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/.
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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.
