DESCRIZIONE:
Il sistema previsionale subseasonal del CNR-ISAC è stato aggiornato il 5 settembre 2024. ll nuovo sistema previsionale si basa su una versione aggiornata del modello GLOBO che gira ad una risoluzione di 0.7 x 0.5 gradi in longitudine e latitudine e 70 livelli verticali. Il modello utilizza il nuovo schema radiativo ECMWF ecRad. Il forecast consiste di un lagged ensemble di 41 membri di cui 11 inizializzati alle ore 00 UTC del giorno di emissione, e 30 inizializzati in gruppi di 10 alle ore 06, 12 e 18 UTC del giorno precedente. I dati di inizializzazione sono forniti dalle analisi del sistema previsionale GEFS del NOAA-NCEP. I reforecast, con cui si calibra il real-time forecast, sono ensemble di 8 membri inizializzati su dati ECMWF ERA5. Gli ensemble reforecast sono inizializzati ogni 5 giorni, dall'1 gennaio al 27 dicembre del periodo 2001-2020.A partire dal marzo 2014, presso l'Istituto CNR-ISAC, sono prodotte previsioni numeriche mensili d'ensemble con il modello di circolazione atmosferica generale GLOBO, sviluppato dal gruppo di ricerca di Meteorologia Dinamica della sede di Bologna. Le previsioni sono emesse ogni settimana nell'ambito di una Intesa tra l'Istituto e il Dipartimento della Protezione Civile nazionale. Il modello è utilizzato con passo di griglia di 0.80 x 0.56 gradi in longitudine e latitudine, rispettivamente, e 54 livelli verticali. L'ensemble è formato da 40 membri, 10 per ogni orario sinottico del giorno di inizializzazione. I campi di inizializzazione del GLOBO sono forniti dalle analisi del sistema previsionale GEFS del NOAA-NCEP. Le anomalie previste dei diversi parametri meteorologici sono riferite al clima 1981-2010. La calibrazione delle anomalie è ottenuta mediante simulazioni di reforecast prodotte inizializzando il GLOBO ogni 5 giorni mediante i dati di rianalisi ECMWF ERA-Interim per il trentennio di riferimento.
DESCRIPTION:
The CNR-ISAC subseasonal forecast system was updated on 5 September 2024. The forecast system is based on a new version of the GLOBO model, running with a resolution of 0.7 x 0.5 degrees latitude and longitude and 70 vertical levels. The model uses the new ECMWF ecRad radiation scheme. The real time forecast is an ensemble of 41 members, with 11 members initialised at 00 UTC on the day of issue and 30 initialised in groups of 10 at 06, 12 and 18 UTC on the previous day. The initialisation data are provided by the analyses of the NOAA-NCEP GEFS forecast system. The reforecasts are 8-member ensembles initialised on ECMWF ERA5 data every 5 days from 1 January to 27 December from 2001 to 2020.Since March 2014, at the CNR-ISAC Institute, monthly ensemble forecasts are produced using the atmospheric general circulation model GLOBO developed at the Institute, in Bologna, by the Dynamic Meteorology research group. Forecasts are issued once a week in the framework of a project supported by the National Civil Protection Agency. The model horizontal grid spacing is 0.80 x 0.56 deg in longitude and latitude, respectively. In the vertical, 54 hybrid levels are used. A total of 40 forecast lagged members is obtained from the analyses of GEFS of NOAA-NCEP by using 10 members for each synoptic time of the initialization day. The anomalies of the atmospheric parameters shown here are referred to the 1981-2010 climate. They are calibrated based on reforecasts that are initialized every 5 days using the ECMWF ERA-Interim dataset and cover the same 30-year period.
