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In December 2019, a contract between CNR and ECMWF was signed for a pilot ACTRIS/EARLINET data provision to the Copernicus Atmosphere Monitoring Service (CAMS). Such pilot contract (CAMS21b) aims to put in place a first data provision for a set of selected stations and it will demonstrate the feasibility of fully traceable and quality-controlled data provision for the whole network.In CAMS21b, the main effort is devoted to design, test and set up the provision of quality-controlled ACTRIS/EARLINET products in Real Real Time (RRT) and/or Near Real Time (NRT) to CAMS. The activities are focused on the automatic centralized data processing and data provision, ensuring the full traceability of the products from the data acquisition level up to the final quality-controlled data level. Most of the activities are done at ARES, the EARLINET/ACTRIS data center node at CNR, for assuring the centralized, harmonized and quality-controlled processing in compliance with FAIR principles.New modules and submodules of the ACTRIS/EARLINET Single Calculus Chain (SCC) as well as optimized algorithms for cloud screening have been designed. Additional procedures were implemented for improving the quality of the data provided in NRT, but also for the quality control of the Level 2 products which are delivered with a time delay.The release of a new version of SCC and of QC procedure is planned for mid-February.The data provision started in October 2020 at the test site of Potenza. A system has been set up for measurement reporting and monitoring of KPIs (Key Performance Indicators). After 3 months of measurements, the overall data provision system showed no critical points.In January 2021, the provision started for a group of 9 stations which are seen as representative for the whole network in terms of instrumental capability, but also ensuring a good geographical coverage of the European continent.In order to accommodate also measurements from non-continuous operation systems, a measurement schedule has been set up, compromising between the need of a large number of measurements and costs/efforts at each station. The measurement schedule has been designed through a representativeness study and foresees 6 slots of measurements per week, 3 in daytime and 3 in nighttime conditions.The successful implementation of the pilot allows the provision of aerosol optical property profiles to the CAMS services. from a set of observational sites distributed over the different European regions. These profiles is expected to be of interest for the assimilation, near real time evaluation and re-analysis evaluation of several CAMS products, including the aerosol load over Europe for air quality issues, atmospheric composition, climate forcing and solar and UV products. This allows for having a systematic solution for looking into specific events as they develop (e.g. the dust plume that you investigated earlier this month or the Californian fires in September), supporting or contradicting model forecasts. This pilot is the first provision of aerosol profiles from a high-quality ground-based network in NRT for this kind of applications. It is expected that these efforts will be continued in the next phase of CAMS/Copernicus (2021-2027).

The occurrence of fog often causes errors in the prediction of the incident solar radiation and the power produced by photovoltaic cells. An accurate fog forecast would benefit solar energy producers and grid operators, who could take coordinated actions to reduce the impact of discontinuity, the main drawback of renewable energy sources. Considering that information on discontinuity is crucial to optimize power production estimation and plant management efficiency, in this work, a fog forecast method based on the output of the Weather Research and Forecasting (WRF) numerical model is presented. The areal extension and temporal duration of a fog event are not easy to predict. In fact, there are many physical processes and boundary conditions that cause fog development, such as the synoptic situation, air stability, wind speed, season, aerosol load, orographic influence, humidity and temperature. These make fog formation a complex and rather localized event. Thus, the results of a fog forecast method based on the output variables of the high spatial resolution WRF model strongly depend on the specific site under investigation. In this work, the thresholds are site-specifically designed so that the implemented method can be generalized to other sites after a preliminary meteorological and climatological study. The proposed method is able to predict fog in the 6–30 h interval after the model run start time; it has been evaluated against METeorological Aerodrome Report data relative to seven selected sites, obtaining an average accuracy of 0.96, probability of detection of 0.83, probability of false detection equal to 0.03 and probability of false alarm of 0.18. The output of the proposed fog forecast method can activate (or not) a specific fog postprocessing layer designed to correct the global horizontal irradiance forecasted by the WRF model in order to optimize the forecast of the irradiance reaching the photovoltaic panels surface.

Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

The High Agri Valley is a tectonically active area in southern Italy characterized by high seismic hazard related to fault systems capable of generating up to M=7 earthquakes (i.e., the 1857 Mw 7 Basilicata earthquake). In addition to the natural seismicity, two different clusters of induced microseismicity were recognized to be caused by industrial operations carried out in the area: (1) the water loading and unloading operations in the Pertusillo artificial reservoir; (2) the wastewater disposal at the Costa Molina 2 injection well. The twofold nature of the recorded seismicity in the High Agri Valley makes it an ideal study area to deepen the understanding of nucleation processes of both natural and anthropogenic earthquakes and to improve the current methodologies for the discrimination between natural and induced seismic events by collecting high quality seismic data. Here we present the dataset gathered by the INSIEME seismic network that was installed in the High Agri Valley within the SIR-MIUR research project INSIEME (INduced Seismicity in Italy: Estimation, Monitoring, and sEismic risk mitigation). The seismic network was planned with the aim to study the two induced seismicity clusters and to collect a full-range of open-access data to be shared with the whole scientific community. The seismic network is composed by 8 stations deployed in an area of 17 km x 11 km around the two clusters of induced microearthquakes and it is equipped with triaxial weak-motion broadband sensors placed at different depths down to 50 m. It allows to detect induced microearthquakes, local/regional earthquakes, and teleseisms from continuous data streams transmitted in real-time to the CNR-IMAA Data Centre. The network has been registered at the International Federation of Digital Seismograph Networks (FDSN) with code 3F. Data collected until the end of the INSIEME project (2019-03-23) are already released with open-access policy through the FDSN webservices and are available from http://www.fdsn.org/networks/detail/3F_2016 (https://doi.org/10.7914/SN/3F_2016; Stabile et al., 2016).

This paper describes the first official release (v1.0) of RTTOV-gb. RTTOV-gb is a FORTRAN 90 code developed by adapting the atmospheric radiative transfer code RTTOV, focused on satellite-observing geometry, to the ground-based observing geometry. RTTOV-gb is designed to simulate ground-based upward-looking microwave radiometer (MWR) observations of atmospheric downwelling natural radiation in the frequency range from 22 to 150 GHz. Given an atmospheric profile of temperature, water vapor, and, optionally, cloud liquid water content, and together with a viewing geometry, RTTOV-gb computes downwelling radiances and brightness temperatures leaving the bottom of the atmosphere in each of the channels of the sensor being simulated. In addition, it provides the sensitivity of observations to the atmospheric thermodynamical state, i.e., the Jacobians. Therefore, RTTOV-gb represents the forward model needed to assimilate ground-based MWR data into numerical weather prediction models, which is currently pursued internationally by several weather services. RTTOV-gb is fully described in a previous paper (De Angelis et al., 2016), while several updates are described here. In particular, two new MWR types and a new parameterization for the atmospheric absorption model have been introduced since the first paper. In addition, estimates of the uncertainty associated with the absorption model and with the fast parameterization are given here. Brightness temperatures (TB) computed with RTTOV-gb v1.0 from radiosonde profiles have been compared with ground-based MWR observations in six channels (23.8, 31.4, 72.5, 82.5, 90.0, and 150.0 GHz). The comparison shows statistics within the expected accuracy. RTTOV-gb is now available to licensed users free of charge from the Numerical Weather Prediction Satellite Application Facility (NWP SAF) website, after registration. Coefficients for four MWR instrument types and two absorption model parameterizations are also freely available from the RTTOV-gb support website.

