基于EnKF排放清单反演方法的关键影响参数评估与优化

以中国一氧化碳(CO)排放反演为例,利用敏感性分析手段评估了集合数目、局地化半径､膨胀因子、观测站点密度和观测数据时间分辨率对排放清单反演的影响.结果表明:站点密度是影响排放反演的最重要参数.在不同站点密度下,反演的中国CO排放总量差异可达34%.同时,站点密度还会影响排放反演对其他参数的敏感性.随着站点密度的降低,排放反演对局地化半径、集合数目和膨胀因子参数变得更为敏感,但对观测数据时间分辨率的敏感性则有所下降.因此在站点稀疏地区,局地化半径是排放反演的主要影响参数,集合数目和膨胀因子次之;但在观测站点密集地区,局地化半径和观测数据时间分辨率是主要的影响参数,而膨胀因子和集合数目的影响相对较小.该研究能够为不同尺度的排放反演开展参数优化提供借鉴.在中国CO排放反演案例(站点密度为1.55个/10⁴km²)中,建议反演参数设置为:集合数目为50、局地化半径为100km、最大似然估计膨胀方案(MLE)、日均或小时观测数据.     

卡尔曼滤波方法在地下水污染源反演中的应用

采用卡尔曼滤波方法反演识别地下水污染源的个数和大概位置.借助一个假想算例,建立地下水系统水流和溶质运移模拟模型,利用灵敏度分析方法筛选出对模拟结果影响最大的参数作为随机变量,对该参数进行抽样,运用蒙特卡罗方法将抽样结果输入模拟模型,生成污染质浓度场.采用卡尔曼滤波方法构造迭代过程,逐个利用采样点处浓度的实测值不断更新综合浓度场.引入模糊集理论表示污染羽,对比综合污染羽和单个污染羽的模糊集来更新各潜在污染源的权重,根据潜在污染源权重大小和综合污染羽收敛形状判断真实污染源的个数和大概位置.算例结果表明：采用卡尔曼滤波方法可以成功反演识别出地下水污染中真实污染源的准确个数和大概位置;引入模糊集理论表示污染羽,通过对比综合污染羽和单个污染羽的模糊集,可以确定各潜在污染源的权重.     

Forecasting turbine problems by means of the state space framework

A forecasting system is set up to improve the diagnosis in a Condition Monitoring Programme of a critical turbine placed at an industrial plant. The system is based on a statistical model in a State Space framework, such that the local mean level of the vibration state of the equipment is estimated directly from the data, based on a continuous-time set up. This model is combined with a cost model in Conditioned Monitoring, by which the time of preventive replacement is produced when the minimum of the expected cost per unit of time is reached into the future. Such measure is a combination of the costs of failure, the costs of a preventive replacement and the probabilities of reaching the alarm levels fixed by some criteria. The system is estimated by Maximum Likelihood and thoroughly tested on the equipment. The main tests relate to statistical properties of the model residuals and a comprehensive comparison with an alternative system, namely a linear trend regression model in continuous time. The system produced a reasonable forecasting performance and sensible time of preventive replacement prediction and outperformed the alternative forecasting system.     

扩展卡尔曼滤波算法在矿用救生舱舱内气体浓度测控中的应用

为有效控制矿用移动式救生舱内气体浓度,确保救生舱内环境质量,保障舱内人员生命安全;根据《煤矿井下紧急避险系统建设管理暂行规定》的指标要求,采用扩展卡尔曼滤波方法对救生舱舱内气体参数的变化过程进行辨识,得出舱内气体浓度的状态空间模型,并应用迭代优化控制方法对气体浓度进行控制调节;在矿用救生舱模型和现场样机中分别进行了模拟和现场试验。结果表明,测控系统能有效控制救生舱舱内气体浓度进行,满足各项指标要求,且具有较好的鲁棒性。     

KALMAN FILTER ESTIMATION AND PREDICTION OF DAILY STREAM FLOWS: II. APPLICATION TO THE POTOMAC RIVER1

Results are reported from an application of the state space formulation and the Kalman filter to real-time forecasting of daily river flows. It is shown that the application of filtering techniques improves the overall forecasting performance of the model. As is true for most hydrologic systems, the model is not completely known. Therefore, the procedures pertaining to on-line parameter and noise statistics estimation, as presented in the first paper, are implemented. The example in this paper shows that these techniques also perform satisfactorily when applied to a real-world situation.     