Related links
WWRP/WCRP S2S Project Subseasonal-to-Seasonal Prediction (S2S) ProjectS2S database at ECMWF
S2S database at CMA
S2S database at IRI/Columbia University
Graphical products of S2S forecasting systems by the S2S Museum
Related Publications
Hitchcock, P., Butler, A., Charlton-Perez, A., Garfinkel, C., Stockdale, T., Anstey, J., Mitchell, D., Domeisen, D. I. V., Wu, T., Lu, Y., Mastrangelo, D., Malguzzi, P., Lin, H., Muncaster, R., Merryfield, B., Sigmond, M., Xiang, B., Jia, L., Hyun, Y.-K., Oh, J., Specq, D., Simpson, I. R., Richter, J. H., Barton, C., Knight, J., Lim, E.-P., and Hendon, H.: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts, Geosci. Model Dev., 2022, https://doi.org/10.5194/gmd-2021-394Davolio S., P. Malguzzi, O. Drofa, D. Mastrangelo and A. Buzzi, 2020: The Piedmont flood of November 1994: a test-bed of forecasting capabilities of the CNR-ISAC meteorological model suite. Bull. of Atmos. Sci.& Technol. (2020). https://doi.org/10.1007/s42865-020-00015-4
Mastrangelo, D. and P. Malguzzi, 2019: Verification of two years of CNR-ISAC subseasonal forecasts. Wea. Forecasting, 34, 331–344, https://doi.org/10.1175/WAF-D-18-0091.1.
Ferrone, A., Mastrangelo, D., and Malguzzi, P.: Multimodel probabilistic prediction of 2-m temperature anomalies on the monthly timescale, Adv. Sci. Res., 14, 123-129, 2017, https://doi.org/10.5194/asr-14-123-2017
Mastrangelo, D. and Malguzzi, P.: CNR-ISAC 2-m temperature monthly forecasts: a first probabilistic evaluation, Adv. Sci. Res., 14, 85-88, 2017, https://doi.org/10.5194/asr-14-85-2017
Vitart, F., C. Ardilouze, A. Bonet, A. Brookshaw, M. Chen, C. Codorean, M. Deque, L. Ferranti, E. Fucile, M. Fuentes, H. Hendon, J. Hodgson, H. Kang, A. Kumar, H. Lin, G. Liu, X. Liu, P. Malguzzi, I. Mallas, M. Manoussakis, D. Mastrangelo, C. MacLachlan, P. McLean, A. Minami, R. Mladek, T. Nakazawa, S. Najm, Y. Nie, M. Rixen, A.W. Robertson, P. Ruti, C. Sun, Y. Takaya, M. Tolstykh, F. Venuti, D. Waliser, S. Woolnough, T. Wu, D. Won, H. Xiao, R. Zaripov, and L. Zhang, 2017: The Subseasonal to Seasonal (S2S) Prediction Project Database. Bull. Amer. Meteor. Soc., 98, 163-173, https://doi.org/10.1175/BAMS-D-16-0017.1
D. Mastrangelo, P. Malguzzi, C. Rendina, O. Drofa, and A. Buzzi: First outcomes from the CNR-ISAC monthly forecasting system, Adv. Sci. Res., 8, 77-82, 2012 doi:10.5194/asr-8-77-2012
Malguzzi, P., Buzzi, A., & Drofa, O. (2011). The Meteorological Global Model GLOBO at the ISAC-CNR of Italy Assessment of 1.5 Yr of Experimental Use for Medium-Range Weather Forecasts, Weather and Forecasting, 26(6), 1045-1055. https://journals.ametsoc.org/view/journals/wefo/26/6/waf-d-11-00027_1.xml
Other Publications
Federico, S., Coscarelli, R., Mastrangelo D., Panegrossi, G., Piervitali, E., Torcasio, R.C., Vulpiani, G. 2024. 'Il regime meteo-climatico', in Siccita`, scarsita` e crisi idriche, Emanuele Romano, Ivan Portoghese (a cura di), Habitat signa 1, 103-143. Roma: Cnr Edizioni. https://doi.org/10.69115/habitatsigna-2024-1/04
Mastrangelo D., Delli Passeri L., Campione E., Malguzzi P., 2021: The contribution of S2S forecasts to the activities of the Italian Civil Protection Department. S2S Newsletter, No. 17 (Aug. 2021) http://s2sprediction.net/file/newsletter/Newsletter%2017_Aug%202021.pdf
Publications using S2S Globo data
Dai, Y., Hitchcock, P., Butler, A. H., Garfinkel, C. I., and Seviour, W. J. M.: Assessing stratospheric contributions to subseasonal predictions of precipitation after the 2018 SSW from SNAPSI, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-484, 2025.
