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Abstract:
Monthly precipitation prediction is of great significance to bridge the gap between short-term weather forecast and seasonal forecast. However, due to the complexity of the climate system, there is still a great deal of uncertainty in the prediction of precipitation at the monthly scale. In order to reduce the uncertainty of the monthly precipitation predictions results，we analyzed the corrective effect of BJP (Bayesian Joint Probabilistic ) and EMOS (Ensemble Model Output Statics) model on precipitation bias prediction using S2S (sub-seasonal to seasonal scale) near-real-time overall forecast data and re-forecast data from five global data centers, including ECMWF, NCEP, UKMO, JMA and KMA. According to the advantages of different models the integrated multi-structure monthly precipitation prediction model was constructed based on EM (Expectation-Maximum) algorithm. The prediction application of monthly precipitation was carried out during the flood season of 1981~2010 in the middle and lower reaches of the Yangtze River, and the result showed as follows. (1)The monthly precipitation prediction results of the ensemble model showed that the Nash efficiency coefficient reached more than 0.6 and the correlation coefficient was 0.83. It indicates that the prediction sequence and the measured sequence of the ensemble model had good consistency. (2) The average relative deviation of the monthly precipitation prediction results is 25%, which effectively reduces the uncertainty of the monthly precipitation prediction results of a single model compared with the monthly precipitation forecast results of a single model. The results provide scientific support for improving the accuracy of drought or flood prediction.
