Ensemble Kalman filter and 4D-Var intercomparison with the Japanese operational global analysis and prediction system

Monthly Weather Review

Volumn 138, Issue 7, 2010, Pages 2846-2866

  Miyoshi, Takemasa ^a   Sato, Yoshiaki ^b   Kadowaki, Takashi ^b  

^a Bldg 142   (United States)

^b JAPAN METEOROLOGICAL AGENCY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BIAS; BIAS CORRECTION; DETERMINISTIC FORECASTS; ENSEMBLE KALMAN FILTER; FOUR-DIMENSIONAL VARIATIONAL DATA ASSIMILATION; GLOBAL ANALYSIS; INTERCOMPARISONS; LOWER TROPOSPHERE; METEOROLOGICAL AGENCY; MULTIPLICATIVE ERRORS; NUMERICAL WEATHER PREDICTION; OPERATIONAL SYSTEMS; OPERATIONAL USE; PERFORMANCE DEGRADATION; PREDICTION SYSTEMS; RADIANCE ASSIMILATION; SYNERGISTIC EFFECT; TEMPERATURE BIAS;

ATMOSPHERICS; DATA PROCESSING; DEGRADATION; KALMAN FILTERS; RADIOSONDES; SATELLITES; WEATHER FORECASTING;

VALUE ENGINEERING;

ACCURACY ASSESSMENT; COMPARATIVE STUDY; COMPUTER SIMULATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; ERROR CORRECTION; EXPERIMENTAL STUDY; FOUR-DIMENSIONAL MODELING; KALMAN FILTER; MODEL VALIDATION; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; PREDICTION; RADIOSONDE; SAMPLING BIAS; SENSITIVITY ANALYSIS; TROPOSPHERE; WEATHER FORECASTING;

EID: 77958470163     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2010MWR3209.1     Document Type: Article

References (50)

1. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.
2. Anderson, J. L., 2007a: An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus, 59A, 210-224.
3. Anderson, J. L., 2007b: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D, 230, 99-111.
4. Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 72-83.
5. Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420-436.
6. Bonavita, M., L. Torrisi, and F. Marcucci, 2008: The ensemble Kalman filter in an operational regional NWP system: Preliminary results with real observations. Quart. J. Roy. Meteor. Soc., 134, 1733-1744.
7. Bonavita, M., L. Torrisi, and F. Marcucci, 2010: Ensemble data assimilation with the CNMCA regional forecasting system. Quart. J. Roy. Meteor. Soc., 136, 132-145.
8. Bowler, N. E., A. Arribas, K. R. Mylne, K. B. Robertson, and S. E. Beare, 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703-722.
9. 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 and single-observation experiments. Mon. Wea. Rev., 138, 1885-1901.
10. 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, 1902-1921.
11. Buizza, R., C. Cardinali, G. Kelly, and J.-N. Thepaut, 2007: The value of targeted observations. ECMWF Newsletter, No. 111, ECMWF, Reading, United Kingdom, 11-20.
12. Dee, D. P., 2004: Variational bias correction of radiance data in the ECMWF system. Proc. ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP, Reading, United Kingdom, ECMWF, 97-112.
13. Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3323-3343.
14. 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.
15. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10 143-10 162.
16. Fertig, E. J., and Coauthors, 2009: Observation bias correction with an ensemble Kalman filter. Tellus, 61A, 210-226.
17. Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723-757.
18. Gustafsson, N., 2007: Discussion on '4D-Var or EnKF?'. Tellus, 59A, 774-777.
19. Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776-2790.
20. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
21. Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123-137.
22. Houtekamer, P. L., and H. L. Mitchell, 2005: Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 3269-3289.
23. 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.
24. Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112-126.
25. JMA, 2007: Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Japan Meteorological Agency, 194 pp.
26. Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437-471.
27. Kalnay, E., H. Li, T. Miyoshi, S.-C. Yang, and J. Ballabrera-Poy, 2007a: 4D-Var or ensemble Kalman filter? Tellus, 59A, 758-773.
28. Kalnay, E., H. Li, T. Miyoshi, S.-C. Yang, and J. Ballabrera-Poy, 2007b: Response to the discussion on "4D-Var or EnKF?" by Nils Gustafsson. Tellus, 59A, 778-780.
29. Kazumori, M., and K. Okamoto, 2004: Direct assimilation of ATOVS brightness temperature (in Japanese). Japan Meteor. Agency, 50, 93-104.
30. Kondo, K., and H. L. Tanaka, 2009: Applying the local ensemble transform Kalman filter to the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). SOLA, 5, 121-124.
31. Li, H., E. Kalnay, and T. Miyoshi, 2009: Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 523-533.
32. Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP-A comparison with 4D-VAR. Quart. J. Roy. Meteor. Soc., 129, 3183-3203.
33. Miyoshi, T., 2005: Ensemble Kalman filter experiments with a primitive-equation global model. Ph.D. dissertation, University of Maryland, College Park, 197 pp.
34. Miyoshi, T., and K. Aranami, 2006: Applying a Four-dimensional Local Ensemble Transform Kalman Filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM). SOLA, 2, 128-131.
35. Miyoshi, T., and Y. Sato, 2007: Assimilating satellite radiances with a Local Ensemble Transform Kalman Filter (LETKF) applied to the JMA Global Model (GSM). SOLA, 3, 37-40.
36. Miyoshi, T., and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 3841-3861.
37. Miyoshi, T., S. Yamane, and T. Enomoto, 2007a: The AFES-LETKF experimental ensemble reanalysis: ALERA. SOLA, 3, 45-48.
38. Miyoshi, T., S. Yamane, and T. Enomoto, 2007b: Localizing the error covariance by physical distances within a Local Ensemble Transform Kalman Filter (LETKF). SOLA, 3, 89-92.
39. Nakagawa, M., 2003: Recent development of a cumulus parameterization scheme for the operational global model at JMA. BMRC Research Rep. 93, 29-32.
40. Ohfuchi, W., and Coauthors, 2004: 10-km mesh meso-scale resolving simulations of the global atmosphere on the Earth Simulator: Preliminary outcomes of AFES (AGCM for the Earth Simulator). J. Earth Simul., 1, 8-34.
41. Onogi, K., and Coauthors, 2007: The JRA-25 Reanalysis. J. Meteor. Soc. Japan, 85, 369-432.
42. Ott, E., and Coauthors, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415-428.
43. Sato, Y., 2007: Introduction of variational bias correction technique into the JMA global data assimilation system. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modeling, Rep. 37, 0126-0127.
44. Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, and S. Iga, 2008: Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations. J. Comput. Phys., 227, 3486-3514.
45. Szunyogh, I., E. J. Kostelich, G. Gyarmati, D. J. Patil, B. R. Hunt, E. Kalnay, E. Ott, and J. A. Yorke, 2005: Assesing a local ensemble Kalman filter: Perfect model experiments with the National Centers for Environmental Prediction global model. Tellus, 57A, 528-545.
46. Szunyogh, I., E. J. Kostelich, G. Gyarmati, E. Kalnay, B. R. Hunt, E. Ott, E. Satterfield, and J. A. Yorke, 2008: A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus, 60A, 113-130.
47. Thepaut, J.-N., 2006: Assimilating only surface pressure observations in 3D and 4D-Var. Proc. ECMWF/GEO Workshop on Atmospheric Re-analysis, Reading, United Kingdom, ECMWF, 107-112.
48. Tippett, M. K., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, 2003: Ensemble square root filters. Mon. Wea. Rev., 131, 1485-1490.
49. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.
50. Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136, 463-482.