Garfinkel, C. I., Lawrence, Z. D., Butler, A. H., Dunn-Sigouin, E., Statnaia, I., Karpechko, A. Y., Koren, G., Abalos, M., Ayarzaguena, B., Barriopedro, D., Calvo, N., de la Camara, A., Charlton-Perez, A., Cohen, J., Domeisen, D. I. V., Garcia-Serrano, J., Hindley, N. P., Jucker, M., Kim, H., Lee, R. W., Lee, S. H., Osman, M., Palmeiro, F. M., Polichtchouk, I., Rao, J., Richter, J. H., Schwartz, C., Son, S.-W., Taguchi, M., Tyrrell, N. L., Wright, C. J., and Wu, R. W.-Y.: A process-based evaluation of biases in extratropical stratosphere-troposphere coupling in subseasonal forecast systems, Weather Clim. Dynam., 6, 171-195, https://doi.org/10.5194/wcd-6-171-2025, 2025.
Rao, J., Garfinkel, C.I., Zhang, X. et al. Subseasonal to seasonal (s2s) prediction of continental cold following the sudden stratospheric warming in the 2022/23 winter. Clim Dyn 63, 75 (2025). https://doi.org/10.1007/s00382-024-07582-8
Jian Rao, Xiaoqi Zhang, Qian Lu, Siming Liu, Prediction of near-surface conditions following the 2023/24 sudden stratospheric warming by the S2S project models, Atmospheric Research, 2024,107882, ISSN 0169-8095, https://doi.org/10.1016/j.atmosres.2024.107882
Zhu, T., Yang, J., Bao, Q., & Vitart, F. (2024). Boreal summer extratropical intraseasonal oscillation prediction in current subseasonal-to-seasonal operational models over Eurasia. Journal of Geophysical Research: Atmospheres, 129, e2024JD042015. https://doi.org/10.1029/2024JD042015
Xiaolei Liu et al. A statistic of the subseasonal forecast skill windows of 2-meter air temperature, Environ. Res. Commun. 6 085002 https://doi.org/10.1088/2515-7620/ad6667
Yang, J., Zhu, T., Vitart, F. et al. Synchronous Eurasian heat extremes tied to boreal summer combined extratropical intraseasonal waves. npj Clim Atmos Sci 7, 169 (2024). https://doi.org/10.1038/s41612-024-00714-1
Ryu, J., Wang, SY., Jeong, JH. et al. Sub-seasonal prediction skill: is the mean state a good model evaluation metric?. Clim Dyn (2024). https://doi.org/10.1007/s00382-024-07315-x
Xiao, H., Xu, P., & Wang, L. (2024). The Unprecedented 2023 North China heatwaves and their S2S predictability. Geophysical Research Letters, 51, e2023GL107642. https://doi.org/10.1029/2023GL107642
Iqura Malik, Vimal Mishra, Sub-seasonal to seasonal (S2S) prediction of dry and wet extremes for climate adaptation in India, Climate Services, Volume 34, 2024, 100457, ISSN 2405-8807, https://doi.org/10.1016/j.cliser.2024.100457.
Wie, J., Kang, J. & Moon, BK. Role of Madden-Julian Oscillation in predicting the 2020 East Asian summer precipitation in subseasonal-to-seasonal models. Sci Rep 14, 865 (2024). https://doi.org/10.1038/s41598-024-51506-9
Liang, X.; Vitart, F.; Wu, T. Evaluation of Probabilistic Forecasts of Extreme Cold Events in S2S Models. Water, 2023, 15, 2795. https://doi.org/10.3390/w15152795
Lu, M. M., Tsai, W. Y. H., Huang, S. F., Cho, Y. M., Sui, C. H., Solis, A. L., & Chen, M. S. (2023).The Philippine springtime (February-April) sub-seasonal rainfall extremes and extended-range forecast skill assessment using the S2S database. Weather and Climate Extremes, 100582.https://doi.org/10.1016/j.wace.2023.100582
Yang, J., Zhu, T. & Vitart, F. An extratropical window of opportunity for subseasonal prediction of East Asian summer surface air temperature. npj Clim Atmos Sci 6, 46 (2023). https://doi.org/10.1038/s41612-023-00384-5
Inatsu, M., M. Matsueda, N. Nakano, and S. Kawazoe, 2023: Prediction skill and practical predictability depending on the initial atmospheric states in S2S forecasts. J. Atmos. Sci., https://doi.org/10.1175/JAS-D-22-0262.1, 2023
