Abstract
Abstract. The precipitation forecasts of three ensemble prediction systems (EPS) and two multi-model ensemble prediction systems (MM EPS) were assessed by comparing with observations from 19 rain gauge stations located in the Dapoling-Wangjiaba sub-catchment of Huaihe Basin for the period from 1 July to 6 August 2008. The sample Probabilistic Distribution Functions (PDF) of gamma distribution, the Relative Operating Characteristic (ROC) diagrams, the percentile precipitation and a heavy rainfall event are analyzed to evaluate the performances of the single and multi-model ensemble prediction system (EPS). The three EPS were from the China Meteorological Administration (CMA); the United States National Centre for Environment Predictions (NCEP); and the European Centre for Medium-Range Weather Forecasts (ECMWF), all were obtained from the TIGGE-CMA archiving centre (THORPEX Interactive Grand Global Ensemble, TIGGE). The MM EPS were created using the equal weighting method for every ensemble member over the test area, the first ( MM-1) consisted of all three EPS, the second (MM-2) consisted of the ECMWF and NCEP EPS. The results demonstrate the level of correspondence between deterioration in predictive skill and extended lead time. Compared with observations and with a lead time of one day, ECMWF performs a little better than other centre's. With over five days in advance, all the three EPS and the two MM EPS don't give reliable probabilistic precipitation forecasts. Both MM EPS can outperform CMA and NCEP for most of the forecasted days, but still perform a little worse than ECMWF. Though variation of daily percentile precipitation and ROC areas show MM-2 outperforms MM-1, gamma distribution indicates much similar performances for all 10-day forecast, and neither is superior to ECMWF.
Highlights
Ensemble forecasting was developed as a result of attempts to understand the limits of deterministic prediction of the atmospheric state by the setting of initial state conditions
The European Centre for Medium-Range Weather Forecasts (ECMWF), the United States National Centre for Environment Predictions (NCEP), and the China Meteorological Administration (CMA) multi-member 1–10 day total precipitation forecasting with initial time 0000 GMT obtained from the TIGGE-CMA portal were used in this study
The probability of predicted daily precipitation of different ensemble prediction systems (EPS) and MM-113and MM-2 is compared in Fig. 2 using fitted gamma distribution functions
Summary
Ensemble forecasting was developed as a result of attempts to understand the limits of deterministic prediction of the atmospheric state by the setting of initial state conditions. Single EPS usually have restrictions in capturing specific atmospheric conditions; multi-model prediction system (MMS) and probabilistic prediction were developed by considering the characteristics of many EPS. TIGGE, a key component of THORPEX (The Observing System Research and Predictability Experiment) was established, providing a very good basis for probabilistic precipitation, which facilitates the establishment of the hydrologic ensemble prediction experiment (HEPEX) (Schaake et al, 2007). Buizza (2008) summarized two of the main advantages of ensemble-based probabilistic forecasts as: the ability of an EPS to predict the most likely scenario; and the ability of an EPS to predict the probability of occurrence of any event, and provide more consistent successive forecasts. The performance of the different ensemble configurations are validated and compared in Sect. 4, and Sect. 5 is the summary and discussion
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