Robust data assimilation in hydrological modeling – A comparison of Kalman and H-infinity filters

Hydrological model and observation errors are often non-Gaussian and/or biased, and the statistical properties of the errors are often unknown or not fully known. Thus, determining the true error covariance matrices is a challenge for data assimilation approaches such as the most widely used Kalman filter (KF) and its extensions, which assume Gaussian error nature and need fully known error statistics. This paper introduces H-infinite filter (HF) to hydrological modeling and compares HF with KF under various model and observation error conditions. HF is basically a robust version of KF. When model performance is not well known, or changes unpredictably, HF may be preferred over KF. HF is especially suitable for the cases where the estimation performance in the worst error case needs to be guaranteed. Through the application of HF to a hypothetical hydrologic model, this paper shows that HF is less sensitive to the uncertainty in the initial condition, corrects system bias more effectively, and converges to true state faster after interruptions than KF. In particular, HF performs better in dealing with instant human inputs (irrigation is used as an example), which are characterized by non-stationary, non-Gaussian and not fully known errors. However HF design can be more difficult than KF design due to the sensitivity of HF performance to design parameters (weights for model and observation error terms). Through sensitivity analysis, this paper shows the existence of a certain range of those parameters, in which the “best” value of the parameters is located. The tuning of HF design parameters, which can be based on users’ prior knowledge on the nature of model and observation errors, is critical for the implementation of HF.     

Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance

Accuracy of the Copernicus snow water equivalent (SWE) product and the impact of SWE calibration and assimilation on modelled SWE and streamflow was evaluated. Daily snowpack measurements were made at 12 locations from 2016 to 2019 across a 4104 km² mixed-forest basin in the Great Lakes region of central Ontario, Canada. Sub-basin daily SWE calculated from these sites, observed discharge, and lake levels were used to calibrate a hydrologic model developed using the Raven modelling framework. Copernicus SWE was bias corrected during the melt period using mean bias subtraction and was compared to daily basin average SWE calculated from the measured data. Bias corrected Copernicus SWE was assimilated into the models using a range of parameters and the parameterizations from the model calibration. The bias corrected Copernicus product agreed well with measured data and provided a good estimate of mean basin SWE demonstrating that the product shows promise for hydrology applications within the study region. Calibration to spatially distributed SWE substantially improved the basin scale SWE estimate while only slightly degrading the flow simulation demonstrating the value of including SWE in a multi-objective calibration formulation. The particle filter experiments yielded the best SWE estimation but moderately degraded the flow simulation. The particle filter experiments constrained by the calibrated snow parameters produced similar results to the experiments using the upper and lower bounds indicating that, in this study, model calibration prior to assimilation was not valuable. The calibrated models exhibited varying levels of skill in estimating SWE but demonstrated similar streamflow performance. This indicates that basin outlet streamflow can be accurately estimated using a model with a poor representation of distributed SWE. This may be sufficient for applications where estimating flow is the primary water management objective. However, in applications where understanding the physical processes of snow accumulation, melt and streamflow generation are important, such as assessing the impact of climate change on water resources, accurate representations of SWE are required and can be improved via multi-objective calibration or data assimilation, as demonstrated in this study.     

适用于海洋动态观测设备嵌入式系统的低功耗管理预测模型

能耗是制约海洋设备在水下工作时长的关键因素。为进一步降低海洋动态观测设备嵌入式系统的动态功耗, 本文针对前人提出的用于预测空闲时间的指数平滑算法所存在的权重系数基于凑数法确定、算法难以适应大波动等问题, 设计了一种新的权重系数, 并构建了一种自适应的低功耗管理预测模型。当模型检测到预测的空闲时间超过工作和休眠状态切换的时间阈值后, 中央处理器关停系统的部分外设, 使系统进入低功耗休眠状态。仿真和单片机实验均表明,本文提出的低功耗管理预测模型能够有效地提高对处理器工作的空闲时间预测的准确性, 且面对突发状况具有良好的自适应性, 可显著降低嵌入式系统的功耗, 有效延长设备的工作时间。     

理论模型分析卡尔曼滤波在航空全张量磁力梯度测量中的应用效果

航空全张量磁力梯度测量数据中包含非常复杂的运动噪声,在频谱图中从低频至高频均有分布,并以白噪声为主,如何有效压制运动噪声是一个较大的挑战.传统的数字滤波只能滤除指定频段的噪声,对于混叠在全张量磁力梯度有用信号中的噪声不能有效分离.鉴于卡尔曼滤波是一种快速、高效和实时的最优估计方法,笔者将其应用到航空全张量磁力梯度数据处...     

