TITLE

Ensemble Data Assimilation without Perturbed Observations

AUTHOR(S)
Whitaker, Jeffrey S.; Hamill, Thomas M.
PUB. DATE
July 2002
SOURCE
Monthly Weather Review;Jul2002, Vol. 130 Issue 7, p1913
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
The ensemble Kalman filter (EnKF) is a data assimilation scheme based on the traditional Kalman filter update equation. An ensemble of forecasts are used to estimate the background-error covariances needed to compute the Kalman gain. It is known that if the same observations and the same gain are used to update each member of the ensemble, the ensemble will systematically underestimate analysis-error covariances. This will cause a degradation of subsequent analyses and may lead to filter divergence. For large ensembles, it is known that this problem can be alleviated by treating the observations as random variables, adding random perturbations to them with the correct statistics. Two important consequences of sampling error in the estimate of analysis-error covariances in the EnKF are discussed here. The first results from the analysis-error covariance being a nonlinear function of the background-error covariance in the Kalman filter. Due to this nonlinearity, analysis-error covariance estimates may be negatively biased, even if the ensemble background-error covariance estimates are unbiased. This problem must be dealt with in any Kalman filter–based ensemble data assimilation scheme. A second consequence of sampling error is particular to schemes like the EnKF that use perturbed observations. While this procedure gives asymptotically correct analysis-error covariance estimates for large ensembles, the addition of perturbed observations adds an additional source of sampling error related to the estimation of the observation-error covariances. In addition to reducing the accuracy of the analysis-error covariance estimate, this extra source of sampling error increases the probability that the analysis-error covariance will be underestimated. Because of this, ensemble data assimilation methods that use perturbed observations are expected to be less accurate than those which do not. Several ensemble filter formulations have recently been proposed that do not...
ACCESSION #
6806564

 

Related Articles

  • A Partitioned Kalman Filter and Smoother. Fukumori, Ichiro // Monthly Weather Review;May2002, Vol. 130 Issue 5, p1370 

    A new approach is advanced for approximating Kalman filtering and smoothing suitable for oceanic and atmospheric data assimilation. The method solves the larger estimation problem by partitioning it into a series of smaller calculations. Errors with small correlation distances are derived by...

  • Robust Adaptive Filter for Small Satellite Attitude Estimation Based on Magnetometer and Gyro. Zhankui Zeng; Shijie Zhang; Yanjun Xing; Xibin Cao // Abstract & Applied Analysis;2014, p1 

    Based on magnetometer and gyro measurement, a sequential scheme is proposed to determine the orbit and attitude of small satellite simultaneously. In order to reduce the impact of orbital errors on attitude estimation, a robust adaptive Kalman filter is developed. It uses a scale factor and an...

  • Comparing Solution Methods for Dynamic Equilibrium Economies. Bauer, Andy; Halton, Nicolas; Rubio-Ramírez, Juan Francisco // Working Paper Series (Federal Reserve Bank of Atlanta);Dec2003, Vol. 2003 Issue 32, p1 

    This paper shows how to use the Kalman filter (Kalman 1960) to back out the shocks of a dynamic stochastic general equilibrium model. In particular, we use the smoothing algorithm as described in Hamilton (1994) to estimate the shocks of a sticky-prices and sticky-wages model using all the...

  • Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood. Fernández-Villaverde, Jesús; Rubio-Ramírez, Juan Francisco // Working Paper Series (Federal Reserve Bank of Atlanta);Feb2004, Vol. 2004 Issue 3, p1 

    This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a sequential Monte Carlo filter proposed by Fernández­Villaverde and Rubio-Ramírez (2004) and the Kalman filter. The sequential Monte Carlo filter exploits the nonlinear...

  • Examination of Analysis and Forecast Errors of High-Resolution Assimilation, Bias Removal, and Digital Filter Initialization with an Ensemble Kalman Filter. Ancell, Brian C. // Monthly Weather Review;Dec2012, Vol. 140 Issue 12, p3992 

    Mesoscale atmospheric data assimilation is becoming an integral part of numerical weather prediction. Modern computational resources now allow assimilation and subsequent forecasting experiments ranging from resolutions of tens of kilometers over regional domains to smaller grids that employ...

  • A novel adaptive traffic prediction AQM algorithm. Na, Zhenyu; Guo, Qing; Gao, Zihe; Zhen, Jiaqi; Wang, Changyu // Telecommunication Systems;Jan2012, Vol. 49 Issue 1, p149 

    In the Internet, network congestion is becoming an intractable problem. Congestion results in longer delay, drastic jitter and excessive packet losses. As a result, quality of service (QoS) of networks deteriorates, and then the quality of experience (QoE) perceived by end users will not be...

  • Fuzzy Digital Filtering: Signal Interpretation. Infante, Juan Carlos García; Juárez, José de Jesús Medel; García, Juan Carlos Sánchez // International Journal of Communications, Network & System Scienc;May2011, Vol. 4 Issue 5, p297 

    The paper makes a description of the fuzzy filter properties considering its operational principles. A digital filter interacts with a reference model signal into real process in order to get the best corresponding answer, having the minimum error at the filter output using the mean square...

  • Applications of wavelet transform to quantum cascade laser spectrometer for atmospheric trace gas measurements. Li, Jingsong; Parchatka, Uwe; Fischer, Horst // Applied Physics B: Lasers & Optics;Sep2012, Vol. 108 Issue 4, p951 

    The application of wavelet transform-based digital filter to quantum cascade laser spectroscopy was investigated by its application to simulated spectra and experimental results obtained with our novel and compact QCL spectrometer, which offers the potential for high sensitive and selective...

  • Application of Genetic Algorithm to Tuning a PID Controller for Glucose Concentration Control. SLAVOV, TSONIO; ROEVA, OLYMPIA // WSEAS Transaction on Systems;Jul2012, Vol. 11 Issue 7, p223 

    The paper presents a feedforward feedback (PID) controller designed for control of glucose concentration during the E. coli fed-batch cultivation process. The controller is used to control the feed rate and to maintain glucose concentration at a desired set point. Taking into account the...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics