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In this study, precipitation retrievals exploiting the available overpasses of passive microwave (PMW) cross-track and conically scanning radiometers in the GPM era are used to monitor the evolution of heavy precipitation systems occurred during the fall 2014 in Italy. Two different physically-based retrieval algorithms (CDRD for SSMIS and PNPR for AMSU/MHS and ATMS) are used in conjunction with official NASA/JAXA GPM instantaneous precipitation products (for AMSR-2 and GMI). The comparison with dual-polarization radar observations at ground evidences the ability of the different sensors to identify different precipitation areas and regimes. This is particularly relevant in presence of complex orography, often found in proximity of coastal areas for the analyzed cases. Analysis of the accuracy and consistency of the retrievals is carried out taking into account the different spatial resolution and viewing geometry of the different radiometers and the different approaches used for the precipitation retrieval.

The ever increasing availability and accuracy of satellite rainfall and soil moisture products is opening new perspectives in the hydrology and civil protection fields, providing new valuable sources of information for flood and landslide prediction. Despite the abundance of this kind of data, the use of satellite products for such purposes in well gauged regions is still very scarce, most likely due to measurement (bias, accuracy) and resolution issues (both spatial and temporal). Satellite rainfall and soil moisture products have been employed for flood and landslide prediction throughout the Italian territory. For an assessment of performances obtained by satellite products, hydrometeorological ground observations of rainfall, temperature and discharge derived from the monitoring network of the Italian Civil Protection Department (period 2010 - 2014) are used. The satellite rainfall products here considered are: 1) the Tropical Rainfall Measurement Mission (TRMM) Multi - satellite Precipitation Analysis (TMPA) 3B42 - RT product, 2) the H05 product from the H - SAF project, and 3) an experimental daily satellite rainfall product obtained by merging instantaneous rainfall rate estimates from cross - track and conical scanning microwave radiometers. Additionally, the satellite soil moisture products provided by the Advanced SCATterometer (ASCAT) and the Soil Moisture and Ocean Salinity (SMOS) mission are used. In this work, on one hand, satellite rainfall observations are used to drive a continuous rainfall - runoff model (MISDc, Brocca et al., 2011). On the other hand, satellite soil moisture data are used to define the initial conditions for a physically - based slope stability model (TRIGRS, Baum et al., 2008), applied in the Umbria region (central Italy), allowing the estimation of the landslide susceptibility over the territory. The modelled landslide susceptibility, obtained with ground and satellite observations, has been compared with a landslide inventory available in the study area. For the investigated period, MISDc model has been used for flood simulation over different basins in Italy (e.g., Brenta, Tanaro, Tevere, Volturno, ...). For landslide susceptibility evaluation, TRIGRS model has been initialized in two different ways. The first considers modelled soil moisture data computed by a Soil Water Balance model forced with in situ meteorological observations (rainfall and air temperature). The second uses satellite soil moisture products in place of modelled data. The analysis has encompassed some significant storm events occurred in the recent years. As far as the floods prediction is concerned, results highlighted that both bias correction and model recalibration are preliminary steps to be carried out for using properly satellite rainfall data. After performing these steps, satellite rainfall data have allowed to achieve results comparable with those obtained by forcing MISDc model with observed data, confirming the feasibility of using satellite rainfall estimation in hydrological applications. As regards the landslide susceptibility analysis, 102 although spatial and temporal resolution of satellite soil moisture data could be considered too coarse, satisfactory results are obtained as demonstrated from the comparison with the landslide inventory map.

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.

