华北汛期降水长期变化规律研究新进展

华北汛期降水的年代际减少,一直是气候学领域关心的重要课题之一。本文扼要回顾了华北汛期旱涝研究的最新代表性成果,主要包括华北汛期起讫的客观识别、华北汛期降水多时间尺度的变化特征、华北汛期降水变化与大气遥相关型的关系,以及华北汛期降水量增多趋势的停滞等。在此基础之上,归纳和总结了该领域需要继续深入研究的问题,如：华北汛期起讫时间的统一性;在华北汛期降水年代际变少的归因分析中,其年际振荡成分衰减的物理原因;华北汛期降水年代际变多趋势停滞的原因;华北汛期降水何时恢复增多等科学问题。     

针对红沿河核电站冷源取水海冰堵塞风险的数值模拟预警设计初探

由于经验和技术缺失，我国冰区滨海核电站的冷源取水安全受到海冰的威胁。数值模拟预警是建立和完善核电冷源取水安全保障系的关键部分。本文以辽东湾东岸的红沿河核电站为例，介绍了 3 种主要的核电冷源安全海冰致险模式，给出了 不同致险模式的数值模拟预警流程，并对数值模拟范围的选取进行重点阐述。结果表明，满足 24 h 预警需求的海冰数值模拟范围面积在数百甚至数千平方公里以上，较大的模拟范围与高网格分辨率对运算能力提出了较高的要求，是目前该数值模拟预警需要解决的突出问题。     

大型斜拉桥索塔变形监测方案的拟订

文章详细讨论了斜拉桥花瓶式塔柱变形监测方案的拟订过程。依据塔柱所能承受的允许荷载正演塔柱允许变形 ;以必要观测精度不掩盖变形点位移为准则 (即满足m≤ (1/ 10 ～ 1/ 2 0 )×Δ ,Δ为变形点的允许位移 )确定塔柱变形监测的必要精度 ;对必要观测精度进一步细化 ,按精度匹配原则量化各项影响误差 ,以此为基础拟订观测方案包括仪器选型 ,方案设计等。该方案充分考虑了被监测对象的结构力学特征 ,具有相当的参考价值。     

南京北郊2011年春季气溶胶粒子的散射特征

利用南京北郊2011年春季积分浊度仪的观测资料,结合PM_2.5质量浓度、能见度和常规气象资料,分析了南京北郊春季气溶胶散射系数的变化特征、散射系数与PM_2.5质量浓度和能见度的关系。结果表明,观测期间气溶胶散射系数平均值为311.5±173.3 Mm^-1,小时平均值出现频率最高的区间为100~200 Mm^-1;散射系数的日变化特征明显,总体为早晚大,中午及午后小。散射系数与PM_2.5质量浓度的变化趋势基本一致,但与能见度呈负相关关系。霾天气期间散射系数日平均值为700.5±341.4 Mm^-1,最高值达到近1 900 Mm^-1;结合地面观测资料、NCEP/NCAR再分析资料和后向轨迹模式分析显示,霾期间气块主要来自南京南部和东南方向。     

量纲分析应用于地震预测的探索

加卸载响应比(LURR)的基本思路是希望能够通过刻画震源区介质的损伤程度,反映地震孕育的进程,从而预测地震.近30年来,很多人对加卸载响应比做了大量基础研究,取得了一系列新的进展.加卸载响应比在地震预测实践中也取得了一定的效果,异常区与地震发生的位置有较好的对应性,但是预测效果仍不够理想.究其原因主要是:在实际预测中对当地的地球物理情况考虑的不够.本文采用量纲分析与加卸载响应比结合的方法,综合考虑当地的地球物理情况,例如剪应变率和平均地震波能量等因素的影响.文中选取1970年以来发生在中国大陆的34个震例资料,通过分析得到了与发震震级和时间相关的无量纲量π₁和π₃,根据对实际数据的拟合,π₁和π₃均与震级成指数关系.在应用于地震预测实践时,首先根据LURR空间扫描结果选取异常区,然后确定异常区的地球物理参数,通过π₁确定震级M,再由π₃确定发震时间T.     

