����GNSS�����Ĵ��ͽ�����ʵʱ��̬�����ģ�ͻ�

???￥??GNSS??????????????????????????????μ???????????λ??仯??????????ɡ??????????????λ??仯????????????г??????????￥?????λ????????????????ü????????????????????????????????λ???????????????RMS????3 mm?????????RMS?????7 mm??     

基于高速公路气象监测数据分析的浓雾预报指标

利用京秦高速公路沿线交通气象监测站实况资料,通过对84个站次的浓雾雾生和雾消各气象要素变化特征进行分析,归纳出高速公路沿线浓雾和强浓雾天气雾生雾消的预报指标。爆发性强浓雾期间能见度少波动,在能见度爆发下降前,温度下降过程中的小幅上升对能见度突然下降有很好的指示作用;相对湿度在能见度爆发下降前1 h内达到80%以上。一般性强浓雾大多数出现在温度波动之后继续直线下降期间;在500 m浓雾出现15 h之前空气相对湿度达90%以上,能见度达50 m之前相对湿度基本达饱和状态。浓雾消散主要有两个方面,因冷空气造成的雾消,预报应着眼于冷空气前锋影响高速公路所在区域的时间;而由辐射升温造成的雾消,预报应着眼于对天空状况和升温速度的判断。     

Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

To improve the accuracy of short-term(0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System(HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting(WRF-ARW)model and the Advanced Regional Prediction System(ARPS) three-dimensional variational data assimilation(3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station(AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting(QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score(FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.     

2014年12月至2015年2月T639、ECMWF及日本模式中期预报性能检验

文章对2014年12月至2015年2月T639、ECMWF（文中简称EC）及日本数值模式的中期预报产品进行了对比分析和检验。结果表明：三个模式对亚洲中高纬环流形势的调整和演变及850 hPa温度转折性变化具有较好的中期预报性能,但都存在随时效增长的系统性偏差;对北方地区温度的预报, EC模式表现为预报较零场偏高,而T639模式表现为预报较零场偏低;对于海平面气压场,EC模式对冷空气前锋的预报与零场更加一致。     

综合地质超前预报在齐岳山隧道施工中的应用

在长大隧道施工过程中,由于工程地质条件的复杂性和不确定性,常会出现如塌方、涌水等地质灾害,为了规避这些地质灾害的发生,在施工过程中实施综合地质超前预报就显得十分有必要。本文以宜(昌)—万(州)铁路齐岳山隧道PDK366+020~PDK365+984段为实例,详细介绍地质分析-TSP地震勘探-超前水平钻探相结合的综合地质超前预报方法,结果证明,该方法具有较高的精度和准确度。     

A hybrid forecasting model for depth-averaged current velocities of underwater gliders

In this paper, we propose a hybrid forecasting model to improve the forecasting accuracy for depth-averaged current velocities (DACVs) of underwater gliders. The hybrid model is based on a discrete wavelet transform (DWT), a deep belief network (DBN), and a least squares support vector machine (LSSVM). The original DACV series are first decomposed into several high- and one low-frequency subseries by DWT. Then, DBN is used for high-frequency component forecasting, and the LSSVM model is adopted for low-frequency subseries. The effectiveness of the proposed model is verified by two groups of DACV data from sea trials in the South China Sea. Based on four general error criteria, the forecast performance of the proposed model is demonstrated. The comparison models include some well-recognized single models and some related hybrid models. The performance of the proposed model outperformed those of the other methods indicated above.     

基于GIS的淄博市地质灾害预警预报系统建设

淄博市地质灾害预警预报系统基于MapGIS K9平台进行二次开发,以数据库和地理底图为基础,集成数据、图像于一体搭建起一个高度信息化、专业化、智能化的地质灾害预警预报及管理平台。此系统的建设能够显著提高淄博市地质灾害气象预警预报的精细化水平,熟练掌握和运用该系统是进行预警分析、提前预报和及时防范地质灾害发生的有效手段。     

佛山市龙卷风活动的特征及环流背景分析

利用常规气象观测资料和NCEP再分析资料,分析了多年来佛山龙卷风的活动特点及其产生的环流背景和环境条件。结果表明:佛山龙卷风集中出现在4—8月;发生时间主要集中在08:00—14:00;发生地域以南海区最多,其次是三水区;16次龙卷风过程可归纳为4种诱生形势:台风外围型、锋面暖区型、地面辐合线型和热带扰动型。分析还发现:佛山龙卷风发生于偏南暖湿气流中,中低层通常有西南或偏南急流叠加配置,并存在强的垂直风切变和中干冷、下暖湿的强不稳定层结及较低的抬升凝结高度。另外,佛山龙卷风的发生还与地形因素关系密切。     

基于EEMD的黄河中上游夏季降水预报方法的研究

传统的统计方法难以很好的对气候系统这一集非线性、非平稳性为一身的多层次系统进行处理。因此集层次化处理和平稳化处理的集合正交经验模态分解技术(EEMD)的提出,为解决上述问题提供了有效的途径。本文选取黄河中上游24个气象观测站的逐月降水资料,结合组合预报和集合预报思路,基于EEMD建立了统计预报模型。其中对降水序列中的高频部分进行了二次平稳化处理,实现对2008—2013年6—8月的降水预报,并用预报评分检测预报效果。结果表明:EEMD模型对黄河中上游夏季降水有着较强的预报能力,在该区域与气候模式和传统的统计方法相比具有更高的精度和更好的应用前景。     

江淮入梅前后气象因子变化特征及预报着眼点探析

针对2010年江淮地区入梅日预报偏差情况,利用2010年6—7月高低空实况资料和NCEP再分析资料,分析了入梅前后湿度、经向风、地转西风急流的变化特征,并结合1985—2005年21 a历史平均状况和近几年的变化特征,分析了江淮地区入梅前后气象因子变化的规律性、普遍性,丰富了江淮地区入梅预报着眼点。研究发现:有些年份地转西风急流从30°N以南北跳到30~37.5°N区域,对江淮地区进入梅雨期有很好的预示作用,且其稳定维持,有利于江淮梅雨期降水的持续。70%湿度区北跳到30°N的时间及持续时间对江淮地区入梅日的预报和梅雨期长度有着较好的指示作用。在30~35°N区域内v850 hPa-v200 hPa风速差值的突然增大和江淮地区入梅有着较好对应关系。这为梅雨的预报提供了新的思路和方法。