A homogeneous linear estimation method for system error in data assimilation

In this paper, a new bias estimation method is proposed and applied in a regional ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) Model. The method is based on a homogeneous linear bias model, and the model bias is estimated using statistics at each assimilation cycle, which is different from the state augmentation methods proposed in previous literatures. The new method provides a good estimation for the model bias of some specific variables, such as sea level pressure (SLP). A series of numerical experiments with EnKF are performed to examine the new method under a severe weather condition. Results show the positive effect of the method on the forecasting of circulation pattern and meso-scale systems, and the reduction of analysis errors. The background error covariance structures of surface variables and the effects of model system bias on EnKF are also studied under the error covariance structures and a new concept ‘correlation scale’ is introduced. However, the new method needs further evaluation with more cases of assimilation.     

基于波动方程预测和双曲Radon变换联合压制表面多次波

基于波动方程预测的表面多次波压制方法可处理复杂地下介质的地震资料,但计算成本较高.基于滤波的多次波压制方法计算效率较高,但其成功应用仅局限于一次波和多次波有明显时差差别的地震数据,对来自速度逆转等复杂介质数据则较难获得满意的压制效果.本文将波动方程预测的反馈迭代法和滤波法有效结合,采用GPU(图形处理器)和CPU协同并行加速计算粗略预测表面多次波,随后在双曲Radon域比较分析原始数据和预测的多次波,设计合理有效的Butterworth型自适应滤波器,滤出原始数据Radon域中的多次波能量,进行Radon反变换后,在时空域将多次波从原始数据中减去,得多次波压制结果.文中对理论模拟的单炮数据、复杂的SMAART模型以及实际地震数据进行了计算,结果表明,结合基于波动方程预测和双曲Radon变换的方法有效突破了两种方法各自的局限性,可高效高精度地压制复杂地下介质的表面多次波.     

Comparison of Ensemble Kalman Filter groundwater-data assimilation methods based on stochastic moment equations and Monte Carlo simulation

Traditional Ensemble Kalman Filter (EnKF) data assimilation requires computationally intensive Monte Carlo (MC) sampling, which suffers from filter inbreeding unless the number of simulations is large. Recently we proposed an alternative EnKF groundwater-data assimilation method that obviates the need for sampling and is free of inbreeding issues. In our new approach, theoretical ensemble moments are approximated directly by solving a system of corresponding stochastic groundwater flow equations. Like MC-based EnKF, our moment equations (ME) approach allows Bayesian updating of system states and parameters in real-time as new data become available. Here we compare the performances and accuracies of the two approaches on two-dimensional transient groundwater flow toward a well pumping water in a synthetic, randomly heterogeneous confined aquifer subject to prescribed head and flux boundary conditions.     

A ＂Dressed＂ Ensemble Kalman Filter Using the Hybrid Coordinate Ocean Model in the Pacific

The computational cost required by the Ensemble Kalman Filter (EnKF) is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation (OI) or three-dimension variational assimilation (3DVAR). Ensemble optimal interpolation (EnOI), a crudely simplified implementation of EnKF, is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF. In this paper, to compromise between computational cost and dynamic covariance, we use the idea of ``dressing' a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance. The term ``dressing' means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles. This dressing EnKF (DrEnKF for short) scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model (HYCOM) over a four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble. Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members. Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors (RMSE) at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.     

Methodology of analysis associating survey results by the filter-pack method with those of precipitation- Acid-base balance on acid-related and alkali-related chemical species in urban ambient air and its influence on the acidification of precipitation -

Continuous weekly monitoring on the concentration of gases and aerosols in urban ambient air by a four-stage filter-pack method was carried out for 7 years in order to study not only the acid-base balance of acid-related (HNO₃, NO₃ ⁻, and non-sea-salt-(nss-)SO₄ ²⁻) and alkali-related (NH₃, NH₄ ⁺, and nss-Ca²⁺) chemical species but also its influence on the acidification of precipitation. The concentrations of the total nitrate (= NO₃⁻ + HNO₃) and nss-SO₄²⁻ showed a similar seasonal variation: high in the summer and low in the winter. The total nitrate and nss-SO₄²⁻ accounted for 0.43 and 0.57 of the acid-related species, respectively, on an equivalent basis. The total ammonium (= NH₃ + NH₄⁺) accounted for more than 0.9 of the alkali-related species, except for a springtime nss-Ca²⁺ episodic peak. The alkali-related species were generally overabundant compared with the acid-related species in the HNO₃-NO₃⁻-nss-SO₄²⁻-NH₃-NH₄⁺-nss-Ca²⁺ system. The alkali-rich distribution was especially pronounced in the winter, but the acid-related species was comparable to the alkali-related species in the summer, which was attributed to the larger H⁺ deposition by precipitation in the summer. This study can provide a methodology to associate survey results obtained by a filter-pack method with those of precipitation.     

