Higher resolution in an operational ensemble Kalman filter

Monthly Weather Review

Volumn 142, Issue 3, 2014, Pages 1143-1162

  Houtekamer, P L ^a   Deng, Xingxiu ^a   Mitchell, Herschel L ^a   Baek, Seung Jong ^a   Gagnon, Normand ^a  

^a ENVIRONMENT CANADA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTING FACILITIES; ENSEMBLE KALMAN FILTER; ENSEMBLE PREDICTION SYSTEMS; HORIZONTAL RESOLUTION; INSTABILITY PROBLEMS; METEOROLOGICAL SERVICE OF CANADA; TEMPORAL RESOLUTION; TIME INTERPOLATION;

MATHEMATICAL MODELS; METEOROLOGY;

KALMAN FILTERS;

ENSEMBLE FORECASTING; INTERPOLATION; KALMAN FILTER; NUMERICAL MODEL; TOPOGRAPHY;

CANADA;

EID: 84896777380     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-13-00138.1     Document Type: Article

References (67)

1. Auligné, T., A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 133, 631-642.
2. Bartello, P., and H. L. Mitchell, 1992: A continuous threedimensional model of short-range forecast error covariances. Tellus, 44A, 217-235.
3. Bélair, S., M. Roch, A.-M. Leduc, P. A. Vaillancourt, S. Laroche, and J. Mailhot, 2009: Medium-range quantitative precipitation forecasts from Canada's new 33-km deterministic global operational system. Wea. Forecasting, 24, 690-708.
4. Bishop, C. H., and D. Hodyss, 2009: Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models. Tellus, 61A, 84-96.
5. Blackadar, A. K., 1962: The vertical distribution of wind and turbulent exchange in a neutral atmosphere. J. Geophys. Res., 67, 3095-3102.
6. Bormann, N., and P. Bauer, 2010: Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data. Quart. J. Roy. Meteor. Soc., 136, 1036-1050.
7. Bougeault, P., and P. Lacarrère, 1989: Parameterization of orographyinduced turbulence in a mesobeta-scalemodel.Mon.Wea. Rev., 117, 1872-1890.
8. Buehner, M., P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He, 2010a: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: Description of single-observation experiments. Mon. Wea. Rev., 138, 1550-1566.
9. Buehner, M., P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He, , 2010b: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part II: One-month experiments with real observations. Mon. Wea. Rev., 138, 1567- 1586.
10. Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719-1724.
11. Candille, G., 2009: The multiensemble approach: The NAEFS example. Mon. Wea. Rev., 137, 1655-1665.
12. Candille, G., C. Côté, P. L. Houtekamer, and G. Pellerin, 2007: Verification of an ensemble prediction system against observations. Mon. Wea. Rev., 135, 2688-2699.
13. Charron, M., G. Pellerin, L. Spacek, P. L. Houtekamer, N. Gagnon, H. L. Mitchell, and L. Michelin, 2010: Toward random sampling of model error in the Canadian Ensemble Prediction System. Mon. Wea. Rev., 138, 1877-1901.
14. Cohn, S. E., and D. F. Parrish, 1991: The behavior of forecast error covariances for a Kalman filter in two dimensions. Mon. Wea. Rev., 119, 1757-1785.
15. Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.
16. Dee, D. P., 1995: On-line estimation of error covariance parameters for atmospheric data assimilation. Mon. Wea. Rev., 123, 1128-1145.
17. Dee, D. P., and A. M. da Silva, 1998: Data assimilation in the presence of forecast bias. Quart. J. Roy. Meteor. Soc., 124, 269-295.
18. Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287-2299.
19. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143-10 162.
20. Fillion, L., H. L. Mitchell, H. Ritchie, and A. Staniforth, 1995: The impact of a digital filter finalization technique in a global data assimilation system. Tellus, 47A, 304-323.
