中国区域秋季旱涝特征预测

利用国家气候中心综合气象干旱指数(CI),以干旱日数为预报对象,以前期500 hPa高度场为预报因子,结合最优子集回归(OSR)方法建立回归预报模型,对1971-2007年期间中国区域秋季干旱形势进行交叉检验预报。针对秋季华西秋雨与华南干旱特征,分别建立以华西洪涝站数与华南干旱站数为预报对象的预报模型,研究OSR方法的预报技巧。研究表明:OSR预报方法对秋季中国区域的干旱日数具有较高的预报技巧,距平相关系数(ACC)在全国大部地区均通过α=0.05的显著性水平检验,中国东部地区整体预报水平高于西部地区,ACC在中国区域平均达到0.45。对于秋季华西洪涝站数与华南干旱站数,OSR方法也体现出较好的预报能力,ACC分别达到0.71和0.55。此外,对华南区域干旱站数的独立预报检验表明,模型能较好地预报出2003-2007年秋季华南地区偏旱的趋势,但对干旱范围的预报有所低估。     

西北地区干旱预测的EOF模型

该文在经验正交函数（EOF）分析的基础上,设计了一种适合于西北地区干旱预测的EOF模型,利用均生函数法、多元回归法和典型相关法对EOF模型进行了有效的预测试验,结果表明这种模型对西北地区的干旱有一定的预报能力。     

灰色预测模型及干旱预测

利用莘县、聊城、高唐3个代表站1960-2009年降水资料,建立了聊城地区降水序列,分析了近50年聊城地区降水量年变化特征,年降水量具有明显减少趋势,干旱有增加的趋势。以此序列为基础重建干旱发生的灾变序列,采用灰色系统GM（1,1）的三种模型探讨了旱灾预测问题。在WindowsXP系统下,用VB6编程计算预测结果,历史拟合效果较好,检验精度较高,尤其以新优化预测模型预测精度最高。对未来年份进行初步预测表明：2011年、2015年前后聊城地区出现干旱年的可能性大。     

灰色GM（1，1）模型在区域干旱预测中的应用

介绍灰色系统理论及其建模原理,利用温州市西山气象站53年的实测降水量资料建立灰色预测GM(1,1)灾变模型,对干旱灾害进行预测,经残差与后验差检验分析,模型精度较高平均达95.5％,并对实测资料进行检验,效果较理想,为温州市抗旱及供水提供必要的预测信息。     

T106数值预报产品在中期大——暴雨预报中的应用

对T10 6数值预报产品性能及其对暴雨预报的能力进行了检验 ,找出了T10 6物理量预报场与我区大———暴雨的相关关系 ,建立了基于灰色预测的逐日降水滚动预报模型 ,该模型对我区降水有一定的预报能力。     

舟山市汛期降水量预报方法

利用基于预测误差平方和 ( PRESS)准则的逐步回归分析和基于残差平方和( RSS)准则的逐步回归分析建立了舟山市 3个县区站汛期 ( 5～ 9月 )降水量的预报模型 ,并对两种方法的预测结果进行预报集成 ,经试报和预报检验表明 ,该模型的预测效果较好     

T106数值预报产品在中期大--暴雨预报中的应用

对T106数值预报产品性能及其对暴雨预报的能力进行了检验,找出了T106物理量预报场与我区大--暴雨的相关关系,建立了基于灰色预测的逐日降水滚动预报模型,该模型对我区降水有一定的预报能力.     

T106数值预报产品在中期大—暴雨预报中的应用

对Ｔ１０６数值预报产品性能及其对暴雨预报的能力进行了检验,找出了Ｔ１０６物理量预报场与我区大－暴雨的相关关系,建立了基于灰色预测的逐日降水滚动预报模型,该模型对我区降水有一定的预报能力。     

