A Multisensor Investigation of Convection During HyMeX SOP1 IOP13

A multisensor analysis of the convective precipitation event occurred over Rome during the IOP13 (October 15th, 2012) of the HyMeX (Hydrological cycle in the Mediterranean eXperiment) Special Observation Period (SOP) 1 is presented. Thanks to the cooperation among Italian meteorological services and scientific community and a specific agreement with NASA-GSFC, different types of devices for meteorological measurements were made available during the HyMeX SOP.1. For investigating this event, used are the 3-D lightning data provided by the LINET, the CNR ISAC dual-pol C-band radar (Polar 55C), located in Rome, the Drop Size Distributions (DSD) collected by the 2D Video Disdrometer (2DVD) and the collocated Micro Rain Radar (MRR) installed at the Radio Meteorology Lab. of "Sapienza" University of Rome, located 14 km from the Polar 55C radar. The relation between microphysical structure and electrical activity during the convective phase of the event was investigated using LINET lightning data and Polar 55C (working both in PPI and RHI scanning mode) observations. Location of regions of high horizontal reflectivity (Zh) values ( > 50 dBz), indicating convective precipitation, were found to be associated to a high number of LINET strokes. In addition, an hydrometeor classification scheme applied to the Polar 55C scans was used to detect graupel and to identify a relation between number of LINET strokes and integrated IWC of graupel along the event. Properties of DSDs measured by the 2DVD and vertical DSD profiles estimated by MRR and their relation with the lighting activity registered by LINET were investigated with specific focus on the transition from convective to stratiform regimes. A good agreement was found between convection detected by these instruments and the number of strokes detected by LINET

Multi-sensor analysis of convective activity in central Italy during the HyMeX SOP 1.1

Abstract. A multi-sensor analysis of convective precipitation events that occurred in central Italy in autumn 2012 during the HyMeX (Hydrological cycle in the Mediterranean experiment) Special Observation Period (SOP) 1.1 is presented. Various microphysical properties of liquid and solid hydrometeors are examined to assess their relationship with lightning activity. The instrumentation used consisted of a C-band dual-polarization weather radar, a 2-D video disdrometer, and the LINET lightning network. Results of T-matrix simulation for graupel were used to (i) tune a fuzzy logic hydrometeor classification algorithm based on Liu and Chandrasekar (2000) for the detection of graupel from C-band dual-polarization radar measurements and (ii) to retrieve graupel ice water content. Graupel mass from radar measurements was related to lightning activity. Three significant case studies were analyzed and linear relations between the total mass of graupel and number of LINET strokes were found with different slopes depending on the nature of the convective event (such as updraft strength and freezing level height) and the radar observational geometry. A high coefficient of determination (R2 = 0.856) and a slope in agreement with satellite measurements and model results for one of the case studies (15 October 2012) were found. Results confirm that one of the key features in the electrical charging of convective clouds is the ice content, although it is not the only one. Parameters of the gamma raindrop size distribution measured by a 2-D video disdrometer revealed the transition from a convective to a stratiform regime. The raindrop size spectra measured by a 2-D video disdrometer were used to partition rain into stratiform and convective classes. These results are further analyzed in relation to radar measurements and to the number of strokes. Lightning activity was not always recorded when the precipitation regime was classified as convective rain. High statistical scores were found for relationships relating lightning activity to graupel aloft

