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

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]

The estimation of the precipitable water vapour content (W) with high temporal and spatial resolution is of great interest to both meteorological and climatological studies. Several methodologies based on remote sensing techniques have been recently developed in order to obtain accurate and frequent measurements of this atmospheric parameter. Among them, the relative low cost and easy deployment of sun–sky radiometers, or sun photometers, operating in several international networks, allowed the development of automatic estimations of W from these instruments with high temporal resolution. However, the great problem of this methodology is the estimation of the sun-photometric calibration parameters. The objective of this paper is to validate a new methodology based on the hypothesis that the calibration parameters characterizing the atmospheric transmittance at 940 nm are dependent on vertical profiles of temperature, air pressure and moisture typical of each measurement site. To obtain the calibration parameters some simultaneously seasonal measurements of W, from independent sources, taken over a large range of solar zenith angle and covering a wide range of W, are needed. In this work yearly GNSS/GPS datasets were used for obtaining a table of photometric calibration constants and the methodology was applied and validated in three European ESR-SKYNET network sites, characterized by different atmospheric and climatic conditions: Rome, Valencia and Aosta. Results were validated against the GNSS/GPS and AErosol RObotic NETwork (AERONET) W estimations. In both the validations the agreement was very high, with a percentage RMSD of about 6, 13 and 8 % in the case of GPS intercomparison at Rome, Aosta and Valencia, respectively, and of 8 % in the case of AERONET comparison in Valencia. [ABSTRACT FROM AUTHOR]

Cloud electrification and related lightning activity in thunderstorms have their origin in the charge separation and resulting distribution of charged iced particles within the cloud. So far, the ice distribution within convective clouds has been investigated mainly by means of ground-based meteorological radars. In this paper we show how the products from Cloud Profiling Radar (CPR) on board CloudSat, a polar satellite of NASA's Earth System Science Pathfinder (ESSP), can be used to obtain information from space on the vertical distribution of ice particles and ice content and relate them to the lightning activity. The analysis has been carried out, focusing on 12 convective events over Italy that crossed CloudSat overpasses during significant lightning activity. The CPR products considered here are the vertical profiles of cloud ice water content (IWC) and the effective radius (ER) of ice particles, which are compared with the number of strokes as measured by a ground lightning network (LINET). Results show a strong correlation between the number of strokes and the vertical distribution of ice particles as depicted by the 94 GHz CPR products: in particular, cloud upper and middle levels, high IWC content and relatively high ER seem to be favourable contributory causes for CG (cloud to ground) stroke occurrence. [ABSTRACT FROM AUTHOR]

Cloud water is an important geophysical quantity that connects the hydrological and radiation characteristics of climate systems and plays an essential role in the global circulation of the atmosphere, water, and energy. However, compared to the contribution of water vapor to precipitation, the understanding of cloud-precipitation transformation and its climate feedback mechanism remains limited. Based on precipitation and temperature datasets of the National Meteorological Observatory and MODIS (Moderate Resolution Imaging Spectroradiometer) satellite remote sensing products, the evolution characteristics of cloud water resources in China over the last twenty years of the 21st century were evaluated. Significant decreasing trends of −3.3 and −4.89 g/m2 decade−1 were found for both the liquid and ice water path. In humid areas with high precipitation, the cloud water path decreased fast. In semiarid areas with an annual precipitation ranging from 500–800 mm, the decreasing trend of the cloud water path was the lowest. The cloud-water period was calculated to represent the relative changes in clouds and precipitation. The national average cloud-water period in China is approximately 12.4 h, with obvious seasonal changes. Over the last 20 years, the cloud water path in dry regions decreased more slowly than that in wet regions, and the cloud-precipitation efficiency significantly increased, which narrowed the climate difference between the dry and wet regions. Finally, the mechanism of the cloud-water period evolution in the different regions were examined from the perspectives of the dynamic and thermal contributions, respectively. Due to the overall low upward moisture flux (UMF) in the dry region, the response of the cloud-water period to the lower tropospheric stability (LTS) mainly first increased and then decreased, which was the opposite in the wet region. The increase in cloud-precipitation efficiency in the dry region of Northwest China is accompanied by a continuous decrease in LTS. The different configurations of regional UMF and LTS play a crucial role in the evolution of cloud-precipitation, which can be used as a diagnostic basis to predict changes in the precipitation intensity to a certain extent. [ABSTRACT FROM AUTHOR]

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. [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]

The present work exploits the satellite measurements from the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) active and passive microwave sensors 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 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 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 microwave measurement analysis. Substantial difference in the rainband precipitation structure is observed, with deeper convection and stronger updraft features during development than 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. The study demonstrates the value of the GPM-CO not only to characterize Medicane Ianos precipitation structure and microphysics processes with unprecedented detail, but also to provide evidence of its exceptional intensity, tropical-like characteristics during its mature phase, and its similarities with tropical cyclones. • Ianos is the most intense Mediterranean hurricane (medicane) on record. • The GPM DPR passes over the mature stage of a medicane for the first time. • Lightning activity is related to GPM-based deep convection features. • Limited graupel growth at mature phase inhibits gravitational charge separation. • Warm rain process are observed in proximity of the medicane eye. [ABSTRACT FROM AUTHOR]

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]