Two-Stage Ensemble Kalman Filter Approach for Data Assimilation Applied to Flow in Fractured Media

Abstract. In this study we implement a data assimilation tool using a 3-D radiation belt model and an ensemble Kalman filter approach. High time and space reanalysis of the electron radiation belt fluxes is obtained over the time period 5 October to 25 October 1990 by combining sparse observations with the Salammbô 3-D model in an optimal way. The convergence of the ensemble Kalman filter is analyzed carefully. The risk of using a biased physical model is discussed and relative consequences are highlighted. Finally, a validation against CRRES data and major improvements compared to pure physics based model are presented.

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A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part II: application in a shallow-water model

Tellus A Dynamic Meteorology and Oceanography ◽

10.3402/tellusa.v64i0.18485 ◽

2012 ◽

Vol 64 (1) ◽

pp. 18485 ◽

Cited By ~ 9

Author(s):

Lili Lei ◽

David R. Stauffer ◽

Aijun Deng

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Shallow Water ◽

Ensemble Kalman Filter ◽

Water Model ◽

Shallow Water Model ◽

Filter Approach

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Using Precipitation Observations in a Mesoscale Short-Range Ensemble Analysis and Forecasting System

Weather and Forecasting ◽

10.1175/2007waf2006108.1 ◽

2008 ◽

Vol 23 (3) ◽

pp. 357-372 ◽

Cited By ~ 5

Author(s):

Tadashi Fujita ◽

David J. Stensrud ◽

David C. Dowell

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Grid Point ◽

Precipitation Data ◽

Simple Method ◽

Forecasting System ◽

Filter Approach ◽

Water Vapor Mixing Ratio ◽

Surface Observations

Abstract A simple method to assimilate precipitation data from a synthesis of radar and gauge data is developed to operate alongside an ensemble Kalman filter that assimilates hourly surface observations. The mesoscale ensemble forecast system consists of 25 members with 30-km grid spacing and incorporates variability in both initial and boundary conditions and model physical process schemes. The precipitation assimilation method only incorporates information on when and where rainfall is observed. Model temperature and water vapor mixing ratio profiles at each grid point are modified if rainfall is observed but not predicted, or if rainfall is predicted but not observed. These modifications act to either increase or decrease, respectively, the likelihood that precipitation develops at that grid point. Two cases are examined in which this technique is applied to assimilate precipitation data every 15 min from 1200 to 1800 UTC, while hourly surface observations are also assimilated at the same time using the more sophisticated ensemble Kalman filter approach. Results show that the simple method for assimilating precipitation data helps the model develop precipitation where it is observed, resulting in the precipitation area being reproduced more accurately than in the run without precipitation-data assimilation, while not negatively influencing the positive results from the surface data assimilation. Improvement is also seen in the reliability of precipitation probabilities for a 1 mm h−1 threshold after the assimilation period, indicating that assimilating precipitation data may provide improved forecasts of the mesoscale environment for a few hours.

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A multimodel comparison of stratospheric ozone data assimilation based on an ensemble Kalman filter approach

Journal of Geophysical Research Atmospheres ◽

10.1002/jgrd.50338 ◽

2013 ◽

Vol 118 (9) ◽

pp. 3848-3868 ◽

Cited By ~ 3

Author(s):

T. Nakamura ◽

H. Akiyoshi ◽

M. Deushi ◽

K. Miyazaki ◽

C. Kobayashi ◽

...

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Stratospheric Ozone ◽

Ozone Data ◽

Filter Approach

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Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter

Hydrology and Earth System Sciences ◽

10.5194/hess-17-3499-2013 ◽

2013 ◽

Vol 17 (9) ◽

pp. 3499-3521 ◽

Cited By ~ 24

Author(s):

V. R. N. Pauwels ◽

G. J. M. De Lannoy ◽

H.-J. Hendricks Franssen ◽

H. Vereecken

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

The State ◽

Simultaneous Estimation ◽

State Variables ◽

Two Stage ◽

Forecast Bias ◽

Error Covariance ◽

Observation Bias

Abstract. In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

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