21. Fisher, M., and H. Auvinen, 2011: Long window 4D-Var. Proc. Seminar on Data Assimilation for Atmosphere and Ocean, Reading, United Kingdom, ECMWF, 189-202.
22. Gagnon, N., and Coauthors, cited 2013: Improvements to the Global Ensemble Prediction System (GEPS) from version 2.0.3 to version 3.0.0. Environment Canada. [Available online at http:// collaboration.cmc.ec.gc.ca/cmc/CMOI/product_guide/docs/lib/ op_systems/doc_opchanges/technote_geps300_20130213_e.pdf.]
23. Gaspari, G., S. E. Cohn, J. Guo, and S. Pawson, 2006: Construction and application of covariance functions with variable lengthfields. Quart. J. Roy. Meteor. Soc., 132, 1815-1838.
24. Gelaro, R., R. H. Langland, S. Pellerin, and R. Todling, 2010: The THORPEX Observation Impact Intercomparison Experiment. Mon. Wea. Rev., 138, 4009-4025.
25. Geleyn, J.-F., 1985: On a simple, parameter-free partition between moistening and precipitation in the Kuo scheme. Mon. Wea. Rev., 113, 405-407.
26. Girard, C., and Coauthors, 2014: Staggered vertical discretization of the Canadian Environmental Multiscale (GEM) model using a coordinate of the log-hydrostatic-pressure type. Mon. Wea. Rev., 142, 1183-1196.
27. Gorin, V. E., and M. D. Tsyrulnikov, 2011: Estimation of multivariate observation-error statistics for AMSU-A data. Mon. Wea. Rev., 139, 3765-3780.
28. Hager, G., and G. Wellein, 2011: Introduction to High Performance Computing for Scientists and Engineers. Chapman and Hall/ CRC, 330 pp.
29. Hamill, T. M., and J. S. Whitaker, 2005: Accounting for the error due to unresolved scales in ensemble data assimilation: A comparison of different approaches. Mon. Wea. Rev., 133, 3132-3147.
30. Hamill, T. M., and J. S. Whitaker, D. T. Kleist, M. Fiorino, and S. G. Benjamin, 2011: Predictions of 2010's tropical cyclones using the GFS and ensemble-based data assimilation methods. Mon. Wea. Rev., 139, 3243-3247.
31. Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea. Forecasting, 15, 559-570.
32. Hollingsworth, A., and P. Lönnberg, 1986: The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus, 38A, 111-136.
33. Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123-137.
34. Houtekamer, P. L., and H. L. Mitchell, 2005: Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 3269-3289.
35. Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 1225-1242.
36. Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604-620.
37. Houtekamer, P. L., H. L. Mitchell, and X. Deng, 2009: Model error representation in an operational ensemble Kalman filter. Mon. Wea. Rev., 137, 2126-2143.
38. Houtekamer, P. L., B. He, and H. L. Mitchell, 2014: Parallel implementation of an ensemble Kalman filter. Mon. Wea. Rev., 142, 1163-1182.
39. Isaksen, L., 2012: Data assimilation on future computer architectures. Proc. Seminar on Data Assimilation for Atmosphere and Ocean, Reading, United Kingdom, ECMWF, 301-322.
40. Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain-Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., Amer. Meteor. Soc., No. 46, 165-170.
41. Kleist, D., and K. Ide, 2012: An OSSE-based evaluation of 4DEnsemble- Var (and hybrid variants) for the NCEP GFS. Proc. Joint CMOS/AMS Congress, 21st Numerical Weather Prediction Conf./25th Weather and Forecasting Conf., Montreal, QC, Canada, CMOS/Amer. Meteor. Soc., 18 pp. [Available online at web2.sca.uqam.ca/;wgne/CMOS/PRESENTATIONS/links/ 5520.pdf.]
42. Laroche, S., and R. Sarrazin, 2013: Impact of radiosonde balloon drift on numerical weather prediction and verification. Wea. Forecasting, 28, 772-782.
43. Lawson, W. G., and J. A. Hansen, 2004: Implications of stochastic and deterministic filters as ensemble-based data assimilation methods in varying regimes of error growth. Mon. Wea. Rev., 132, 1966-1981.
