检索结果-内蒙古大学图书馆

Model and observation-error covariance matrix information in the physical nudging equations

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2025年第0期

作者： Conti, Giovanni van leeuwen, peter jan Anderson, Jeffrey CMCC Fdn Euromediterranean Ctr Climate Change I-40127 Bologna Italy Colorado State Univ Dept Atmospher Sci Ft Collins CO USA NSF Natl Ctr Atmospher Res NSF NCAR Boulder CO USA

In this work we show how to extend the deterministic physical nudging scheme in order to include two important ingredients, the model and observation-error covariance matrices, which are common features of classical data-assimilation schemes. The method exploits the relation between a stochastic differential equation and the evolution of its probability density via the Fokker-Planck equation. Observations are introduced by evolving the posterior probability density backward in time to obtain a so-called smoother. To obtain a computationally feasible scheme, we used the small-time approximation, resulting in an efficient nudging scheme built from first principles. We explored the capabilities of this new nudging method with the low-dimensional Lorenz 1963 model and a surface quasi-geostrophic turbulence model on a 128x128$$ 128\times 128 $$ grid, with many degrees of freedom. We show that the new method is more accurate than a 3DVar at similar computational cost, and is accurate and easy to implement in the high-dimensional system. The new scheme has the potential to be used in extremely high-dimensional systems, because ensemble integrations and adjoint models are avoided.

关键词： data assimilation Fokker-Planck udging optimal-nudging statistical physics

来源：评论

学校读者我要写书评

暂无评论

Online state and time-varying parameter estimation using the implicit equal-weights particle filter

引用

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2024年第761期150卷 2087-2103页

作者： Satoh, Mineto van leeuwen, peter jan Nakano, Shin'ya SOKENDAI Grad Inst Adv Studies Tachikawa Japan NEC Corp Ltd Data Sci Labs Kawasaki Japan Univ Reading Dept Meteorol Reading England Univ Reading Natl Ctr Earth Observat Reading England Colorado State Univ Dept Atmospher Sci Ft Collins CO USA Res Org Informat & Syst Inst Stat Math Tachikawa Japan SOKENDAI Grad Inst Adv Studies Tachikawa 1908562 Japan

A method is proposed for resilient and efficient estimation of the states and time-varying parameters in nonlinear high-dimensional systems through a sequential data assimilation process. The importance of estimating time-varying parameters lies not only in improving prediction accuracy but also in determining when model characteristics change. We propose a particle-filter-based method that incorporates nudging techniques inspired by optimization algorithms in machine learning by taking advantage of the flexibility of the proposal density in particle filtering. However, as the model resolution and number of observations increase, filter degeneracy tends to be the obstacle to implementing the particle filter. Therefore, this proposed method is combined with the implicit equal-weights particle filter (IEWPF), in which all particle weights are equal. The method is validated using the 1000-dimensional linear model with an additive parameter and the 1000-dimensional Lorenz-96 model, where the forcing term is parameterized. The method is shown to be capable of resilient and efficient parameter estimation for parameter changes over time in our application with a linear observation operator. This leads to the conjecture that it applies to realistic geophysical, climate, and other problems. We developed a state and parameter estimation method based on an implicit equal-weight particle filter, incorporating optimization-inspired nudging. The graphs compare the parameter trajectories of (a) the augmented method, (b) results with a correlated covariance matrix, and (c) results with nudging in addition to (b), using a 1000-dimensional Lorenz-96 model with parameterized forcing terms. The solid line (blue) represents each of the 20 ensemble members, and the dashed line (red) represents the true parameter values. The proposed method (c) avoids filter degeneracy and can track abrupt changes in all three parameters. image

关键词： data assimilation nondegeneracy parameter estimation particle filter

来源：评论

学校读者我要写书评

暂无评论

An Implementation of the Particle Flow Filter in an Atmospheric Model

引用

MONTHLY WEATHER REVIEW 2024年第10期152卷 2247-2264页

作者： Hu, Chih-Chi van leeuwen, peter jan Anderson, Jeffrey L. Colorado State Univ Ft Collins CO 80523 USA Univ Reading Whiteknights House Reading Berks England NSF Natl Ctr Atmospher Res Boulder CO 80305 USA

The particle flow filter (PFF) shows promise for fully nonlinear data assimilation (DA) in high-dimensional systems. However, its application in atmospheric models has been relatively unexplored. In this study, we develop a new algorithm, PFF-DART, in order to conduct DA for high-dimensional atmospheric models. PFF-DART combines the PFF and the two-step ensemble filtering algorithm in the Data Assimilation Research Testbed (DART), exploiting the highly parallel structure of DART. To evaluate the performance of PFF-DART, we conduct an observing system simulation experiment (OSSE) in a simplified atmospheric general circulation model and compare the performance of PFF-DART with an existing linear and Gaussian DA method. Using the PFF-DART algorithm, we demonstrate, for the first time, the capability of the PFF to yield stable results in a yearlong cycling DA OSSE. Moreover, PFF-DART retains the important ability of the PFF to improve the assimilation of nonlinear and non-Gaussian observations. Finally, we emphasize that PFF-DART is a versatile algorithm that can be integrated with numerous other non-Gaussian DA techniques. This quality makes it a promising method for further investigation within a more sophisticated numerical weather prediction model in the future.

