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Four-dimensional Ensemble-variational Data Assimilation for Global Deterministic Weather Prediction : Volume 20, Issue 5 (24/09/2013)

By Buehner, M.

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Book Id: WPLBN0003990302
Format Type: PDF Article :
File Size: Pages 14
Reproduction Date: 2015

Title: Four-dimensional Ensemble-variational Data Assimilation for Global Deterministic Weather Prediction : Volume 20, Issue 5 (24/09/2013)  
Author: Buehner, M.
Volume: Vol. 20, Issue 5
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Charette, C., Buehner, M., & Morneau, J. (2013). Four-dimensional Ensemble-variational Data Assimilation for Global Deterministic Weather Prediction : Volume 20, Issue 5 (24/09/2013). Retrieved from http://cloud-library.org/


Description
Description: Data Assimilation and Satellite Meteorology Research Section, Environment Canada, Dorval, Quebec, Canada. The goal of this study is to evaluate a version of the ensemble-variational data assimilation approach (EnVar) for possible replacement of 4D-Var at Environment Canada for global deterministic weather prediction. This implementation of EnVar relies on 4-D ensemble covariances, obtained from an ensemble Kalman filter, that are combined in a vertically dependent weighted average with simple static covariances. Verification results are presented from a set of data assimilation experiments over two separate 6-week periods that used assimilated observations and model configuration very similar to the currently operational system. To help interpret the comparison of EnVar versus 4D-Var, additional experiments using 3D-Var and a version of EnVar with only 3-D ensemble covariances are also evaluated. To improve the rate of convergence for all approaches evaluated (including EnVar), an estimate of the cost function Hessian generated by the quasi-Newton minimization algorithm is cycled from one analysis to the next.

Analyses from EnVar (with 4-D ensemble covariances) nearly always produce improved, and never degraded, forecasts when compared with 3D-Var. Comparisons with 4D-Var show that forecasts from EnVar analyses have either similar or better scores in the troposphere of the tropics and the winter extra-tropical region. However, in the summer extra-tropical region the medium-range forecasts from EnVar have either similar or worse scores than 4D-Var in the troposphere. In contrast, the 6 h forecasts from EnVar are significantly better than 4D-Var relative to radiosonde observations for both periods and in all regions. The use of 4-D versus 3-D ensemble covariances only results in small improvements in forecast quality. By contrast, the improvements from using 4D-Var versus 3D-Var are much larger. Measurement of the fit of the background and analyzed states to the observations suggests that EnVar and 4D-Var can both make better use of observations distributed over time than 3D-Var. In summary, the results from this study suggest that the EnVar approach is a viable alternative to 4D-Var, especially when the simplicity and computational efficiency of EnVar are considered. Additional research is required to understand the seasonal dependence of the difference in forecast quality between EnVar and 4D-Var in the extra-tropics.


Summary
Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction

Excerpt
Canadian Meteorological Centre (CMC): Improvements to the Global Deterministic Prediction System (from version 2.2.2 to 3.0.0), and related changes to the Regional Deterministic Prediction System (from version 3.0.0 to 3.1.0), Canadian Meteorological Centre technical note, available at: http://collaboration.cmc.ec.gc.ca/cmc/CMOI/product_guide/docs/lib/op_systems/doc_opchanges/technote_gdps300_20130213_e.pdf, 2013.; Caya, A., Sun, J., and Snyder, C.: A comparison between the 4dvar and the ensemble Kalman filter techniques for radar data assimilation, Mon. Weather Rev., 133, 3081–3094, 2005.; Houtekamer, P. L. and Mitchell, H. L.: Data Assimilation Using an Ensemble Kalman Filter Technique, Mon. Weather Rev., 126, 796–811, 1998.; Charron, M., Pellerin, G., Spacek, L., Houtekamer, P. L., Gagnon, N., Mitchell, H. L., and Michelin, L.: Toward Random Sampling of Model Error in the Canadian Ensemble Prediction System, Mon. Weather Rev., 138, 1877–1901, 2010.; Charron M., Polavarapu, S., Buehner, M., Vaillancourt, P. A., Charette, C., Roch, M., Morneau, J., Garand, L., Aparicio, J. M., MacPherson, S., Pellerin, S., St-James, J., and Heilliette, S.: The stratospheric extension of the Canadian global deterministic medium range weather forecasting system and its impact on tropospheric forecasts, Mon. Weather Rev., 140, 1924–1944, 2012.; Clayton, A. M., Lorenc, A. C., and Barker, D. M.: Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office, Q. J. Roy. Meteor. Soc., 139, 1445–1461, doi:10.1002/qj.2054, 2012.; Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828, 2011.; Fairbairn, D., Pring, S. R., Lorenc, A. C., and Roulstone, I.: A comparison of 4DVar with ensemble data assimilation methods, Q. J. Roy. Meteor. Soc., doi:10.1002/qj.2135, online first, 2013.; Gauthier, P., Tanguay, M., Laroche, S., Pellerin, S., and Morneau, J.: Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada, Mon. Weather Rev., 135, 2339–2354, 2007.; Gilbert, J.-Ch., and Lemaréchal, C.: Some numerical experiments with variable-storage quasi-Newton algorithms, Mathematical Programming, 45, 407–435, 1989.; Hamill, T. M. and Snyder, C.: A hybrid ensemble kalman filter–3d variational analysis scheme, Mon. Weather Rev., 128, 2905–2919, 2000.; Gustafsson, N.: Discussion on 4D-Var or EnKF?, Tellus A, 59, 774–777, doi:10.1111/j.1600-0870.2007.00262.x, 2007.; Honda, Y., Nishijima, M., Kopizumi, K., Ohta, Y., Tamiya, K., Kawabata, T., and Tsuyuki, T.: A pre-operational variational data assimilation system for a non-hydrostatic model at the Japan Meteorological Agency: Formulation and preliminary results, Q. J. Roy. Meteor. Soc., 131, 3465–3475, 2005.; Houtekamer, P. L. and Mitchell, H. L.: Ensemble Kalman filtering, Q. J. Roy. Meteor. Soc., 131, 3269–3289, 2005.; Bishop, C. H., Hodyss, D., Steinle, P., Simms, H., Clayton, A. M., Lorenc, A. C., Barker, D. M., and Buehner, M.: Efficient ensemble covari

 

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