基于雷达资料的定量降水预报外推算法试验

摘要：目前贵州在短时临近降水预报客观算法方面研究匮乏，而短时强降水常常给贵州造成严重的洪涝灾害和地质灾害。将短时临近降水预报应用于气象防灾减灾、电网输电线路安全预警、水利防洪自动化方面是未来重要的研究应用方向。本文基于灾害天气短时临近系统（Severe Weather Automatic Nowcast System，SWAN）的定量降水预报(QPF)产品进行0～2小时QPF外推试验研究。利用贵州省地面自动站数据、SWAN 输出的1h-QPF产品、SWAN 1h定量降水估测(QPE)产品。选取2018年3次典型暴雨个例进行试验，采用融合订正技术，利用相似离度算法对降水强度位相进行调整、Weibull分布算法对降水极值分布进行订正、改进后的交叉相关法(Improved Cross-correlation Extrapolation Method，COTREC)进行降水外推预报。通过主客观检验对比，初步说明基于Weibull分布算法的降水极值订正对降水极值分布有很好的模拟效果。融合订正外推的贵州省短时临近定量降水预报产品（Nowcasting precipitation forecast of GuiZhou，下文简写为GZ_NPF）在大于5mm以上量级降水的TS检验中评分更高。GZ_NPF的空报率明显降低，但空报率降低的代价是漏报率相对提高。GZ_NPF总的相对误差减小，格点总降水量与实况总降水量更为接近。试验说明新的融合订正外推算法提高了0～2h短时临近降水的预报能力。     

The 3-hour-interval prediction of ground-level temperature in South Korea using dynamic linear models

The 3-hour-interval prediction of ground-level temperature from 00 h out to 45 h in South Korea(38 stations) is performed using the DLM (dynamic linear model) in order to eliminate the systematic error of numerical model forecasts. Numerical model forecasts and observations are used as input values of the DLM. According to the comparison of the DLM forecasts to the KFM (Kalman filter model) forecasts with RMSE and bias, the DLM is useful to improve the accuracy of prediction.     

砂岩型铀矿成矿预测评价信息系统研究

基于Arcview3．2a平台的二次开发工具,采用Avenue和Microsoft VB6．0语言,设计开发了地漫砂岩型铀矿成矿预测评价信息系统。信息平台应用地理信息系统(GIS)强大的空间分析功能以及空间和属性数据的管理和查询功能,以某盆地的控矿地质、盆地构造、地质异常多深信息数据为例,建立了砂岩型铀矿成矿预测评价信息系统。系统对该盆地的地质、遥感、地球物理数据进行了基于空间分析和成矿预测的数据处理,圈定出14片远景区,其中4号远景区已钻探见矿。该系统的开发成功对铀矿的成矿预测评价进入数字化、信息化和可视化的空间信息处理具有重要的试验意义。     

Meteorological monitoring of an active volcano: Implications for eruption prediction

Rainfall data collected on and around the Soufriere Hills Volcano, Montserrat between 1998 and 2003 were analysed to assess the impact on primary volcanic activity, defined here as pyroclastic flows, dome collapses, and explosions. Fifteen such rainfall-triggered events were identified. If greater than 20 mm of rain fell on a particular day, the probability of a dome collapse occurring on that day increased by a factor of 6.3% to 9.2%, compared to a randomly chosen day. Similarly, the probability of observing pyroclastic flows and explosions on a day with > 20 mm of rainfall increased by factors of 2.6 and 5.4, respectively. These statistically significant links increased as the rainfall threshold was increased. Seventy percent of these rainfall-induced dome collapse episodes occurred on the same calendar day (most within a few hours) as the onset of intense rainfall, but an extra 3 occurred one or two calendar days later. The state of the volcano was important, with the rainfall–volcanic activity link being strongest during periods of unstable dome growth and weakest during periods of no dome growth or after a recent major collapse.Over 50% of the heavy rain days were associated with large-scale weather systems that can potentially be forecast up to a few days ahead. However, the remaining heavy rain days were associated with small-scale, essentially unpredictable weather systems. There was significant variability in the amount of rainfall recorded by different rain gauges, reflecting topographic variations around the volcano but also the inherent small-scale variability within an individual weather system. Hence, any monitoring/warning program is recommended to use a network, rather than just a single gauge. The seasonal cycle in rainfall was pronounced, with nearly all the heavy rain days occurring in the May–December wet season. Hence, the dome was at its most vulnerable at the beginning of the wet season after a period of uninterrupted growth. Interannual variability in rainfall was related to tropical Pacific and Atlantic sea surface temperature anomalies, and holds out the prospect of some limited skill in volcanic hazard forecasts at even longer lead times.     

