43 results on '"Puca, Silvia"'
Search Results
2. Improving the lightning forecast with the WRF model and lightning data assimilation: Results of a two-seasons numerical experiment over Italy
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Federico, Stefano, Torcasio, Rosa Claudia, Popova, Jana, Sokol, Zbyněk, Pop, Lukáš, Lagasio, Martina, Lynn, Barry H., Puca, Silvia, and Dietrich, Stefano
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- 2024
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3. A 13-year long strokes statistical analysis over the Central Mediterranean area
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Petracca, Marco, Federico, Stefano, Roberto, Nicoletta, Puca, Silvia, D'Adderio, Leo Pio, Torcasio, Rosa Claudia, and Dietrich, Stefano
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- 2024
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4. Near real-time generation of a country-level burned area database for Italy from Sentinel-2 data and active fire detections
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Pulvirenti, Luca, Squicciarino, Giuseppe, Fiori, Elisabetta, Negro, Dario, Gollini, Andrea, and Puca, Silvia
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- 2023
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5. Soil moisture products consistency for operational drought monitoring in Europe.
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Gaona, Jaime, Bavera, Davide, Fioravanti, Guido, Hahn, Sebastian, Stradiotti, Pietro, Filippucci, Paolo, Camici, Stefania, Ciabatta, Luca, Mossaffa, Hamidreza, Puca, Silvia, Roberto, Nicoletta, and Brocca, Luca
- Abstract
The roadmap to enable operational soil moisture (SM) monitoring for meteorologic and hydrological early warning is challenged by the uncertainty within the available remote sensing and modelling products. This study addressed two relevant uncertainties: the residual trends in the series and the spatial consistency. While the latter has been often revisited to validate remote sensing and modelling products against in-situ data, the former is often disregarded in studies addressing SM changes. This study evaluated three SM products: (1) the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) active Advanced SCATterometer (ASCAT)-derived dataset, (2) the passive subset of the European Space Agency (ESA) - Climate Change Initiative (CCIp), and (3) the modelled dataset from the European Drought Observatory (EDO). The analysis was carried out over Europe in the period 2007–2022 at 10-day temporal scales. We obtained that even these popular datasets are subject to patches of spatial inconsistency and residual trends when compared to the in-situ data from the International Soil Moisture Network (ISMN). In view of the great complementarity shown by the active and passive remote sensing and the modelled SM estimates, two merged products are proposed and tested against in-situ data. Results indicate that combining H SAF ASCAT, CCIp and EDO equals or surpasses the spatial and temporal consistency of the individual SM products alone, even when only the near-real-time products of H SAF ASCAT and EDO are combined. Thus, merging remote sensing and modelled SM products is advantageous for enhanced spatial and temporal operational monitoring of SM at the European scale. [ABSTRACT FROM AUTHOR]
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- 2024
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6. The impact of global navigation satellite system (GNSS) zenith total delay data assimilation on the short-term precipitable water vapor and precipitation forecast over Italy using the Weather Research and Forecasting (WRF) model.
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Torcasio, Rosa Claudia, Mascitelli, Alessandra, Realini, Eugenio, Barindelli, Stefano, Tagliaferro, Giulio, Puca, Silvia, Dietrich, Stefano, and Federico, Stefano
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PRECIPITABLE water ,GLOBAL Positioning System ,METEOROLOGICAL research ,WEATHER forecasting ,PRECIPITATION forecasting ,KALMAN filtering ,ATMOSPHERIC water vapor measurement ,FORECASTING - Abstract
The impact of assimilating GNSS-ZTD (global navigation satellite system–zenith total delay) on the precipitable water vapor and precipitation forecast over Italy is studied for the month of October 2019, which was characterized by several moderate to intense precipitation events, especially over northwestern Italy. The WRF (Weather Research and Forecasting) model, version 4.1.3, is used with its 3D-Var data assimilation system to assimilate ZTD observations from 388 GNSS receivers distributed over the country. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area. The water vapor forecast is verified for the forecast hours of 1–6 h after the last data assimilation time. Results show that WRF underestimates the atmospheric water vapor content for the period, and GNSS-ZTD data assimilation improves this underestimation. The precipitation forecast is verified in the phases of 0–3 and 3–6 h after the last data assimilation time using more than 3000 rain gauges spread over Italy. The application of GNSS-ZTD data assimilation to a case study improved the precipitation forecast by increasing the rainfall maximum and by better focusing the precipitation pattern over northeastern Italy, with the main drawback being the prediction of false alarms. Considering the study over the whole period, GNSS-ZTD data assimilation had a positive impact on rainfall forecast, with an improvement in the performance up to 6 h and with statistically significant results for moderate to intense rainfall thresholds (25–30 mm (3 h) -1). [ABSTRACT FROM AUTHOR]
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- 2023
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7. Rainfall-runoff modelling by using SM2RAIN-derived and state-of-the-art satellite rainfall products over Italy
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Ciabatta, Luca, Brocca, Luca, Massari, Christian, Moramarco, Tommaso, Gabellani, Simone, Puca, Silvia, and Wagner, Wolfgang
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- 2016
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8. Integration of Satellite Soil Moisture and Rainfall Observations over the Italian Territory
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Ciabatta, Luca, Brocca, Luca, Massari, Christian, Moramarco, Tommaso, Puca, Silvia, Rinollo, Angelo, Gabellani, Simone, and Wagner, Wolfgang
