Exploring Assimilation Order to Improve Assimilation with Serial Ensemble Kalman Filter

2020 ◽  
pp. 2150068
Author(s):  
Juan Zhao ◽  
Chengwu Zhao ◽  
Hongze Leng ◽  
Junqiang Song ◽  
Zhilin Wang
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Localization Algorithm ◽  
Optimal Parameters ◽  
Forgetting Factor ◽  
Computation Cost ◽  
Localization Radius

Serial ensemble Kalman filter (EnKF) is a kind of EnKF which treats observations serially during every assimilation step. The assimilation order can be generated by different rules and has significant impacts on the performance of serial EnKF when localization algorithm is applied. In this study, we seek to examine and better understand the characteristics of various ordering methods when they are applied in the serial EnKF. The results show that different ordering methods demonstrate almost the same changes in analysis as the localization radius changing. Moreover, the optimal parameters of localization radius and forgetting factor of serial EnKF are found varying among different ordering rules. In addition, a novel rule for confirming the assimilation order is proposed to further improve the performance of serial EnKF. The observations are sorted from “better” to “worse” (OBS-BtoW), which are evaluated by estimating the distance of analysis between the prior and observations. Compared with the existing ordering methods, the proposed method can improve the performance at a very small computation cost without needing future forecasts and the truth.

scholarly journals On the Choice of an Optimal Localization Radius in Ensemble Kalman Filter Methods

2014 ◽  
Vol 142 (6) ◽  
pp. 2165-2175 ◽  
Author(s):  
Paul Kirchgessner ◽  
Lars Nerger ◽  
Angelika Bunse-Gerstner
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Degrees Of Freedom ◽  
Localization Length ◽  
Ensemble Size ◽  
Localization Radius ◽  
Filter Methods ◽  
Local Observation ◽  
Optimal Localization ◽  
Analysis Quality

Abstract In data assimilation applications using ensemble Kalman filter methods, localization is necessary to make the method work with high-dimensional geophysical models. For ensemble square root Kalman filters, domain localization (DL) and observation localization (OL) are commonly used. Depending on the localization method, appropriate values have to be chosen for the localization parameters, such as the localization length and the weight function. Although frequently used, the properties of the localization techniques are not fully investigated. Thus, up to now an optimal choice for these parameters is a priori unknown and they are generally found by expensive numerical experiments. In this study, the relationship between the localization length and the ensemble size in DL and OL is studied using twin experiments with the Lorenz-96 model and a two-dimensional shallow-water model. For both models, it is found that the optimal localization length for DL and OL depends linearly on an effective local observation dimension that is given by the sum of the observation weights. In the experiments no influence of the model dynamics on the optimal localization length was observed. The effective observation dimension defines the degrees of freedom that are required for assimilating observations, while the ensemble size defines the available degrees of freedom. Setting the localization radius such that the effective local observation dimension equals the ensemble size yields an adaptive localization radius. Its performance is tested using a global ocean model. The experiments show that the analysis quality using the adaptive localization is similar to the analysis quality of an optimally tuned constant localization radius.

scholarly journals Assessing the Impact of the Covariance Localization Radius when Assimilating Infrared Brightness Temperature Observations Using an Ensemble Kalman Filter

2012 ◽  
Vol 140 (2) ◽  
pp. 543-561 ◽  
Author(s):  
Jason A. Otkin
Keyword(s):  
Kalman Filter ◽  
Brightness Temperature ◽  
Ensemble Kalman Filter ◽  
Short Range ◽  
Infrared Observations ◽  
Localization Radius ◽  
Temperature Observations ◽  
The Impact ◽  
Infrared Brightness ◽  
Cloud Analysis

