偏振激光散斑的相关性研究

采用发送一束线偏振激光产生散斑的方法，研究线偏振激光散斑、＂平均椭圆偏振＂激光散斑以及非偏振激光散斑的相关性和精细结构。指出：当漫射器为一片毛玻璃时，散斑激光是线偏振的．其方向与入射激光束相同；当漫射器为涂无光白漆的毛玻璃时，散斑激光是部分或全部退偏的，涂层越厚，退偏越甚。对于强线偏振散斑场，当偏振方向改变时，散斑的精细结构无明显变化，即使是两个正交方向上的散斑图，也保持良好的相关性。对于非偏振散斑场，当照明激光束的偏振方向改变时．散斑精细结构有明显变化。     

FIO-ESM气候模式北太平洋海表温度和降水的季节性预报技巧评估

The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.     

使用间隔气象数据的余水位分析

余水位是潮汐预报中的"报不准"部分。在现有条件下,许多验潮站在对水位数据进行连续采集的同时,对气象数据还使用间隔时间采集的方式(若干小时采集一次)。使用间隔气象数据,按风向不同对气象数据、余水位数据进行分组,对风与余水位关系进行定性分析,对风-压与余水位关系进行拟合分析,并结合实例,对方法进行说明和检视。     

Numerical simulation of the Rongcharong paleolandslide river-blocking event: implication for the longevity of the landslide dam

Landslides - Numerous paleolandslide dams are distributed along the upper reaches of the Jinsha River under the special geological setting of the Tibetan Plateau. A field investigation revealed...     

Numerical simulation of the Qulong Paleolandslide Dam event in the late pleistocene using the finite volume type shallow water model

Natural Hazards - The Qulong paleolandslide dam event lies in the Benzilan-Batang zone of the upper Jinsha River. The Jinsha River is one of the most extensive water resources in southwest China....     

Interannual Variability and Scenarios Projection of Sea Ice in Bohai Sea Part I: Variation Characteristics and Interannual Hindcast

The Bohai Sea is one of the southernmost areas for sea ice formation in the northern hemisphere.Sea ice disasters in this body of water severely affect marine activities and the safety of coastal residents.In this study,we analyze the variation characteristics of the sea ice in the Bohai Sea and establish an annual regression model based on predictable mode analysis method.The results show the following:1)From 1970 to 2018,the average ice grade is(2.6±0.8),with a maximum of 4.5 and a minimum of 1.0.Liaodong Bay(LDB)has the heaviest ice conditions in the Bohai Sea,followed by Bohai Bay(BHB)and Laizhou Bay(LZB).Interannual variation is obvious in all three bays,but the linear decreasing trend is significant only in BHB.2)Three modes are obtained from empirical orthogonal function analysis,namely,single polarity mode with the same sign of anomaly in all of the three bays and strong interannual variability(82.0%),the north–south dipole mode with BHB and LZB showing an opposite sign of anomalies to that in LDB and strong decadal variations(14.5%),and a linear trend mode(3.5%).Critical factors are analyzed and regression equations are established for all the principal components,and then an annual hindcast model is established by synthesizing the results of the three modes.This model provides an annual spatial prediction of the sea ice in the Bohai Sea for the first time,and meets the demand of operational sea ice forecasting.     

可解释性长短期记忆模型用于预测湖泊总磷浓度变化

对湖泊总磷的变化预测和来源识别对水资源调度和流域生态治理有着重要的意义,然而复杂的生化反应和水动力条件导致的非平稳性给湖泊总磷浓度的准确预测带来极大的困难。为克服这一挑战,本文引入了基于加权回归的季节趋势分解(seasonal and trend decomposition using Loess,STL)技术和夏普利加法(SHapley additive exPlanations,SHAP)结合长短期记忆网络(long short-term memory neural network,LSTM)和门控循环单元(gated recurrent unit,GRU)构建了一个可解释的预测框架,以增强对湖泊总磷浓度演变的预测并提高其可解释性。研究表明:(1)在骆马湖总磷浓度的预测中,该框架拥有较好的预报精度(R²=0.878),优于LSTM和卷积长短期记忆模型(convolutional neural networks and long short term memory network,CNN-LSTM)。当预测时间步长增加到8 h时,该框架有效提高了总磷浓度的预测精度,平均相对误差和均方根误差分别降低了47.1%和33.3%。从预测趋势来看,骆马湖在汛期的总磷平均浓度为0.158 mg/L,相较于非汛期的平均浓度,增加了202.1%。(2)运河来水是骆马湖总磷浓度最重要的影响因素,贡献权重为60.0%,并且不同断面(三湾、三场)的污染源受水动力、气象等因素的影响存在显著的时空差异。本文凸显了神经网络模型在预警水体污染方面的可实施性,并且为提高传统神经网络的学习能力和可解释性的开发与验证提供了重要方向。     

风与余水位延迟效应关系的研究

针对实测水位资料中存在余水位的问题,进行气象因素中风与余水位的相关性以及风对余水位的延迟效应的研究。用统计分析法对青岛站数据分析得出,偏北风对减水贡献较大,偏南风对增水贡献较大;进一步对比分析得出:风对余水位的影响有一定的延迟性,延迟时间的长短与风速的大小成线性关系,风速越大延迟时间越短,造成的余水位变化幅度越大。通过本文分析可以为进一步研究余水位的特性、改进水位预报精度提供一定的参考。     

基于鲸鱼优化算法的长短期记忆模型水库洪水预报

洪涝灾害是世界主要自然灾害之一,优化洪水预报方案对防洪决策至关重要,然而传统水文模型存在参数多、调参受人为因素影响,泛化能力弱等问题。针对上述问题,本文提出基于改进的鲸鱼优化算法和长短期记忆网络构建自动优化参数的WOA-LSTM模型,通过优化神经网络结构进一步增强该模型的稳定性和精确度,并且建立不同预见期下的洪水预报模型来分析讨论神经网络结构与预报期之间的关系。以横锦水库流域1986—1997年洪水资料为例,其中以流域7个雨量站点的降雨以及横锦站水文资料为输入,不同预见期下洪水过程作为输出,以1986—1993年作为模型的率定期,1994—1997年作为模型的检验期,研究结果表明:(1)以峰现时差、确定性系数、径流深误差和洪峰流量误差作为评价指标,相比较于LSTM模型和新安江模型对检验期的模拟结果表明WOA-LSTM模型拥有更高的精度、预报结果更稳定;(2)结合置换特征值和SHAP法分析模型特征值重要性,增强了神经网络模型的可解释性;(3)通过改变神经网络结构在一定程度避免由于预见期增加和数据关联性下降而导致的模型预报精度下降的问题,最终实验表明该模型在预见期1~6 h下都可以满足横锦水库的洪水预报要求,可以为当地的防洪决策提供依据。