44. Liu, Z.-Q., and F. Rabier, 2003: The potential of high-density observations for numerical weather prediction: A study with simulated observations. Quart. J. Roy. Meteor. Soc., 129, 3013- 3035.
45. Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21, 289-307.
46. Mailhot, J., R. Sarrazin, B. Bilodeau, N. Brunet, and G. Pellerin, 1997: Development of the 35-km version of the Canadian regional forecast system. Atmos.-Ocean, 35, 1-28.
47. McFarlane, N. A., 1987: The effect of orographically excited gravity-wave drag on the general circulation of the lower stratosphere and troposphere. J. Atmos. Sci., 44, 1775-1800.
48. McTaggart-Cowan, R., C. Girard, A. Plante, and M. Desgagné, 2011: The utility of upper-boundary nesting in NWP. Mon. Wea. Rev., 139, 2117-2144.
49. Ménard, R., and R. Daley, 1996: The application of Kalman smoother theory to the estimation of 4DVAR error statistics. Tellus, 48A, 221-237.
50. Miller, R. N., M. Ghil, and F. Gauthiez, 1994: Advanced data assimilation in strongly nonlinear dynamical systems. J. Atmos. Sci., 51, 1037-1056.
51. Mitchell, H. L., P. L. Houtekamer, and G. Pellerin, 2002: Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Wea. Rev., 130, 2791-2808.
52. Moore, G. E., 1965: Cramming more components onto integrated circuits. Electronics, 38, 114-117.
53. Navarra, A., J. L. Kinter III, and J. Tribbia, 2010: Crucial experiments in climate science. Bull. Amer. Meteor. Soc., 91, 343-352.
54. Noilhan, J., and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536-549.
55. Rabier, F., A. McNally, E. Andersson, P. Courtier, P. Undén, J. Eyre, A. Hollingsworth, and F. Bouttier, 1998: TheECMWF implementation of three-dimensional variational assimilation (3D-Var). II: Structure functions. Quart. J. Roy. Meteor. Soc., 124, 1809-1829.
56. Rutherford, I. D., 1976: An operational 3-dimensional multivariate statistical objective analysis scheme. Proc. JOC Study Group Conf. on Four-Dimensional Data Assimilation, Paris, France, WMO/JCSU, 98-121.
57. Seidel, D. J., B. Sun, M. Pettey, and A. Reale, 2011: Global radiosonde balloon drift statistics. J. Geophys. Res., 116, D07102, doi:10.1029/2010JD014891.
58. Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 3079-3102.
59. Snyder, C., 2012: Particle filters, the "optimal" proposal and highdimensional systems. Proc. Seminar on Data Assimilation for Atmosphere and Ocean, Reading, United Kingdom,ECMWF, 161-170.
60. Snyder, C., T. Bengtsson, P. Bickel, and J. Anderson, 2008: Obstacles to high-dimensional particle filtering. Mon. Wea. Rev., 136, 4629- 4640.
61. Stoffelen, A., and D. Anderson, 1997: Ambiguity removal and assimilation of scatterometer data. Quart. J. Roy. Meteor. Soc., 123, 491-518.
62. Talagrand, O., R. Vautard, and B. Strauss, 1999: Evaluation of probabilistic prediction systems. Proc. Workshop on Predictability, Reading, United Kingdom, ECMWF, 1-25.
63. Talagrand, O., G. Candille, and L. Descamps, 2007: On some aspects of validation of probabilistic prediction. Proc. Workshop on Ensemble Prediction, Reading, United Kingdom, ECMWF, 1-12.
64. Todling, R., 2013: Comparing two approaches for assessing observation impact. Mon. Wea. Rev., 141, 1484-1505.
65. Trémolet, Y., 2007: Incremental 4D-Var convergence study. Tellus, 59A, 706-718.
66. Zhang, F., Y. Weng, J. A. Sippel, Z. Meng, and C. H. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter.Mon. Wea. Rev., 137, 2105- 2125.
67. Zupanski, D., 1997: A general weak constraint applicable to operational 4DVAR data assimilation systems. Mon. Wea. Rev., 125, 2274-2292.