfall rate map, taking into account the radar artifacts, the partial beam blockage, the height of measurements with respect to the freezing layer, the beam broadening and the rain path attenuation. The geostationary Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) provides high-quality measurements across twelve visible (VIS) and infrared spectral (IR) bands. Several tech- niques have been developed exploiting VIS or IR observations (individually or in a combined way) to relate the cloud features to the surface precipitation. One of them is the blending Rapid Update (RU) technique combining IR observations with precipitation estimates obtained from spaceborne passive microwave radiometers, and used operationally within the EUMETSAT H SAF program. Within the collaborative agreement between DPC and the Institute of Atmospheric Sciences and Climate (ISAC) of the National Research Council (CNR) a new blended algorithm has been developed that combines the ground radar precipitation estimates and the IR observations from SEVIRI to provide a precipitation map over the whole Italian territory every 15 minutes, at 1 km spatial resolution. The goal is to integrate the low QI ground radar mea- surements with the precipitation estimates inferred from IR observations. The IR precipitation estimates are based on regression curves built from a training dataset based on coincident IR and high QI radar observations. The present work shows the preliminary results of the blended algorithm. The validation is carried out through a comparative study with the Italian rain gauges network.

The Ka–Ku Dual-Frequency Precipitation Radar (DPR) and the Microwave Imager on board the Global Precipitation Measurement (GPM) mission core satellite have been collecting data for more than 3 years, providing precipitation products over the globe, including oceans and remote areas where ground-based precipitation measurements are not available. The main objective of this work is to validate the GPM-DPR products over a key climatic region with complex orography such as the Italian territory. The performances of the DPR precipitation rate products are evaluated over an 18-month period (July 2015–December 2016) using both radar and rain gauge data. The ground reference network is composed of 22 weather radars and more than 3000 rain gauges. DPR dual-frequency products generally show better performance with respect to the single-frequency (i.e., Ka- or Ku-band only) products, especially when ground radar data are taken as reference. A sensitivity analysis with respect to season and rainfall intensity is also carried out. It was found that the normal scan (NS) product outperforms the high-sensitivity scan (HS) and matched scan (MS) during the summer season. A deeper analysis is carried out to investigate the larger discrepancies between the DPR-NS product and ground reference data. The most relevant improvement of the DPR products’ performance was found by limiting the comparison to the upscaled radar data with a higher quality index. The resulting scores in comparison with ground radars are mean error (ME) = −0.44 mm h−1, RMSE = 3.57 mm h−1, and fractional standard error (FSE) = 142%, with the POD = 65% and FAR = 1% for rainfall above 0.5 mm h−1.

A network of seven two-dimensional video disdrometers (2DVD), which were operated during the Midlatitude Continental Convective Clouds Experiment (MC3E) in northern Oklahoma, are employed to investigate the spatial variability of raindrop size distribution (DSD) within the footprint of the dual-frequency precipitation radar (DPR) on board the National Aeronautics and Space Administration’s Global Precipitation Measurement (GPM) mission core satellite. One-minute 2DVD DSD observations were interpolated uniformly to 13 points distributed within a nearly circular DPR footprint through an inverse distance weighting method. The presence of deep continental showers was a unique feature of the dataset resulting in a higher mean rain rate R with respect to previous studies. As a measure of spatial variability for the interpolated data, a three-parameter exponential function was applied to paired correlations of three parameters of normalized gamma DSD, R, reflectivity, and attenuation at Ka- and Ku-band frequencies of DPR (Z_Ka, Z_Ku, k_Ka, and k_Ku, respectively). The symmetry of the interpolated sites allowed quantifying the directional differences in correlations at the same distance. The correlation distances d0 of R, k_Ka, and k_Ku were approximately 10 km and were not sensitive to the choice of four rain thresholds used in this study. The d0 of Z_Ku, on the other hand, ranged from 29 to 20 km between different rain thresholds. The coefficient of variation (CV) remained less than 0.5 for most of the samples for a given physical parameter, but a CV of greater than 1.0 was also observed in noticeable samples, especially for the shape parameter and Z_Ku.

Artificial rainfall is widely used to study the surface runoff process but several problems are related to the reproducibility of natural rainstorms. A new rainfall simulator and a collection system were designed and tested in the laboratory and in the field. The rainfall simulator consists of four independent lines of low-cost pressure washing nozzles operated at a pressure of 80 mbar which number and position causes the rainfall intensity delivered on the plot. The spatial rainfall distribution and his intensity were measured with 63 rainfall gauges covering the whole plot The Joss Waldvogel Disdrometer was used to characterize the rainfall produced by the rainfall simulator. The drop size distribution was obtained. The drop size spectrum ranges from 0.25 mm to 3.3 mm and its shape is the same to that one produced by a natural rainfall. The rainfall intensity varies from approximately 31 to 62 mm/h and it is sufficiently spatially uniform (Christiansen's coefficient of uniformity is 0.62 to 0.75) over the plot. Field tests were carried out in on a grassy field with silt-loam soil in Orroli, Sardinia in July and August 2010. The values of the mean rainfall intensities obtained from field data are in accord with the laboratory values. The field site measurement includes the surface runoff, evaluated using a dedicated tipping bucket flow meter, and the soil water content measured throughout the field experiments. The results showed the good performance of this new rainfall simulator that offers the possibility to reproduce natural rainfall to gather parameters needed for hydrologic modeling. The entire designed system offers the possibility to carry out reliable measurements of the surface runoff under different rain intensities and also allows one to measure this on different temporal scales by taking into account the differen environmental conditions.