基于WRF模式的云贵川渝地质灾害气象预报系统的应用

介绍了国家气象中心和中国科学院成都山地灾害与环境研究所联合研发的基于ArcGis9.1的云贵川渝地区地质灾害预报系统。该系统是在可拓模型理论基础上建立的,其优点在于能将气象要素和地面要素紧密地结合在一起,形成雨—地耦合的区域泥石流预报模型。系统采用区域精细化数值天气预报模式WRF提供的高时空分辨率的精细降雨预报作为模型的动态输入,实现地质灾害发生概率的预报。目前,系统已经在中央气象台进行业务运行,每日在精细化数值天气预报系统提供的1小时间隔的数值降水预报支持下,定时启动预报系统。该预报产品目前已成为区域精细化地质灾害预报的重要参考。为了进行地质灾害预报效果的检验和评估,首先利用2007年6—7月份实况降水对WRF模型的预报降水进行检验。结果表明:中尺度数值预报模式WRF对区域性、持续时间长的暴雨过程预报能力较高,对于地质灾害预报服务具有较好的应用价值。此外,对2007年7月2—5日发生在四川盆地东部的部分地区的群发地质灾害个例分析及对2007年7月份该地质灾害预报系统的整体预报效果进行检验,结果表明:系统囊括了该次灾情大部分灾情点,总体预报准确率较高,业务中具有一定的应用价值。     

偏最小二乘回归模型在辽宁汛期降水预测中的应用

利用1951~2011年中国160个气象站逐月降水、温度、74项环流指数和NCEP再分析海表温度资料,采用偏最小二乘回归(PLSR)方法,结合均生函数构造预报量周期性因子,建立辽宁省汛期平均降水量及其5站(沈阳、朝阳、营口、丹东和大连)汛期降水量预测模型,并进行预测效果检验分析。结果表明:采用均生函数构造预报量周期性因子,在一定程度上弥补了气候预测统计模型高相关性因子的不足,从而使辽宁汛期平均降水量PLSR模型的试报均方根误差降低约10 mm。PLSR模型由于较好地解决了预报因子之间的多重相关性问题,其预测效果较逐步回归模型有明显提高,对2002~2011年辽宁5站汛期降水量试报的Ps评分平均值为72.6%,比逐步回归模型提高了10.3%。     

Drought prediction

Drought prediction has been an age-old problem, but in more recent times the magnitude of the Sahelian drought has brought into focus the need to improve the techniques for predicting such droughts with some measure of accuracy. Prom the present state of knowledge, drought prediction is difficult, if not impossible. Two major approaches appear to be prominent in the search for appropriate techniques. These include the use of teleconnections and the development of numerical models.Essentially, time-lagged teleconnections rely on the assumption that changes induced in any one area of the world may produce changes elsewhere on a world-wide scale, though time-lagged. The 1972-73 El Nino off the west coast of South America is a good case in point. The development of numerical models which allow the incorporation of not only climatic factors but also man's impact on the natural environment, offers some ray of hope that the general circulation models (GCMs) may help to improve the techniques of drought prediction.     

Operational regional prediction

Summary Regional operational prediction requires the development, implementation and steady improvement of a modelling system which comprises different components like the data assimilation scheme, numerical forecast model, postprocessing and application modules as well as packages for monitoring, diagnostics and verification. The text highlights the impact of some of those components like lateral boundary conditions, data assimilation and statistical postprocessing on the quality of operational regional prediction systems and proposes some important research directions for an improvement of the systems from the operational point of view.With 14 Figures     

国内外雾预报技术研究进展

大雾是引起低能见度的主要天气现象.提高雾的预报技术水平是确保交通安全的重要措施.从统计和数值预报两个方面,回顾了过去几十年来国内外在雾预报技术上的主要研究进展,并总结了各种方法的特点及存在的缺陷.在某些情形下,新统计方法的应用提高了雾的预报准确率,但仍然无法摆脱统计方法本身的缺陷.相比较而言,数值模式在大雾预报方面具有更广泛的应用和更大的潜力.在目前的计算机水平下,使用高分辨率的一维雾模式与中尺度天气模式相结合的方法,在一定程度上可以提高雾的预报准确率,该方法在大雾易发区的机场及高速公路沿线具有重要的应用价值.随着计算机能力的不断提高,包含大雾形成和演变的各种复杂过程、具有先进资料同化过程的高分辨率三维雾模式,以及集合数值预报系统将是未来的发展方向.     

