Experiments of DSAEF_LTP Model with Two Improved Parameters for Accumulated Precipitation of Landfalling Tropical Cyclones over Southeast China

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.     

强台风“菲特”(1323)极端降水研究进展

台风暴雨灾害是台风三类灾害(暴雨、大风、风暴潮)之首,而台风极端降水是暴雨灾害的直接原因,对台风极端降水的研究有利于增强对台风极端降水机理的认识和提高极端降水的预报水平。强台风“菲特”(1323)具有登陆强度历史罕见、降雨强度大、影响范围广、引发灾害重等特点,本文对“菲特”极端降水特征及其形成机理研究进行了回顾和总结。“菲特”的强降水过程主要分为两个阶段,造成了杭州湾一带和浙闽交界处两个强降水中心。“菲特”极端降水之所以产生,源于环境因子、地形和内部条件多尺度相互作用:环境因子涉及双台风作用、弱冷空气侵入、台风倒槽、垂直风切变和高空急流等,其中“丹娜丝”台风外围偏东气流源源不断的水汽输送是“菲特”极端降水形成的关键物理因子;山脉等地形增幅作用是浙江余姚等地出现历史性强降水的重要原因;水汽辐合和凝结与霰的融化和对流区雨滴的迁移是暴雨增幅重要的内部因素。     

强台风“菲特”(1323)极端降水研究进展

台风暴雨灾害是台风三类灾害(暴雨、大风、风暴潮)之首,而台风极端降水是暴雨灾害的直接原因,对台风极端降水的研究有利于增强对台风极端降水机理的认识和提高极端降水的预报水平。强台风“菲特”(1323)具有登陆强度历史罕见、降雨强度大、影响范围广、引发灾害重等特点,本文对“菲特”极端降水特征及其形成机理研究进行了回顾和总结。“菲特”的强降水过程主要分为两个阶段,造成了杭州湾一带和浙闽交界处两个强降水中心。“菲特”极端降水之所以产生,源于环境因子、地形和内部条件多尺度相互作用:环境因子涉及双台风作用、弱冷空气侵入、台风倒槽、垂直风切变和高空急流等,其中“丹娜丝”台风外围偏东气流源源不断的水汽输送是“菲特”极端降水形成的关键物理因子;山脉等地形增幅作用是浙江余姚等地出现历史性强降水的重要原因;水汽辐合和凝结与霰的融化和对流区雨滴的迁移是暴雨增幅重要的内部因素。     

高重复率卫星激光测距中一种回波实时识别方法

高重复率卫星激光测距(SLR)采用低能量激光,回波率较低且探测器暗噪声数明显增多,尤其在白天测距强背景噪声时,回波信号更是难于识别。该文提出了两次滤波的回波实时识别方法。为验证此方法的可行性,使用了Graz激光站2kHz实际测距数据,模拟了高重复率测距过程。结果表明,此方法在对高重复率激光测距回波识别中是有效的,为开展kHz激光测距打下了技术基础。     

事件计时器在卫星激光测距中的应用

传统时间间隔的测量速率受限于被测事件的时间间隔，事件计时概念的引入可以很好地提高测量速率。事件计时器是高重频（kHz级）卫星激光测距（SLR）技术的主要设备。该文介绍了事件计时器A032．ET的性能以及其在卫星激光测距中的应用，为我国开展高重频SLR研究提供了技术支持。