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- 2015
9. Analysis and Processing of the COSMO-SkyMed Second Generation Images of the 2022 Marche (Central Italy) Flood.
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Pulvirenti, Luca, Squicciarino, Giuseppe, Fiori, Elisabetta, Candela, Laura, and Puca, Silvia
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SYNTHETIC aperture radar ,IMAGE segmentation ,FLOODS ,FUZZY logic ,OPTICAL images ,DATA mapping ,EMERGENCY medical services - Abstract
The use of SAR data for flood mapping is well established. However, the problem of the missed detection of rapidly evolving floods remains. Recently, the Italian Space Agency deployed the COSMO-SkyMed Second Generation (CSG) constellation, with an on-demand capability that makes it possible to reduce the number of missed floods. However, for on-demand SAR acquisitions, pre-flood images are generally not available, and change-detection methods, commonly adopted for flood mapping using SAR, cannot be applied. This study focused on the high-resolution CSG images of a flood that occurred in central Italy. An accurate analysis of the radar responses of the different targets included in the scenes observed by GSG was performed. Then, a methodology to detect floods using high-resolution single SAR images was developed. The methodology was based on image segmentation and fuzzy logic. Image segmentation allowed us to analyze homogeneous areas in the CSG images. Fuzzy logic was used to integrate the SAR data with ancillary information that was useful when change-detection methods could not be applied. A comparison with the maps produced by the Copernicus Emergency Service, using high-resolution optical images, demonstrated the reliability of the methodology. [ABSTRACT FROM AUTHOR]
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- 2023
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10. The impact of GNSS Zenith Total Delay data assimilation on the short-term precipitable water vapor and precipitation forecast over Italy using the WRF model.
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Torcasio, Rosa Claudia, Mascitelli, Alessandra, Realini, Eugenio, Barindelli, Stefano, Tagliaferro, Giulio, Puca, Silvia, Dietrich, Stefano, and Federico, Stefano
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PRECIPITABLE water ,PRECIPITATION forecasting ,GLOBAL Positioning System ,ATMOSPHERIC water vapor ,FORECASTING ,METEOROLOGICAL research ,WATER vapor transport - Abstract
The impact of assimilating GNSS-ZTD (Global Navigation Satellite Systems - Zenith Total Delay) on the precipitable water vapor and precipitation forecast over Italy is studied for the month of October 2019, characterized by several moderate to intense precipitation events. The WRF (Weather Research and Forecasting) model 4.1.3 is used with its 3DVar data assimilation system to assimilate ZTD observations from 388 GNSS receivers distributed over the country. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area. Results show that WRF underestimates the atmospheric water vapor content for the period, and GNSS-ZTD data assimilation improves this underestimation. The water vapor forecast is verified for the forecast hours 1-6h after the last data assimilation time, while the precipitation forecast is verified in the phases 0-3h and 3-6h after the last data assimilation time. More than 3000 rain gauges spread over Italy were used to verify the precipitation forecast. The application of GNSS-ZTD data assimilation to a case study showed an improvement of the precipitation forecast in different ways, the main drawback being the prediction of false alarms. Considering the study over the whole period, GNSS-ZTD data assimilation had a positive impact on the precipitable water vapor and rainfall forecast, with an improvement of the performance up to 6 hours, and with statistically significant results for moderate to intense rainfall thresholds (25-30 mm/3h). [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
11. IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021).
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Avanzi, Francesco, Gabellani, Simone, Delogu, Fabio, Silvestro, Francesco, Pignone, Flavio, Bruno, Giulia, Pulvirenti, Luca, Squicciarino, Giuseppe, Fiori, Elisabetta, Rossi, Lauro, Puca, Silvia, Toniazzo, Alexander, Giordano, Pietro, Falzacappa, Marco, Ratto, Sara, Stevenin, Hervè, Cardillo, Antonio, Fioletti, Matteo, Cazzuli, Orietta, and Cremonese, Edoardo
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MODIS (Spectroradiometer) ,SNOW accumulation ,AUTOMATIC meteorological stations ,STANDARD deviations ,GLOBAL warming - Abstract
We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 10 3 km2) – a transitional continental-to-Mediterranean region where snow plays an important but still poorly constrained societal and ecological role. IT-SNOW provides ∼ 500 m daily maps of snow water equivalent (SWE), snow depth, bulk snow density, and liquid water content for the initial period 1 September 2010–31 August 2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel-2, MODIS (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 350 on-the-ground snow depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in situ snow data from three focus regions (Aosta Valley, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 mm for in situ, measured snow depth and snow water equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available at 10.5281/zenodo.7034956 and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate. [ABSTRACT FROM AUTHOR]
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- 2023
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12. Multi-Sensor Data Analysis of an Intense Weather Event: The July 2021 Lake Como Case Study.