汶川余震序列的加卸载响应比分析

该文首先将汶川余震序列分成西南、 东北两段, 用加卸载响应比方法分别进行分析, 结果表明震后两段的响应比值都比较低, 明显小于1, 这可判断汶川地震属于主余震型地震; 且地震序列中的强余震(M≥5.0)发生前加卸载响应比值出现明显异常, 大都是加卸载响应比出现峰值, 之后发生强余震。 考虑到主震后余震的时空丛集现象, 结合ETAS模型对原有的加卸载响应比计算式进行改造, 并分析了主震之后15天的改造后响应比值, 发现改造后的响应比在一定程度上消除了丛集的影响, 并展示了对紧随主震发生的强余震的预测能力。     

基于高光谱遥感的渤海海冰厚度半经验模型

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness(SEMSIT) that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands(spectral dimension information) are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.     

Resonance Effect in Interaction Between South Asian Summer Monsoon and ENSO During 1958–2018

The study has shown that the shear component of the vertical integrated kinetic energy (Ks) over the box (40oE–100oE, 0o–20oN) can be used as a measure of the intensity of the South Asian summer monsoon (SASM). Based on its value averaged between June and August, the SASM can be divided into strong and weak monsoon episodes. Between 1958 and 2018, there existed 16 (16) strong (weak) monsoon episodes. Based on the calendar year, the relationship between the SASM and the ENSO episodes can be grouped into six patterns: weak monsoon - El Ni?o (WM-EN), normal monsoon - El Ni?o (NM-EN), weak monsoon - non ENSO (WM-NE), strong monsoon - La Ni?a (SM-LN), normal monsoon - La Ni?a (NM-LN) and strong monsoon - non ENSO (SM-NE). Previous studies have suggested that the WM-EN and SM-LN patterns reflect the correlated relationship between the SASM and El Ni?o/Southern Oscillation (ENSO) events. Therefore, we name these two strongly coupled categories WM-EN and SM-LN as the resonance effect. Two important circulations, Walker circulation (WC) and zonal Asian monsoon circulation (MC), in the vertical plane are found to be not always correlated. MC is controlled by thermal gradients between the Asian landmass and the tropical Indian Ocean, while the WC associated with the ENSO event is primarily the east-west thermal gradient between the tropical South Pacific and the tropical Indian Ocean. Furthermore, the gradient directions caused by different surface thermal conditions are different. The main factor for the resonance effect is the phenomenon that the symbols of SSTA in the tropical Indian Ocean and the equatorial eastern Pacific are the same, but are opposite to that of the SSTA near the maritime continent.     

全新世以来泰国湾古气候演化历史———来自地球化学和矿物学证据

为了对海表温度(SeaSurfaceTemperature,SST)和海表盐度(SeaSurfaceSalinity,SSS)数据进行精确的短期预报,基于多站位海洋观测浮标获取的海表温度和海表盐度数据,利用反向传播(BackPropagation,BP)和径向基函数(RadialBasisFunction,RBF)两种神经网络方法开展了短期预测。首先,在预测时长固定为5d的情况下,对比不同训练时长的预测结果的均方误差(MeanSquaredError,MSE),进而确定以20d的观测数据作为训练集的预测结果均方误差最小。然后,以 PAPA 站观测浮标获取的2009年1月、4月、7月和10月各月的前20d温盐数据作为训练集,分别训练BP和 RBF神经网络,将训练好的2种神经网络模型应用于各月第21至25日的温盐数据预测。结果表明:BP和 RBF神经网络均能有效预测海表温盐数据的季节性变化,但 RBF神经网络对不同预测时间的整体预测效果优于 BP神经网络。多站点数据的预测实验进一步验证了 RBF神经网络模型具有较强适用性和更高的准确性。RBF神经网络模型可以作为海表温盐数据短期预报的有力工具。