An analysis of the error space of a high-resolution implementation of the GHER hydrodynamic model in the Mediterranean Sea

An ensemble of 250 model setups covering the Mediterranean Sea is built by perturbing various parameters: the bathymetry, the initial conditions, atmospheric forcing fields (air temperature, cloud coverage, wind), and internal model parameters (diffusion coefficients). The ensemble is then forwarded in time using the GHER hydrodynamic model, allowing to obtain information about the expected error associated with the forecast in a natural way. The evolution of this error is analyzed. In particular, we examine the time evolution and stationarity of its spatial average, and the spatial distribution of the error at different instants, by means of its first to fourth order moments, and of empirical orthogonal functions. We verify whether the a posteriori error distribution is Gaussian using the Anderson-Darling test. From these results, we are able to assess what parameters and forcing fields are most critical for the forecast. Qualitative conclusions are obtained throughout the text, in accordance with our expectations. Moreover, quantitative estimations of the expected error are also given.     

The representer method for two-phase flow in porous media

The representer method is applied to a one-dimensional two-phase flow model in porous media; capillary pressure and gravity are neglected. The Euler–Lagrange equations must be linearized, and one such linearization is presented here. The representer method is applied to the linear system iteratively until convergence, though a rigorous proof of convergence is out of reach. The linearization chosen is easy to calculate but does not converge for certain weights; however, a simple damping restores convergence at the cost of extra iterations. Numerical experiments are performed that illustrate the method, and quick comparison to the ensemble Kalman smoother is made. This research was supported by NSF grant EIA-0121523.     

一种汛期降水分布的客观集成预报方法

该文应用么枕生提出的中心聚类方法确定每个台站所能代表的站 ,在这些台站上对降水距平百分率进行平均 .这种平均对每个代表站逐站进行 ,由此得出的降水分布作为集成预报的对象 .应用最小预测误差平方和逐步算法逐站建立集成预报方程 .应用这一方法对在国家气候中心汛期预报中使用多年的 4个旱涝预报模型进行了集成预报试验 ,然后对每张预报图进行评分 .结果说明集成预报的评分高于 4个原始预报模型的评分 .对于 1 998年长江中下游和嫩江流域的异常洪涝 ,集成预报的评分亦高于 4个原始预报模型的评分     

热带气旋路径动力释用预报的集合预报方案

考虑到热带气旋中心定位的不确定以及热带气旋（以下简称ＴＣ）路径动力释用预报对ＴＣ中心位置初值的敏感性,本文以ＴＣ路径动力释用预报方案为基础,应用集合预报原理构造了具有５０个成员的集合预报试验方案,经１９９７和１９９８年１５个ＴＣ的１１８次试验对比,结果表明,５０个成员的集合预报方案２的改进效果较好,其平均距离误差较业务预报的平均距离误差有较显著的减小。集合预报原理应用于ＴＣ路径动力释用预报的可行性     

The Role of Changes in the Annual Cycle in Earlier Onset of Climatic Spring in Northern China

Climatic changes in the onset of spring in northern China associated with changes in the annual cycle and with a recent warming trend were quantified using a recently developed adaptive data analysis tool, the Ensemble Empirical Mode Decomposition. The study was based on a homogenized daily surface air temperature (SAT) dataset for the period 1955–2003. The annual cycle here is referred to as a refined modulated annual cycle (MAC). The results show that spring at Beijing has arrived significantly earlier by about 2.98 d (10 yr)-1, of which about 1.85 d (10 yr)-1 is due to changes in the annual cycle and 1.13 d (10 yr)-1 due to the long-term warming trend. Variations in the MAC component explain about 92.5% of the total variance in the Beijing daily SAT series and could cause as much as a 20-day shift in the onset of spring from one year to another. The onset of spring has been advancing all over northern China, but more significant in the east than in the west part of the region. These differences are somehow unexplainable by the zonal pattern of the warming trend over the whole region, but can be explained by opposite changes in the spring phase of the MAC, i.e. advancing in the east while delaying in the west. In the east of northern China, the change in the spring phase of MAC explains 40%–60% of the spring onset trend and is attributable to a weakening Asian winter monsoon. The average sea level pressure in Siberia (55°–80°N, 50°–110°E), an index of the strength of the winter monsoon, could serve as a potential short-term predictor for the onset of spring in the east of northern China.     

非线性滤波在含“开关”过程的资料同化中的应用研究

利用一个描述实际数值天气预报模式中比湿在单格线上随时间发展的偏微分方程作为控制方程,研究分析了非线性滤波方法在含开关过程的资料同化中的有效性和可行性。首先在贝叶斯理论框架下,讨论了一般情形的非线性滤波方法,然后对基于粒子滤波(PF)和基于集合卡尔曼滤波(EnKF)的两种同化方法进行对比,由于EnKF是通过对集合成员的统计分析得到的误差分布的一阶矩和二阶矩来近似真实误差分布的,所以当用高斯分布近似真实误差分布所产生的误差较大时,基于EnKF的同化方法得到的结果也会有较大的误差。最后分别从观测算子为线性和非线性、观测误差为高斯型和非高斯型4种情形进行数值试验,结果显示当观测误差为高斯型时,无论观测算子为线性还是非线性,基于PF和基于EnKF的同化方法都能克服由开关过程给资料同化带来的困难,给出满意的同化结果;而当观测误差为非高斯型时,EnKF出现滤波不稳定,产生了非理想的同化结果,但PF方法仍然能够有效地发挥作用,给出满意的同化结果。     