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Mascitelli, Alessandra, Petracca, Marco, Puca, Silvia, Realini, Eugenio, Gatti, Andrea, Biondi, Riccardo, Anesiadou, Aikaterini, Brocca, Luca, Vulpiani, Gianfranco, Torcasio, Rosa Claudia, Federico, Stefano, Oriente, Antonio, and Dietrich, Stefano
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HAILSTORMS ,DATA analysis ,GLOBAL Positioning System ,WEATHER ,RAIN gauges ,LAKES - Abstract
A comprehensive analysis of the July 2021 event that occurred on Lake Como (Italy), during which heavy hailstorms and floods affected the surroundings of Lake, is presented. The study provides a detailed analysis of the event using different observation sources currently available. The employed techniques include both conventional (rain gauges, radar, atmospheric sounding) and non-conventional (satellite-based Earth observation products, GNSS, and lightning detection network) observations for hydro-meteorological analysis. The study is split in three main topics: event description by satellite-based observations; long-term analysis by the ERA5 model and ASCAT soil water index; and short-term analysis by lightning data, GNSS delays and radar-VIL. The added value of the work is the near-real-time analysis of some of the datasets used, which opens up the potential for use in alerting systems, showing considerable application possibilities in NWP modeling, where it can also be useful for the implementation of early warning systems. The results highlight the validity of the different techniques and the consistency among the observations. This result, therefore, leads to the conclusion that a joint use of the innovative techniques with the operational ones can bring reliability in the description of events. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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13. IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations (2010-2021).
- Author
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Avanzi, Francesco, Gabellani, Simone, Delogu, Fabio, Silvestro, Francesco, Pignone, Flavio, Bruno, Giulia, Pulvirenti, Luca, Squicciarino, Giuseppe, Fiori, Elisabetta, Rossi, Lauro, Puca, Silvia, Toniazzo, Alexander, Giordano, Pietro, Falzacappa, Marco, Ratto, Sara, Stevenin, Hervé, Cardillo, Antonio, Fioletti, Matteo, Cazzuli, Orietta, and Cremonese, Edoardo
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AUTOMATIC meteorological stations ,SNOW accumulation ,STANDARD deviations - Abstract
We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (300k+ km²) covering a transitional continental-to-Mediterranean region where snow plays an important, but still poorly constrained societal and ecological role. IT-SNOW provides ∼500-m, daily maps of Snow Water Equivalent (SWE), snow depth, bulk-snow density, and liquid water content for the 5 period 01/09/2010 - 31/08/2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 350+ on-the-ground snow-depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in-situ snow data from three focus regions (Aosta Valley, Lombardia, and Molise) shows little to none mean bias compared to the former, and Root Mean Square Errors on the order of 30 to 60 cm and 90 to 300 mm for in-situ, measured snow depth and Snow Water Equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak SWE and annual streamflow that are in line with expectations for this mixed rain-snow region (22% on average). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of SWE across the Italian landscape at peak accumulation, which on average occurs on the 4th of March. Nearly 52% of mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23%), and central Apennines (5%). IT-SNOW is freely available with the following DOI: https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources - a crucial endevor in a warming and drier climate. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
14. A Year-Long Total Lightning Forecast over Italy with a Dynamic Lightning Scheme and WRF.
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Federico, Stefano, Torcasio, Rosa Claudia, Lagasio, Martina, Lynn, Barry H., Puca, Silvia, and Dietrich, Stefano
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METEOROLOGICAL research ,WEATHER forecasting ,FORECASTING ,POTENTIAL energy ,MICROPHYSICS - Abstract
Lightning is an important threat to life and properties and its forecast is important for several applications. In this paper, we show the performance of the "dynamic lightning scheme" for next-day total strokes forecast. The predictions were compared against strokes recorded by a ground observational network for a forecast period spanning one year. Specifically, a total of 162 case studies were selected between 1 March 2020 and 28 February 2021, characterized by at least 3000 observed strokes over Italy. The events span a broad range of lightning intensity from about 3000 to 600,000 strokes in one day: 69 cases occurred in summer, 46 in fall, 18 in winter, and 29 in spring. The meteorological driver was the Weather Research and Forecasting (WRF) model (version 4.1) and we focused on the next-day forecast. Strokes were simulated by adding three extra variables to WRF, namely, the potential energies for positive and negative cloud to ground flashes and intracloud strokes. Each potential energy is advected by WRF, it is built by the electrification processes occurring into the cloud, and it is dissipated by lightning. Observed strokes were remapped onto the WRF model grid with a 3 km horizontal resolution for comparison with the strokes forecast. Results are discussed for the whole year and for different seasons. Moreover, statistics are presented for the land and the sea. In general, the results of this study show that lightning forecast with the dynamic lightning scheme and WRF model was successful for Italy; nevertheless, a careful inspection of forecast performance is necessary for tuning the scheme. This tuning is dependent on the season. A numerical experiment changing the microphysics scheme used in WRF shows the sensitivity of the results according to the choice of the microphysics scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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15. SWING, The Score-Weighted Improved NowcastinG Algorithm: Description and Application.
