Comparing limited-area 3DVAR and hybrid variational-ensemble data assimilation methods for typhoon track forecasts: Sensitivity to outer loops and vortex relocation

Monthly Weather Review

Volumn 141, Issue 12, 2013, Pages 4350-4372

  Schwartz, Craig S ^a   Liu, Zhiquan ^a   Huang, Xiang Yu ^a   Kuo, Ying Hwa ^a   Fong, Chin Tzu ^b  

^a NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^b CENTRAL WEATHER BUREAU   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

HURRICANES;

BACKGROUND-ERROR COVARIANCES; DATA ASSIMILATION; DATA ASSIMILATION METHODS; ENSEMBLE ADJUSTMENT KALMAN FILTER; HORIZONTAL GRID SPACING; RELOCATION TECHNIQUES; TYPHOON TRACK FORECASTS; WEATHER RESEARCH AND FORECASTING MODELS;

WEATHER FORECASTING;

COMPARATIVE STUDY; DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; KALMAN FILTER; THREE-DIMENSIONAL MODELING; TYPHOON; WEATHER FORECASTING;

EID: 84884813239     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-13-00028.1     Document Type: Article

References (74)

1. Ancell, B. C., 2012: Examination of analysis and forecast errors of high-resolution assimilation, bias removal, and digital filter initialization with an ensemble Kalman filter. Mon. Wea. Rev., 140, 3992-4004.
2. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.
3. Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634-642.
4. Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 72-83.
5. Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Arellano, 2009: The Data Assimilation Research Testbed: Acommunity facility. Bull. Amer. Meteor. Soc., 90, 1283-1296.
6. Barker, D. M., W. Huang, Y.-R. Guo, A. Bourgeois, and X. N. Xio, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897-914.
7. Barker, D. M., and Coauthors, 2012: The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA. Bull. Amer. Meteor. Soc., 93, 831-843.
8. Berner, J., G. J. Shutts, M. Leutbecher, and T. N. Palmer, 2009: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603-626.
9. 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.
10. Buehner, M., 2005: Ensemble-derived stationary and flow-dependent background error covariances: Evaluation in a quasi-operational NWP setting. Quart. J. Roy. Meteor. Soc., 131, 1013-1043.
11. 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, 1550-1566.
12. 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.
13. Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719-1724.
14. Campbell, W. F., C. H. Bishop, and D. Hodyss, 2010: Vertical covariance localization for satellite radiances in ensemble Kalman filters. Mon. Wea. Rev., 138, 282-290.
15. Cavallo, S. M., R. D. Torn, C. Snyder, C. Davis, W. Wang, and J. Done, 2013: Evaluation of the Advanced Hurricane WRF data assimilation system for the 2009 Atlantic hurricane season. Mon. Wea. Rev., 141, 523-541.
16. Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface/ hydrology model with the Penn State/NCAR MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569-585.
17. Chou, M.-D., and M. J. Suarez, 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo. 104606, Vol. 3, 85 pp.
18. Clayton, A. M., A. C. Lorenc, and D. M. Barker, 2012: Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office. Quart. J. Roy. Meteor. Soc., 139, 1445-1461, doi:10.1002/qj.2054.
19. Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994:Astrategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367-1387.
20. Elsberry, R. L., and P. A. Harr, 2008: Tropical cyclone structure (TCS08) field experiment science basis, observational platforms, and strategy.Asia-Pac. J. Atmos. Sci., 44 (3), 209-231.
21. Etherton, B. J., and C. H. Bishop, 2004: Resilience of hybrid ensemble/3DVAR analysis schemes to model error and ensemble covariance error. Mon. Wea. Rev., 132, 1065-1080.
22. 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.