Precipitation estimation from passive microwave radiometry based on physically based profile retrieval algorithms must be aided by a microphysical generator providing structure information on the lower portions of the cloud, consistent with the upper-cloud structures that are sensed. One of the sources for this information is mesoscale model simulations involving explicit or parameterized microphysics. Such microphysical information can be then associated to brightness temperature signatures by using radiative transfer models, forming what are referred to as cloud‐radiation databases. In this study cloud‐radiation databases from three different storm simulations involving two different mesoscale models run at cloud scales are developed and analyzed. Each database relates a set of microphysical profile realizations describing the space‐time properties of a given precipitating storm to multifrequency brightness temperatures associated to a measuring radiometer. In calculating the multifrequency signatures associated with the individual microphysical profiles over model space‐time, the authors form what are called brightness temperature model manifolds. Their dimensionality is determined by the number of frequencies carried by the measuring radiometer. By then forming an analogous measurement manifold based on the actual radiometer observations, the radiative consistency between the model representation of a rain cloud and the measured representation are compared. In the analysis, the authors explore how various microphysical, macrophysical, and environmental factors affect the nature of the model manifolds, and how these factors produce or mitigate mismatch between the measurement and model manifolds. Various methods are examined that can be used to eliminate such mismatch. The various cloud‐radiation databases are also used with a simplified profile retrieval algorithm to examine the sensitivity of the retrieved hydrometeor profiles and surface rainrates to the different microphysical, macrophysical, and environmental factors of the simulated storms. The results emphasize the need for physical retrieval algorithms to account for a number of these factors, thus preventing biased interpretation of the rain properties of precipitating storms, and minimizing rms uncertainties in the retrieved quantities.

The Special Observation Period (SOP1), part of the HyMeX campaign (Hydrological cycle in the Mediterranean Experiments, 5 September–6 November 2012), was dedicated to heavy precipitation events and flash floods in the western Mediterranean, and three Italian hydro-meteorological monitoring sites were identified: Liguria–Tuscany, northeastern Italy and central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models, including hydrological models and marine models, allowed an unprecedented monitoring and analysis of high-impact weather events around the Italian hydro-meteorological sites. This activity has seen strong collaboration between the Italian scientific and operational communities. In this paper an overview of the Italian organization during SOP1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in northeastern Italy, IOP13 (15–16 October 2012) in central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems, including the hydrological impacts. The usefulness of having different weather forecast operational chains characterized by different numerical weather prediction models and/or different model set up or initial conditions is finally shown for one of the events (IOP19).

A new cloud dynamics and radiation database (CDRD) precipitation retrieval algorithm for satellite passive microwave (PMW) radiometer measurements has been developed. It represents a modification to and an improvement upon the conventional cloud radiation database (CRD) algorithms, which have always been prone to ambiguity. This part 2 paper of a series describes the methodology of the algorithm and the modeling verification analysis involved in creating a synthetic CDRD database for the Europe/Mediterranean basin region. This is followed by a proof-of-concept analysis, which demonstrates that the underlying CDRD theory based on use of meteorological parameters for reducing retrieval ambiguity is valid. This paper uses a regional/mesoscale model, applied in cloud resolving model (CRM) mode, to produce a large set of numerical simulations of precipitating storms and extended precipitating systems. The simulations are used for selection of millions of meteorological/microphysical vertical profiles within which surface rainfall is identified. For each of these profiles, top-of-atmosphere brightness temperature (TB) vectors are calculated (the vector dimension associated with the number of relevant cm-mm wavelengths and polarizations), based on an elaborate radiative-transfer equation (RTE) model system (RMS) coupled to the CRM. This entire body of simulation information is organized into the CDRD database, then used as a priori knowledge to guide a physical Bayesian retrieval algorithm in obtaining rainfall and associated precipitation parameters from the PMW satellite observations. We first prove the physical validity of our CRM-RMS simulations, by showing that the simulated TBs are in close agreement with observations. Agreement is demonstrated using dual-channel-frequency TB manifold sections, which quantify the degree of overlap between the simulated and observed TBs extracted from the full manifolds. Nevertheless, the salient result of this paper is a pro- f that the underlying CDRD theory is valid, found by combining subdivisions of the invoked meteorological parameter ranges of values and showing that such meteorological partitioning associates itself with distinct microphysical profiles. It is then shown that these profiles give rise to similar TB vectors, proving the existence of ambiguity in a CRD-type algorithm. Finally, we show that the CDRD methodology provides significant improvements in reducing retrieval ambiguity and retrieval error, especially for land surface backgrounds where contrasts are typically small between the rainfall TB signatures and surface emission signatures.

