Numerical study of current fields near the Changjiang Estuary and impact of Quick-EnKF assimilation

A 30-d current numerical simulation is running for the Yangshan Port, the Changjiang Estuary, the Hangzhou Bay and their adjacent seas using a finite volume coastal ocean model (FVCOM), with Changjiang River runoff and wind effect being considered. At the open boundary, this model is driven by the water level obtained from prediction including eight main partial tides. After the harmonic analysis, the cotidal chart and the iso-amplitude line as well as the current ellipse distribution map are displayed to illustrate the propagation property of a tidal wave. Horizontal velocity of both the U and V components coincides with the actual measurement, which shows that the model result is credible to describe the hydrodynamic pattern in this sea area. On this basis, real-time current data from high-frequency radar is assimilated with the implementation of quick ensemble Kalman filter, which takes the variation tendency of the state vector to compute the analysis field, instead of integrating the field for N (the number of ensemble) times as it used to in the standard EnKF, aiming at raising the efficiency of computation, reducing the error of prediction and at the same time, improving the forecast effect.     

WRF模式中微物理和积云对流参数化方案对台风“莫拉克”模拟敏感性分析

基于WRF模式,研究了不同微物理和积云对流参数化方案对0908号台风"莫拉克"的路径移动、强度变化和降水过程模拟的敏感性。结果显示,积云对流参数化方案对台风"莫拉克"的路径和强度模拟起主导作用,采用Kain-Fritsch积云对流方案模拟的72 h平均路径误差较小;降水量的模拟主要取决于微物理参数化方案,而降水分布的好坏更依赖于积云对流参数化方案,而采用Thompson微物理和Grell-Devenyi积云对流方案的试验导致累积降水极值的偏干误差较大。积云对流方案对环境场和潜热释放模拟存在差异,导致路径和强度、温度廓线和垂直运动的模拟结果不同,而微物理方案对不同相态降水粒子的垂直分布结构模拟存在差异,从而导致降水模拟的差别。此外,由不同试验构造的集合平均能减少单个成员模拟路径和降水的不确定性,特别在强降水方面能减小空报数和漏报数,提高TS评分,改善模拟效果。     

非常规油气藏地质建模与数值模拟一体化技术研究

非常规油气藏地质建模与数值模拟是通过对地质、地震、油藏综合研究,建立储层精细地质模型。首先建立构造模型,同时运用地震反演的成果结合单井岩相数据建立岩相模型,利用钻井泥浆漏失、油气田和单井的实际生产情况和微地震监测,描述油气藏内发育的天然裂缝和人工压裂裂缝,最终利用岩相模型控制建立孔隙度和饱和度等属性模型,为数值模拟提供能够反映与实际地质情况相符的三维油气藏粗化地质模型。在此模型的基础上,进行生产历史拟合、产量预测,并制定合理的开发方案。     

基于支持向量机无限集成学习方法的遥感图像分类

基于支持向量机的无限集成学习方法(SVM-based IEL)是机器学习领域新兴起的一种集成学习方法。本文将SVM-based IEL引入遥感图像的分类领域,并同时将SVM、Bagging、AdaBoost和SVM-based IEL等方法应用于遥感图像分类。实验表明:Bagging方法可以提高遥感图像的分类精度,而AdaBoost却降低了遥感图像的分类精度;同时,与SVM、有限集成的学习方法相比,SVM-based IEL方法具有可以显著地提高遥感图像的分类精度、分类效率的优势。     

Modelling radar-rainfall estimation uncertainties using elliptical and Archimedean copulas with different marginal distributions

Abstract

Given that radar-based rainfall has been broadly applied in hydrological studies, quantitative modelling of its uncertainty is critically important, as the error of input rainfall is the main source of error in hydrological modelling. Using an ensemble of rainfall estimates is an elegant solution to characterize the uncertainty of radar-based rainfall and its spatial and temporal variability. This paper has fully formulated an ensemble generator for radar precipitation estimation based on the copula method. Each ensemble member is a probable realization that represents the unknown true rainfall field based on the distribution of radar rainfall (RR) error and its spatial error structure. An uncertainty model consisting of a deterministic component and a random error factor is presented based on the distribution of gauge rainfall conditioned on the radar rainfall (GR|RR). Two kinds of copulas (elliptical and Archimedean copulas) are introduced to generate random errors, which are imposed by the deterministic component. The elliptical copulas (e.g. Gaussian and t-copula) generate the random errors based on the multivariate distribution, typically of decomposition of the error correlation matrix using the LU decomposition algorithm. The Archimedean copulas (e.g. Clayton and Gumbel) utilize the conditional dependence between different radar pixels to obtain random errors. Based on those, a case application is carried out in the Brue catchment located in southwest England. The results show that the simulated uncertainty bands of rainfall encompass most of the reference raingauge measurements with good agreement between the simulated and observed spatial dependences. This indicates that the proposed scheme is a statistically reliable method in ensemble radar rainfall generation and is a useful tool for describing radar rainfall uncertainty.

