南极中山站1995年异常天气特点的分析

本文以第十一次南极考察队在中山站气象观测和天气分析预报资料收集情况，对中山站现场观测资料分析统计，并与建站以来历次考察队在中山站观测资料进行对比，得出了１９９５年中山站天气比历次观测年份异常，在１～５月份天气特点雪多，阴天多，日照短，７月份气温异常高，月平均气温比一般年份要高出９．８℃，大风日数２８天，极大风速达４７．２ｍ／ｓ。     

Predict typhoon-induced storm surge deviation in a principal component back-propagation neural network model

To reduce typhoon-caused damages, numerical and empirical methods are often used to forecast typhoon storm surge. However, typhoon surge is a complex nonlinear process that is difficult to forecast accurately. We applied a principal component back-propagation neural network (PCBPNN) to predict the deviation in typhoon storm surge, in which data of the typhoon, upstream flood, and historical case studies were involved. With principal component analysis, 15 input factors were reduced to five principal components, and the application of the model was improved. Observation data from Huangpu Park in Shanghai, China were used to test the feasibility of the model. The results indicate that the model is capable of predicting a 12-hour warning before a typhoon surge.     

Impact of the National Tsunami Hazard Mitigation Program on Operations of the Richard H. Hagemeyer Pacific Tsunami Warning Center

The first 7 years of the National Tsunami Hazard Mitigation Program (NTHMP) have had a significant positive impact on operations of the Richard H. Hagemeyer Pacific Tsunami Warning Center (PTWC). As a result of its seismic project, the amount and quality of real-time seismic data flowing into PTWC has increased dramatically, enabling more rapid, accurate, and detailed analyses of seismic events with tsunamigenic potential. Its tsunameter project is now providing real-time tsunameter data from seven strategic locations in the deep ocean to more accurately measure tsunami waves as they propagate from likely source regions toward shorelines at risk. These data have already been used operationally to help evaluate potential tsunami threats. A new type of tsunami run-up gauge has been deployed in Hawaii to more rapidly assess local tsunamis. Lastly, numerical modeling of tsunamis done with support from the NTHMP is beginning to provide tools for real-time tsunami forecasting that should reduce the incidence of unnecessary warnings and provide more accurate forecasts for destructive tsunamis.     

High resolution numerical weather prediction over the Indian subcontinent

In this study, the Florida State University Global Spectral Model (FSUGSM), in association with a high-resolution nested regional spectral model (FSUNRSM), is used for short-range weather forecasts over the Indian domain. Three-day forecasts for each day of August 1998 were performed using different versions of the FSUGSM and FSUNRSM and were compared with the observed fields (analysis) obtained from the European Center for Medium Range Weather Forecasts (ECMWF). The impact of physical initialization (a procedure that assimilates observed rain rates into the model atmosphere through a set of reverse algorithms) on rainfall forecasts was examined in detail. A very high nowcasting skill for precipitation is obtained through the use of high-resolution physical initialization applied at the regional model level. Higher skills in wind and precipitation forecasts over the Indian summer monsoon region are achieved using this version of the regional model with physical initialization. A relatively new concept, called the ‘multimodel/multianalysis superensemble’ is described in this paper and is applied for the wind and precipitation forecasts over the Indian subcontinent. Large improvement in forecast skills of wind at 850 hPa level over the Indian subcontinent is shown possible through the use of the multimodel superensemble. The multianalysis superensemble approach that uses the latest satellite data from the Tropical Rainfall Measuring Mission (TRMM) and the Defense Meteorological Satellite Program (DMSP) has shown significant improvement in the skills of precipitation forecasts over the Indian monsoon region.     

GRAPES_TCM模式对影响浙江省热带气旋的降水预报评估

基于2006-2009年有热带气旋影响浙江省期间气象站(包括自动站)资料及上海台风研究所的GRAPES_TCM模式输出降水资料,应用TS和BS评分方法,对模式在浙江省的热带气旋降水预报能力进行了评估.结果表明模式晴雨准确率和小雨TS评分24 h和36 h预报相对高,48 h开始随预报时效逐渐下降,中雨、大雨和暴雨TS评分随时效变化趋势不明显,而大暴雨TS评分有较大的随机性.漏报是影响小雨和中雨TS评分的主要原因,空、漏报对大雨和暴雨TS评分的影响基本相同,空报是影响大暴雨TS评分的主要原因.相同预报时效的TS评分随降水等级的提高而减小.BS评分发现模式对大雨及以下量级降水预报站次比实际偏少,对暴雨预报相对接近实际出现站次,对70 mm以上的大暴雨模式有过度预报倾向.与宁波市气象台主观预报比较,对晴雨和小雨预报,相同时效的主观预报一般高于客观,主观暴雨预报TS评分比客观预报一般高出30％以上.GRAPES_TCM模式对于影响程度高的TC降水预报能力相对高.20时输出预报与08时相比,对于晴雨和小雨预报,48 h以内差异不大,而60 h和72 h预报准确率,20时明显高于08时.对于中雨及以上等级降水,20时预报TS评分一般比08时好,主要得益于漏报率的降低.     

Bias correction of sea surface temperature retrospective fore-casts in the South China Sea

Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have been developed in this study: a backpropagation neural network(BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function(EOF) analysis and BPNN(named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South...     

降水偏差订正的频率(或面积)匹配方法介绍和分析

针对AREM模式降水预报的偏差特征,开展了基于频率(或面积)匹配方法的降水偏差订正试验,重点介绍了该方法的原理和实现过程,并对订正前后的结果进行了系统检验,深入分析了该方法的优缺点并指出了可能的改进方向。经过3个月降水集中期(2012年6—8月)的逐日试验分析结果表明:(1)该方法能显著改善模式降水预报中雨量和雨区范围的系统性偏差,订正后降水预报的范围和平均强度与实况更加接近;(2)偏差愈大订正效果愈好;(3)原理上此法不能订正降水的落区位置偏差,但通过改变雨区范围的大小,订正后降水预报的TS和ETS的评分也有一定程度提高,尤其是小雨量段,订正使数值预报的"有雨或无雨"的定性降水预报的质量得到明显改善。针对该方法"不能改进降水落区偏差"的局限性,提出了5种可以改进和尝试的方法,同时指出,该方法和原理可以用于单站降水预报、雾和水文的流域预报的偏差订正。     

一次低涡暴雨过程发生机制及其模式预报分析

为了揭示低涡暴雨发生机制,认识高分辨率的区域数值模式对低涡暴雨类天气的预报能力,应用多种观测资料,NCEP再分析资料和区域数值模式WRF和GRAPES预报资料,分析了2012年7月20—23日西南地区一次高原涡和西南涡带来的大暴雨过程。研究结果表明:(1)高原低涡与西南低涡耦合有利于低涡发展维持,中层的正涡度平流、低层的辐合上升运动是低涡发展的重要机制。低涡强烈发展时期,对流系统发展极为强盛,-64℃云盖呈圆形。(2)对流层中低层低涡的维持和发展,使四川盆地处于辐合上升环流控制中,提供了有利于降水的动、热力条件,是盆地强降水发生的重要机制。(3)两个模式都较好地反映了低涡影响下的盆地大暴雨过程。与实况的差异主要表现在降水发生时间提前,降水落区移动偏快或偏慢,有利于降水的动、热力场更强等等。相对而言,WRF模式预报与实况更接近。模式预报的低涡位置及伴随的物理量演变决定了降水预报的差异。