Within the H-SAF program (Satellite Application Facility on Support to Operational Hydrology and Water Management, http://hsaf.meteoam.it) we have developed two different passive microwave precipitation retrieval algorithms, one based on a physically-based Bayesian approach for conical scanning radiometers, and the other one based on Neural Network approach for cross-track scanning radiometers. The foundation of both algorithms is the use of a Cloud Dynamic and Radiation Database (CDRD) built for European and the Mediterranean regions. The database is generated upon the microphysical and dynamical output of 60 simulations of different precipitation events carried out using the cloud resolving model Non-hydrostatic Modeling System (NMS), coupled with a radiative transfer model. Brightness temperatures (TBs) are computed at the frequencies and spatial resolutions of cross track scanning radiometers (NOAA and MetOp-A AMSU/MHS) and of conical scanning radiometers (SSMIS, TMI), with different surface emissivity conditions, relative to the surface conditions of each simulation. Since both algorithms use the same physical foundation (same cloud model simulations, microphysics parameterization, and radiative transfer model), it is reasonable to expect consistency of the precipitation retrievals from cross-track and conical scanning radiometers for the same event. This consistency, besides the accuracy of the retrievals, is necessary in order to be able to fully exploit all cross-track and conical scanning radiometer overpasses for a specific event (available at about 3 hour time interval), and to be able to use both algorithms for monitoring precipitation at higher spatial/temporal resolution (i.e., blending with IR observations), as well as for nowcasting and/or hydrological applications. The two algorithms are undergoing continuous development, such as the use of dynamical/meteorological variables, as well as topography, season, and geographical location, as ancillary information to characterize the observed event, and mitigate the ambiguity of the (many) cloud microphysical structures (and rainfall rates at the ground) associated to any given set of measured multichannel TBs. A verification study of the latest versions of the two algorithms is being carried out within the H-SAF program, where the rainfall estimates are compared against radar observations and rain gauge network measurements. We will present the main characteristics of the two algorithms, and we will discuss their strengths and potentials, and future challenges based on the results of the verification study for different precipitation events in Europe and in the Mediterranean area.

Within the EUMETSAT H-SAF program (Satellite Application Facility on Support to Operational Hydrology and Water Management, http://hsaf.meteoam.it) we have developed two different passive microwave precipitation retrieval algorithms, one based on a physically-based Bayesian approach for conically scanning radiometers (i.e., SSMIS), and the other one based on Neural Network approach for cross-track scanning radiometers (i.e., AMSU-A/MHS). The two algorithms are based on the same physical foundation, i.e., same cloud-radiation model simulations to be used as a priori information in the Bayesian solver and as training dataset in the neural network approach. They also use similar procedures for screening of non-precipitating pixels, identification of frozen background surface, presence of snowfall, and determination of a pixel based quality index of the surface precipitation retrievals. These procedures are calibrated according to the different characteristics (i.e., viewing angle, horizontal resolution, channel frequencies) of the cross-track and conically scanning radiometers used. The two algorithms use dynamical/meteorological/environmental variables as ancillary information to characterize the observed event, and mitigate the ambiguity of the cloud microphysical structures (and rainfall rates at the ground) associated to any given set of measured multichannel brightness temperatures. A verification study of the latest versions of the two algorithms has been carried out within the H-SAF program, where the rainfall estimates are compared against radar observations and rain gauge network measurements for several precipitation events in Europe, characterized by different environmental, meteorological, dynamical conditions, and by different precipitation regimes. We will present the main results of this study, discussing strengths and potentials of the two algorithms in relation to the different characteristics of the observed events. In addition the consistency of the retrievals from close in time overpasses of the cross-track and conically scanning radiometers for the same event will be discussed. Consistency, besides accuracy of the retrievals, is necessary in order to be able to fully exploit all current of future cross-track and conically scanning radiometer overpasses for monitoring precipitation, and to be able to use them in conjunction with IR GEO observations in blending/morphing resolution enhancing techniques for nowcasting and/or hydrological applications.

