An application of the LTP＿DSEF model to heavy precipitation forecasts of landfalling tropical cyclones over China in 2018

Recently, a track-similarity-based Dynamical-Statistical Ensemble Forecast(LTP_DSEF) model has been developed in an attempt to predict heavy rainfall from Landfalling Tropical cyclones(LTCs). In this study, the LTP_DSEF model is applied to predicting heavy precipitation associated with 10 LTCs occurring over China in 2018. The best forecast scheme of the model with optimized parameters is obtained after testing 3452 different schemes for the 10 LTCs. Then, its performance is compared to that of three operational dynamical models. Results show that the LTP_DSEF model has advantages over the three dynamical models in predicting heavy precipitation accumulated after landfall, especially for rainfall amounts greater than 250 mm. The model also provides superior or slightly inferior heavy rainfall forecast performance for individual LTCs compared to the three dynamical models. In particular, the LTP_DSEF model can predict heavy rainfall with valuable threat scores associated with certain LTCs, which is not possible with the three dynamical models. Moreover, the model can reasonably capture the distribution of heavier accumulated rainfall, albeit with widespread coverage compared to observations. The preliminary results suggest that the LTP_DSEF model can provide useful forecast guidance for heavy accumulated rainfall of LTCs despite its limited variables included in the model.

Analysis of a Heavy Rainfall Event over Beijing During 21-22 July2012 Based on High Resolution Model Analyses and Forecasts

The heaviest rainfall over 61 yr hit Beijing during 21-22 July 2012.Characterized by great rainfall amount and intensity,wide range,and high impact,this record-breaking heavy rainfall caused dozens of deaths and extensive damage.Despite favorable synoptic conditions,operational forecasts underestimated the precipitation amount and were late at predicting the rainfall start time.To gain a better understanding of the performance of mesoscale models,verification of high-resolution forecasts and analyses from the WRFbased BJ-RUCv2.0 model with a horizontal grid spacing of 3 km is carried out.The results show that water vapor is very rich and a quasi-linear precipitation system produces a rather concentrated rain area.Moreover,model forecasts are first verified statistically using equitable threat score and BIAS score.The BJ-RUCv2.0forecasts under-predict the rainfall with southwestward displacement error and time delay of the extreme precipitation.Further quantitative analysis based on the contiguous rain area method indicates that major errors for total precipitation（〉 5 mm h^（-1）） are due to inaccurate precipitation location and pattern,while forecast errors for heavy rainfall（〉 20 mm h^（-1）） mainly come from precipitation intensity.Finally,the possible causes for the poor model performance are discussed through diagnosing large-scale circulation and physical parameters（water vapor flux and instability conditions） of the BJ-RUCv2.0 model output.