23. Guo, Y.-R., H.-C. Lin, X. X. Ma, X.-Y. Huang, C. T. Terng, and Y.-H. Kuo, 2006: Impact of WRF-Var (3DVar) background error statistics on typhoon analysis and forecast. Extended Abstracts, WRF Users' Workshop, Boulder, CO, NCAR, P4.2. [Available online at http://www.mmm.ucar.edu/wrf/users/ workshops/WS2006/abstracts/PSession04/P4_2_Guo.pdf.]
24. Hamill, T.M., and C. Snyder, 2000:Ahybrid ensemble Kalman filter-3Dvariational analysis scheme. Mon. Wea. Rev., 128, 2905-2919.
25. Hamill, T.M., J. S.Whitaker, M. Fiorino, and S. G. Benjamin, 2011a: Global ensemble predictions of 2009's tropical cyclones initialized with an ensemble Kalman filter. Mon. Wea. Rev., 139, 668-688.
26. Hamill, T.M., J. S. Whitaker, D. T. Kleist, M. Fiorino, and S. G. Benjamin, 2011b: Predictions of 2010's tropical cyclones using the GFS and ensemble-based data assimilation methods. Mon. Wea. Rev., 139, 3243-3247.
27. Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318-2341.
28. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
29. Houtekamer, P. L., and 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.
30. Hsiao, L.-F., C. S. Liou, T. C. Yeh, Y. R. Guo, D. S. Chen, K. N. Huang, C. T. Terng, and J. H. Chen, 2010: A vortex relocation scheme for tropical cyclone initialization in Advanced Research WRF. Mon. Wea. Rev., 138, 3298-3315.
31. Hsiao, L.-F., D.-S. Chen, Y.-H. Kuo, Y.-R. Guo, T.-C. Yeh, J.-S. Hong, C.-T. Fong, and C.-S. Lee, 2012: Application of WRF3DVAR to operational typhoon prediction in Taiwan: Impact of outer loop and partial cycling approaches. Wea. Forecasting, 27, 1249-1263.
32. Huang, X.-Y., and P. Lynch, 1993: Diabatic digital filter initialization: Application to the HIRLAM model. Mon. Wea. Rev., 121, 589-603.
33. Huang, X.-Y., and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Wea. Rev., 137, 299-314.
34. Ide, K., P. Courtier, M. Ghil, and A. Lorenc, 1997: Unified notation for data assimilation: Operational, sequential and variational. J. Meteor. Soc. Japan, 75, 181-189.
35. Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170-181.
36. Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/ detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784-2802.
37. 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., No. 24, Amer. Meteor. Soc., 165-170.
38. Kurihara, Y., M. A. Bender, R. E. Tuleya, and R. J. Ross, 1995: Improvements in the GFDL hurricane prediction system. Mon. Wea. Rev., 123, 2791-2801.
39. Liu, Q., T. Marchok, H. Pan, M. Bender, and S. Lord, 2000: Improvements in hurricane initialization and forecasting at NCEP with global and regional (GFDL) models. NCEP/EMC Tech. Procedures Bull. 472, 7 pp. [Available online at http:// 205.156.64.206/om/tpb/472.htm.]
40. Liu, Z., C. S. Schwartz, C. Snyder, and S.-Y. Ha, 2012: Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filter. Mon. Wea. Rev., 140, 4017-4034.
41. 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.
42. Lorenc, A. C., and Coauthors, 2000: The Met. Office global threedimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 126, 2991-3012.
43. Lynch, P., 1997: The Dolph-Chebyshev window: A simple optimal filter. Mon. Wea. Rev., 125, 655-660.
44. Lynch, P., and X.-Y. Huang, 1992: Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev., 120, 1019-1034.
45. Ma, L.-M., and Z.-M. Tan, 2009: Improving the behavior of the cumulus parameterization for tropical cyclone prediction: Convection trigger. Atmos. Res., 92 (2), 190-211.
46. Meng, Z., and F. Zhang, 2008a: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study. Mon. Wea. Rev., 136, 522-540.
47. Meng, Z., and F. Zhang, 2008b: Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV: Comparison with 3DVar in a month-long experiment. Mon. Wea. Rev., 136, 3671-3682.
48. Miyoshi, T., Y. Sato, and T. Kadowaki, 2010: Ensemble Kalman filter and 4D-Var intercomparison with the Japanese Operational Global Analysis and Prediction System. Mon. Wea. Rev., 138, 2846-2866.
49. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 (D14), 16 663-16 682.
50. Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