The purpose of this work is to explore the effect of temporal sampling on the accuracy of the hourly mean Surface Solar Irradiance (SSI) estimation. An upgraded version of the Advanced Model for the Estimation of Surface Solar Irradiance from Satellite (AMESIS), exploiting data from the Meteosat Second Generation Rapid Scanning Service (MSG-RSS), has been used to evaluate the SSI. The assessment of the new version of AMESIS has been carried out against data from two pyranometers located in Southern (Tito) and Northern (Ispra) Italy at an altitude of 760 m and 220 m, respectively. The statistical analysis of the comparison between hourly mean SSI estimates based on temporal sampling every five minutes shows a quantitative improvement compared to those based on 15-minute sampling. In particular, for the whole dataset in Tito, the correlation increases from 0.979 to 0.998, the Root Mean Square Error (RMSE) decreases from 45.16 W/m2 to 13.19 W/m2 and the Mean Bias Error (MBE) is reduced from −0.67 W/m2 to −0.02 W/m2. For the whole dataset in Ispra, the correlation increases from 0.995 to 0.998, the RMSE decreases from 24.85 W/m2 to 15.59 W/m2, whereas the MBE increases from 3.84 W/m2 to 4.58 W/m2. This preliminary assessment shows that higher temporal sampling can improve SSI monitoring over areas featuring frequent and rapid solar irradiance variation.

Previsione a breve termine dell'irradianza solare mediante tecniche di Cloud Motion Vector applicate ai dati MSG-SEVIRI

This paper presents a geostatistical downscaling procedure to improve the spatial resolution of precipitation data. The kriging method with external drift has been applied to surface rain intensity (SRI) data obtained through the Operative Precipitation Estimation at Microwave Frequencies (OPEMW), which is an algorithm for rain rate retrieval based on Advanced Microwave Sounding Units (AMSU) and Microwave Humidity Sounder (MHS) observations. SRI data have been downscaled from coarse initial resolution of AMSU-B/MHS radiometers to the fine resolution of Spinning Enhanced Visible and InfraRed Imager (SEVIRI) flying on board the Meteosat Second Generation (MSG) satellite. Orographic variables, such as slope, aspect and elevation, are used as auxiliary data in kriging with external drift, together with observations from Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI) in the water vapor band (6.2 µm and 7.3 µm) and in thermal-infrared (10.8 µm and 8.7 µm). The validation is performed against measurements from a network of ground-based rain gauges in Southern Italy. It is shown that the approach provides higher accuracy with respect to ordinary kriging, given a choice of auxiliary variables that depends on precipitation type, here classified as convective or stratiform. Mean values of correlation (0.52), bias (0.91 mm/h) and root mean square error (2.38 mm/h) demonstrate an improvement by +13%, −37%, and −8%, respectively, for estimates derived by kriging with external drift with respect to the ordinary kriging.

Il documento descrive la lista dei test eseguiti per validare la PoC Radware presso l'Istituto di Metodologia per l'Analisi Ambientale (IMAA) del Consiglio Nazionale delle Ricerche (CNR) avente sede nell'Area di Ricerca di Potenza. L'apparato hardware Radware DefensePro (DP) è stato installato in modalità "in-line", ovvero posizionato tra il circuito in fibra ottica L2 GARR e il border router del CNR-IMAA. Nelle architetture di tipo "In-Line" il DefensePro diventa un elemento "in linea" per tutto il traffico, sia esso di tipo Inbound che Outbound.

Studio, prototipazione e sperimentazione di un servizio cloud per la stima e la previsione della fonte primaria per il management degli impianti di produzione di energia solare

Detection of clouds over polar areas from satellite radiometric measurements in the visible and IR atmospheric window region is rather difficult because of the high albedo of snow, possible ice covered surfaces, very low humidity, and the usual presence of atmospheric temperature inversion. Cold and highly reflective polar surfaces provide little thermal and visible contrast between clouds and the background surface. Moreover, due to the presence of temperature inversion, clouds are not always identifiable as being colder than the background. In addition, low humidity often causes polar clouds to be optically thin. Finally, polar clouds are usually composed of a mixture of ice and water, which leads to an unclear spectral signature. Single and bi-spectral threshold methods are sometimes inappropriate due to a large variability of surface emissivity and cloud conditions. The objective of this study is to demonstrate the crucial role played by surface emissivity in the detection of polar winter clouds and the potential improvement offered by infrared hyperspectral observations, such as from the Infrared Atmospheric Sounding Interferometer (IASI). In this paper a new approach for cloud detection is proposed and validated exploiting active measurements from satellite sensors, i.e., the CloudSat cloud profiling radar (CPR) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For a homogenous IASI field of view (FOVs), the proposed cloud detection scheme tallies with the combined CPR and CALIOP product in classifying 98.11% of the FOVs as cloudy and also classifies 97.54% of the FOVs as clear. The Hansen Kuipers discriminant reaches 0.95.