Precipitation products from the hydrology SAF

Abstract. The EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) was established by the EUMETSAT Council on 3 July 2005, starting activity on 1 September 2005. The Italian Meteorological Service serves as Leading Entity on behalf of twelve European member countries. H-SAF products include precipitation, soil moisture and snow parameters. Some products are based only on satellite observations, while other products are based on the assimilation of satellite measurements/products into numerical models. In addition to product development and generation, H-SAF includes a product validation program and a hydrological validation program that are coordinated, respectively, by the Italian Department of Civil Protection and by the Polish Institute of Meteorology and Water Management. The National Center of Aeronautical Meteorology and Climatology (CNMCA) of the Italian Air Force is responsible for operational product generation and dissemination. In this paper we describe the H-SAF precipitation algorithms and products, which have been developed by the Italian Institute of Atmospheric Sciences and Climate (in collaboration with the international community) and by CNMCA during the Development Phase (DP, 2005–2010) and the first Continuous Development and Operations Phase (CDOP-1, 2010–2012). The precipitation products are based on passive microwave measurements obtained from radiometers onboard different sun-synchronous low-Earth-orbiting satellites (especially, the SSM/I and SSMIS radiometers onboard DMSP satellites and the AMSU-A + AMSU-B/MHS radiometer suites onboard EPS-MetOp and NOAA-POES satellites), as well as on combined infrared/passive microwave measurements in which the passive microwave precipitation estimates are used in conjunction with SEVIRI images from the geostationary MSG satellite. Moreover, the H-SAF product generation and dissemination chain and independent product validation activities are described. Also, the H-SAF program and its associated activities that currently are being carried out or are planned to be performed within the second CDOP phase (CDOP-2, 2012–2017) are presented in some detail. Insofar as CDOP-2 is concerned, it is emphasized that all algorithms and processing schemes will be improved and enhanced so as to extend them to satellites that will be operational within this decade – particularly the geostationary Meteosat Third Generation satellites and the low-Earth-orbiting Core Observatory of the international Global Precipitation Measurement mission. Finally, the role of H-SAF within the international science and operations community is explained.</p

The validation service of the hydrological SAF geostationary and polar satellite precipitation products

Abstract. The development phase (DP) of the EUMETSAT Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF) led to the design and implementation of several precipitation products, after 5 yr (2005–2010) of activity. Presently, five precipitation estimation algorithms based on data from passive microwave and infrared sensors, on board geostationary and sun-synchronous platforms, function in operational mode at the H-SAF hosting institute to provide near real-time precipitation products at different spatial and temporal resolutions. In order to evaluate the precipitation product accuracy, a validation activity has been established since the beginning of the project. A Precipitation Product Validation Group (PPVG) works in parallel with the development of the estimation algorithms with two aims: to provide the algorithm developers with indications to refine algorithms and products, and to evaluate the error structure to be associated with the operational products. In this paper, the framework of the PPVG is presented: (a) the characteristics of the ground reference data available to H-SAF (i.e. radar and rain gauge networks), (b) the agreed upon validation strategy settled among the eight European countries participating in the PPVG, and (c) the steps of the validation procedures. The quality of the reference data is discussed, and the efforts for its improvement are outlined, with special emphasis on the definition of a ground radar quality map and on the implementation of a suitable rain gauge interpolation algorithm. The work done during the H-SAF development phase has led the PPVG to converge into a common validation procedure among the members, taking advantage of the experience acquired by each one of them in the validation of H-SAF products. The methodology is presented here, indicating the main steps of the validation procedure (ground data quality control, spatial interpolation, up-scaling of radar data vs. satellite grid, statistical score evaluation, case study analysis). Finally, an overview of the results is presented, focusing on the monthly statistical indicators, referred to the satellite product performances over different seasons and areas

The passive microwave empirical cold surface classification algorithm (PESCA): Application to GMI and ATMS

This paper describes a new Passive Microwave Empirical Cold Surface Classification Algorithm (PESCA) developed for snow-cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, since several studies have highlighted the influence of snow-cover radiative properties on the falling-snow passive microwave signature. The developed method is based on the exploitation of the lower-frequency channels (&lt;90 GHz), common to most microwave radiometers. The method applied to the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the cross-track-scanning Advanced Technology Microwave Sounder (ATMS) is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust both for sensors in dry conditions (total precipitable water&lt;10 mm) and for mean surface elevation&lt; 2500 m, independent of the cloud cover. The algorithm shows very good performance for cold temperatures (2-m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 and 280 K, where 280K is assumed as the maximum temperature limit for PESCA (overall detection statistics: probability of detection is 0.98 for ATMS and 0.92 for GMI, false alarm ratio is 0.01 for ATMS and 0.08 for GMI, and Heidke skill score is 0.72 for ATMS and 0.69 for GMI). Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometries, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes also appear to be different at high frequency (&gt;90 GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically scanning and cross-track-scanning radiometers, including the future operational EUMETSAT Polar System Second Generation (EPS-SG) mission microwave radiometers