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Lagasio, Martina, Campo, Lorenzo, Milelli, Massimo, Mazzarella, Vincenzo, Poletti, Maria Laura, Silvestro, Francesco, Ferraris, Luca, Federico, Stefano, Puca, Silvia, and Parodi, Antonio
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NUMERICAL weather forecasting ,ATMOSPHERIC models ,RAINFALL frequencies ,ALGORITHMS ,DETERMINISTIC algorithms - Abstract
Because of the ongoing climate change, the frequency of extreme rainfall events at the global scale is expected to increase, resulting in higher social and economic impacts. Thus, improving the forecast accuracy and the risk communication is a fundamental goal to limit social and economic damages. Both Numerical Weather Prediction (NWP) and radar-based nowcasting systems still have open issues, mainly in terms of precipitation correct time/space localization predictability and rapid forecast accuracy decay, respectively. Trying to overcome these issues, this work aims to present a nowcasting system combining an NWP model (WRF), using a 3 h rapid update cycling 3DVAR assimilation of radar reflectivity data, with the radar-based nowcasting system PhaSt through a blending technique. Moreover, an innovative post-processing algorithm named SWING (Score-Weighted Improved NowcastinG) has been developed in order to take into account the timely and spatial uncertainty in the convective field simulation. The overarching goal is to pave the way for an easy and automatic communication of the heavy rainfall warning derived by the nowcasting procedure. The results obtained applying the SWING algorithm over a case study of 22 days in the fall 2019 season suggest that the algorithm could improve the predictive capability of a traditional deterministic nowcasting forecast system, keeping a useful forecast timing and thus integrating the current forecast procedures. Eventually, the main advantage of the SWING algorithm is also its very high versatility, since it could be used with any meteorological model also in a multi-model forecast approach. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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16. H-SAF precipitation products quality assessment and meteorological applications
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Kaňák, Ján, Ľuboslav Okon, Petracca, Marco, Puca, Silvia, Sk, Jan Kanak@Shmu, and Sk, Luboslav Okon@Shmu
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- 2020
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17. The reconstruction of significant wave height time series by using a neural network approach
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Arena, Felice and Puca, Silvia
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Ocean waves -- Research ,Ocean waves -- Models ,Science and technology - Abstract
A Multivariate Neural Network (MNN) algorithm is proposed for the reconstruction of significant wave height time series, without any increase of the error of the MNN output with the number of modelled data. The algorithm uses a weighted error function during the learning phase, to improve the modelling of the higher significant wave height. The ability of the MNN to reconstruct sea storms is tested by applying the equivalent triangular storm model. Finally an application to the NOAA buoys moored off California shows a good performance of the MNN algorithm, both during sea storms and calm time periods.
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- 2004
18. Assimilation of remote sensing observations into a continuous distributed hydrological model: Impacts on the hydrologic cycle.
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Laiolo, Paola, Gabellani, Simone, Campo, Lorenzo, Cenci, Luca, Silvestro, Francesco, Delogu, Fabio, Boni, Giorgio, Rudari, Roberto, Puca, Silvia, and Pisani, Anna Rita
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- 2015
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19. Validation of remote sensing soil moisture products with a distributed continuous hydrological model.
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Laiolo, Paola, Gabellani, Simone, Pulvirenti, Luca, Boni, Giorgio, Rudari, Roberto, Delogu, Fabio, Silvestro, Francesco, Campo, Lorenzo, Fascetti, Fabio, Pierdicca, Nazzareno, Crapolicchio, Raffaele, Hasenauer, Stefan, and Puca, Silvia
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- 2014
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20. Comparison of microwave passive and active observations of soil moisture.
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Pierdicca, Nazzareno, Pulvirenti, Luca, Santarelli, Andrea, Crapolicchio, Raffaele, Talone, Marco, and Puca, Silvia
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This paper describes the first outcomes of an activity aiming at validating the H-SAF soil moisture products derived from Metop-ASCAT data. For this purpose, an extensive comparison between SMOS and ASCAT derived soil moisture retrievals has been accomplished by considering the 25 km resolution ASCAT products and the SMOS L2 products. Both Europe and Northern Africa have been considered and data acquired during 2010 have been used. The procedure that has been followed to accomplish the comparison is described together with the first results. The way the ASCAT soil moisture relative index has been converted into a volumetric moisture content, which represents a critical aspect of the comparison, is also described. Results have demonstrated that, after the conversion of the H-SAF estimates into absolute volumetric soil moisture, the two products show a relatively good degree of correlation. Additional factors, such as spatial property features are also preliminary investigated. [ABSTRACT FROM PUBLISHER]
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- 2012
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21. Generalization to Other Phenomena.
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Bellomo, Nicola, Tirozzi, Brunello, Puca, Silvia, Pittalis, Stefano, Bruschi, Antonello, Morucci, Sara, Ferraro, Enrico, and Corsini, Stefano
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In this chapter we deal with applications to situations different from the ones analyzed in the previous chapters in order to show the wide range of applications of the artificial neural network (ANN). Therefore, we apply the methods discussed in the previous chapters to the case of buoys moored off the the California coast, the postprocessing of temperatures for a certain gauge station in the south of Italy and to forecasting precipitation. In the first section we describe the structure of the network and of the optimal error chosen for the data reconstruction of the California coast. In the second section we show the application of this NN algorithm, proposed in Chapter 4 and applied in Chapter 7, and how it is able to model the SWH measured by the NOAA (National Oceanic and Atmospheric Association) National Data Buoy Center (NDBC-USA) near the California coast. In this application the NN uses as input the data of either one or more correlated data series from buoys located near San Francisco. The reconstruction of this time series is proposed by using different input data. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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22. Extreme-Event Analysis.
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Bellomo, Nicola, Tirozzi, Brunello, Puca, Silvia, Pittalis, Stefano, Bruschi, Antonello, Morucci, Sara, Ferraro, Enrico, and Corsini, Stefano
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In this chapter we discuss how we selected a model for describing the probability distribution of the extreme events of sea waves and how the model has been made to suit the structure of the data. We also discuss the model's consequences, such as the evaluation of an m-year return level. Also, we check whether the time series obtained by means of artificial neural network (ANN) reconstruction has the same extreme events probability distribution as the original one. We further discuss the definition of a special ANN method dedicated to the reconstruction of single selected extreme or large events. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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23. Application of Approximation Theory and ARIMA Models.