Understanding the dynamical mechanism of year-to-year incremental prediction by nonlinear time series prediction theory

基于年际增量预测(YIP)方法的数学原理,推导得出新的非线性时间序列预测(NP)方法,并从理论上证明了两种预测方法在一定条件下的等效性。以长江中下游地区的夏季降水为例,新的NP方法在使用年际增量作为强迫项时,可以取得与YIP相当的预测效果。而且,通过调整重构维数等参数,能够将预测模型重构于高于一维的相空间中,进而可获得更好的预测效果。研究发现YIP方法除了有物理基础外,还有隐含的动力基础,新NP模型有助于提高短期气候预测的水平。     

Adaptive observations for hurricane prediction

Summary This study proposes a method that can be used to provide guidelines to aircraft reconnaissance for hurricane observations. The method combines numerical weather prediction (NWP) model with a statistical approach to target adaptive observations over areas where the hurricane predictions are very sensitive to the initial analysis for the NWP-model. A single model experiment is performed using regular initial analysis, while 50 other ensemble runs are performed from randomly perturbed initial states. Under the perfect model assumption, the single model experiment serves as a true state. The method first computes the forecast error variances at a certain verification time, e.g. hour 48, and then locates the maximum centers of variances. After the locations of the maximum forecast error variances are known, various correlations of different variables between these maximum variance points and the perturbation fields at the target time, e.g. hour 12, are calculated to identify those locations at the target time, over where the observational errors might be responsible for the growth of forecast error variances at the verification time. Statistically, these correlation fields indicate where the most sensitive areas are at the target time, i.e. where the need for additional observations is suggested. Hurricane Fran of 1996 is used to test the proposed method. The reason for choosing this case is that, during the first 48 hour forecast, the track forecast from NWP-model was very close to the best track. Two additional experiments were designed to examine the method. One experiment updates predicted variables at the target time (12 h) over the areas, to where the proposed method indicates the forecast would be sensitive. The updating combines observations (or truth) with the first guess (predicted) fields. Another experiment also modifies predicted variables at the target time (12 h), but over the areas where the method indicates the forecast errors are less correlated to. The results show that the modification has greatly reduced the forecast error variances at the verification time (48 h) in the first experiment, however it has a very little impact on the variance fields at the forecast hour (48 h) in the second experiment. It is very clear from our experiments, that the proposed method is able to identify sensitive areas, where additional observations can help to reduce hurricane forecast errors from an NWP-model. Received July 19, 1999 Revised November 28, 1999     

连环犯罪的时空预测方法

基于英国的连环犯罪调查数据,使用ArcGIS9.2软件对犯罪地点进行缓冲区分析,将全范围内的可能犯罪地点进行重分类,然后采用Rossmo公式建模预测分析犯罪地点,得到罪犯居住地的可能性分布,最后通过时间序列分析方法对下次行凶的可能时间和地点进行了预测.     

地质气象灾害的定点预报

本文针对地质灾害中与气象因素结合最为紧密的滑坡和泥石流两种灾害类型,从灾害成因、预报依据、预报方法、系统建设等方面阐述了定点地质气象灾害的预警与预报的理论框架,对于气象部门与国土部门联合开展地质气象灾害预警预报做出了科学的解释.     

地质气象灾害的定点预报

本文针对地质灾害中与气象因素结合最为紧密的滑坡和泥石流两种灾害类型,从灾害成因、预报依据、预报方法、系统建设等方面阐述了定点地质气象灾害的预警与预报的理论框架,对于气象部门与国土部门联合开展地质气象灾害预警预报做出了科学的解释。     

Uganda rainfall variability and prediction

This study analyzes large-scale controls on Uganda’s rainfall. Unlike past work, here, a May–October season is used because of the year-round nature of agricultural production, vegetation sensitivity to rainfall, and disease transmission. The Uganda rainfall record exhibits steady oscillations of ～3 and 6 years over 1950–2013. Correlation maps at two-season lead time resolve the subtropical ridge over global oceans as an important feature. Multi-variate environmental predictors include Dec–May south Indian Ocean sea surface temperature, east African upper zonal wind, and South Atlantic wind streamfunction, providing a 33% fit to May–Oct rainfall time series. Composite analysis indicates that cool-phase El Niño Southern Oscillation supports increased May–Oct Uganda rainfall via a zonal overturning lower westerly/upper easterly atmospheric circulation. Sea temperature anomalies are positive in the east Atlantic and negative in the west Indian Ocean in respect of wet seasons. The northern Hadley Cell plays a role in limiting the northward march of the equatorial trough from May to October. An analysis of early season floods found that moist inflow from the west Indian Ocean converges over Uganda, generating diurnal thunderstorm clusters that drift southwestward producing high runoff.     

Statistical method of precipitation prediction

Precipitation records from Salamanca (Spain) are being analyzed to detect temporal oscillations. This paper shows a method to identify these signals in order to determine the behaviour of the precipitation time series in this region.