Extended Abstract

due to errors in the measurements themselves but it is linked to representativeness issues due to theinability of a point observation to represent an averaged behaviour that can be fairly compared to a modelfield.Precipitation forecasts for a period of about a year and a half and range up to t+54 are verifiedagainst a precipitation analysis built using the observed reports of 24 accumulated precipitation from theEuropean high resolution network data. The precipitation analysis comprises of an areal value of theobserved precipitation and information on the observed precipitation distribution for each grid point. TheEPS ECMWF and the AEMET-SREPS (LAM) ensembles precipitation forecasts are scored against theirown analysisThe BSS shows slight improvements when using O-OP for both ECMWF EPS and AEMET-SREPS for precipitation thresholds above 5mm/24h. This is in agreement with the findings of CT08. Theimprovements are due to an improvement in the BSS resolution component and a decrease in the BSuncertainty term, which are in excess of the degradation of the BSS reliability component. This degradationsignals that the standard method over-estimates the reliability of the system.The timeseries of the percent increase in BSS_res for both systems indicate a seasonal behaviour withwinter being the period when the O-OP method is the most beneficial. The largest increase is for theECMWF EPS pointing to the changes to the model convections in November 2007. Such changes haveimproved the ability of the EPS to produce conditional probabilities, given different forecasts that differfrom the climatic average.6. ReferencesBowler, N. E., 2006: Explicitly Accounting for Observation Error in Categorical Verification of Forecasts.Mon. Wea. Rev., 134, 1600-1606Buizza, R. and Palmer, T., 1995: The singular vectors structure of the atmospheric general circulation. J.Atmos. Sci., 52, 1434-1456.Buizza, R., Richardson, D. S., Palmer, T. N. 2003. Benefits of increased resolution in the ECMWFensemble system and comparison with poor-man's ensembles. Q. J. R. Meteorol. Soc. 129, 1269-1288.Candille, G. and Talagrand, O., 2008. Impact of observational error on the validation of ensembleprediction systems. Q. J. R. Meteorol. Soc., 134: 959-971.Cherubini, T., Ghelli, A., and Lalaurette, F., 2002: Verification of precipitation forecasts over the Alpineregion using a high-density observing network. Weather and Forecasting, 17, 238–248.Ciach, G. J. and Witold F. Krajewski, 1999: Radar–Rain Gauge Comparisons under ObservationalUncertainties. J. Appl. Meteor., 38, 1519-1525García-Moya, J.A., Callado A., Santos C., Santos-Muñoz D. and Simarro J., 2009: Predictability of ShortrangeForecasting: A Multi-Model Approach. AEMET Technical Report NT SPPE-1, pp. 30.Ghelli, A. and Lalaurette, F., 2000: Verifying precipitation forecasts using up-scaled observations.ECMWF Newsletter 87, ECMWF, Reading, United Kingdom, 9–17.Ghelli, A., and Primo, C., 2009: On the use of the extreme dependency score to investigate theperformance of a NWP model for rare events. Meteorol. Apps., 16 , p 537-544-204-