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Bellomo, Nicola, Tirozzi, Brunello, Puca, Silvia, Pittalis, Stefano, Bruschi, Antonello, Morucci, Sara, Ferraro, Enrico, and Corsini, Stefano
- Abstract
In this chapter we describe other algorithms as a possible alternative to artificial neural network (ANN) method for solving the reconstruction problem. Many algorithms, unlike ANN and simply NN, have been used for solving analogous problems. We selected two algorithms: the approximation operators which are a different version of ANN, already studied and explained in detail in Chapter 5, and the classical autoregressive integrated moving average (ARIMA) models widely used in the framework of time-series analysis. We apply both of them to our problem and we show with some examples that the ANN models have a much better performance. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
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24. Application of ANN to Sea Time Series.
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Bellomo, Nicola, Tirozzi, Brunello, Puca, Silvia, Pittalis, Stefano, Bruschi, Antonello, Morucci, Sara, Ferraro, Enrico, and Corsini, Stefano
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In this chapter we will show the application of the algorithms and methods explained in Chapter 4 to the time series of sea level (SL) and sea wave height (SWH) measurements. As specified in Chapter 2, SL is the height of the tide, and SWH is the significant wave height. The phenomenologies of the two time series are different and each has its own problems. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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25. Extreme-Value Theory.
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Bellomo, Nicola, Tirozzi, Brunello, Puca, Silvia, Pittalis, Stefano, Bruschi, Antonello, Morucci, Sara, Ferraro, Enrico, and Corsini, Stefano
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The statistics of waves is important in understanding the forces acting on the sea shore and for determining its evolution. Interaction among waves and winds is crucial for wave motion. Knowledge of the probability of occurrence of extreme events is necessary for designing secure structures in the sea environment. Extreme-value theory provides powerful tools to evaluate the probability of extreme events. In this chapter our aim is to collect several contributions to the theory of extreme events in order to make a self-contained exposition. We present a selection of the papers which seem best suited to our procedures, aims and tastes ([55], [56], [16], [19], [8], [29], [17]). Theorems are outlined without giving the proofs, which can be found in the quoted literature; we prefer to underline their importance for operations on the data. The chapter is divided in two parts. The first describes the method for deriving the distribution of the maxima in the case of independent random variables from the statistics of the exceedances of the time series over a certain threshold. This method is called POT (peak over threshold) and will be used in Chapter 9 to show the results for sea measurements. Section 6.1 also gives the fundamentals of the theory. The hypothesis of independent random variables is very restrictive and obliges the researcher to extract subsequences of i.i.d. variables from stationary processes, getting too few data in the case of long-range correlations. In the second part we deal with theorems and useful results for weakly dependent data. There is an introduction to each section with exact mathematical statements where the ideas are explained in simple and more intuitive terms. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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26. Approximation Theory.
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Bellomo, Nicola, Tirozzi, Brunello, Puca, Silvia, Pittalis, Stefano, Bruschi, Antonello, Morucci, Sara, Ferraro, Enrico, and Corsini, Stefano
- Abstract
In this chapter we discuss and show some results for the use of the neural network (NN) as a complete set of functions. The fact that the combination of the sigmoidal function corresponding to an NN can approximate any function is a simple consequence of the Stone-Weierstrass theorem and so such an approach is a convincing one. Furthermore, in the case of approximation theory the synaptic weights are given by some a priori estimates and in many cases could be directly evaluated from the data. This approach has, as a drawback, more errors than the NN constructed using the procedures described in the previous chapter. [ABSTRACT FROM AUTHOR]
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- 2006
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27. Artificial Neural Networks.
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Bellomo, Nicola, Tirozzi, Brunello, Puca, Silvia, Pittalis, Stefano, Bruschi, Antonello, Morucci, Sara, Ferraro, Enrico, and Corsini, Stefano
- Abstract
In this chapter we explain some fundamental concepts used in neural networks (NNs) with special regard to the ones applied to forecasts and data analysis. The structure of the back-propagation NN is shown in connection with its use for time-series analysis. The concepts of learning and training errors are explained in some detail and the main types of learning algorithms are exhibited. We also give some exact estimates of the probability that the learning error differs from the training error by more than a small quantity and a priori bounds of the learning error using extreme-value theory for the simple perceptron. Since NNs are also analyzed as a universal approximation of functions, we expose some useful properties of NNs treated in this way in Chapter 5. These topics do not cover all the literature dealing with NN application and theory, but they are good examples of the present situation. Heuristic remarks and rigorously proven statements are also distinguished using mathematical language for exactly proven facts and ordinary language for conjectures and hypotheses. This type of presentation suits the general aim of our book: we want to start from the very beginning of the theory to arrive at the very end of some applications of NNs. In the literature, especially in the applications, there is often confusion between proven facts and conjectures. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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28. The Wave Amplitude Model.
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Bellomo, Nicola, Tirozzi, Brunello, Puca, Silvia, Pittalis, Stefano, Bruschi, Antonello, Morucci, Sara, Ferraro, Enrico, and Corsini, Stefano
- Abstract
In this chapter we explain the main ideas and open problems of the model of wind waves. It is important to include this presentation in our book since we will compare the results of neural network (NN) reconstruction with those of the wave amplitude model (WAM) model. This comparison is done to check the order of magnitude of the significant wave height (SWH) reconstructed by means of the NN. Moreover, an understanding of this chapter is useful to obtain a good comprehension of the sea phenomena such as the sea evolution, the interaction among the sea and the wind and the formulation of the problem of dissipation forces. [ABSTRACT FROM AUTHOR]
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- 2006
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29. Basic Notions on Waves and Tides.
