Towards an improved ensemble precipitation forecast: A probabilistic post-processing approach

Abstract

Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique.Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the © 2017. This manuscript version is made available under the Elsevier user license http://www.elsevier.com/open-access/userlicense/1.0/ 2 Western United States. Results show that both techniques are successful in producing reliable 25 and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable 26 improvement in the ensemble forecast in both deterministic and probabilistic verification, in 27 particular in characterizing the extreme events in wet seasons. 28 Post-Processing; Precipitation; Copulas; Climate Forecast System; Hydrologic Forecasting 29 65The procedure is based on the bivariate probability distribution between the observation and the 66 single-value precipitation forecast. In the past few years, various methods were applied to meet 67 this objective. Kelly and Krzysztofowicz (1997) developed a bivariate meta-Gaussian 68 distribution function based on a normal quantile transformation of two variables according to the 69 4 Gaussian law in the Bayesian Forecasting System (BFS). The method was later used by 70 Krzysztofowicz and Herr (2001) to assess the uncertainty in the precipitation data. Clark and 71 Hay (2004) employed Model Output Statistics (MOS) to downscale the model outputs of Global 72 Forecast System (GFS), a medium range forecast system, developed in the National Weather 73 Service (NWS) cooperative network. To preserve and represent space-time variability of climate 74 variables, Clark et al. (2004) introduced a procedure, the so called Schaake Shuffle 75 reconstructing the ensemble members according to the historical values. Schaake...

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