51. Rainwater, S., and B. Hunt, 2013: Mixed resolution ensemble data assimilation. Mon. Wea. Rev., 141, 3007-3021.
52. Schwartz, C. S., Z. Liu, Y. Chen, and X.-Y. Huang, 2012: Impact of assimilating microwave radiances with a limited-area ensemble data assimilation system on forecasts of Typhoon Morakot. Wea. Forecasting, 27, 424-437.
53. Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 3079-3102.
54. Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech Note NCAR/TN-4751STR, 113 pp. [Available from UCAR Communications, P. O. Box 3000, Boulder, CO 80307.]
55. Tao, W.-K., and J. Simpson, 1993: The Goddard cumulus ensemble model. Part I: Model description. Terr.Atmos.Oceanic Sci., 4, 35-72.
56. Tao, W.-K., and Coauthors, 2003: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97-137.
57. Torn, R. D., 2010: Performance of a mesoscale ensemble Kalman filter (EnKF) during the NOAA high-resolution hurricane test. Mon. Wea. Rev., 138, 4375-4392.
58. Torn, R. D., and G. J. Hakim, 2009: Ensemble data assimilation applied to RAINEX observations of Hurricane Katrina (2005). Mon. Wea. Rev., 137, 2817-2829.
59. Torn, R. D., and G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limitedarea ensemble Kalman filters. Mon.Wea. Rev., 134, 2490-2502.
60. Wang, J., and Coauthors, 2010: Water vapor variability and comparisons in the subtropical Pacific from The Observing System Research and Predictability Experiment-Pacific Asian Regional Campaign (T-PARC) driftsonde, Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), and reanalyses. J. Geophys. Res., 115, D21108, doi:10.1029/2010JD014494.
61. Wang, X., 2011: Application of the WRF Hybrid ETKF-3DVAR data assimilation system for hurricane track forecasts. Wea. Forecasting, 26, 868-884.
62. Wang, X., T. M. Hamill, J. S. Whitaker, and C. H. Bishop, 2007a: A comparison of hybrid ensemble transform Kalman filter-OI and ensemble square-root filter analysis schemes. Mon. Wea. Rev., 135, 1055-1076.
63. Wang, X., C. Snyder, and T. M. Hamill, 2007b: On the theoretical equivalence of differently proposed ensemble/3D-Var hybrid analysis schemes. Mon. Wea. Rev., 135, 222-227.
64. Wang, X., D. Barker, C. Snyder, and T. M. Hamill, 2008a: A hybrid ETKF-3DVARdata assimilation scheme for theWRFmodel. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116-5131.
65. Wang, X., D. Barker, C. Snyder, and T. M. Hamill, 2008b: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 5132-5147.
66. Wang, X., T. M. Hamill, J. S. Whitaker, and C. H. Bishop, 2009: A comparison of the hybrid and EnSRF analysis schemes in the presence of model error due to unresolved scales. Mon. Wea. Rev., 137, 3219-3232.
67. Wang, X., D. F. Parrish, D. T. Kleist, and J. S. Whitaker, 2013: GSI 3DVAR-based ensemble-variational hybrid data assimilation for NCEP Global Forecast System: Single-resolution experiments. Mon. Wea. Rev., 141, 4098-4117.
68. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.
69. Whitaker, J. S., and 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.
70. Zhang, F., Y. Weng, J. A. Sippel, Z. Meng, and C. H. Bishop, 2009a: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 2105-2125.
71. Zhang, F., M. Zhang, and J. A. Hansen, 2009b: Coupling ensemble Kalman filter with four-dimensional variational data assimilation. Adv. Atmos. Sci., 26, 1-8.
72. Zhang, F., M. Zhang, and J. A. Hansen, and J. Poterjoy, 2013: E3DVar: Coupling an ensemble Kalman filter with three-dimensional variational data assimilation in a limited-area weather prediction model and comparison to E4DVar. Mon. Wea. Rev., 141, 900-917.
73. Zhang, M., and F. Zhang, 2012: E4DVar: Coupling an ensemble Kalman filter with four-dimensional variational data assimilation in a limited-area weather prediction model. Mon. Wea. Rev., 140, 587-600.
74. Zhang, M., and F. Zhang, X.-Y. Huang, and X. Zhang, 2011: Intercomparison of an ensemble Kalman filter with three-and four-dimensional variational data assimilation methods in a limited-area model over the month of June 2003. Mon. Wea. Rev., 139, 566-572.