Editor D. Koutsoyiannis; Associate editor S. Grimaldi     

Evaluation of a global ensemble flood prediction system in Peru

ABSTRACT

Flood early warning systems play a more substantial role in risk mitigation than ever before. Hydrological forecasts, which are an essential part of these systems, are used to trigger action against floods around the world. This research presents an evaluation framework, where the skills of the Global Flood Awareness System (GloFAS) are assessed in Peru for the years 2009–2015. Simulated GloFAS discharges are compared against observed ones for 10 river gauges. Forecasts skills are assessed from two perspectives: (i) by calculating verification scores at every river section against simulated discharges and (ii) by comparing the flood signals against reported events. On average, river sections with higher discharges and larger upstream areas perform better. Raw forecasts provide correct flood signals for 82% of the reported floods, but exhibit low verification scores. Post-processing of raw forecasts improves most verification scores, but reduces the percentage of the correctly forecasted reported events to 65%.     

Evaluation of the Added Value of Probabilistic Nowcasting Ensemble Forecasts on Regional Ensemble Forecasts

Ensemble forecasting systems have become an important tool for estimating the uncertainties in initial conditions and model formulations and they are receiving increased attention from various applications. The Regional Ensemble Prediction System(REPS), which has operated at the Beijing Meteorological Service(BMS) since 2017, allows for probabilistic forecasts. However, it still suffers from systematic deficiencies during the first couple of forecast hours. This paper presents an integrated prob...     

Ideal Case Study of Adaptive Localization in Storm-scale Ensemble Kalman Filter Assimilation

This study explores the use of the hierarchical ensemble filter to determine the localized influence of ob-servations in the Weather Research and Forecasting ensemble square root filtering (WRF-EnSRF) assimilation system. With error correlations between observations and background field state variables considered, the adaptive localization approach is applied to conduct a series of ideal storm-scale data assimilation experiments using simulated Doppler radar data. Comparisons between adaptive and empirical localization methods are made, and the feasibility of adaptive locali-zation for storm-scale ensemble Kalman filter assimilation is demonstrated. Unlike empirical localization, which relies on prior knowledge of distance between observations and background field, the hierarchical ensemble filter provides con-tinuously updating localization influence weights adaptively. The adaptive scheme improves assimilation quality during rapid storm development and enhances assimilation of reflectivity observations. The characteristics of both the observation type and the storm development stage should be considered when identifying the most appropriate localization method. Ultimately, combining empirical and adaptive methods can optimize assimilation quality.     

西北干旱区阿克苏河径流对气候波动的多尺度响应

基于阿克苏河流域1960~2010年的气象、水文观测数据,利用集合经验模态分解（EEMD）方法,对研究期内阿克苏河径流时间序列进行多尺度的分析,并探讨其在不同时间尺度上的振荡模态结构特征及其对气候因子的多尺度响应。结果表明：① 近50年来,阿克苏河年径流整体上呈现出显著的非线性增加趋势,且其变化在年际尺度上表现出准3 a和准6~7 a的周期性波动,在年代际尺度上表现出准13 a和准25 a的周期性变化;② 各周期分量的方差贡献率表明,年际振荡在径流长期变化中占据主导地位,年代际尺度在径流变化过程中也起着重要作用。重构的径流年际变化能够较为详细地描述原始径流序列在研究时期内的波动趋势,重构的径流年代际变化则有效揭示了阿克苏河径流在不同年代丰、枯水期交替出现的状态。③ 在年际尺度上径流与气温、降水和潜在蒸发都表现为不显著的正相关关系,而在年代际尺度上,径流量与气温和降水均表现为显著的正相关关系,与潜在蒸发表现为显著的负相关关系,且在年代际尺度上相关性和显著性明显强于年际尺度,表明年代际尺度更适于评价径流对气候波动的响应。结果表明EEMD是一种甄别非线性趋势和尺度循环的有效方法。     

Implementation of a one-dimensional enthalpy sea-ice model in a simple pycnocline prediction model for sea-ice data assimilation studies

To further explore enthalpy-based sea-ice assimilation, a one-dimensional(1D) enthalpy sea-ice model is implemented into a simple pycnocline prediction model. The 1D enthalpy sea-ice model includes the physical processes such as brine expulsion, flushing, and salt diffusion. After being coupled with the atmosphere and ocean components, the enthalpy sea-ice model can be integrated stably and serves as an important modulator of model variability. Results from a twin experiment show that the sea-ice data assimilation in the enthalpy space can produce smaller root-mean-square errors of model variables than the traditional scheme that assimilates the observations of ice concentration, especially for slow-varying states. This study provides some insights into the improvement of sea-ice data assimilation in a coupled general circulation model.