0～6 h定量降雪(雨)预报技术及其在温哥华冬奥会中的应用

利用中尺度数值预报模式与雷达回波外推技术相结合,围绕SNOWV10项目的科学目标,在自动判识降水相态(雨、雪或雨雪混合物)基础上,利用雷达反演降雪(雨)关系,建立短时(0～6 h)定量降雪(雨)预报方法,并集成到GRAPES-SWIFT临近预报系统,为温哥华冬奥会提供实时场馆预报产品.从2010年2月冬奥会使用情况看,所建立的定量降雪(雨)预报技术,一定程度满足高纬度冬季降雪(雨)临近预报预警的需求,但降水(雨或雪)预报量级偏小,针对场馆的降水预报起止时间节奏偏差较大,各时次预报一致性有待改进.     

Geology of Gold Ore Deposits in Western Linyi of Shandong Province,China

1IntroductionWestern Linyi of Shandong Province is one of the most important gold ore mineralization concen-tration areas. Tongjing skarn type gold copper de-posit, Jinchang skarn type gold copper deposit and Duijinshan porphyry type gold deposit were found in 1960's (Edited committee, 1996). Following the discovery of Guilaizhuang gold ore deposit related to alkaline rocks in 1988, the western Linyi region becomes one of the extensively studied areas of gold deposits in China (Lin etal.,…     

全球变暖背景下广东省近60年来极端气温时空变化特征分析

根据1951―2010年广东省26个气象站点的气温资料,统计了6个极端气温指数,采用线性倾向估计、Mann-kendall趋势和突变检验等方法对广东省极端气温时空变化特征进行了分析。结果表明,在时间上,近60年来广东省气温总体上具有倾向极端的趋势,但倾向极端高温的趋势不明显,而倾向极端低温的趋势却十分明显。6个极端气温指数均检测到有突变发生,突变时间集中在20世纪90年代和21世纪初期,除了指数TX90%(暖日天数),其余5个指数——SU(夏日天数)、FD(霜日天数)、TN90%(暖夜天数)、TX10%(冷日天数)、TN10%(冷夜天数)在突变后均呈现增加趋势,并且速度加快。与1961―1980年气温倾向极端的趋势不明显相比,1981―2010年气温倾向极端的趋势非常明显,并且具有倾向极端高温和极端低温的双向趋势。而在空间差异上,4个相对阈值指数在空间上的分布均为镶嵌式的分布,大致呈现出中部与东西两端一致,北部的变化幅度最小。     

利用相关性对自动气象站资料进行时空一致性检测

通过对广东省456个自动气象站2003-2005年的逐时资料进行处理,得到滤去天和年周期的温度、变温和1 h降水资料序列,并利用该序列作了滞后1 h自相关和空间相关分析。结果表明,滞后自相关系数含有时间特性,而要素站点空间分布则具有空间特性。选取2006年自动气象站个例,利用空间连续性和时间连续性原理,对气象要素时间滞后自相关系数在空间上的分布特性,进行时间、空间一致性检测,发现使用该方法可以容易地检查出资料缺测率偏高的站点。此外还总结出温度与降水观测资料的质量情况及规律。     

有云环境下MODIS亮温资料的变分同化Ⅱ——对暴雨预报的影响

卫星资料提供了大量关于云和雨的观测信息,在暴雨预报中可发挥巨大的作用,然而在数值模式资料同化中的应用水平仍然不高,特别是红外辐射资料的应用。由于有云环境下辐射传输过程的模拟难度很大,因此通常只同化晴空环境下的红外辐射资料。基于GRAPES-3DVAR(Global and Regional Assimilation and Prediction Enhanced System,全球/区域同化预报系统),根据RTTOV辐射传输模式(fast radiative transfer model for TOVS,快速辐射传输模式)的特点,增加云水含量、云冰水含量和云量作为同化系统控制变量,在改进辐射传输模式对红外资料模拟的同时,利用红外资料调整初始云参数和大气参数。针对2007年5月26日南海季风爆发后广东地区的一次暴雨过程,选取MODIS(Moderate Resolution Imaging Spectroradiometer,中分辨成像光谱仪)传感器水汽(第27)和云顶观测(第36)通道进行了同化试验,利用WRF(Weather Research and Forecasting Model,天气研究和预报模式)进行了数值模拟,结果表明同化MODIS资料,可以改进初始场水汽和温度分布,间接调整高空风场,调整趋势符合卫星观测,对短时降水预报有正面影响。     

GRAPES区域模式水汽平流方案的比较与改进

与欧拉显式时间差分方法相比,GRAPES区域模式采用半隐半拉格朗日时间差分方案可增加时间步长且不影响稳定性,而且模式积分可有较高的计算效率和准确性。半拉格朗日法需要用到内插算法来预测下一时刻的值,对于水汽场的内插值来说,常常会造成预报值的过饱和或者是负值,需要进行特殊处理。比较GRAPES模式的准单调半拉格朗日方案(QMSL)和高精度正定保形方案(PRM),分析模式的降水预报、形势预报,同时初步总结了两方案的优缺点。在参考LCSL(Linear Constraint Semi-Lagarangain)方案的基础上,改进QMSL方案,通过连续试验运行,表明新方案基本稳定可靠,对于降水预报、形势预报有一定的改进,在台风预报试验中也有良好的表现。     

Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

Storm surges pose significant danger and havoc to the coastal residents' safety, property, and lives, particularly at offshore locations with shallow water levels. Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans. In addition to experienced predictions and numerical models, artificial intelligence (AI) techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations. Convolutional neural network (CNN) and long short-term memory (LSTM) are two of the most important models among AI techniques. However, they have been scarcely utilised for surge level (SL) forecasting, and combinations of the two models are even rarer. This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information. The architectures of the CNN, LSTM, and two sequential techniques of combining the models (LSTM–CNN and CNN–LSTM) were constructed via a trial-and-error approach and knowledge obtained from previous studies. As a case study, 11 a of hourly observed SL and wind data of the Xiuying Station, Hainan Province, China, were organised as inputs for training to verify the feasibility and superiority of the proposed models. The results show that CNN and LSTM had evident advantages over support vector regression (SVR) and multilayer perceptron (MLP), and the combined models outperformed the individual models (CNN and LSTM), mostly by 4％–6％. However, on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges, the accuracy was found to improve by over 10％ at all forecasting steps.     

Estimate of contribution of near-inertial waves to the velocity shear in the Bay of Bengal based on mooring observations from 2013 to 2014

Near-inertial motions contribute most of the velocity shear in the upper ocean. In the Bay of Bengal (BoB), the annual-mean energy flux from the wind to near-inertial motions in the mixed layer in 2013 is dominated by tropical cyclone (TC) processes. However, due to the lack of long-term observations of velocity profiles, our knowledge about interior near-inertial waves (NIWs) as well as their shear features is limited. In this study, we quantified the contribution of NIWs to shear by integrating the wavenumber-frequency spectra estimated from velocity profiles in the upper layers (40?440 m) of the southern BoB from April 2013 to May 2014. It is shown that the annual-mean proportion of near-inertial shear out of the total is approximately 50%, and the high contribution is mainly due to the enhancement of the TC processes during which the near-inertial shear accounts for nearly 80% of the total. In the steady monsoon seasons, the near-inertial shear is dominant to or at least comparable with the subinertial shear. The contribution of NIWs to the total shear is lower during the summer monsoon than during the winter monsoon owing to more active mesoscale eddies and higher subinertial shear during the summer monsoon. The Doppler shifting of the M₂ internal tide has little effect on the main results since the proportion of shear from the tidal motions is much lower than that from the near-inertial and subinertial motions.