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A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part I: application in the Lorenz system Cover

A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part I: application in the Lorenz system

Open Access
|Dec 2012

Figures & Tables

Fig. 1. 

The temporal weighting function w of nudging, where t is the model time, t o is the observation time and τ N is the half-period of nudging time window (after Stauffer and Seaman, 1990).

Fig. 2. 

Schematic showing the procedures of the HNEnKF approach. The trapezoid around the observation denotes the temporal nudging weighting function over the nudging time window.

Table 1. Experimental design

Experiment name Experiment description CTRL Assimilate no observations EnKF Assimilate observations by EnKF EnKS Assimilate observations by EnKS Nudging Assimilate observations by traditional nudging (diagonal terms only in eq. (8) with nudging coefficients of 10) IAU Assimilate observations by IAU HNEnKF-D Assimilate observations by HNEnKF with diagonal elements only of the hybrid nudging magnitude matrix HNEnKF-A (HNEnKF) Assimilate observations by HNEnKF with all elements of the hybrid nudging magnitude matrix
Fig. 3. 

The average RMS errors for the 3000-step dynamic-analysis period set-up for the control run without data assimilation, traditional nudging and two kinds of HNEnKF approaches described in Table 1 for (a) perfect model and (b) imperfect model.

Fig. 4. 

The average hybrid nudging coefficients in the 3000-step period set-up in each equation for (a) experiment HNEnKF-D and (b) experiment HNEnKF-A. The dashed line denotes the magnitude of the nudging coefficient used in the traditional nudging approach.

Fig. 5. 

The average performance metrics over the 3000-step dynamic-analysis period set-up for the different data assimilation methods described in Table 1 with various observation frequencies of one per 10 time steps (OF10), one per 25 time steps (OF25) and one per 50 time steps (OF50), under a perfect model assumption for (a) RMS error and (b) DP. Smaller values are more desirable for both metrics.

Fig. 6. 

Same as Fig. 5, except for the imperfect model.

Fig. 7. 

The ensemble spread and error of the 3000-step period set-up using an observation frequency one per 25 time steps. (a) Perfect model and (b) imperfect model.

Table 2. The average RMS error in the 100 random initial conditions set-up for the data assimilation methods described in Table 1 with the default observation frequency (one per 25 time steps) for the perfect model

EnKF EnKS Nudging IAU HNEnKF EnKF 0.84 + + + + EnKS 0.48 + + + Nudging 2.70 + + IAU 1.66 + HNEnKF 1.54

[i] There is significant difference (+) and no significant difference (−).

Table 3. Same as Table 2, except for the DP

EnKF EnKS Nudging IAU HNEnKF EnKF 0.74 + + + + EnKS 0.20 + − + Nudging 0.22 − + IAU 0.20 + HNEnKF 0.16

Table 4. Same as Table 2, except for the imperfect model

EnKF EnKS Nudging IAU HNEnKF EnKF 2.73 + − + EnKS 1.87 + + Nudging 3.01 + IAU HNEnKF 2.64

Table 5. Same as Table 4, except for the DP

EnKF EnKS Nudging IAU HNEnKF EnKF 2.96 + + + EnKS 0.58 + + Nudging 0.36 + IAU HNEnKF 0.50
Fig. 8. 

The average ratios of the hybrid nudging terms in each equation compared with the sum of the physical terms for the 3000-step period set-up for Experiment HNEnKF. The dashed line denotes a ratio of 0.1, where the nudging term is an order of magnitude smaller than the sum of the physical forcing terms.

Table 6. Total CPU time cost of different data assimilation schemes with the default observation frequency in the 100 initial conditions set-up

Experiment EnKF EnKS Nudging IAU HNEnKF CPU time (s) 16 3414 4 5 16
Language: English
Page range: 18484 - 18484
Submitted on: Jan 24, 2011
Published on: Dec 1, 2012
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2012 Lili Lei, David R. Stauffer, Sue Ellen Haupt, George S. Young, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.