Yan, Y., Zhu, C., & Liu, B. (2023). Subseasonal predictability of the July 2021 extreme rainfall event over Henan China in S2S operational models. Journal of Geophysical Research: Atmospheres, 128, e2022JD037879. https://doi.org/10.1029/2022JD037879, 2023
Brum, M. and Schwanenberg, D.: Long-term evaluation of the Sub-seasonal to Seasonal (S2S) dataset and derived hydrological forecasts at the catchment scale, EGUsphere [preprint],https://doi.org/10.5194/egusphere-2022-419, 2022
Chwat, D., Garfinkel, C. I., Chen, W., & Rao, J. (2022). Which Sudden Stratospheric Warming Events are Most Predictable? Journal of Geophysical Research: Atmospheres, 127, e2022JD037521. https://doi.org/10.1029/2022JD037521
Li, W., Song, J., Hsu, P., & Wang, Y. (2022). Evaluation of the Forecast Performance for Week-2 Winter Surface Air Temperature from the Model for Prediction Across Scales - Atmosphere (MPAS-A), Weather and Forecasting. https://doi.org/10.1175/WAF-D-22-0054.1
Deoras, A., Turner, A.G. and Hunt, K.M.R. (2022), The structure of strong Indian monsoon low-pressure systems in Subseasonal-to-Seasonal prediction models. Q J R Meteorol Soc. https://doi.org/10.1002/qj.4296
Wie, J.; Kang, J.; Moon, B.-K. Superensemble Approach to S2S Model for Predicting Surface Air Temperature in Summer in East Asia from 2016 to 2020. Atmosphere 2022, 13, 701. https://doi.org/10.3390/atmos13050701
Cowan, T., Wheeler, M.C., de Burgh-Day, C. et al. Multi-week prediction of livestock chill conditions associated with the northwest Queensland floods of February 2019. Sci Rep 12, 5907 (2022). https://doi.org/10.1038/s41598-022-09666-z
Lin, H., Mo, R., & Vitart, F. (2022). The 2021 western North American heatwave and its subseasonal predictions. Geophysical Research Letters, 49, e2021GL097036. https://doi.org/10.1029/2021GL097036
Lawrence, Z. D., Abalos, M., Ayarzagüena, B., Barriopedro, D., Butler, A. H., Calvo, N., de la Cámara, A., Charlton-Perez, A., Domeisen, D. I. V., Dunn-Sigouin, E., García-Serrano, J., Garfinkel, C. I., Hindley, N. P., Jia, L., Jucker, M., Karpechko, A. Y., Kim, H., Lang, A. L., Lee, S. H., Lin, P., Osman, M., Palmeiro, F. M., Perlwitz, J., Polichtchouk, I., Richter, J. H., Schwartz, C., Son, S.-W., Statnaia, I., Taguchi, M., Tyrrell, N. L., Wright, C. J., and Wu, R. W.-Y.: Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems, Weather Clim. Dynam., 3, 977-1001, https://doi.org/10.5194/wcd-3-977-2022, 2022.
Stan, C., Zheng, C., Chang, E. K., Domeisen, D. I., Garfinkel, C. I., Jenney, A. M., Kim, H., Lim, Y., Lin, H., Robertson, A., Schwartz, C., Vitart, F., Wang, J., & Yadav, P. (2022). Advances in the prediction of MJO-Teleconnections in the S2S forecast systems, Bulletin of the American Meteorological Society (2022). https://journals.ametsoc.org/view/journals/bams/aop/BAMS-D-21-0130.1/BAMS-D-21-0130.1.xml
Yan, Y., Liu, B., Zhu, C. et al.: Subseasonal forecast barrier of the North Atlantic oscillation in S2S models during the extreme mei-yu rainfall event in 2020. Clim Dyn (2021). https://doi.org/10.1007/s00382-021-06076-1
The January 2021 Sudden Stratospheric Warming and Its Prediction in Subseasonal to Seasonal Models. Jian Rao,Chaim I. Garfinkel,Tongwen Wu,Yixiong Lu,Qian Lu,Zhuoqi Liang, 2021,https://doi.org/10.1029/2021JD035057
Lin, H., Huang, Z., Hendon, H., & Brunet, G. (2021). NAO Influence on the MJO and its Prediction Skill in the Subseasonal-to-Seasonal Prediction Models, Journal of Climate, 34(23), 9425-9442. https://journals.ametsoc.org/view/journals/clim/34/23/JCLI-D-21-0153.1.xml
Schwartz, C., Garfinkel, C. I., Yadav, P., Chen, W., and Domeisen, D.: Stationary Waves and Upward Troposphere-Stratosphere Coupling in S2S Models, Weather Clim. Dynam., 3, 679-692, https://doi.org/10.5194/wcd-2021-58, 2022.
