World Library  


Add to Book Shelf
Flag as Inappropriate
Email this Book

Controlling Instabilities Along a 3Dvar Analysis Cycle by Assimilating in the Unstable Subspace: a Comparison with the Enkf : Volume 15, Issue 4 (01/07/2008)

By Carrassi, A.

Click here to view

Book Id: WPLBN0003977496
Format Type: PDF Article :
File Size: Pages 19
Reproduction Date: 2015

Title: Controlling Instabilities Along a 3Dvar Analysis Cycle by Assimilating in the Unstable Subspace: a Comparison with the Enkf : Volume 15, Issue 4 (01/07/2008)  
Author: Carrassi, A.
Volume: Vol. 15, Issue 4
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2008
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Descamps, L., Talagrand, O., Trevisan, A., Carrassi, A., & Uboldi, F. (2008). Controlling Instabilities Along a 3Dvar Analysis Cycle by Assimilating in the Unstable Subspace: a Comparison with the Enkf : Volume 15, Issue 4 (01/07/2008). Retrieved from http://hawaiilibrary.net/


Description
Description: Royal Meteorological Institute of Belgium – RMI, Bruxelles, Belgium. A hybrid scheme obtained by combining 3DVar with the Assimilation in the Unstable Subspace (3DVar-AUS) is tested in a QG model, under perfect model conditions, with a fixed observational network, with and without observational noise. The AUS scheme, originally formulated to assimilate adaptive observations, is used here to assimilate the fixed observations that are found in the region of local maxima of BDAS vectors (Bred vectors subject to assimilation), while the remaining observations are assimilated by 3DVar. The performance of the hybrid scheme is compared with that of 3DVar and of an EnKF. The improvement gained by 3DVar-AUS and the EnKF with respect to 3DVar alone is similar in the present model and observational configuration, while 3DVar-AUS outperforms the EnKF during the forecast stage. The 3DVar-AUS algorithm is easy to implement and the results obtained in the idealized conditions of this study encourage further investigation toward an implementation in more realistic contexts.

Summary
Controlling instabilities along a 3DVar analysis cycle by assimilating in the unstable subspace: a comparison with the EnKF

Excerpt
Anderson, J.: A local least squares framework for ensemble filtering, Mon. Wea. Rev., 131, 634–642, 2003.; Anderson, J. and Anderson, S.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilation and forecast, Mon. Wea. Rev., 127, 2741–2758, 1999.; Benettin, G., Galgani, L., Giorgilli, A., and Strelcyn, J.: Lyapunov characteristic exponents for smooth dynamical systems and for Hamiltonian systems; a method for computing them, Meccanica, 15, 9–30, 1980.; Bergman, K H.: Multivariate analysis of temperature and winds using optimum interpolation., Mon. Wea. Rev., 107, 1423–1444, 1979.; Bleck, R.: Simulation of coastal upwelling frontogenesis with an isopycnic coordinate model, J. Geophys. Res., 83C, 6163–6172, 1978.; Carrassi, A., Trevisan, A., and Uboldi, F.: Adaptive observations and assimilation in the unstable subspace by breeding on the data-assimilation system, Tellus, 59A, 101–113, 2007.; Whitaker, J. and Hamill, T.: Ensemble data assimilation without perturbed observations, Mon. Wea. Rev., 130, 1913–1924, 2002.; Carrassi, A., Ghil, M., Trevisan, A., and Uboldi, F.: Data Assimilation as a nonlinear dynamical system problem: Stability and convergence of the prediction-assimilation system, Chaos, 18, 023112, 2008.; Corazza, M., Kalnay, E., Patil, D., Yang, S.-C., Morss, R., Cai, M., Szunyogh, I., Hunt, B., and Yorke, J.: Use of the breeding technique to estimate the structure of the analysis error of the day, Nonlin. Processes Geophys., 10, 233–243, 2003.; Corazza, M., Kalnay, E., and Yang, S.-C.: An implementation of the Local Ensemble Kalman filter for a simple quasi-geostrophic model: Results and comparison with a 3D-Var data assimilation system, Nonlin. Processes Geophys., 14, 89–101, 2007.; Descamps, L. and Talagrand, O.: On some aspects of the definition of initial conditions for ensemble prediction, Mon. Wea. Rev., 135, 3260–3272, 2007.; Etherton, B. and Bishop, C.: Resilience of hybrid ensemble/3DVAR analysis schemes to model error and ensemble covariance error, Mon. Wea. Rev., 132, 1065–1080, 2004.; Evensen, G.: Inverse Methods and Data Assimilation in Nonlinear Ocean Models, Physica D, 77, 108–129, 1994.; Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Oc. Dyn., 53, 343–367, 2003.; Evensen, G.: Sampling strategies and square root analysis schemes for the EnKF, Oc. Dyn., 53, 539–560, 2004.; Gaspari, G. and Cohn, S.: Construction of correlation functions in two and three dimensions, Quart. J. Roy. Meteor. Soc., 125, 723–757, 1999.; Hamill, T M. and Snyder, C.: A hybrid ensemble Kalman filter 3D variational scheme., Mon. Wea. Rev., 129, 2905–2919, 2000.; Houtekamer, P L.: Global and local skill forecast, Mon. Wea. Rev., 121, 1834–1846, 1993.; Houtekamer, P L. and Mitchell, H L.: A sequential ensemble Kalman filter fot atmospheric data assimilation, Mon. Wea. Rev., 129, 123–137, 2001.; Hunt, B., Kostelich, E., and Szunyogh, I.: Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter, Physica D, p. in print, 2007.; Ide, K., Courtier, P., Ghil, M., and Lorenc, A.: Unified notation for data assimilation: Operational, variational and sequential, J. Met. Soc. Japan, 75, 181–189, 1997.; Jazwinski, A H.: Stochastic Processes and Filtering Theory, Academic Press, 1970.; Langland, R H.: Observation Impact during the North Atlantic TReC-2003, Mon. Wea. Rev., 133, 2297–2309, 2005.; Lorenz, E.: Predictability: A problem partly solved., Proc. Seminar on\ Predictability Vol 1, ECMWF, Reading, Berkshire, UK, 1–18, 1996.; Morss, R., Emanuel, K., and Snyder, C.: Idealized adaptive observation strategies for improving numerical weather prediction, J. Atmos. Sci., 58, 210–232, 2001.; Morss, R E.: Adaptive observations: Idealized sampling strategies for improving numerical weather prediction., PhD thesis, Massachusetts Institute of Technology, 1999.; Ochotta, T., Gebhardt, C., Saupe, D., and Wergen, W.: Adaptive th

 

Click To View

Additional Books


  • Electromagnetic and Mechanical Control o... (by )
  • Role of the Hydrological Cycle in Regula... (by )
  • On the Hamiltonian Approach: Application... (by )
  • Numerical Simulations of the Charging of... (by )
  • A Barnes-hut Scheme for Simulating Fault... (by )
  • Energetics of Internal Solitary Waves in... (by )
  • Non-equilibrium Effects of Core-cooling ... (by )
  • Quantifying Soil Complexity Using Networ... (by )
  • Bounded Lognormal Cascades as Quasi-mult... (by )
  • Nonlinear Optimization Set Pair Analysis... (by )
  • Statistical Analysis of Stromboli Vlp Tr... (by )
  • Estimating the Diffusive Heat Flux Acros... (by )
Scroll Left
Scroll Right

 



Copyright © World Library Foundation. All rights reserved. eBooks from Hawaii eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.