关键词： Atmosphere Bayesian methods Kalman fi lters Numerical analysis/modeling Uncertainty Data assimilation

来源：评论

学校读者我要写书评

暂无评论

A non-parametric way to estimate observation errors based on ensemble innovations

引用

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2024年第761期150卷 2296-2315页

作者： Hu, Chih-Chi van leeuwen, peter jan Geer, Alan J. Colorado State Univ Dept Atmospher Sci Ft Collins CO USA Univ Reading Dept Meteorol Reading England European Ctr Medium Range Weather Forecasts Reading England Colorado State Univ Dept Atmospher Sci Ft Collins CO 80526 USA

Previous studies that inferred the observation error statistics from the innovation statistics can only provide the second moment of the error probability density function (pdf). However, the observation errors are sometimes non-Gaussian, for example, for observation operators with unknown representation errors, or for bounded observations. In this study, we propose a new method, the Deconvolution-based Observation Error Estimation (DOEE), to infer the full observation error pdf. DOEE does not rely on linear assumptions on the observation operator, the optimality of the data assimilation algorithm, or implicit Gaussian assumptions on the error pdf. The main assumption of DOEE is the availability of an ensemble of background forecasts following the independent and identically distributed (i.i.d.) assumption. We conduct idealized experiments to demonstrate the ability of the DOEE to accurately retrieve a non-Gaussian (bimodal, skewed, or bounded) observation error pdf. We then apply the DOEE to construct a state-dependent observation error model for satellite radiances by stratifying the observation errors based on cloud amount. In general, we find that the observation error pdfs in many categories are skewed. By adding a new predictor, total column water vapor (TCWV), into the state-dependent model, we find that for cloudy pixels, when TCWV is small, the observation error pdfs are quite similar and Gaussian-like, whereas when TCWV is large, the observation error pdfs differ for different cloud amount, while all of them are positively biased. This result suggests that exploring other predictors, like cloud type, might improve the stratification of the observation error model. We also discuss ways to include a non-parametric observation error pdf into modern data assimilation schemes, including a consideration of the strong-constraint 4D-Var perspective, as well as the implications for other observation types including observations with bounded range. We propose a new

关键词： Bounded observation data assimilation non-Gaussian distribution nonlinear observation observation error satellite radiance state-dependent observation error

来源：评论

学校读者我要写书评

暂无评论

Iterative Ensemble Smoothers for Data Assimilation in Coupled Nonlinear Multiscale Models

引用

Monthly Weather Review 2024年第6期152卷 1277-1301页

作者： Evensen, Geir Vossepoel, Femke C. van leeuwen, peter jan Bergen Norway Nansen Environmental and Remote Sensing Center Bergen Norway Department of Geoscience and Engineering Delft University of Technology Delft Netherlands Department of Atmospheric Sciences Colorado State University Fort CollinsCO United States

This paper identifies and explains particular differences and properties of adjoint-free iterative ensemble methods initially developed for parameter estimation in petroleum models. The aim is to demonstrate the methods' potential for sequential data assimilation in coupled and multiscale unstable dynamical systems. For this study, we have introduced a new nonlinear and coupled multiscale model based on two Kuramoto-Sivashinsky equations operating on different scales where a coupling term relaxes the two model variables toward each other. This model provides a convenient testbed for studying data assimilation in highly nonlinear and coupled multiscale systems. We show that the model coupling leads to cross covariance between the two models' variables, allowing for a combined update of both models. The measurements of one model's variable will also influence the other and contribute to a more consistent estimate. Second, the new model allows us to examine the properties of iterative ensemble smoothers and assimilation updates over finitelength assimilation windows. We discuss the impact of varying the assimilation windows' length relative to the model's predictability time scale. Furthermore, we show that iterative ensemble smoothers significantly improve the solution's accuracy compared to the standard ensemble Kalman filter update. Results and discussion provide an enhanced understanding of the ensemble methods' potential implementation and use in operational weather- and climate-prediction systems. © 2024 American Meteorological Society.