- Author
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Bellomo, Nicola, Tirozzi, Brunello, Puca, Silvia, Pittalis, Stefano, Bruschi, Antonello, Morucci, Sara, Ferraro, Enrico, and Corsini, Stefano
- Abstract
The characteristics of wave motion are important for understanding the evolution of a coast. In the first three sections of this chapter we give the definitions of the quantities describing waves and a description of the current instruments and methodologies for their measurement. We describe the network of buoys used for attaining the significant wave height (SWH) time series analyzed in this book. We use a similar approach for tides: some of the theory and the corresponding network of buoys are described. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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30. Introduction.
- Author
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Bellomo, Nicola, Tirozzi, Brunello, Puca, Silvia, Pittalis, Stefano, Bruschi, Antonello, Morucci, Sara, Ferraro, Enrico, and Corsini, Stefano
- Abstract
There are some important reasons for writing this book about neural networks (NNs) even though many books and articles on this topic have already been published. One reason is that we deal with the application of NNs as an algorithm for data analysis in the field of sea phenomena. This is quite a new field of application for the NN; few papers have yet been published using this approach. NNs have a double significance: one is that they provide very flexible and adaptable algorithms to handle data analysis and the other is that some versions of these algorithms can be used to describe the behavior of real neurons. The first meaning of NN is the one which is dealt with in this book. We do not deal with all the possible applications to the complex world of time series, which is impossible to do in the space of one book (it would require an entire encyclopedia), but only with the applications of NNs to time series of sea levels and other related important variables. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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31. A nowcasting tool for the evolution of convective cells using the water vapor absorption and infrared window channels of the Meteosat Second Generation.
- Author
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Puca, Silvia, De Leonibus, Luigi, Zauli, Francesco, Rosci, Paolo, Musmanno, Leonardo, and Biron, Daniele
- Published
- 2005
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32. Impact of Radar Reflectivity and Lightning Data Assimilation on the Rainfall Forecast and Predictability of a Summer Convective Thunderstorm in Southern Italy.
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Federico, Stefano, Torcasio, Rosa Claudia, Puca, Silvia, Vulpiani, Gianfranco, Comellas Prat, Albert, Dietrich, Stefano, and Avolio, Elenio
- Subjects
THUNDERSTORMS ,RADAR ,LIGHTNING ,PRECIPITATION forecasting ,FORECASTING ,SUMMER - Abstract
Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm of rainfall in 3 h. The aim is to investigate the event predictability and a potential way to improve the precipitation forecast. To reach this aim, lightning (LDA) and radar reflectivity data assimilation (RDA) was applied. LDA was able to trigger deep convection over Palermo, with high precision, whereas the RDA had a key role in the prediction of the amount of rainfall. The simultaneous assimilation of both data sources gave the best results. An alert for a moderate–intense forecast could have been issued one hour and a half before the storm developed over the city, even if predicting only half of the total rainfall. A satisfactory prediction of the amount of rainfall could have been issued at 14:30 UTC, when precipitation was already affecting the city. Although the study is centered on a single event, it highlights the need for rapidly updated forecast cycles with data assimilation at the local scale, for a better prediction of similar events. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
33. Conclusions.
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Bellomo, Nicola, Tirozzi, Brunello, Puca, Silvia, Pittalis, Stefano, Bruschi, Antonello, Morucci, Sara, Ferraro, Enrico, and Corsini, Stefano
- Abstract
We have analyzed and explained many topics in this book. They differ very much but are all connected with the sea events and the algorithm selected for developing the analysis. The choice among the different algorithms has not been simple; we think that we have solved it in the optimal way, according to our taste and interests. The first principle used for collecting the various chapters has been to bring together all the theoretical and experimental facts concerning the sea time series and the phenomenology of the waves. Thus, the first three chapters have been devoted to an exposition of the main phenomenology (Chapters 1, 2) of the sea events as well as historical information. The measuring techniques are discussed and displayed in some detail so that the reader can feel the complexity of the process of collecting the data and also the practical and historical motivations which led to the development of such instruments. The waves and tides are distinguished and analyzed in detail. Chapter 3 covers the theoretical model currently used for forecasting sea waves. We discussed briefly the problems of the construction of the WAM which is used in Europe for the prediction of waves. We emphasize the fact that the actual derivation of this model disregards the free boundary nature of the problem. In Chapter 7 we compared the results of NN reconstruction with the output of the astronomical model for a check of the results and with the output of the WAM to check the order of magnitudes of the NN outputs. Chapters 4 and 5 give the main aspects and fundamentals of the theory and practice of NNs. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
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34. Impact of space-borne estimates of hydrological variables in early warning systems: analysis of recent severe weather events over Europe.
