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In recent work, three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation (DSAEF_LTP model) have been introduced, namely, tropical cyclone (TC) track, TC landfall season, and TC intensity. In the present study, we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added. Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018. The first experiment was designed as the DSAEF_LTP model with TC track, TC landfall season, and intensity (DSAEF_LTP-1). The other three experiments were based on the first experiment, but with new ensemble forecast schemes added (DSAEF_LTP-2), new similarity regions added (DSAEF_LTP-3), and both added (DSAEF_LTP- 4), respectively. Results showed that, after new similarity regions added into the model (DSAEF_LTP-3), the forecasting performance of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥250 mm and ≥100 mm) improved, and the sum of the threat score (TS250 + TS100) increased by 4.44%. Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1, the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously (DSAEF_LTP-4), with the TS increasing by 25.36%. Moreover, the forecasting performance of the four experiments was compared with four operational numerical weather prediction models, and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall. Finally, some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed. 相似文献
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The Dynamical-Statistical-Analog Ensemble Forecast model for landfalling tropical cyclones (TCs) precipitation (DSAEF_LTP) utilises an operational numerical weather prediction (NWP) model for the forecast track, while the precipitation forecast is obtained by finding analog cyclones, and making a precipitation forecast from an ensemble of the analogs. This study addresses TCs that occurred from 2004 to 2019 in Southeast China with 47 TCs as training samples and 18 TCs for independent forecast experiments. Experiments use four model versions. The control experiment DSAEF_LTP_1 includes three factors including TC track, landfall season, and TC intensity to determine analogs. Versions DSAEF_LTP_2, DSAEF_LTP_3, and DSAEF_LTP_4 respectively integrate improved similarity region, improved ensemble method, and improvements in both parameters. Results show that the DSAEF_LTP model with new values of similarity region and ensemble method (DSAEF_LTP_4) performs best in the simulation experiment, while the DSAEF_LTP model with new values only of ensemble method (DSAEF_LTP_3) performs best in the forecast experiment. The reason for the difference between simulation (training sample) and forecast (independent sample) may be that the proportion of TC with typical tracks (southeast to northwest movement or landfall over Southeast China) has changed significantly between samples. Forecast performance is compared with that of three global dynamical models (ECMWF, GRAPES, and GFS) and a regional dynamical model (SMS-WARMS). The DSAEF_LTP model performs better than the dynamical models and tends to produce more false alarms in accumulated forecast precipitation above 250 mm and 100 mm. Compared with TCs without heavy precipitation or typical tracks, TCs with these characteristics are better forecasted by the DSAEF_LTP model. 相似文献
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最近,一种基于路径相似的登陆热带气旋降水动力统计集合预报(LTP_DSEF)模型被发展用来预报登陆热带气旋(LTC)带来的强降水.文章把LTP_DSEF模型应用于2018年登陆中国的10个热带气旋(TC)的强过程降水预报,通过测试模型的3452套预报方案确定了对这10个LTC的最佳方案,然后将其性能与三家动力模式(ECMWF、GFS和GRAPES)进行对比.结果表明:LTP_DSEF模型在预报LTC的较强过程降水方面与三家动力模式相比很有优势,特别是预报250mm以上量级的过程降水;对单TC, LTP_DSEF模型预报LTC过程降水的能力优于或者略逊于三家动力模式,特别在三家动力模式对某些TC的强降水均无预报能力时,模型仍能提供宝贵的大于零的TS值;此外虽然与实况相比该模型预测的强降水范围倾向偏大,但它在多数情况下能合理地捕捉到强降水的落区.初步研究表明,尽管LTP_DSEF模型只引入了TC路径和登陆时间两个相似性变量,但它已能为LTC的强过程降水预报提供非常有用的指导. 相似文献
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基于路径相似的登陆热带气旋降水之动力-统计集合预报模型 总被引：1，自引：0，他引：1
数值天气预报(NWP)过去几十年在热带气旋(TC)预报方面的最大进步是越来越准确的路径预报。对于登陆TC降水的预报,目前以数值模式为代表的技术手段预报能力还十分有限。围绕动力-统计结合之方法研究,初步发展了登陆热带气旋降水(LTP)预报的一种新方法:基于路径相似的登陆热带气旋降水之动力统计集合预报(LTP_DSEF)模型。该方法主要分为五步:TC路径预报、相似路径TC识别、其他特征相似性的判别、TC降水集合预报和最佳预报方案选择;涉及两个关键技术:TC降水分离的客观天气图分析法(OSAT)和TC路径相似面积指数(TSAI)。LTP DSEF模型对2012-2016年影响华南地区出现最大日降水量≥100 mm的21个TC的定量降水预报(QPF)试验结果显示,该模型对登陆TC过程降水的预报结果优于动力模式。登陆TC过程降水≥50 mm情况下,建模样本和独立样本平均TS评分均高于动力模式(EC、GFS、T639)相应的最好表现。对LTP_DSEF模型三个最佳方案的参数取值分析显示,起报时刻参数设定为最临近影响时刻即TC对陆地产生降水的前一天12:00 UTC、集合参数取最大值时预报效果稳定趋好。 相似文献
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利用汕尾市2013—2018年间115个自动气象站逐小时降雨量资料,对汕尾地区短历时强降水时空分布特征以及与年总降水量的关系进行研究。分析表明:(1)2013—2018年汕尾市短历时强降雨的次数大体呈递增趋势,各年发生的次数差异较大;(2)短历时强降雨具有明显的季节变化和日变化特征,最易发生在6月,时段集中在下午至傍晚;(3)短历时强降水主要集中在莲花山脉东南侧的海丰县大部及陆丰市东北部,包括莲花山、公平、梅陇、海城、八万等地区;(4)短历时强降雨发生次数多的年份降水量也大,两者具有很强的相关性。 相似文献
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In this study, the Dynamical-Statistical-Analog Ensemble Forecast model (DSAEF_LTP model) for landfalling tropical cyclone (LTC) precipitation was employed to simulate the precipitation of 10 LTCs that occurred Pover China in 2018. With adding parameter‘similarity region scheme’(SRS) values and introducing TC intensity into the generalized initial value (GIV), four groups of precipitation simulation experiments were designed to verify the forecasting ability of the improved model for more TC samples. Results show that the simulation ability of the DSAEF_LTP model can be optimized regardless of whether adding SRS values only, or introducing TC intensity into GIV, while the experiment with both the two improvements shows a more prominent advantage in simulating the heavier precipitation of LTCs. Compared with four NWP models (i.e., ECMWF, GFS, GRAPES and SMS-WARMS), the overall forecasting performance of the DSAEF_LTP model achieves a better result in simulating precipitation at the thresholds over 250 mm and performs slightly better than NWP models at the thresholds over 100 mm. 相似文献
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