关键词： Kalman filters

来源：评论

学校读者我要写书评

暂无评论

Dynamics and Predictability of Tropical Cyclone Rapid Intensification in Ensemble Simulations of Hurricane Patricia (2015)

引用

JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 2022年第8期127卷 e2021JD036079-e2021JD036079页

作者： Tao, Dandan van leeuwen, peter jan Bell, Michael Ying, Yue Colorado State Univ Dept Atmospher Sci Ft Collins CO 80523 USA Natl Ctr Atmospher Res Adv Study Program POB 3000 Boulder CO 80307 USA

Hurricane Patricia (2015) over the eastern Pacific was a record-breaking tropical cyclone (TC) under a very favorable environment during its rapid intensification (RI) period, which makes it an optimal real case for studying RI dynamics and predictability. In this study, we performed ensemble Kalman filter analyses at Patricia's early development stage using both traditional observations and the Office of Naval Research Tropical Cyclone Intensity (TCI) field campaign data. It is shown that assimilating the inner-core TCI observations produces a stronger initial vortex and significantly improves the prediction of RI. Analysis of observation sensitivity experiments shows that the deep-layer dropsonde observations have high impact on both the primary and secondary circulations for the entire troposphere while the radar observations have the most impact on the primary circulations near aircraft flight level. A wide range of intensification scenarios are obtained through two sets of ensemble forecasts initialized with and without assimilating the TCI data prior to the RI onset. Verification of the ensemble forecasts against the TCI observations during the RI period shows that forecast errors toward later stages can originate from two different error sources at early stages of the vortex structure: One is a timing error from a delayed vortex development such that the TC evolution is the same but shifted in time;the other is due to a totally different storm such that there is no moment in time the simulated storm can obtain a correct TC structure.

关键词： tropical cyclone rapid intensification radial structure angular momentum

来源：评论

学校读者我要写书评

暂无评论

A particle flow filter for high-dimensional system applications

引用

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2021年第737期147卷 2352-2374页

作者： Hu, Chih-Chi van leeuwen, peter jan Colorado State Univ Dept Atmospher Sci Ft Collins CO 80526 USA Univ Reading Dept Meteorol Reading Berks England

A novel particle filter proposed recently, the particle flow filter (PFF), avoids the long-existing weight degeneracy problem in particle filters and, therefore, has great potential to be applied in high-dimensional systems. The PFF adopts the idea of a particle flow, which sequentially pushes the particles from the prior to the posterior distribution, without changing the weight of each particle. The essence of the PFF is that it assumes the particle flow is embedded in a reproducing kernel Hilbert space, so that a practical solution for the particle flow is obtained. The particle flow is independent of the choice of kernel in the limit of an infinite number of particles. Given a finite number of particles, we have found that a scalar kernel fails in high-dimensional and sparsely observed settings. A new matrix-valued kernel is proposed that prevents the collapse of the marginal distribution of observed variables in a high-dimensional system. The performance of the PFF is tested and compared with a well-tuned local ensemble transform Kalman filter (LETKF) using the 1,000-dimensional Lorenz 96 model. It is shown that the PFF is comparable to the LETKF for linear observations, except that explicit covariance inflation is not necessary for the PFF. For nonlinear observations, the PFF outperforms LETKF and is able to capture the multimodal likelihood behavior, demonstrating that the PFF is a viable path to fully nonlinear geophysical data assimilation.

关键词： high‐ dimensional system kernel embedding non‐ Gaussian distribution nonlinear data assimilation particle filters particle flows

来源：评论

学校读者我要写书评

暂无评论

Effects of mis-specified time-correlated model error in the (ensemble) Kalman Smoother

引用

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2021年第734期147卷 573-588页

作者： Ren, Haonan Amezcua, Javier van leeuwen, peter jan Univ Reading Dept Meteorol Reading RG6 6BB Berks England Colorado State Univ Dept Atmospher Sci Ft Collins CO 80523 USA

Data assimilation is often performed under the perfect model assumption. Although there is an increasing amount of research accounting for model errors in data assimilation, the impact of an incorrect specification of the model errors on the data assimilation results has not been thoroughly assessed. We investigate the effect that an inaccurate time correlation in the model error description can have on data assimilation results, deriving analytical results using a Kalman Smoother for a one-dimensional system. The analytical results are evaluated numerically to generate useful illustrations. For a higher-dimensional system, we use an ensemble Kalman Smoother. Strong dependence on observation density is found. For a single observation at the end of the window, the posterior variance is a concave function of the guessed decorrelation time-scale used in the data assimilation process. This is due to an increasing prior variance with that time-scale, combined with a decreasing tendency from larger observation influence. With an increasing number of observations, the posterior variance decreases with increasing guessed decorrelation time-scale because the prior variance effect becomes less important. On the other hand, the posterior mean-square error has a convex shape as a function of the guessed time-scale with a minimum where the guessed time-scale is equal to the real decorrelation time-scale. With more observations, the impact of the difference between two decorrelation time-scales on the posterior mean-square error reduces. Furthermore, we show that the correct model error decorrelation time-scale can be estimated over several time windows using state augmentation in the ensemble Kalman Smoother. Since model errors are significant and significantly time correlated in real geophysical systems such as the atmosphere, this contribution opens up a next step in improving prediction of these systems.

