Around 10 Terabites have been already allocated to the CHFP dataserver at CIMA

CHFP Data Access

Following the specifications in the CHFP documentation, and trying to be as broad as possible, we have organized directories as follows: CHFP atm surface monthly variables daily variables levels daily variables invariant ocn . . lnd . . Within each "variable" directory, test files were copied. Files names follow the following convention: vble_frecuency_model_exp_yyyymm_ti.nc where: vble = variable frequency = daily / monthly exp = experiment YYYYMM = year and month (initial time) ti = number of times the file

We have doubts about whether there is any specific definition about how the CHFP files should be organized and in particular, how many months of predictions each CHFP file should contain. There are no instructions about that in the CHFP documentation. We noticed however, that in the seasonal directories of the ECMWF Data Server for ENSEMBLES, each file contains the multi-member prediction of a specific variable starting on a specific year and month, with prediction lengths between 7 and 14 months.

CHFP data at CIMA: status IRI: Contacts have been made. Issues in formatting the data following the CHFP guidelines RSMAS: Some data has been transferred already to CIMA via ftp, (ccsm3_0 model outputs. 1982-1998 period , atm (2.5x2.5), ocn (1x1), surface and levels variables. Files are in netcdf format but they do not fully follow the CHFP guidelines. GFDL: Last year some test files have been provided. Doubts about the prediction lengths.

Training based on the use of CPT software from IRI Overall Objective: to increase local and regional capacity on the use of seasonal prediction tailored to user needs in different socioeconomic sectors of Latin America. The course will consist of two weeks of lectures, seminars, discussions and practical exercises in computer lab. Main topics are: 1) From global to regional seasonal predictions; 2) Translating science into applied knowledge to inform decision making; 3) Use of climate information: Experiences and needs in disaster risk reduction; in the agricultural, water management sector; and the health sector Participants (40): Students or young scientists from Latin America and the Caribbean region (Universities, NWS, Water agencies, Agricultural agencies) Training based on the use of CPT software from IRI https://iaibr3.iai.int/twiki/bin/view/TIClimatePredictions2010