L'efficienza energetica è diventata una tematica fondamentale, data l'importanza di ridurre al minimo le risorse consumate e quindi ridurre le emissioni di CO2. Per poter applicare politiche di risparmio energetico è necessario mettere in campo prima di tutto sistemi capaci monitorare lo stato energetico degli edifici e in un secondo momento di potere elaborare i dati acquisiti. In tal modo è possibile intervenire e ridurre significativamente gli sprechi di energia che fanno aumentare drasticamente i consumi e di conseguenza l'emissione di CO2. Il CNR - Istituto di Metodologie per l'Analisi Ambientale, forte della propria missione legata all'osservazione della terra e allo sviluppo di tecniche avanzate di monitoraggio ambientale, ha sviluppato un sistema di monitoraggio dei valori di temperatura e umidità della struttura dell'Area di Ricerca di Potenza del CNR, al fine di ottimizzare i consumi energetici relativi al riscaldamento e al raffreddamento dell'edificio.

L'obiettivo principale della rete EARLINET (European Aerosol Research Lidar NETwork) è quello di costruire un database contenente i profili verticali dei più importanti parametri ottici utili alla caratterizzazione degli aerosol atmosferici. Attualmente il database di EARLINET, gestito e ospitato presso il Centro di Elaborazione Dati del CNR-IMAA, costituisce il più grande e rappresentativo database di parametri ottici aerosolici ottenuti a partire da misure LIDAR. Esso è fruibile liberamente dalla comunità scientifica e permette la realizzazione di numerosi e molteplici studi riguardanti gli aerosol su scala europea. Affinché il database sia caratterizzato sempre dalla massima qualità possibile dei prodotti finali e allo stesso tempo sia garantita l'omogeneità di dati provenienti da differenti tipologie di sistemi lidar, la rete EARLINET, nell'ambito dell'infrastruttura di ricerca ACTRIS, ha implementato un rigido programma di quality assurance che tutte le stazioni affiliate sono tenute a rispettare. In mancanza dei requisiti previsti dal programma di quality assurance non è possibile sottomettere dati all'interno del database di EARLINET. Per semplificare e velocizzare l'inserimento dei dati acquisiti dalle stazioni della rete EARLINET, è stata sviluppata una piattaforma web che poi va a popolare il database MySQL esistente. Il rapporto tecnico prodotto svolge anche il ruolo di una guida di riferimento per il personale tecnico che dovrà occuparsi della gestione delle strumentazioni e dell'archiviazione dei dati acquisti (tramite la gestione del geo-database esistente), per poi procedere alla loro elaborazione tramite la messa a punto di catene di calcolo. L'implementazione di detto programma di quality assurance ha costituito e tutt'ora costituisce una delle principali attività della rete EARLINET e rappresenta un requisito indispensabile per stabilire e monitorare nel tempo le performance dei differenti sistemi Lidar che fanno parte della rete.

Metadata is a vital information embedded within an electronic file about a container of data or a single file data. By accessing an electronic file's metadata, a user can access a variety of information that drives him/her in a quick selection of the datasets to extract from a data archive according to its specific application. Two types of metadata exist: structural metadata and descriptive metadata. Structural metadata is data about the containers of data. Descriptive metadata uses individual instances of application data or the data content.