A verification study is conducted for the Cloud Dynamics and Radiation Database (CDRD) rainfall algorithm over the southern Mediterranean Basin (25–36° N / 25° W–45° E), based on TRMM Microwave Imager (TMI) passive microwave (PMW) brightness temperature (TB) measurements. The CDRD's required synthetic database is generated from a high resolution, nonhydrostatic cloud resolving model equipped with a precision RTE model. This dual modeling system is used to simulate numerous and precipitating storms over Europe and across the Mediterranean Basin as the database's foundation. Extracted from the simulations for the database are the a priori knowledge variables involving many thousands of synthetic precipitating microphysical-meteorological profiles and their concomitant synthetic multi-frequency PMW TBs. This information provides the probability framework for the CDRD algorithm's Bayesian inversion process and thus the means to relate measured satellite TBs to the most probable rainfall solutions. As the newest algorithm feature, the CDRD scheme makes use of meteorological parameters (tags) of observed precipitation systems, obtained from global NWP model synthesized initialization data, for both precipitation regime recognition and Bayesian retrieval constraints. These two steps effectively reduce non-uniqueness problems in obtaining retrieval solutions. Comparison of TMI-based CDRD retrievals to coincident TRMM Precipitation Radar (PR) retrievals, used as ground truth, provides the means to assess the algorithm's performance and the twofold use of meteorological tag information for control and constraint. PR-retrieved cloud property parameters (e.g., freezing level) are also compared to NWP model tags to further verify algorithm design performance. The verification procedure utilizes a full year (2010) of TMI and PR measurements, indicating CDRD over-ocean and over-land results are within 0.10 and 0.17 mm hr-1 of the PR means, with accompanying 1.24 and 2.94 mm hr-1 rms errors. The CDRD algorithm is also compared to the TRMM TMI facility algorithm (2al2-v7), exhibiting slight improvement over water (in correlation coefficient) and significantly improvement over land. Error statistics indicate CDRD vis-a-vis 2al2 over-land improvements of 78% and 14% in regards to mean error and rms error, respectively.

The EUMETSAT Satellite Application Facility on support to Operational Hydrology and Water Management (H-SAF) was established by the EUMETSAT Council on July 3, 2005 and started activity on September 1, 2005. The development programme duration was 5 years, ending on August 31, 2010. Soon after, the first phase of a follow-on Continuous Operations-Development Programme (CDOP-1) started with a 18-month period of duration. A following second phase (CDOP-2) has started on March 1, 2012 to provide long-term perspective (five years) to the initiative. The Italian Meteorological Service serves as Host Institute on behalf of 12 European member countries. H-SAF products include precipitation, soil moisture and snow parameters. Some products are based only on satellite observations (OBS), while other products are based on the assimilation of satellite measurements/products into numerical models (ASS). In addition to products development and generation, H-SAF includes a product validation programme and a hydrological validation programme. The Italian Civil Protection Department (DPC) coordinates the validation programme. Here, we will focus on the OBS precipitation products that are based on algorithms developed by CNR-ISAC in collaboration with the international community. While some of these products are being generated during the CDOP-2 phase, other products were first generated during the H-SAF development programme, then were under continuous development during CDOP-1 in order to improve their performance, and are now operationally generated at the Italian Centro Nazionale di Meteorologia e Climatologia Aeronautica (CNMCA), which is responsible of operational product generation and dissemination. Specifically, we will present and discuss the algorithms on which all these precipitation products are based. We will also discuss the activities that have been planned for CDOP-2, in order to enhance and improve algorithms and processing schemes and extend them to satellites that will be operational in the 2012-2017 timeframe - with special emphasis on the GEO Meteosat Third Generation (MTG) satellite which is scheduled to be launched by EUMETSAT in 2016, and on the LEO Core Observatory of the Global Precipitation Measurement (GPM) mission which will launched by NASA and JAXA in 2013.