Endris, H. S., Hirons, L., Segele, Z. T., Gudoshava, M., Woolnough, S., & Artan, G. A. (2021). Evaluation of the Skill of Monthly Precipitation Forecasts from Global Prediction Systems over the Greater Horn of Africa, Weather and Forecasting, 36(4), 1275-1298. https://journals.ametsoc.org/view/journals/wefo/36/4/WAF-D-20-0177.1.xml
Pei-Ning Feng & Hai Lin (2021) Modulation of the MJO-Related Teleconnection by the QBO in Subseasonal-to-Seasonal Prediction Models, Atmosphere-Ocean, https://doi.org/10.1080/07055900.2021.1944045
Zheng, C., Kar-Man Chang, E., Kim, H., Zhang, M., & Wang, W. (2021). Subseasonal Prediction of Wintertime Northern Hemisphere Extratropical Cyclone Activity by SubX and S2S Models, Weather and Forecasting, 36(1), 75-89.https://doi.org/10.1175/WAF-D-20-0157.1
Feng, P., Lin, H., Derome, J., & Merlis, T. M. (2021). Forecast Skill of the NAO in the Subseasonal-to-Seasonal Prediction Models, Journal of Climate, 34(12), 4757-4769, https://doi.org/10.1175/JCLI-D-20-0430.1
Deoras, A., Hunt, K. M. R., & Turner, A. G. (2021). Comparison of the Prediction of Indian Monsoon Low Pressure Systems by Subseasonal-to-Seasonal Prediction Models, Weather and Forecasting, 36(3), 859-877. https://journals.ametsoc.org/view/journals/wefo/36/3/WAF-D-20-0081.1.xml
Kueh, MT., Lin, CY. The 2018 summer heatwaves over northwestern Europe and its extended-range prediction. Sci Rep 10, 19283 (2020). https://doi.org/10.1038/s41598-020-76181-4
Rao, J., Garfinkel, C. I., White, I. P., & Schwartz, C. (2020). The Southern Hemisphere minor sudden stratospheric warming in September 2019 and its predictions in S2S models. Journal of Geophysical Research: Atmospheres, 125, e2020JD032723. https://doi.org/10.1029/2020JD032723
Pan, B., K. Hsu, A. AghaKouchak, S. Sorooshian, and W. Higgins, 2019: Precipitation Prediction Skill for the West Coast United States: From Short to Extended Range. J. Climate, 32, 161-182, https://doi.org/10.1175/JCLI-D-18-0355.1
Rao, J., Garfinkel, C. I., Chen, H., & White, I. P. (2019). The 2019 New Year stratospheric sudden warming and its real-time predictions in multiple S2S models. Journal of Geophysical Research: Atmospheres, 124, 11155-11174. https://doi.org/10.1029/2019JD030826
Wang, S., Sobel, A.H., Tippett, M.K. et al. Prediction and predictability of tropical intraseasonal convection: seasonal dependence and the Maritime Continent prediction barrier. Clim Dyn 52, 6015-6031 (2019). https://doi.org/10.1007/s00382-018-4492-9
Zhou, Y., Yang, B., Chen, H. et al. Effects of the Madden-Julian Oscillation on 2-m air temperature prediction over China during boreal winter in the S2S database. Clim Dyn 52, 6671-6689 (2019). https://doi.org/10.1007/s00382-018-4538-z
Wang, S., Tippett, M. K., Sobel, A. H., Martin, Z., & Vitart, F. ( 2019). Impact of the QBO on prediction and predictability of the MJO convection. Journal of Geophysical Research: Atmospheres, 124, 11766-11782. https://doi.org/10.1029/2019JD030575
Li, W., J. Chen, L. Li, H. Chen, B. Liu, C. Xu, and X. Li, 2019: Evaluation and Bias Correction of S2S Precipitation for Hydrological Extremes. J. Hydrometeor., 20, 1887-1906, https://doi.org/10.1175/JHM-D-19-0042.1