浚县地震台地磁秒采样数据高频干扰分析

为识别及分析浚县地震台地磁秒采样观测数据中的高频干扰成分,采用别尔采夫滤波剔除其日波及半日波,然后通过时频分析得到高频干扰时段。通过查询工作日志及进行毕奥萨伐尔定律分析,认为浚县地震台地磁秒采样数据所受高频干扰除磁暴外,主要来自电焊施工和雷电,同时发现2种干扰类型有不同的典型干扰形态。     

基于粒子滤波的PDR/地磁指纹室内定位

针对行人航位推算(PDR)定位存在误差累积和地磁指纹不唯一导致的误匹配问题,本文改进了基于粒子滤波的PDR/地磁指纹室内定位方法.在PDR定位过程中利用地图信息控制粒子权重更新,得到较为准确的位置信息后,利用动态时间规整(DTW)算法在PDR推算位置基础上进行快速序列匹配,获取最优位置估计.试验结果表明,融合定位方法有...     

广西西江流域致洪暴雨过程中尺度特征及机制分析

利用广西逐站逐小时降水量、1°×1°逐小时FY-2C的TBB资料和1°×1°逐6 h NCEP/NCAR再分析资料,应用空间25点平滑滤波方法,分析了2005年6月18~22日西江流域致洪暴雨过程的中尺度特征及其发生、发展的机制。结果表明:这次致洪暴雨过程的降水时空分布不均,具有明显的中尺度特征。雨团集中出现在TBB值的低值中心、低值中心北侧等值线密集的地方以及等值线曲率大的地方。镶嵌于大尺度西南气流中的中尺度涡旋系统在西江流域反复生成,导致对流云团不断生成和加强,是此次暴雨产生的直接原因。暴雨区上空中尺度经向垂直环流圈的存在对中尺度涡旋和对流云团的发生和发展具有促进作用。中低层弱的对流不稳定与强的水平风垂直切变和湿斜压性共存是此次暴雨产生和持续发展的重要机制。     

卫星雷达联合重构大尺度流域降水场

给出了一种用GMS-5卫星估计3h降水的方法,并利用地面测雨雷达和卡尔曼滤波器提取误差因子,用于在雷达探测范围以外校准卫星估计的降水,从而获得大尺度流域的(淮河流域)降水分布。在史灌河子流域的比较试验表明：卫星雷达联合重构的降水场具有比卫星估计降水更高的精度,3h降水估计的相对误差从31％下降到13％,和地面降水场的相关系数也有明显提高。     

云南初夏罕见暴雨天气的中尺度特征

应用Barnus带通滤方法，对2001年5月30日－6月2日云南初夏罕见的暴雨天气过程进行了尺度分离，初步揭示了此次暴雨天气发生的流场、能量场和动力场的中尺度特征，对提高暴雨天气预报能力具有积极意义。     

大气对外强迫低频遥响应的数值模拟 Ⅰ:对赤道东太平洋SSTA的响应

本文通过在IAP-GCM上实现的数值模拟,研究了大气对赤道东太平洋地区暖SSTA的响应,着重讨论了大气遥响应的性质和演变过程. 数值模拟清楚地表明,大气对异常外源的响应主要是一种具有30—60天周期的低频遥响应.分析不同地区和不同季节这种强迫响应的30—60天低频振荡的结构及活动,可以清楚看到它与大气中实际存在着的30—60天振荡极为相似.因此也可以认为,大气对海温异常的强迫遥响应是激发产生全球大气30—60天振荡的重要机制.