引入最优自适应因子的状态模型法航空重力测量

引入最优自适应比例因子以改善状态模型法航空重力测量的精度,并尝试将其应用到我国困难地区的重力测量.把重力扰动当作状态量引入Kalman滤波进行最优估计,并引入最优自适应因子调节状态信息的权阵,提高重力扰动的最终解算精度.利用新疆地区不同航次和航高的实测数据,计算了垂直向下方向上的重力扰动.与全球重力场模型EGM2008的对比分析表明,差值中误差在10mGal左右,接近国家在困难地区重力测量精度的限差要求.     

Unscented Kalman Filter trained neural networks based rudder roll stabilization system for ship in waves

The large roll motion of ships sailing in the seaway is undesirable because it may lead to the seasickness of crew and unsafety of vessels and cargoes, thus it needs to be reduced. The aim of this study is to design a rudder roll stabilization system based on Radial Basis Function Neural Network (RBFNN) control algorithm for ship advancing in the seaway only through rudder actions. In the proposed stabilization system, the course keeping controller and the roll damping controller were accomplished by utilizing modified Unscented Kalman Filter (UKF) training algorithm, and implemented in parallel to maintain the orientation and reduce roll motion simultaneously. The nonlinear mathematical model, which includes manoeuvring characteristics and wave disturbances, was adopted to analyse ship’s responses. Various sailing states and the external wave disturbances were considered to validate the performance and robustness of the proposed roll stabilizer. The results indicate that the designed control system performs better than the Back Propagation (BP) neural networks based control system and conventional Proportional-Derivative (PD) based control system in terms of reducing roll motion for ship in waves.     

地球系统模式FIO-ESM对2016-2017年La Ni(n)a事件及其对中国近海地区影响的预测

最近的观测表明赤道太平洋中部及东部的水温略低于拉尼娜事件的阈值,但大气与海洋的状态还不足以完全支持转为弱拉尼娜现象.本研究基于地球系统模式FIO-ESM和集合调整卡尔曼滤波同化方案建立的短期气候同化和预测系统,进行了1992-01-01-2016-10-31的模式同化,结果表明同化系统能够为预测提供较好的初始场.随后对2016-2017年拉尼娜事件的状态以及中国近海地区气温和降水异常进行了未来6个月的预测,结果表明赤道太平洋会在2016年年底继续降温,Ni(n)o3.4区海温异常将持续略低于拉尼娜事件的阈值-0.5 ℃,说明2016-2017年为弱拉尼娜事件,2017年春季东太平洋继续降温,表明此次拉尼娜事件可能会持续较长时间.预测结果同时也表明2016年冬季至2017年春季中国近海地区存在着北高南低的气温异常分布,中国南部地区降水存在负异常.拉尼娜带来的极端天气与气候异常会对中国沿岸地区带来巨大影响,但总体来说2016-2017年拉尼娜事件对中国的影响相对较弱.     

数字滤波初始化(DFI)在高水平分辨率模式中的应用

数字滤波初始化应用的前提假设之一是不平衡重力惯性波在频率谱上与有天气意义的波动不重叠。随着水平分辨率的不断提高,数值模式能够解析的重力波具有波长更短、频率更高的性质,模式中有天气意义的波动与高频噪声的界线更加模糊,因此,数字滤波初始化在高分辨率模式中的应用性能尚未明确。以一次深对流过程为例,在3 km水平分辨率条件下采用不同滤波参数配置进行数字滤波初始化试验,试图对高水平分辨率预报初始场中高频噪声特征、如何区分具有天气意义的高频信号和虚假噪声、数字滤波初始化在数值预报模式(WRF)高水平分辨率条件下的应用性能等一系列相关问题给出初步的认识。试验结果表明,在针对高分辨率模式进行数字滤波初始化时,原有应用于低分辨率模式的初始化配置参数已不适用,必须根据高分辨率噪声的特点重新设计相应的滤波方案。即使在"无噪声"状态下,有天气意义的信息在模式中也具有与虚假高频噪声一致的表现形式,即大的地面气压变化倾向。数字滤波初始化的确能对特定切断频率的高频振荡进行滤波,但无法区分其性质究竟是具有天气意义的高频信号,还仅仅只是虚假的高频噪声。如果要达到滤除初始场中高频噪声的理想效果、并且消除积分过程中的高频振荡,必须延长滤波时间窗,但这是以增加额外的积分时间、以及有可能损害气象上有意义的波动为代价的。滤波效果越显著,滤波结果对初始场的改变越大,这两个方面实质上是有冲突的,因此,在应用数字滤波初始化时必须在滤波效果可接受和保证预报性能两方面之间进行妥协。最后就数字滤波初始化在高分辨率有限区域数值模式中的可能应用提出了具体的建议。     

基于UKF的GPS非线性动态滤波算法

介绍了一种Unscented卡尔曼滤波算法，它通过确定性采样获得一组采样点，可获得更多的观测假设，对系统状态统计特性的估计更加准确，同时该算法无需对系统方程进行线性化，避免了传统的EKF算法由于线性化引入的误差。本文将UKF算法用于GPS非线性动态滤波技术中，建立了仿真模型并定义了仿真条件，与EKF算法的仿真结果相比，在系统状态统计特性未知的情况下，UKF算法对系统状态的估计更准确，定位精度更高。