Dynamic multi-resolution spatial models

Data from remote-sensing platforms play an important role in monitoring environmental processes, such as the distribution of stratospheric ozone. Remote-sense data are typically spatial, temporal, and massive. Existing prediction methods such as kriging are computationally infeasible. The multi-resolution spatial model (MRSM) captures nonstationary spatial dependence and produces fast optimal estimates using a change-of-resolution Kalman filter. However, past data can provide valuable information about the current status of the process being investigated. In this article, we incorporate the temporal dependence into the process by developing a dynamic MRSM. An application of the dynamic MRSM to a month of daily total column ozone data is presented, and on a given day the results of posterior inference are compared to those for the spatial-only MRSM. It is apparent that there are advantages to using the dynamic MRSM in regions where data are missing, such as when a whole swath of satellite data is missing.     

The Extended Kalman Filter (EKF) as a tool for the assimilation of high frequency water quality data

The Extended Kalman Filter (EKF) was applied to the analysis of high frequency field measurements of dissolved oxygen (DO), water temperature, salinity, collected by multiparametric sensors in the lagoon of Venice. This paper focuses on the practical aspects of the implementation of the EKF as a data assimilation technique and does not deal with the problems associated with the identification of the model. In this regard, the EKF has proved to be a useful tool for the updating of the estimates of the parameters of a simple DO-chlorophyll model, which can be used for linking the high frequency data to meteorological forcings, such as solar radiation and wind, and to other low frequency measurements of water quality parameters, such as the concentrations of Chlorophyll a and nutrients. The model can subsequently be used as a tool for checking the consistency of all this data, and may also be employed for controlling the quality of the data collected by the multiparametric sensors.     

Optimization of ecosystem model parameters using spatio-temporal soil moisture information

Parameters in process-based terrestrial ecosystem models are often nonlinearly related to the water flux to the atmosphere, and they also change temporally and spatially. Therefore, for estimating soil moisture, process-based terrestrial ecosystem models inevitably need to specify spatially and temporally variant model parameters. This study presents a two-stage data assimilation scheme (TSDA) to spatially and temporally optimize some key parameters of an ecosystem model which are closely related to soil moisture. At the first stage, a simplified ecosystem model, namely the Boreal Ecosystem Productivity Simulator (BEPS), is used to obtain the prior estimation of daily soil moisture. After the spatial distribution of 0–10 cm surface soil moisture is derived from remote sensing, an Ensemble Kalman Filter is used to minimize the difference between the remote sensing model results, through optimizing some model parameters spatially. At the second stage, BEPS is reinitialized using the optimized parameters to provide the updated model predictions of daily soil moisture. TSDA has been applied to an arid and semi-arid area of northwest China, and the performance of the model for estimating daily 0–10 cm soil moisture after parameter optimization was validated using field measurements. Results indicate that the TSDA developed in this study is robust and efficient in both temporal and spatial model parameter optimization. After performing the optimization, the correlation (r²) between model-predicted 0–10 cm soil moisture and field measurement increased from 0.66 to 0.75. It is demonstrated that spatial and temporal optimization of ecosystem model parameters can not only improve the model prediction of daily soil moisture but also help to understand the spatial and temporal variation of some key parameters in an ecosystem model and the corresponding ecological mechanisms controlling the variation.     

基于U-D分解的卡尔曼滤波法在地下水污染源识别中的应用

采用U-D分解的方法对常规卡尔曼滤波进行改进,以提高反演结果的有效性和可靠性.借助假想算例,引入模糊集合理论,将污染羽以模糊集的形式表示,利用U-D分解的卡尔曼滤波方法不断更新复合污染羽,通过复合污染羽和单个污染羽间模糊集的对比更新潜在污染源权重,进而达到真实污染源位置识别的目的.算例结果表明该方法与常规卡尔曼滤波方法相比具有更高的可靠性和有效性,能更好的达到污染源位置识别的目的.     

基于集合均方根滤波的太湖叶绿素a浓度估算与预测

叶绿素a浓度作为表征水质状况的重要参数之一,反映了水体富营养化程度和藻类含量,是决定水体的反射光谱特征的重要因素,也是水质遥感领域研究较多的一项水质参数.研究叶绿素a浓度的遥感定量反演可以为湖泊水质监测与评价提供新的思路和方法.本研究发展了一个基于集合均方根滤波和风生流的污染物扩散模型的数据同化方案,并结合2010年5月20日的太湖3个浮标观测站点的观测数据进行了同化实验.首先对太湖叶绿素a浓度进行同化估算,然后利用优化后的估算结果对太湖叶绿素a浓度进行了为期6 h的预报.在同化阶段,均方根误差分别从1.58、1.025、2.76降低到了0.465、0.276、1.01,平均相对误差也从0.2降低到了0.05、0.046、0.069.在预报阶段,均方根误差从1.486、1.143、2.38降低到了0.017、0.147、0.23,平均相对误差也从0.2降低到了0.002、0.025、0.019.结果表明,利用集合均方根滤波的数据同化方法可以有效地提高太湖叶绿素a浓度的估算与预报精度.