- Author
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Puca, Silvia
- Subjects
- *
SOIL moisture measurement , *SEVERE storms , *EMERGENCY management , *SYSTEM analysis , *CIVIL defense , *SOIL moisture - Abstract
Many severe precipitation events occurred in Europe during the last decade and caused casualties and damages to the historical heritage and natural environment. Protection of civilians and safeguard of the territory are the kay mandates of a civil Protection (CP) agency. This is achieved through activities that mitigate hydro-meteorological risks, such as flooding and droughts. European CP agencies, supported by meteorological and hydrological institutes, aim to assess risk scenarios, to monitor and supervise events and risk levels, providing early warning to National and local authorities.Hydro meteorological risk management consists of three phases: pre-event (forecast), event (early warning, and monitoring) and post-event (recovery and survey).Near real time accurate estimations of hydrological variables such as precipitation and soil moisture are invaluable to the CP agencies, enable them to issue early warnings and plan for disaster relief at the local level.Besides measurements of key hydrological variables by ground-based instruments, often affected by a limited spatial coverage, advanced satellite-based precipitation and soil moisture products developed within different international programs, are becoming available and accessible to users in near-real time. The assessment of the accuracy and reliability of such products is necessary in order to be able to optimally exploit them for hydro-meteorological applications.The comparison of satellite-derived rainfall and soil moisture estimates with respect to ground-based measurements is a challenging task, both because of the temporal and spatial variability of the fields and the inherently different measurement types. This activity is particularly complex in Europe due to the heterogeneity in the ground-based networks of the different countries, due to sampling strategy and processing methodologies. For the validation and assessment of satellite-derived soil moisture products, these issues are exacerbated by the limited availability of ground observation networks that can be used as benchmark. Moreover, the measuring stations cover only small areas over Europe. As an example, in Central Italy only 14 probes are distributed over an area of about 8000 km2.In this study, a comparison between satellite derived products and ground data is presented, according to the EUMETSAT H SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) Validation protocols. Since 2005, eleven European countries joined in the framework of the H SAF to perform the satellite products monitoring and ground verification in their own regions of interest. Moreover, recent severe rainfall events are selected in order to understand how the main satellite product characteristics, i.e. accuracy, spatial pattern and resolution, update frequency and latency, impact the efficiency of a hydro-meteorological early warning system at a local level in an operational framework. State-of-the-art satellite rainfall and soil moisture products, obtained through international space agencies and programmes (EUMETSAT H SAF, ESA, NASA, and JAXA), are used over different European countries as forcing input for an early warning system for meteorological/hydrological events. [ABSTRACT FROM AUTHOR]
- Published
- 2019
35. A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data.
- Author
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Pulvirenti, Luca, Squicciarino, Giuseppe, Fiori, Elisabetta, Ferraris, Luca, Puca, Silvia, and Schumann, Guy J.-P.
- Subjects
WATER use ,INTERMEDIATE goods ,EMERGENCY management ,FLOODS ,FUZZY logic ,WATER storage - Abstract
An automated tool for pre-operational mapping of floods and inland waters using Sentinel-1 data is presented. The acronym AUTOWADE (AUTOmatic Water Areas DEtector) is used to denote it. The tool provides the end user (Italian Department of Civil Protection) with a continuous, near real-time (NRT) monitoring of the extent of inland water surfaces (floodwater and permanent water). It implements the following operations: downloading of Sentinel-1 products; preprocessing of the products and storage of the resulting geocoded and calibrated data; generation of the intermediate products, such as the exclusion mask; application of a floodwater/permanent water mapping algorithm; generation of the output layer, i.e., a map of floodwater/permanent water; delivery of the output layer to the end user. The open floodwater/permanent water mapping algorithm implemented in AUTOWADE is based on a new approach, denoted as buffer-from-edge (BFE), which combines different techniques, such as clustering, edge filtering, automatic thresholding and region growing. AUTOWADE copes also with the typical presence of gaps in the flood maps caused by undetected flooded vegetation. An attempt to partially fill these gaps by analyzing vegetated areas adjacent to open water is performed by another algorithm implemented in the tool, based on the fuzzy logic. The BFE approach has been validated offline using maps produced by the Copernicus Emergency Management Service. Validation has given good results with a F1-score larger than 0.87 and a kappa coefficient larger than 0.80. The algorithm to detect flooded vegetation has been visually compared with optical data and aerial photos; its capability to fill some of the gaps present in flood maps has been confirmed. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
36. RAINBOW: An Operational Oriented Combined IR-Algorithm.
- Author
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D'Adderio, Leo Pio, Puca, Silvia, Vulpiani, Gianfranco, Petracca, Marco, Sanò, Paolo, and Dietrich, Stefano
- Subjects
- *
RAIN gauges , *METEOROLOGICAL satellites , *STANDARD deviations , *ALGORITHMS , *BRIGHTNESS temperature , *SATELLITE-based remote sensing , *MIMO radar - Abstract
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]
- Published
- 2020
- Full Text
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37. Application of Lightning Data Assimilation for the 10 October 2018 Case Study over Sardinia.
- Author
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Torcasio, Rosa Claudia, Federico, Stefano, Puca, Silvia, Vulpiani, Gianfranco, Comellas Prat, Albert, and Dietrich, Stefano
- Subjects
ATMOSPHERIC models ,LIGHTNING ,NUMERICAL weather forecasting ,CASE studies ,FALSE alarms ,FORECASTING - Abstract
On 10 October 2018 an intense storm, characterized by heavy rainfall, hit the Sardinia island, reaching a peak of 452 mm of rain measured in 24 h. Among others, two particularly intense phases were registered between 3 and 6 UTC (Universal Coordinated Time), and between 18 and 24 UTC. The forecast of this case study is challenging because the precipitation was heavy and localized. In particular, the meteorological model used in this paper, provides a good prediction only for the second period over the eastern part of the Sardinia island. In this work, we study the impact of lightning data assimilation and horizontal grid resolution on the Very Short-term Forecast (VSF, 3 and 1 h) for this challenging case, using the RAMS@ISAC meteorological model. The comparison between the 3 h VSF control run and the simulations with lightning data assimilation shows the considerable improvement given by lightning data assimilation, especially for the precipitation that occurred in the eastern part of the island. Reducing the VSF range to 1 h, resulted in higher model performance with a good precipitation prediction over eastern and south-central Sardinia. In addition, the comparison between simulated and observed reflectivity shows an important improvement of simulations with lightning data assimilation compared to the control forecast. However, simulations assimilating lightning overestimated the precipitation in the last part of the day. The increasing of the horizontal resolution to 2 km grid spacing reduces the false alarms and improves the model performance. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