关键词： data assimilation linear model model error temporal auto‐ correlation

来源：评论

学校读者我要写书评

暂无评论

Model Error Estimation Using the Expectation Maximization Algorithm and a Particle Flow Filter

引用

SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION 2021年第2期9卷 681-707页

作者： Magdalena Lucini, Maria van leeuwen, peter jan Pulido, Manuel UNNE CONICET RA-3400 Corrientes Argentina Univ Reading Dept Meteorol Reading RG6 6AH Berks England Colorado State Univ Dept Atmospher Sci Ft Collins CO 80523 USA

Model error covariances play a central role in the performance of data assimilation methods applied to nonlinear state-space models. However, these covariances are largely unknown in most of the applications. A misspecification of the model error covariance has a strong impact on the computation of the posterior probability density function, leading to unreliable estimations and even to a total failure of the assimilation procedure. In this work, we propose the combination of the expectation maximization (EM) algorithm with an efficient particle filter to estimate the model error covariance using a batch of observations. Based on the EM algorithm principles, the proposed method encompasses two stages: the expectation stage, in which a particle filter is used with the present updated value of the model error covariance as given to find the probability density function that maximizes the likelihood, followed by a maximization stage, in which the expectation under the probability density function found in the expectation step is maximized as a function of the elements of the model error covariance. This novel algorithm here presented combines the EM algorithm with a fixed point algorithm and does not require a particle smoother to approximate the posterior densities. We demonstrate that the new method accurately and efficiently solves the linear model problem. Furthermore, for the chaotic nonlinear Lorenz-96 model the method is stable even for observation error covariance 10 times larger than the estimated model error covariance matrix and also is successful in moderately large dimensional situations where the dimension of the estimated matrix is 40 x 40.

关键词： particle filters state-space models model error covariance EM algorithm

来源：评论

学校读者我要写书评

暂无评论

Rain-Induced Stratification of the Equatorial Indian Ocean and Its Potential Feedback to the Atmosphere

引用

JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS 2022年第3期127卷 e2021JC018025-e2021JC018025页

作者： Shackelford, Kyle DeMott, Charlotte A. van leeuwen, peter jan Thompson, Elizabeth Hagos, Samson Colorado State Univ Dept Atmospher Sci Ft Collins CO 80523 USA Univ Reading Dept Meteorol Reading Berks England NOAA Phys Sci Lab Boulder CO USA Pacific Northwest Natl Lab Richland WA 99352 USA

Surface freshening through precipitation can act to stably stratify the upper ocean, forming a rain layer (RL). RLs inhibit subsurface vertical mixing, isolating deeper ocean layers from the atmosphere. This process has been studied using observations and idealized simulations. The present ocean modeling study builds upon this body of work by incorporating spatially resolved and realistic atmospheric forcing. Fine-scale observations of the upper ocean collected during the Dynamics of the Madden-Julian Oscillation field campaign are used to verify the General Ocean Turbulence Model (GOTM). Spatiotemporal characteristics of equatorial Indian Ocean RLs are then investigated by forcing a 2D array of GOTM columns with realistic and well-resolved output from an existing regional atmospheric simulation. RL influence on the ocean-atmosphere system is evaluated through analysis of RL-induced modification to surface fluxes and sea surface temperature (SST). This analysis demonstrates that RLs cool the ocean surface on time scales longer than the associated precipitation event. A second simulation with identical atmospheric forcing to that in the first, but with rainfall set to zero, is performed to investigate the role of rain temperature and salinity stratification in maintaining cold SST anomalies within RLs. Approximately one third, or 0.1 degrees C, of the SST reduction within RLs can be attributed to rain effects, while the remainder is attributed to changes in atmospheric temperature and humidity. The prolonged RL-induced SST anomalies enhance SST gradients that have been shown to favor the initiation of atmospheric convection. These findings encourage continued research of RL feedbacks to the atmosphere.

关键词： Surface freshening air-sea interaction tropical convection ocean stratification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：