The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

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

Simulating lightning into the RAMS model: implementation and preliminary results

This paper shows the results of a tailored version of a previously published methodology, designed to simulate lightning activity, implemented into the Regional Atmospheric Modeling System (RAMS). The method gives the flash density at the resolution of the RAMS grid scale allowing for a detailed analysis of the evolution of simulated lightning activity. The system is applied in detail to two case studies occurred over the Lazio Region, in Central Italy. Simulations are compared with the lightning activity detected by the LINET network. The cases refer to two thunderstorms of different intensity which occurred, respectively, on 20 October 2011 and on 15 October 2012. The number of flashes simulated (observed) over Lazio is 19435 (16231) for the first case and 7012 (4820) for the second case, and the model correctly reproduces the larger number of flashes that characterized the 20 October 2011 event compared to the 15 October 2012 event. There are, however, errors in timing and positioning of the convection, whose magnitude depends on the case study, which mirrors in timing and positioning errors of the lightning distribution. For the 20 October 2011 case study, spatial errors are of the order of a few tens of kilometres and the timing of the event is correctly simulated. For the 15 October 2012 case study, the spatial error in the positioning of the convection is of the order of 100 km and the event has a longer duration in the simulation than in the reality. To assess objectively the performance of the methodology, standard scores are presented for four additional case studies. Scores show the ability of the methodology to simulate the daily lightning activity for different spatial scales and for two different minimum thresholds of flash number density. The performance decreases at finer spatial scales and for higher thresholds. The comparison of simulated and observed lighting activity is an immediate and powerful tool to assess the model ability to reproduce the intensity and the evolution of the convection. This shows the importance of using computationally efficient lightning schemes, such as the one described in this paper, in forecast models

Comparison of the GPM DPR Single- and Double-Frequency Products Over the Mediterranean Area

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

Analysis of long-\uad\u2010term precipitation pattern over Antarctica derived from satellite-\uad\u2010borne radar

Mass accumulation is a key geophysical parameter in understanding the Antarctic climate and its role in the global system. The local mass variation is driven by a number of different mechanisms: the deposition of snow and ice crystals on the surface from the atmosphere is generally modified by strong surface winds and variations in temperature and humidity at the ground, making it difficult to measure directly the accumulation by a sparse network of ground based instruments. Moreover, the low cloud total water/ice content and the varying radiative properties of the ground pose problems in the retrieval of precipitation from passive space-borne sensors at all frequencies. Finally, numerical models, despite their high spatial and temporal resolution, show discordant results and are difficult to be validated using ground-based measurements. A significant improvement in the knowledge of the atmospheric contribution to the mass balance over Antarctica is possible by using active space-borne instruments, such as the Cloud Profiling Radar (CPR) on board the low earth orbit CloudSat satellite, launched in 2006 and still operating. The radar measures the vertical profile of reflectivity at 94 GHz (sensitive to small ice particles) providing narrow vertical crosssections of clouds along the satellite track. The aim of this work is to show that, after accounting for the characteristics of precipitation and the eect of surface on reflectivity in Antarctica, the CPR can retrieve snowfall rates on a single event temporal scale. Furthermore, the CPR, despite its limited temporal and spatial sampling capabilities, also effectively observes the annual snowfall cycle in this region. Two years of CloudSat data over Antarctica are analyzed and converted in water equivalent snowfall rate. Two different approaches for precipitation estimates are considered in this work. The results are analyzed in terms of annual and monthly averages, as well as in terms of instantaneous values. The derived snowfall maps are compared with ERA-Interim reanalysis and with in situ measurements, showing overall agreement. The effects of coastlines in enhancing precipitation rates and cloud precipitation efficiency are recognized. A significant seasonal signal also affects the averaged spatial extent of snowfall patterns. A comparison with snow accumulation ground measurements of single snowfall events shows consistency with the CPR retrievals: all the retrieved snowfall episodes correspond to an increase of snow accumulation at the ground, while several episodes of increase of snow stack height are not related to significant retrieved snowfall rate, likely indicating the local contribution of blowing snow. The results show that CPR can be a valuable source of snowfall rate data in Antarctica that can be used at dierent temporal scales, providing support to the sparse network of ground-based instruments both for numerical model validation and climatological studies

The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer

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 &gt; 0.90, ME &lt; &minus;0.22 mm h&minus;1, RMSE &lt; 2.75 mm h&minus;1 and FSE% &lt; 100% for rainfall rates lower than 1 mm h&minus;1 and around 30&ndash;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