38. An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data.
- Author
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Pulvirenti, Luca, Squicciarino, Giuseppe, Fiori, Elisabetta, Fiorucci, Paolo, Ferraris, Luca, Negro, Dario, Gollini, Andrea, Severino, Massimiliano, and Puca, Silvia
- Subjects
FOREST mapping ,NORMALIZED difference vegetation index ,SALVAGE logging ,IMAGE processing ,EMERGENCY management - Abstract
A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the Italian territory to support the Italian Civil Protection Department in the management of one of the major natural hazards, which affects the territory. The processing chain includes a Sentinel-2 data procurement component, an image processing algorithm, and the delivery of the map to the end-user. The data procurement component searches every day for the most updated products into different archives. The image processing part represents the core of AUTOBAM and implements an algorithm for burned forest areas mapping that uses, as fundamental parameters, the relativized form of the delta normalized burn ratio and the normalized difference vegetation index. The minimum mapping unit is 1 ha. The algorithm implemented in the image processing block is validated off-line using maps of burned areas produced by the Copernicus Emergency Management Service. The results of the validation shows an overall accuracy (considering the classes of burned and unburned areas) larger than 95% and a kappa coefficient larger than 80%. For what concerns the class of burned areas, the commission error is around 1%−3%, except for one case where it reaches 25%, while the omission error ranges between 6% and 25%. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
39. Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography.
- Author
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Piazzi, Gaia, Tanis, Cemal Melih, Kuter, Semih, Simsek, Burak, Puca, Silvia, Toniazzo, Alexander, Takala, Matias, Akyürek, Zuhal, Gabellani, Simone, and Arslan, Ali Nadir
- Subjects
METEOROLOGICAL satellites ,SNOW ,SNOW cover ,WATER management ,PHOTOGRAPHY ,REMOTE sensing - Abstract
Information on snow properties is of critical relevance for a wide range of scientific studies and operational applications, mainly for hydrological purposes. However, the ground-based monitoring of snow dynamics is a challenging task, especially over complex topography and under harsh environmental conditions. Remote sensing is a powerful resource providing snow observations at a large scale. This study addresses the potential of using Sentinel-2 high-resolution imagery to assess moderate-resolution snow products, namely H10—Snow detection (SN-OBS-1) and H12—Effective snow cover (SN-OBS-3) supplied by the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). With the aim of investigating the reliability of reference data, the consistency of Sentinel-2 observations is evaluated against both in-situ snow measurements and webcam digital imagery. The study area encompasses three different regions, located in Finland, the Italian Alps and Turkey, to comprehensively analyze the selected satellite products over both mountainous and flat areas having different snow seasonality. The results over the winter seasons 2016/17 and 2017/18 show a satisfying agreement between Sentinel-2 data and ground-based observations, both in terms of snow extent and fractional snow cover. H-SAF products prove to be consistent with the high-resolution imagery, especially over flat areas. Indeed, while vegetation only slightly affects the detection of snow cover, the complex topography more strongly impacts product performances. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
40. Comparison between H02B/H18 and 2A-DPR precipitation products over MSG full disk area according to the H-SAF validation methodology.
- Author
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Petracca, Marco, Kanak, Jan, Porcù, Federico, Iwanski, Rafal, Lapeta, Bozena, Diószeghy, Márta, Szenyán, Ildikó, Baguis, Pierre, Roulin, Emmanuel, Oztopal, Ahmet, Krahe, Peter, Kunkel, Asta, Artinian, Eram, Chervenkov, Hristo, Cacciamani, Carlo, Toniazzo, Alexander, Vulpiani, Gianfranco, and Puca, Silvia
- Published
- 2019
41. Global validation of the different H SAF soil moisture products.
- Author
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Delogu, Fabio, Hahn, Sebastian, Gabellani, Simone, Puca, Silvia, and Brocca, Luca
- Published
- 2019
42. Assessment of Ground-Reference Data and Validation of the H-SAF Precipitation Products in Brazil.
- Author
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Martins Costa do Amaral, Lia, Barbieri, Stefano, Vila, Daniel, Puca, Silvia, Vulpiani, Gianfranco, Panegrossi, Giulia, Biscaro, Thiago, Sanò, Paolo, Petracca, Marco, Marra, Anna Cinzia, Gosset, Marielle, and Dietrich, Stefano
- Subjects
RAINFALL ,METEOROLOGICAL precipitation ,RADAR ,RAIN gauges ,RADAR meteorology - Abstract
The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades; however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Paraíba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor. These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q 2 V u 15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms. Differently, for the Vale do Paraíba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
43. Development and Evaluation of the Ground Radar and Infrared Satellite Combined Algorithm for the Italian Peninsula.
- Author
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D'Adderio, Leo Pio, Vulpiani, Gianfranco, Puca, Silvia, Panegrossi, Giulia, Sanò, Paolo, Marra, Anna Cinzia, and Dietrich, Stefano
- Published
- 2018
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