Numerous fields of atmospheric and hydrological sciences require the parametric form of the raindrop size distribution (DSD) to estimate the rainfall rate from radar observables as well as in cloud resolving and weather forecasting models. This study aims to investigate the capability of the Johnson SB distribution (JSB) in estimating rain integral parameters. Specifically, rainfall rate (R), reflectivity factor (Z) and mean mass diameter (Dmass) estimated by JSB are compared with those estimated by a three-parameter Gamma distribution, widely used by radar meteorologists and atmospheric physicists to model natural DSD. A large dataset consisting of more than 155,000 one-minute DSD, from six field campaigns of Ground Validation (GV) program of NASA/JAXA Global Precipitation Measurement mission (GPM), is used to test the performance of both JSB and Gamma distribution. The available datasets cover a wide range of rain regimes because of the field campaigns were carried out in different seasons and locations. Correlation coefficient, bias, root mean square error (RMSE) and fractional standard error (FSE) between estimated and measured integral parameters are calculated to compare the performances of the two distributions. The capability of JSB in estimating the integral parameters, especially R and Z, resulted very close to that of Gamma distribution. In particular, for light precipitation, JSB is superior to Gamma distribution in estimating R with FSE of 11% with respect to values ranging between 25% and 37% about for Gamma. Comparison of the estimated and measured DSDs shows that the JSB distribution reproduces the natural DSD quite accurately.

Precipitation is a key geophysical parameter in understanding the Antarctic climate. However, the particular environmental conditions of the Continent make it difficult to measure directly solid precipitation rate and accumulation from either ground based instruments or passive space-borne sensors. A significant improvement in the study of solid precipitation over Antarctica is possible by using active space-borne instruments: the Cloud Profiling Radar (CPR), a nadir-pointing 94 GHz radar, on board the low earth orbit CloudSat satellite. Five years (2006-2011) of CPR data and products over Antarctica are analyzed to investigate the characteristics of solid precipitation. The aim of this work is twofold: 1) to compare a global snowfall rate retrieval algorithm (Kulie and Bennartz, 2009) with the official CloudSat product (2C-SNOW-PROFILE) over the Antarctic environment, evaluating the sensitivity of the estimated snow fields to: ground clutter, choice of reflectivity-snowfall rate relationship (Z-S), presence of melting snow/liquid precipitation; 2) to provide snow fall rates and accumulation at different scales over Antarctica, evaluating the impact of background physiography and seasonal cycle on the precipitation distribution. Further comparisons are also performed with ERA-Interim snowfall fields and point-like snow stack height measurements by acoustic depth gauges. Results show that the difference between the Kulie and Bennartz (2009) algorithm and the 2C-SNOW-PROFILE product is mainly due to the choice of the Z-S relationship. Furthermore, despite the CPR limited temporal and spatial sampling capabilities, CPR is able to evidence precipitation characteristics difficult to study from conventional ground-based instruments, at spatial and temporal scales of interest for the study of the hydrological cycle over Antarctica. This is of particular relevance given that the CPR follow-on mission on EarthCare will ensure a long-term coverage.

Pludix is an X-band disdrometer based on the Doppler effect, that provides rainfall intensity and drop size distribution output, and exhibits potential capabilities as a Present Weather Sensor (PWS). A Present Weather Sensor is an instrument capable of automatically detecting weather phenomena present at the time of observation. In order to verify the latter, two different methods have been developed to identify the precipitation types according to the WMO codes in its table 4680. With the first method (P-method), twelve precipitation codes are identified by analysing the characteristics of the Pludix power spectrum. With the second method (S-method), a statistical one based on a pattern recognition type algorithm, twenty-three precipitation codes are identified. With both methods main precipitation types are identified: drizzle, rain, snow, hail, ice crystals, mixed precipitation. Pludix is compared with the reference observations of a human observer during a campaign performed at the weather station of Wasserkuppe (central Germany, 950 m a.s.l.). Two years of data are analysed (December 2000–January 2003); in this time interval there was a fair amount of both liquid and solid precipitation events, allowing us to evaluate the instrument capabilities. Pludix is also compared with the Vaisala FD12P PWS and with the optical disdrometer Parsivel which were tested in the same campaign. Since the S-method has only recently been developed and it is under investigation, the present analysis is mainly focusing on the P-method. The results show that Pludix performs quite well in distinguishing the precipitation type and it is generally in agreement with the human observations, especially for the rain case. Pludix also detects some situations (especially rain-showers with hail) that the human observer sometimes does not detect. The instrument is less sensitive to the slight precipitation, especially because it samples on a small measurement volume. We also test the performance of Pludix in measuring the rainfall-intensity, finding that it works better than the other instruments in the case of rain.