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Meteorological ensembles are a collection of scenarios for future weatherdelivered by a meteorological center. Such ensembles form the main source ofvaluable information for probabilistic forecasting which aims at producing apredictive probability distribution of the quantity of interest instead of asingle best guess estimate. Unfortunately, ensembles cannot generally beconsidered as a sample from such a predictive probability distribution withouta preliminary post-processing treatment to calibrate the ensemble. Two mainfamilies of post-processing methods, either competing such as BMA orcollaborative such as EMOS, can be found in the literature. This paper proposesa mixed effect model belonging to the collaborative family. The structure ofthe model is based on the hypothesis of invariance under the relabelling of theensemble members. Its interesting specificities are as follows: 1)exchangeability, which contributes to parsimony, with a latent pivot variablesynthesizing the essential meteorological features of the ensembles, 2) amulti-ensemble implementation, allowing to take advantage of variousinformation so as to increase the sharpness of the forecasting procedure. Focusis cast onto Normal statistical structures, first with a direct application fortemperatures, then with its Tobit extension for precipitation. Inference isperformed by EM algorithms with recourse made to stochastic conditionalsimulations in the precipitation case. After checking its good behavior onartificial data, the proposed post-processing technique is applied totemperature and precipitation ensemble forecasts produced over five riverbasins managed by Hydro-Qu$\'e$bec. These ensemble forecasts were extractedfrom the THORPEX Interactive Grand Global Ensemble (TIGGE) database. Theresults indicate that post-processed ensemble are calibrated and generallysharper than the raw ensembles.

Post-processing multi-ensemble temperature and precipitation forecasts through an Exchangeable Gamma Normal model and its Tobit extension