安康水电站精细化降水预报的检验

降水精细化数值预报模式的发展是开展精细化降水预报业务的理想途径,而模式本地化中的误差评估是当前开展业务应用的重要环节。基于此,运用误差分析、晴雨预报准确率、降水TS评分方法评估陕西精细化数值预报攻关团队提供的2016年5月1日~9月30日安康水电站降水预报。结果表明：随着降水时效的增长,降水的预报准确率呈减小趋势;大雨以上的降水过程预报较好且未出现漏报,但量值与实况有差异,预报值小于实况值;20时起报的预报准确率大于08时起报的准确率且夜间的高于白天;降水日数多的月份TS评分预报准确率高于降水日数少的月份。比较安康和石泉的结果发现,安康的预报准确率明显优于石泉,主要原因是安康降水日数比石泉多,且大的降水过程比石泉少。逐1 h、3 h的72 h以内的降水预报可以为安康水电厂水利调度提供参考。     

低频电磁波与地倾斜传感器对地震预报分析与研究

5.12大地震对国家造成了一次重大的损失,则对地震预报的研究则显得尤为重要。本文认为地震发生的机理是板块挤压破裂学说。在地震动发生前后都会出现一些宏观或微观异常,在地震发生前其微观异常主要表现为电磁波、地壳倾斜、地应力等异常。本方通过对低频电磁波和地倾斜进行测量和研究,可通过数据多台站共享来进行地震预报分析。     

台湾海峡1997年夏季和1998年冬季两航次颗粒有机碳研究

1997～1998年台湾海峡两个航次的数据表明,颗粒有机碳(POC)的平均含量为0．120±0．009mg/dm3,表层POC浓度在夏季和冬季分别存在几个高值区。POC在随深度变化的剖面分布图中,最大值出现在10m层,底层水明显受再悬浮影响,这一剖面的变化规律普遍存在于整个海区及不同季节。夏季POC的深度变化幅度要显著大于冬季,这与垂直混合作用有关。POC与Chl－a相关性较差,暗示以Chl－a估算浮游植物的不确定性及POC产生及来源的复杂性。     

State Estimation of the North Pacific Ocean by a Four-Dimensional Variational Data Assimilation Experiment

A four-dimensional variational data assimilation system has been applied to an experiment to describe the dynamic state of the North Pacific Ocean. A synthesis of available observational records and a sophisticated ocean general circulation model produces a dynamically consistent dataset, which, in contrast to the nudging approach, provides realistic features of the seasonally-varying ocean circulation with no artificial sources/sinks for temperature and salinity fields. This new dataset enables us to estimate heat and water mass transports in addition to the qualification of water mass formation and movement processes. A sensitivity experiment on our assimilation system reveals that the origin of the North Pacific Intermediate Water can be traced back to the Sea of Okhotsk and the Bering Sea in the subarctic region and to the subtropical Kuroshio region further south. These results demonstrate that our data assimilation system is a very powerful tool for the identification and characterization of ocean variabilities and for our understanding of the dynamic state of ocean circulation. This revised version was published online in July 2006 with corrections to the Cover Date.     

Studies on twelve common bivalve larvae,with notes on bivalve spawning seasons in New Zealand

Twelve common bivalve larvae occurring in the plankton from the Bay of Islands (35°15'S, 174°10'E), Wellington Harbour (41°16'S, 174°51'E), and off Raumati Beach (40°56'S, 174°58'E), New Zealand, during 1970–72 are described and, wherever possible, provisionally identified. The seasonal occurrences of these larvae in the plankton are also described. Information on the spawning cycles of some New Zealand adult bivalves is reviewed; although some species have a short (4 months or less) spawning season, for most it is much longer, possibly with ‘trickle’ spawning through several months of the year.     

A baroclinic typhoon model with a moving multi-nested grid and variational adjustment initialization Ⅱ. Forecast experiments

INTRODUCTIONBeingoneofthemestdisastrousweathersystemsinfluencingthesoutheaStcoastofChinainsurnmerandautumn,typhoonsalsocausestrongstormsUrgeSandseawavesI'eSultinginsevereoceandisasters,whichthreaten~theasternChina'sagriculture,navigation,offshoreoila...     

质点-网格海冰模式在渤海的数值预报试验

在海冰动力学和热力学的研究基础上,开发应用于渤海的质点-网格海冰模式.该模式采用质点-网格法,有效地避免了传统海冰模式的数值扩散问题.该模式采用冰厚分布函数取代传统渤海业务海冰预报模式中平整冰、堆积冰和开阔水的三类海冰分型.应用质点-网格海冰模式对2003年～2004年冬季渤海冰情进行了业务化逐日数值预报试验,并对预报结果进行统计检验和分析比较.     

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.     

大鹏湾岬湾型海滩季节性变化特征及原因分析——以官湖海滩为例

随着全球海平面的上升及极端气象的频发,全球海滩总体呈现出一定的退化现象,海滩保护成为海岸带生态修复的焦点问题之一。我国华南地区岬湾型海滩分布广泛,以深圳市大鹏湾官湖海滩为代表,基于2020—2021年实测海滩剖面高程数据,分析岬湾型海滩季节性变化特征。研究表明,官湖海滩剖面坡度夏秋缓冬春陡,夏秋侵蚀冬春淤积;海滩沉积物粒径季节性变化不明显。海滩剖面形态受风浪、平均潮位的季节性变化控制,以夏秋季为例,平均潮位逐渐升高,南向波浪强度较大,在二者的共同作用下,海滩后滨侵蚀明显,泥沙离岸输运,并在前滨淤积。补沙方案宜在夏秋季进行,且重点区域为官湖海滩东侧与观海湾海滩,防御方案应主要削弱南向波浪。     

COMPARATIVE EXPERIMENTS OF DYNAMICAL FORECAST AND STATISTICAL FORECAST*

After analyzing the advantages and disadvantages of dynamical system and statistical system.some simple models with the equations of Lorenz system and auto-regresslon model have been respectively set up,and comparative experiments conducted.The result of the research demonstrates that in chaotic parametric domain,the accuracy of statistical forecast is higher than that of dynamical forecast,while in non-chaotic parametric domain,dynamical forecast is more accurate than statistical forecast.