Quantitative Precipitation Forecast (QPF) is a challenging issue in seamless prediction. QPF faces the following difficulties: (i) single rather than multiple model products are still used; (ii) most QPF methods require long-term training samples not easily available, and (iii) local features are insufficiently reflected. In this work, a multi-model blending (MMB) algorithm with supplemental grid points (SGPs) is experimented to overcome these shortcomings.
The MMB algorithm includes three steps: (1) single-model bias-correction, (2) dynamic weight MMB, and (3) light-precipitation elimination. In step 1, quantile mapping (QM) is used and SGPs are configured to expand the sample size. The SGPs are chosen based on similarity of topography, spatial distance, and climatic characteristics of local precipitation. In step 2, the dynamic weight MMB uses the idea of ensemble forecasting: a precipitation process can be forecast if more than 40% of the models predict such a case; moreover, threat score (TS) is used to update the weights of ensemble members. Finally, in step 3, the number of false alarms of light precipitation is reduced, thus alleviating unreasonable expansion of the precipitation area caused by the blending of multiple models.
Verification results show that using the MMB algorithm has effectively improved the TS and bias score (BS) for blended 6-h QPF. The rate of increase in TS for heavy rainfall (25-mm threshold) reaches 20%−40%; in particular, the improvement has reached 47.6% for forecast lead time of 24 h, compared with the ECMWF model. Meanwhile, the BS is closer to 1, which is better than any single-model forecast. In sum, the QPF using MMB with SGPs shows great potential to further improve the present operational QPF in China.
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