Accounting for Representativeness in the Verification of Ensemble Precipitation Forecasts

Abstract

AbstractSpatial variability of precipitation is analyzed to characterize to what extent precipitation observed at a single location is representative of precipitation over a larger area. Characterization of precipitation representativeness is made in probabilistic terms using a parametric approach, namely, by fitting a censored shifted gamma distribution to observation measurements. Parameters are estimated and analyzed for independent precipitation datasets, among which one is based on high-density gauge measurements. The results of this analysis serve as a basis for accounting for representativeness error in an ensemble verification process. Uncertainty associated with the scale mismatch between forecast and observation is accounted for by applying a perturbed-ensemble approach before the computation of scores. Verification results reveal a large impact of representativeness error on precipitation forecast reliability and skill estimates. The parametric model and estimated coefficients presented in this study could be used directly for forecast postprocessing to partly compensate for the limitation of any modeling system in terms of precipitation subgrid-scale variability.