Precipitation occurring over the Mediterranean basin is typically unusually difficult to measure due to variability resulting from the irregular terrain. Radar based precipitation measurements also are compromised by the terrain, which contaminates reflectivity with excessive ground cover and blocks the radar beam at low elevation angles. That same terrain also is instrumental in providing for the localization of rain producing storms that results in long term and flash flooding situations, especially in the fall

AMERICAN METEOROLOGICAL SOCIETY | 1229 On 9–10 November 2001, Algeria was struck with its most devastating f lood since records were first kept in 1908 (Fig. 1). The f lood was caused by an intense meso-β1-scale cyclone that struck the west side of the city of Algiers, producing accumulated rainfall up to ~285 mm and onshore winds of ~33 m s−1, resulting in some 740 deaths. Fortunately, the overall cyclogenesis event was detected by the European Centre for Medium-Range Weather Forecast (ECMWF) model, enabling the Office National de la Meteorologie de Algier (ONMA) to issue a f lood forecast as early as 5 November. Whereas the apparent predictability of the event suggests a controlling role by large-scale forcing, closer examination reveals that significant mesoscale development led to the actual weather pattern within the Algiers locale.

The objective of this paper is to evaluate the potential of a Bayesian inversion algorithm using microwave multisensor data for the retrieval of surface rainfall rate and cloud parameters. The retrieval scheme is based on the maximum a posteriori probability (MAP) method, extended for the use of both spaceborne passive and active microwave data. The MAP technique for precipitation profiling is also proposed to approach the problem of the radar-swath synthetic broadening; that is, the capability to exploit the combined information also where only radiometric data are available. In order to show an application to airborne data, two case studies are selected within the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE). They refer to a stratiform storm region and an intense squall line of two mesoscale convective systems, which occurred over the ocean on February 20 and 22, 1993, respectively. The estimated rainfall rates and columnar hydrometeor contents derived from the proposed algorithms are compared to each other and to radar estimates based on reflectivity-rainrate (Z-R) relationships. Results in terms of reflectivity profiles and upwelling brightness temperatures, reconstructed from the estimated cloud structures, are also discussed. A database of combined measurements acquired at nadir during various TOGA-COARE flights, is used for applying the radar-swath synthetic broadening technique in the case of an along-track radar-failure countermeasure. A simulated test of the latter technique is performed using the case studies of February 20 and 22, 1993.

Rainfall retrieval estimation algorithms, based on passive and active microwave sensor data, are applied to along-track nadir-looking observations of a cyclone over ocean that occurred on February 8, 1993 during TOGA-COARE. The estimated rainfall rates derived from the radiometer data are compared with those obtained from ARMAR radar. Results in terms of reflectivity profiles and upwelling brightness temperatures, reconstructed from the estimated cloud structures, are discussed.

In the perspective of flood hazard multisensor monitoring, the event of 4–6 November, 1994 over Northern Italy is described and analyzed in this paper using traditional ground‐based rainfall observations and remote sensing techniques. Satellite imagery is used with different objectives: the interpretation of SAR measurements allows the identification of flooded areas, Meteosat infrared images show high intensity rain areas and SSM/I passive microwave data provide estimates of the rain‐rates. The IFA‐SAP algorithm, a profile based retrieval technique for estimating rainfall rates and precipitating cloud parameters from spacebome multifrequency microwave radiometers, has been used in the latter case. The use of multisensor observations considerably supplements conventional monitoring systems which prove inadequate for many hydrological purposes. The present paper is written in the spirit of encouraging a posteriori analyses of observed heavy rainfall events so as to explore the potential of a real‐time oper...