GIS空间关系在北京市水务普查中的应用

在北京市第一次水务普查工作中,水务空间数据处理是一项非常重要的工作,而水务对象间的空间关系的正确性是水务空间数据处理成功的重要指标之一。本文分析了北京市水务普查空间数据处理中的空间对象间存在的GIS空间关系,然后详细阐述了基于GIS空间关系的水务空间数据处理方法,最后对水务普查空间成果在应急保障和水务精细化信息管理中的应用进行了展望。     

GLON ASS 系统可用性进展

介绍了GLONASS系统现代化的进展,从卫星可见性和位置精度因子两方面探讨了其可用性。通过中国境内不同纬度IGS 站实测数据分析,表明了大多数时间内可观测GLONASS卫星超过了4颗。目前,GLONASS在全球范围内位置精度因子较小,星座分布也较合理,在经历现代化进程后,其可用性已有了大幅提高。     

刚性连接的POS与相机集成系统集成检校试验精度分析

本文把国产POS和国产相机进行刚性连接,将系统误差检校参数用于修正随后的不同区域的POS观测值,利用由此得到的POS外方位元素结合航空影像进行直接地理定位,结果证明该方法用于直接测图是满足精度要求的。     

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.     

Experiments with the Improved Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation over South China

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.     

亚洲热带气候态夏季风涌传播特征及其对中国降水的影响

基于1979—2020年逐日的NOAA向外长波辐射资料、NCEP/NCAR再分析风场资料,以及全球CMAP再分析降水资料,探讨了气候态亚洲热带夏季风涌的传播过程及与我国夏季相应的降水联系。分析结果表明,主汛期亚洲热带气候态夏季风季节内振荡(CISO)活动是亚洲夏季风活动的主要特征,随时间北传的亚洲热带夏季风CISO称为亚洲热带夏季风涌,主要有南亚夏季风涌和南海夏季风涌。亚洲热带夏季风涌的传播可分为四个阶段。在亚洲热带夏季风涌的发展阶段,印度洋区域低频气旋与对流活跃,孟加拉湾和南海热带区域被低频东风控制,我国大部分地区无降水发生,降水中心位于两广地区。当进入亚洲热带夏季风涌活跃阶段,孟加拉湾和南海热带地区低频气旋和对流活跃,东亚低频“PJ”波列显著,我国降水中心北移到长江以南的附近区域。亚洲热带夏季风涌减弱阶段,孟加拉湾与南海低频气旋消亡,对流减弱,低频西风加强,日本南部附近为低频反气旋控制,我国长江中下游低频南风活跃,降水中心也北移到长江中下游地区,而华南地区已基本无降水,此阶段的大气低频环流场与亚洲热带夏季风涌发展阶段基本相反。进入亚洲热带夏季风涌间歇阶段时,孟加拉湾和南海热带地区低频反气旋活跃,对流不显著,日本南部附近的低频反气旋北移减弱,我国东部基本在低频南风的控制下,降水中心也逐步北移到华北-朝鲜半岛一带,此时的大气低频环流场与亚洲季风涌活跃阶段基本相反。      

一次模态变异中尺度对流系统的模拟与分析

利用NCEP FNL分析资料及南京多普勒雷达观测,借助WRF模式,对2017年8月19日发生在长江中下游地区的一次中尺度对流系统(MCS)进行模拟和诊断分析。此次MCS组织模态PS(Parallel Stratiform)型和TS(Trailing Stratiform)型共存,开始为带状结构,最后演变为强弓状飑线。气旋切变和低空急流是此次过程的重要影响系统,而午后强烈发展的地面锋触发了此次强对流。在垂直风切变和冷池共同作用下,西侧初始对流发展为PS型模态,东侧发展为TS型模态。由于PS型模态的中低层垂直风切变发生转向,导致其消散。TS型模态附近冷池和垂直风切变相配合,且在后向入流(Rear Inflow Jets,RIJ)作用下发展成为强弓状飑线;RIJ受中低层涡旋对影响而发展增强,其中气旋式涡旋主要由涡度方程中拉伸项决定,而反气旋式涡旋则主要由倾侧项决定。      

台风大风预报研究回顾

大风是台风引发的三大灾害之一,考虑到现有的台风大风研究相对较少、预报经验也比较不足,因此对其预报方法进行回顾总结具有十分重要的意义。从经验预报、统计预报、数值模式预报及数值预报产品释用等4个方向梳理了台风大风预报技术的研究与应用现状:天气图、卫星云图和雷达图是经验预报中非常重要的工具;统计预报根据方法的不同可细分为回归预报、客观相似预报和气候持续性预报;数值模式预报方面,国内外多家机构都研发了相应的台风大风预报产品;数值预报产品的释用目前则包含了动力释用、统计释用和人工智能释用。同时总结了上述4类预报方法各自的优势和不足。最后,讨论了未来研究与实际业务中进一步提高台风大风预报能力的可能发展方向。     

三江源地区降雪量演变特征及其对径流的影响

利用1961-2019年三江源地区19个气象台站逐日气温、降水量等气象观测数据,分析了三江源地区降雪量、降雪日数和雪雨比的时空演变特征及其对径流量的影响。结果表明：（1）1961-2019年三江源地区平均降雪量为146.5 mm,降雪量以14.8 mm·（10a）^-1的速率在减少,1985-1999年为降雪量偏多期,2000年以来为降雪量偏少期;（2）三江源地区年平均雪雨比以4.0%·（10a）-1的速率在减少,长江源区西部及黄河源区西部是雪雨比减少最为明显的区域,其余地区减少速率相对缓慢;（3）三江源地区年平均降雪日数为91天,曲麻莱、五道梁、沱沱河一带以及黄河源区中西部是降雪日数的大值区,降雪日数以14 d·（10a）^-1的速率在减少,黄河源区西部是降雪日数减少最为明显的区域;（4）三江源地区各月降雪量及降雪日数均呈双峰型分布,降雪量和降雪日数最多出现在5月,小雪易出现在3月或4月,中雪和大雪以上量级在秋末、春季出现的概率最高;（5）随着海拔的抬升,降雪量和降雪日数增加,随经度的增加而减少;冷季降雪量多的年份,径流量也随之增大,且径流量相...