A regional-scale Observing System Simulation Experiment is used to examine how changes in the horizontal covariance localization radius employed during the assimilation of infrared brightness temperature observations in an ensemble Kalman filter assimilation system impacts the accuracy of atmospheric analyses and short-range model forecasts. The case study tracks the evolution of several extratropical weather systems that occurred across the contiguous United States during 7–8 January 2008. Overall, the results indicate that assimilating 8.5-μm brightness temperatures improves the cloud analysis and forecast accuracy, but has the tendency to degrade the water vapor mixing ratio and thermodynamic fields unless a small localization radius is used. Vertical cross sections showed that varying the localization radius had a minimal impact on the shape of the analysis increments; however, their magnitude consistently increased with increasing localization radius. By the end of the assimilation period, the moisture, temperature, cloud, and wind errors generally decreased with decreasing localization radius and became similar to the Control case in which only conventional observations were assimilated if the shortest localization radius was used. Short-range ensemble forecasts showed that the large positive impact of the infrared observations on the final cloud analysis diminished rapidly during the forecast period, which indicates that it is difficult to maintain beneficial changes to the cloud analysis if the moisture and thermodynamic forcing controlling the cloud evolution are not simultaneously improved. These results show that although assimilation of infrared observations consistently improves the cloud field regardless of the length of the localization radius, it may be necessary to use a smaller radius to also improve the accuracy of the moisture and thermodynamic fields.

scholarly journals Impact of MPEX Upsonde Observations on Ensemble Analyses and Forecasts of the 31 May 2013 Convective Event over Oklahoma

2016 ◽  
Vol 144 (8) ◽  
pp. 2889-2913 ◽  
Author(s):  
Stacey M. Hitchcock ◽  
Michael C. Coniglio ◽  
Kent H. Knopfmeier
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Mobile Systems ◽  
Observation Error ◽  
Ensemble Spread ◽  
Convective Event ◽  
Localization Radius ◽  
Standard Value ◽  
The Impact ◽  
Vertical Localization

Abstract This study examines the impact of assimilating three radiosonde profiles obtained from ground-based mobile systems during the Mesoscale Predictability Experiment (MPEX) on analyses and convection-permitting model forecasts of the 31 May 2013 convective event over Oklahoma. These radiosonde profiles (in addition to standard observations) are assimilated into a 36-member mesoscale ensemble using an ensemble Kalman filter (EnKF) before embedding a convection-permitting (3 km) grid and running a full ensemble of 9-h forecasts. This set of 3-km forecasts is compared to a control run that does not assimilate the MPEX soundings. The analysis of low- to midlevel moisture is impacted the most by the assimilation, but coherent mesoscale differences in temperature and wind are also seen, primarily downstream of the location of the soundings. The ensemble of forecasts of convection on the 3-km grid are improved the most in the first three hours of the forecast in a region where the analyzed position of low-level frontal convergence and midlevel moisture was improved on the mesoscale grid. Later forecasts of the upscale growth of intense convection over central Oklahoma are improved somewhat, but larger ensemble spread lowers confidence in the significance of the improvements. Changes in the horizontal localization radius from the standard value applied to the MPEX sounding assimilation alters the specific times that the forecasts are improved in the first three hours of the forecasts, while changes to the vertical localization radius and specified temperature and wind observation error result in little to no improvements in the forecasts.

Adaptive Localization for Tropical Cyclones With Satellite Radiances in an Ensemble Kalman Filter

2021 ◽  
Author(s):  
Chen Wang ◽  
Lili Lei ◽  
Zhe-Min Tan ◽  
Kekuan Chu
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Adaptive Method ◽  
State Variables ◽  
Localization Algorithm ◽  
Sampling Errors ◽  
Satellite Platform ◽  
Geophysical Application ◽  
Vertical Location ◽  
Radiance Data

<p>One important aspect of successfully implementing an ensemble Kalman filter (EnKF) in a high dimensional geophysical application is covariance localization. But for satellite radiances whose vertical locations are not well defined, covariance localization is not straightforward. The global group filter (GGF) is an adaptive localization algorithm, which can provide adaptively estimated localization parameters including the localization width and vertical location of observations for each channel and every satellite platform of radiance data, and for different regions and times. This adaptive method is based on sample correlations between ensemble priors of observations and state variables, aiming to minimize sampling errors of estimated sample correlations. The adaptively estimated localization parameters are examined here for typhoon Yutu (2018), using the regional model WRF and a cycling EnKF system. The benefits of differentiating the localization parameters for TC and non-TC regions and varying the localization parameters with time are investigated. Results from the 6-h priors verified relative to the conventional and radiance observations show that the adaptively estimated localization parameters generally produce smaller errors than the default Gaspari and Cohn (GC) localization. The adaptively estimated localization parameters better capture the onset of RI and yield improved intensity and structure forecasts for typhoon Yutu (2018) compared to the default GC localization. The time-varying localization parameters have slightly advantages over the time-constant localization parameters. Further improvements are achieved by differentiating the localization parameters for TC and non-TC regions.</p>