潞西盆地浅层地下水脆弱性预测分析

运用DRASTIC方法，评价潞西盆地浅层地下水脆弱性，结合区内人类工程活动因子。对潞西盆地远期地下水脆弱性进行预测分析，成果可为区内土地规划、备用水源地的选择、垃圾填埋场的选址及实现水资源可持续利用提供合理依据。     

基于改进的Elman神经网络的中长期径流预报

径流中长期预报长期以来一直都是人们关注的热点研究问题。现行的径流预报方法很多,传统的有时间序列法,多元回归分析法等,这些方法虽然简单易用,但是如果预报对象提供的样本容量偏小或者因子选择不够合理,都会造成预报精度偏差过大,难于有效的指导工程应用。鉴于此,本文提出一种改进的采用局部回归的Elman神经网络方法。并应用到凤滩水库优化调度的径流预报中。结果表明,与回归分析法、BP网络相比较,该方法不仅提高了算法的效率,而且提高了预报的精度,在径流预报中具有有效性和优越性。     

洪水预报信息利用问题研究与讨论

分析了洪水预报研究现状,讨论了实时洪水预报系统研究中信息利用方面存在的问题,提出了值得进一步研究的关键性问题、研究思路和研究技术路线。     

精细化MOS相对湿度预报方法研究

利用2003年5~9月MM5模式每隔1 h的站点基本要素预报场和物理量诊断场资料,以及相应时段内宁夏25个测站的相对湿度自记观测资料,同时采用多元线性和逐步回归2种MOS统计方法,预报宁夏25个测站5~9月48 h逐时相对湿度。对2004年夏季6~8月预报效果检验表明:MOS方法制作宁夏48 h逐时相对湿度预报结果是可用的或是可参考的;2种MOS统计方法预报结果相近,逐步回归方法比多元线性方法预报效果稍好,08:00预报误差明显低于20:00;当天气形势变化较平稳时,MOS预报结果稳定,平均绝对误差控制在10%左右;当有明显的变温等特殊天气时,误差变率起伏波动大,预报结果不稳定。     

双流机场低能见度天气预报方法研究

在信息量较大, 而预报对象与预报因子的关系又不清楚的状况下, 智能机器学习方法是解决这类问题的较好手段。利用1997—2001年成都站的常规探空资料和双流机场的地面观测资料, 使用支持向量机 (Support Vector Machines, 简称SVM) 方法, 选取多种核函数进行双流机场低能见度天气的预报建模试验。测试结果表明:以径向基函数和拉普拉斯函数构造的SVM预报模型实验效果最好, Ts评分分别为0.287和0.292, 远高于双流机场低能见度天气出现的频率 (0.155)。试验结果还表明:以径向基函数构造的SVM预报模型空报较多, 漏报较少; 而以拉普拉斯函数构造的SVM预报模型空报较少, 漏报较多。因此, 如果强调模型对低能见度天气预报的准确性, 则应采用以拉普拉斯函数构造的预报模型, 如果强调对低能见度天气的预防性, 则应采用以径向基函数构造的预报模型。     

APPLICATION OF BDA SCHEME IN TYPHOON TRACK PREDICTION

The MM5, which is the PSU/NCAR mesoscale nonhydrostatic limited-area model, and its adjoining modeling system are used in this paper. Taking T106 analysis data as background field the authors generate an optimal initial condition of a typhoon by using two bogus data assimilation schemes, and conduct some numerical simulating experiments. The results of No.9608 typhoon (Gloria) show that the optimal initial field have some dramatic improvements, such as inaccurate position of typhoon center, weaker typhoon circulation and incomplete inner structure of the typhoon, which are caused by shortage of data over the sea. Some improvements have been made in the track forecast. Through several comparing experiments, the initialization optimized by BDA scheme is found to be more reasonable than GFDL scheme and its typhoon track forecast is better.