基于离散余弦变换的集合Kalman滤波方法对河流相油藏渗透率场的反演

针对河流相油藏参数场的非高斯性及油藏生产数据与地质模型之间的非线性关系,提出了耦合离散余弦变换的半迭代集合Kalman滤波方法对河流相油藏进行生产历史拟合及反演.将河流相油藏非高斯渗透率场利用离散余弦变换变为类似高斯分布的DCT(Discrete cosine transform)系数,利用能量压缩特性对DCT系数进行...     

An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering

The ensemble Kalman filter (EnKF) is a commonly used real-time data assimilation algorithm in various disciplines. Here, the EnKF is applied, in a hydrogeological context, to condition log-conductivity realizations on log-conductivity and transient piezometric head data. In this case, the state vector is made up of log-conductivities and piezometric heads over a discretized aquifer domain, the forecast model is a groundwater flow numerical model, and the transient piezometric head data are sequentially assimilated to update the state vector. It is well known that all Kalman filters perform optimally for linear forecast models and a multiGaussian-distributed state vector. Of the different Kalman filters, the EnKF provides a robust solution to address non-linearities; however, it does not handle well non-Gaussian state-vector distributions. In the standard EnKF, as time passes and more state observations are assimilated, the distributions become closer to Gaussian, even if the initial ones are clearly non-Gaussian. A new method is proposed that transforms the original state vector into a new vector that is univariate Gaussian at all times. Back transforming the vector after the filtering ensures that the initial non-Gaussian univariate distributions of the state-vector components are preserved throughout. The proposed method is based in normal-score transforming each variable for all locations and all time steps. This new method, termed the normal-score ensemble Kalman filter (NS-EnKF), is demonstrated in a synthetic bimodal aquifer resembling a fluvial deposit, and it is compared to the standard EnKF. The proposed method performs better than the standard EnKF in all aspects analyzed (log-conductivity characterization and flow and transport predictions).     

集合预报误差在GRAPES全球四维变分同化中的应用研究Ⅰ：局地化方案设计与动力平衡特征分析

通过引入流依赖的集合预报误差,使得同化分析与天气形势紧密相关,是改善初值分析质量的重要途径。文中在GRAPES（Global Regional Assimilation and PrEdiction System）全球四维变分资料同化（4DVar）中研究了如何有效应用集合预报误差,包括增加扩展控制变量时如何降低其计算消耗以及如何在局地化过程中保持不同变量之间的动力平衡。利用高斯分布的谱滤波实现水平局地化,利用垂直正交经验函数分解实现垂直局地化,并采用前8个主导特征模态来限制控制变量空间维数增加。引入20至180个集合样本,在水平二维局地化情形下,控制变量总数的增长可以限制在1.1—1.8倍,而在三维局地化情形下,控制变量总数的增长限制在1.7—7.1倍。对60个集合样本和1°水平分辨率内循环,4DVar引入扩展控制变量后墙钟时间增加了约30%。进一步,通过采用在非平衡分析变量上进行水平局地化,然后再将风压地转平衡关系重新叠加到非平衡分析变量上,使得分析更好地保持了风压平衡关系,初始场地面气压倾向变化减小。此外,虽然垂直局地化对分析平衡影响较大,但依靠目标函数中的数字滤波弱约束,分析变量之间仍能较好满足动力平衡关系。结果表明,GRAPES全球4DVar中发展的增加扩展控制变量、谱滤波实现水平局地化、非平衡分析变量进行水平局地化等有效应用集合预报误差的方法,适合集合样本数超过100个的情况,在分析质量改善的同时,4DVar系统的计算和存储消耗没有显著增加。      

A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America

Abstract

It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations. To better manage the natural resources, and to better understand the closely linked Earth systems, the concept of Digital Earth has been strongly promoted since US Vice President Al Gore's speech in 1998. One core element of Digital Earth is the use and integration of remote sensing data. Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use, but also to detect more subtle changes related for instance to climate change. To uncover global change effects on vegetation activity and phenology, it is important to establish high quality time series characterizing the past situation against which the current state can be compared. With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution. Particular emphasis was put on noise removal. Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale. Without temporal smoothing, subtle spatio-temporal patterns in vegetation composition, density and phenology would be hidden by atmospheric noise and undetected clouds. Such noise is immanent in data that have undergone solely a maximum value compositing. Within the present study, the Whittaker smoother (WS) was applied to a SPOT VGT time series. The WS balances the fidelity to the observations with the roughness of the smoothed curve. The algorithm is extremely fast, gives continuous control over smoothness with only one parameter, and interpolates automatically. The filtering efficiently removed the negatively biased noise present in the original data, while preserving the overall shape of the curves showing vegetation growth and development. Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data. Analysis of the data also revealed spatially consistent key phenological markers. Extracted seasonality parameters followed a clear meridional trend. Compared to the unfiltered data, the filtered time series increased the separability of various land cover classes. It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.