Benefits of smoothing backgrounds and radar reflectivity observations for multiscale data assimilation with an ensemble Kalman filter at convective scales: A proof of concept study

2022 ◽  
Author(s):  
Frédéric Fabry
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
System Simulation ◽  
Proof Of Concept ◽  
Single Cycle ◽  
Forecast Period ◽  
Simulation Experiments ◽  
Radar Observations ◽  
Localization Radius ◽  
Forecast Quality

Abstract In the ensemble Kalman filter (EnKF), the covariance localization radius is usually small when assimilating radar observations because of high density of the radar observations. This makes the region away from precipitation difficult to correct if no other observations are available, as there is no reason to correct the background. To correct errors away from the innovating radar observations, a multiscale localization (MLoc) method adapted to dense observations like those from radar is proposed. In this method, different scales are corrected successively by using the same reflectivity observations, but with different degree of smoothing and localization radius at each step. In the context of observing system simulation experiments, single and multiple assimilation experiments are conducted with the MLoc method. Results show that the MLoc assimilation updates areas that are away from the innovative observations and improves on average the analysis and forecast quality in single cycle and cycling assimilation experiments. The forecast gains are maintained until the end of the forecast period, illustrating the benefits of correcting different scales.

Ensemble Kalman Filter for Out of Sequence Measurement Tracking

2012 ◽  
Vol 132 (10) ◽  
pp. 1617-1625
Author(s):  
Sirichai Pornsarayouth ◽  
Masaki Yamakita
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter

Data assimilation with the weighted ensemble Kalman filter

2010 ◽  
Author(s):  
Nicolas Papadakis ◽  
Etienne Mémin ◽  
Anne Cuzol ◽  
Nicolas Gengembre
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter

scholarly journals Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

2021 ◽  
Vol 14 (6) ◽  
Author(s):  
Jinming Yang ◽  
Chengzhi Li
Keyword(s):  
Remote Sensing ◽  
Kalman Filter ◽  
Water Resource ◽  
Ensemble Kalman Filter ◽  
Snow Depth ◽  
Resource Assessment ◽  
Measured Data ◽  
Hydrological Process ◽  
Depth Data ◽  
Water Resource Assessment

AbstractSnow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.

scholarly journals Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter

2021 ◽  
Vol 11 (7) ◽  
pp. 2898
Author(s):  
Humberto C. Godinez ◽  
Esteban Rougier
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Failure Modes ◽  
Split Hopkinson Pressure Bar ◽  
Peak Stress ◽  
Discrete Element ◽  
Material Behavior ◽  
Model Parameters ◽  
Hopkinson Pressure Bar ◽  
Parameter Values

Simulation of fracture initiation, propagation, and arrest is a problem of interest for many applications in the scientific community. There are a number of numerical methods used for this purpose, and among the most widely accepted is the combined finite-discrete element method (FDEM). To model fracture with FDEM, material behavior is described by specifying a combination of elastic properties, strengths (in the normal and tangential directions), and energy dissipated in failure modes I and II, which are modeled by incorporating a parameterized softening curve defining a post-peak stress-displacement relationship unique to each material. In this work, we implement a data assimilation method to estimate key model parameter values with the objective of improving the calibration processes for FDEM fracture simulations. Specifically, we implement the ensemble Kalman filter assimilation method to the Hybrid Optimization Software Suite (HOSS), a FDEM-based code which was developed for the simulation of fracture and fragmentation behavior. We present a set of assimilation experiments to match the numerical results obtained for a Split Hopkinson Pressure Bar (SHPB) model with experimental observations for granite. We achieved this by calibrating a subset of model parameters. The results show a steady convergence of the assimilated parameter values towards observed time/stress curves from the SHPB observations. In particular, both tensile and shear strengths seem to be converging faster than the other parameters considered.

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

2020 ◽  
pp. 1-21
Author(s):  
Lanhua Hou ◽  
Xiaosu Xu ◽  
Yiqing Yao ◽  
Di Wang ◽  
Jinwu Tong
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Moving Average ◽  
Integrated System ◽  
Measurement Noise ◽  
Noise Estimation ◽  
Doppler Velocity ◽  
Forgetting Factor ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Abstract The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

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