Adaptive observations in ensemble data assimilation

نویسنده

  • Bahri Uzunoglu
چکیده

An important question in ensemble based data assimilation scheme is how to configure our observations to correctly capture the important features in either our atmospheric or oceanic models given a set of ensembles. In this paper a systematic approach for effective sensor placement is formulated to evaluate how to target our observations. This method is based on a criterion of Shannon information entropy and condition number of our ensemble subspace covariance matrix yielding adaptive observation configurations. The theory behind this method is presented as well as an example illustrated with a global shallow water equations model on the sphere. 2007 Elsevier B.V. All rights reserved.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Adaptive Observation Strategies with the Local Ensemble Transform Kalman Filter

Adaptive observation strategies (AOS) aim to improve forecasts by adding additional observations at a few locations that have no standard observations. Lorenz and Emanuel (1998) designed experiments to evaluate different adaptive strategies with Lorenz 40-variable model. Routine observations are observed over “land” (grid points from 21 to 40) every 6 hours. One adaptive point is chosen from on...

متن کامل

Using Improved Background-Error Covariances from an Ensemble Kalman Filter for Adaptive Observations

A method for determining adaptive observation locations is demonstrated. This method is based on optimal estimation (Kalman filter) theory; it determines the observation location that will maximize the expected improvement, which can be measured in terms of the expected reduction in analysis or forecast variance. This technique requires an accurate model for background error statistics that var...

متن کامل

Highly-Scalable Algorithms for Ensemble Data Assimilation

A complete ensemble data assimilation algorithm can be composed into three basic computational tasks: advancing an ensemble of model forecasts, computing ensembles of forward operators for available observations, and assimilating the observations to modify the ensemble of model states. Each of these tasks requires a fundamentally different pattern of communication when implemented on current ge...

متن کامل

Adaptive Sampling with the Ensemble Transform Kalman Filter. Part II: Field Program Implementation

The practical application of the ensemble transform Kalman filter (ET KF), used in recent Winter Storm Reconnaissance (WSR) programs by the National Centers for Environmental Prediction (NCEP), is described. The ET KF assesses the value of targeted observations taken at future times in improving forecasts for preselected critical events. It is based on a serial assimilation framework that makes...

متن کامل

Preventing catastrophic filter divergence using adaptive additive inflation

Ensemble based filtering or data assimilation methods have proved to be indispensable tools in atmosphere and ocean science as they allow computationally cheap, low dimensional ensemble state approximation for extremely high dimensional turbulent dynamical systems. For sparse, accurate and infrequent observations, which are typical in data assimilation of geophysical systems, ensemble filtering...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2007