A new algorithm for the estimation of atmospheric temperature (T) and water vapor (WV) vertical profiles in nonprecipitating conditions is presented. The microwave random forest temperature and water vapor (MiRTaW) profiling algorithm is based on the random forest (RF) technique and it uses microwave (MW) sounding from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. Three different data sources were chosen for both training and validation purposes, namely, the ERA-Interim from the European Centre for Medium-Range Weather Forecasts (ECMWF), the Infrared Atmospheric Sounding Interferometer Atmospheric Temperature Water Vapour and Surface Skin Temperature (IASI L2 v6) from the Meteorological Operational satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the radiosonde observations from the Integrated Global Radiosonde Archive (IGRA). The period from 2012 to 2016 was considered in the training dataset; particular attention was paid to the instance selection procedure, in order to reduce the full training dataset with negligible information loss. The out-of-bag (OOB) error was computed and used to select the optimal RF parameters. Different RFs were trained, one for each vertical level: 32 levels for T (within 10–1000 hPa) and 23 levels for WV (200–1000 hPa). The validation of the MiRTaW profiling algorithm was conducted on a dataset from 2017. The mean bias error (MBE) of T vertical profiles ranges within about (−0.4–0.4) K, while for the WV mixing ratio, the MBE starts at ~0.5 g/kg near the surface and decreases to ~0 g/kg at 200 hPa level, in line with the expectations.

The Advanced Model for the Estimation of Surface Solar Irradiance (AMESIS) was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) to derive surface solar irradiance from SEVIRI radiometer on board the MSG geostationary satellite. The operational version of AMESIS has been running continuously at IMAA-CNR over all of Italy since 2017 in support to the monitoring of photovoltaic plants. The AMESIS operative model provides two different estimations of the surface solar irradiance: one is obtained considering only the low-resolution channels (SSI_VIS), while the other also takes into account the high-resolution HRV channel (SSI_HRV). This paper shows the difference between these two products against simultaneous ground-based observations from a network of 63 pyranometers for different sky conditions (clear, overcast and partially cloudy). Comparable statistical scores have been obtained for both AMESIS products in clear and cloud situation. In terms of bias and correlation coefficient over partially cloudy sky, better performances are found for SSI_HRV (0.34 W/m2 and 0.995, respectively) than SSI_VIS (−33.69 W/m2 and 0.862) at the expense of the greater run-time necessary to process HRV data channel.

La diffusione tra gli utenti di nuove apparecchiature (laptop, smartphone, tablet) dotate di interfacce wireless ha reso indispensabile l'implementazione di "Wireless Local Area Network" (WLAN) che permettano agli utilizzatori di lavorare in mobilità all'interno delle strutture. Queste ragioni, unite alla obsolescenza della rete in funzione, hanno spinto il CNR- IMAA alla realizzazione di una nuova rete wireless di nuova concezione in grado di garantire agli utenti sia livelli prestazionali che di sicurezza comparabili alla rete cablata. Particolare attenzione è stata rivolta ai sistemi di autenticazione federata come Eduroam (Education Roaming), un'infrastruttura basata su una rete di server RADIUS che utilizza lo standard IEEE 802.1x e ha lo scopo di facilitare l'accesso agli utenti mobili delle reti della ricerca delle organizzazioni partecipanti. In Italia la federazione Eduroam è coordinata dal Consortium GARR.

Nell'ambito della collaborazione tra il CNR-Istituto di Metodologie per l'Analisi Ambientale e il Gestore Servizi Energetici (GSE) è stato implementato e validato un modello di previsione a 48/72 ore dell'irradianza solare al suolo e della produzione di potenza oraria da fotovoltaico, basato sui dati di output di un modello di previsione meteo ad alta risoluzione spaziale e temporale.

Le infrastrutture informatiche sono uno strumento essenziale per poter svolgere le attività di ricerca, in particolare per quelle che richiedono servizi avanzati riguardo la rete (grandi moli di dati) e riguardo la sicurezza (dei dati archiviati). Il CNR - Istituto di Metodologie per l'Analisi Ambientale ha da sempre investito risorse, sia in termini di personale fortemente qualificato, sia in termini di hardware/software per poter gestire "in casa" qualsiasi necessità informatica senza utilizzare servizi di hosting o housing esterni, quindi la progettazione e gestione di una rete performante e affidabile ha un ruolo cruciale all'interno dell'Istituto. Questa scelta consente principalmente di avere una rete informatica all'avanguardia in grado di supportare appieno le attività di ricerca e svolgere anche un ruolo di sperimentazione nel campo dell'ICT per essere sempre al passo con le nuove tecnologie. Le attività di implementazione e gestione della rete rientrano nelle competenze del "Centro Calcolo e Servizi di Rete" e sono concentrate nella sede di Tito Scalo, dove l'IMAA si occupa anche della gestione della rete e della telefonia dell'Area di Ricerca di Potenza, e delle due Unità di Supporto Operative dell'IBAM e dell'ISM del CNR, nonché al Consorzio pubblico-privato TeRN. Oltre ai servizi di supporto alla ricerca, dal Centro Calcolo dell'IMAA vengono implementati tutti i servizi necessari al funzionamento della rete come ad esempio server DNS per la gestione dei domini "imaa.cnr.it" e "pz.cnr.it", server web per i siti web d'Istituto, dei gruppi di ricerca e dei progetti in essere, server mail per la posta elettronica, server sFTP per lo scambio di dati e molto altro ancora. Con l'entrata in funzione di GARR-X, l'infrastruttura di rete a banda ultralarga dell'istruzione e della ricerca italiana, l'Area di Ricerca di Potenza del CNR è stata dotata di un nuovo collegamento con un notevole aumento della banda a disposizione. In particolare è stato realizzato un circuito in fibra ottica spenta (dark fiber) fino al PoP GARR di Macchia Roman presente nell'Università degli Studi della Basilicata, in grado di assicurare una banda simmetrica di 1 Gbps, e un circuito di backup in rame da 6 Mbps da utilizzare in caso di guasti sulla linea principale. A seguito di questi upgrade è stato necessario riprogettare l'infrastruttura di rete anche con l'aggiunta di nuovi apparati, interventi necessari a gestire correttamente la banda a disposizione sia in termini prestazionali, sia in termini di sicurezza informatica. Particolare attenzione è stata dedicata anche all'implementazione di IPv6 (Internet Protocol Version 6), un nuovo protocollo definito dall'IETF per affiancare e sostituire in futuro l'attuale versione dell'Internet Protocol, ovvero IPv4. IPv6 risolve molti problemi legati a IPv4 primo tra tutti il limitato numero di indirizzi IP pubblici disponibili che sono in esaurimento.

Nell'ambito della collaborazione tra CNR - IMAA e GSE è stato implementato un indice di previsione della nebbia per 6 particolari città italiane (Varese, Ferrara, Foggia, Macerata, Viterbo e Chieti). Tra i diversi metodi di previsione presenti in letteratura si è scelto di utilizzare un indice chiamato FOG INDEX (FI) e ricavato a partire da alcuni dati meteorologici quali la temperatura al suolo, la temperatura a 850 hPa, la temperatura di rugiada al suolo e l'intensità del vento a 850 hPa. Il FI è un indice con una scala che va da 1 a 100 i cui valori più bassi indicano la più alta probabilità di formazione della nebbia. La previsione è fornita quotidianamente per tutte le ore che vanno dalle 4 alle 21 del giorno successivo. Al fine di validare la bontà o meno di tale indice è stata condotta un'analisi nel periodo che va da Dicembre 2013 a Marzo 2014 per 4 dei 6 siti (Varese, Ferrara, Foggia e Viterbo) utilizzando come verità a terra i dati METAR sulla visibilità orizzontale misurata in km (relativa ai più o meno vicini aeroporti) e, laddove si è riscontrata scarsa correlazione, aggiungendo come ulteriore parametro di confronto le immagini Meteosat provenienti dal sensore SEVIRI nelle bande del visibile e del vicino infrarosso. I risultati di questa prima fase di analisi quantitativa sono stati sintetizzati in diverse tabelle con lo scopo di indicare la presenza o meno di correlazione tra la previsione, i dati METAR e le immagini MSG. La fase successiva ha riguardato una sintesi statistica dei dati in nostro possesso. Assimilando il comportamento del FI a quello di un classificatore binario, si è proceduto al calcolo della soglia ottima di separazione tra le probabilità di formazione e non formazione della nebbia. Infine si sono ricavati indici prestazionali quali la probabilità d'errore, l'affidabilità e le curve ROC che hanno confermato la bontà della previsione fornita da tale indice nei siti considerati.