Previsioni meteorologiche CNR-ISAC
GLOBO monthly forecast

CNR-ISAC, Bologna

globo
Globo monthly forecast: N. H., Europe, ItalyPrevisioni probabilistiche mensili del modello Globo

Ensemble monthly forecasts of the Globo model


    DESCRIZIONE:

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.

Il sistema previsionale mensile del CNR-ISAC partecipa al Subseasonal-to-Seasonal Prediction (S2S) Project. I dati a partire dal novembre 2015 sono disponibili per il download, in quasi tempo reale e tramite log in, presso i siti ECMWF e CMA.
Inoltre, vari prodotti grafici basati sulle previsioni S2s sono anche resi disponibili dal dr. Mio Matsueda sul sito http://gpvjma.ccs.hpcc.jp/S2S/.

    DESCRIPTION:

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.

The CNR-ISAC monthly forecasting system is involved in the joint WWRP/WCRP Subseasonal-to-Seasonal Prediction (S2S) Project. Data are contributed in quasi real time to the S2S database since November 2015 and are available for download, through log in, at ECMWF and CMA.
Also, different graphical products based on the S2S forecasts are made available by dr. Mio Matsueda at http://gpvjma.ccs.hpcc.jp/S2S/.


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. Discuss. [preprint], https://doi .org/10.5194/gmd-2021-394, in review, 2022.

Davolio 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

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 showing S2S Globo data

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. Discuss. [preprint], https://doi.org/10.5194/wcd-2022-12, 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. Discuss. [preprint], https://doi.org/10.5194/wcd-2021-58, in review, 2021.

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

Forecast Skill of the NAO in the Subseasonal to-Seasonal Prediction Models, Pei-Ning Feng, Hai Lin, Jacques Derome, and Timothy M. Merlis 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

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

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

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

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


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

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