D. I., Domeisen, Butler, A. H., Charlton-Perez, A. J., Ayarzaguena, B., Baldwin, M. P., Dunn-Sigouin, E. et al. (2020). The role of the stratosphere in subseasonal to seasonal prediction: 2. Predictability arising from stratosphere-troposphere coupling. Journal of Geophysical Research: Atmospheres, 125, e2019JD030923. https://doi.org/10.1029/2019JD030923
Domeisen, D. I. V., Butler, A. H., Charlton-Perez, A. J., Ayarzaguena, B., Baldwin, M. P., Dunn-Sigouin, E., et al (2020). The role of the stratosphere in subseasonal to seasonal prediction: 1. Predictability of the stratosphere. Journal of Geophysical Research: Atmospheres, 125, e2019JD030920.https://doi.org/10.1029/2019JD030920
Hai Lin, Ruping Mo, Frederic Vitart & Cristiana Stan (2019) Eastern Canada Flooding 2017 and its Subseasonal Predictions, Atmosphere-Ocean, 57:3, 195-207, https://doi.org/10.1080/07055900.2018.1547679
Minami, A., & Takaya, Y. (2020). Enhanced Northern Hemisphere correlation skill of subseasonal predictions in the strong negative phase of the Arctic Oscillation. Journal of Geophysical Research: Atmospheres, 125, e2019JD031268. https://doi.org/10.1029/2019JD031268
de Andrade, F.M., Coelho, C.A.S. & Cavalcanti, I.F.A. Global precipitation hindcast quality assessment of the Subseasonal to Seasonal (S2S) prediction project models. Clim Dyn 52, 5451-5475 (2019). https://doi.org/10.1007/s00382-018-4457-z
Quinting, J. F., & Vitart, F. (2019). Representation of synoptic-scale Rossby wave packets and blocking in the S2S prediction project database. Geophysical Research Letters, 46, 1070-1078. https://doi.org/10.1029/2018GL081381
Zheng, C., Chang, E. K.-M., Kim, H., Zhang, M., & Wang, W. (2019). Subseasonal to seasonal prediction of wintertime northern hemisphere extratropical cyclone activity by S2S and NMME models. Journal of Geophysical Research: Atmospheres, 124, 12057-12077. https://doi.org/10.1029/2019JD031252
Son, S.-W., Kim, H., Song, K., Kim, S.-W., Martineau, P., Hyun, Y.-K., & Kim, Y. (2020). Extratropical prediction skill of the Subseasonal-to-Seasonal (S2S) prediction models. Journal of Geophysical Research: Atmospheres, 125, e2019JD031273. https://doi.org/10.1029/2019JD031273
Lim, Y., Son, S., Marshall, A.G. et al. Influence of the QBO on MJO prediction skill in the subseasonal-to-seasonal prediction models. Clim Dyn 53, 1681-1695 (2019). https://doi.org/10.1007/s00382-019-04719-y
Lim, Y., S. Son, and D. Kim, 2018: MJO Prediction Skill of the Subseasonal-to-Seasonal Prediction Models. J. Climate, 31, 4075-4094, https://doi.org/10.1175/JCLI-D-17-0545.1
Jie, W., Vitart, F., Wu, T. and Liu, X. (2017), Simulations of the Asian summer monsoon in the subseasonal to seasonal prediction project (S2S) database. Q.J.R. Meteorol. Soc., 143: 2282-2295. https://doi.org/10.1002/qj.3085
Vitart, F. (2017), Madden-Julian Oscillation prediction and teleconnections in the S2S database.Q.J.R. Meteorol. Soc, 143: 2210-2220. https://doi.org/10.1002/qj.3079
All plots shown in these pages are created with the NCAR Command Language (Version 6.1.2) [Software]. (2013).
Boulder, Colorado: UCAR/NCAR/CISL/VETS. http://dx.doi.org/10.5065/D6WD3XH5
This work by CNR-ISAC is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
