A GSI-based coupled EnSRF-En3DVar hybrid data assimilation system for the operational rapid refresh model: Tests at a reduced resolution

Monthly Weather Review

Volumn 142, Issue 10, 2014, Pages 3756-3780

  Pan, Yujie ^a,b   Zhu, Kefeng ^a,c   Xue, Ming ^c,d   Wang, Xuguang ^d   Hu, Ming ^e   Benjamin, Stanley G ^f   Weygandt, Stephen S ^c   Whitaker, Jeffrey S ^c  

^a Center for Analysis and Prediction of Storms   (United States)

^b NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^c NANJING UNIVERSITY   (China)

^d UNIVERSITY OF OKLAHOMA   (United States)

^e UNIVERSITY OF COLORADO   (United States)

^f NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HYBRID DATUM; REDUCED RESOLUTION;

COVARIANCE ANALYSIS; DATA ASSIMILATION; DOWNSCALING; ENSEMBLE FORECASTING; INTERPOLATION; NUMERICAL MODEL; PRECIPITATION ASSESSMENT; WEATHER FORECASTING;

EID: 84907426920     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-13-00242.1     Document Type: Article

References (75)

1. Anderson, J.L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903, doi:10.1175/ 1520-0493(2001)129,2884:AEAKFF.2.0.CO;2.
2. Anderson, J.L., 2007: An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus, 59A, 210-224, doi:10.1111/ j.1600-0870.2006.00216.x.
3. Anderson, J.L., 2012: Localization and sampling error correction in ensemble Kalman filter data assimilation. Mon. Wea. Rev., 140, 2359- 2371, doi:10.1175/MWR-D-11-00013.1.
4. Barker, D.M., W. Huang, Y.R. Guo, A.J. Bourgeois, and Q.N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897-914, doi:10.1175/1520-0493(2004)132,0897: ATVDAS.2.0.CO;2.
5. Benjamin, S.G., and Coauthors, 2004: An hourly assimilation- forecast cycle: The RUC. Mon. Wea. Rev., 132, 495-518, doi:10.1175/1520-0493(2004)132,0495:AHACTR.2.0.CO;2.
6. Benjamin, S.G., and Coauthors, 2009: Technical review of rapid refresh/RUC project. NOAA/ESRL/GSD internal review, 3 Nov 2009. [Available online at http://ruc.noaa.gov/pdf/RR-RUC-TR_11_3_ 2009.pdf.]
7. 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, doi:10.1175/ 1520-0493(2001)129,0420:ASWTET.2.0.CO;2.
8. Bonavita, M., L. Torrisi, and F. Marcucci, 2008: The ensemble Kalman filter in an operational regionalNWPsystem: Preliminary results with real observations. Quart. J. Roy. Meteor. Soc., 134, 1733-1744, doi:10.1002/qj.313.
9. Bonavita, M., L. Isaksen, and E. Hólm, 2012: On the use of EDA background error variances in the ECMWF 4D-Var. Quart. J. Roy. Meteor. Soc., 138, 1540-1559, doi:10.1002/qj.1899.
10. Brown, B., J.H. Gotway, R. Bullock, E. Gilleland, T. Fowler, D. Ahijevych, and T. Jensen, 2009: The Model Evaluation Tools (MET): Community tools for forecast evaluation. 25th Conf. on Int. Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc., 9A.6. [Available online at https://ams.confex.com/ams/pdfpapers/151349.pdf.]
11. Buehner, M., 2005: Ensemble-derived stationary and flowdependent background-error covariances: Evaluation in a quasi-operational NWP setting. Quart. J. Roy. Meteor. Soc., 131, 1013-1043, doi:10.1256/qj.04.15.
12. Brown, B., and A. Mahidjiba, 2010: Sensitivity of global ensemble forecasts to the initial ensemble mean and perturbations: Comparison of EnKF, singular vector, and 4D-Var approaches. Mon. Wea. Rev., 138, 3886-3904, doi:10.1175/2010MWR3296.1.
13. Brown, B., 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, doi:10.1175/2009MWR3157.1.
14. Brown, B., 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, doi:10.1175/2009MWR3158.1.
15. Burgers, G., P.J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719-1724, doi:10.1175/1520-0493(1998)126,1719: ASITEK.2.0.CO;2.
16. 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, doi:10.1175/ 2009MWR3017.1.
17. 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, doi:10.1175/ MWR3414.1.
18. Courtier, P., and Coauthors, 1998: The ECMWF implementation of three-dimensional variational assimilation (3D-Var). I: formulation. Quart. J. Roy. Meteor. Soc., 124, 1783-1807, doi:10.1002/ qj.49712455002.
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, doi:10.1029/94JC00572.
20. Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343-367, doi:10.1007/s10236-003-0036-9.
21. Fujita, T., D.J. Stensrud, and D.C. Dowell, 2007: Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties. Mon. Wea. Rev., 135, 1846-1868, doi:10.1175/MWR3391.1.
22. Gandin, L.S., and A.H. Murphy, 1992: Equitable skill scores for categorical forecasts. Mon. Wea. Rev., 120, 361-370, doi:10.1175/ 1520-0493(1992)120,0361:ESSFCF.2.0.CO;2.
23. Gao, J.-D., M. Xue, K. Brewster, and K.K. Droegemeier, 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457-469, doi:10.1175/1520-0426(2004)021,0457: ATVDAM.2.0.CO;2.
24. Gaspari, G., and S.E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723-757, doi:10.1002/qj.49712555417.
25. Ge, G., J. Gao, M. Xue, and K.K. Droegemeier, 2012: Diagnostic pressure equation as a weak constraint in a storm-scale three dimensional variational radar data assimilation system. J. Atmos. Oceanic Technol., 29, 1075-1092, doi:10.1175/ JTECH-D-11-00201.1.
26. Hamill, T.M., and C. Snyder, 2000: A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Wea. Rev., 128, 2905-2919, doi:10.1175/1520-0493(2000)128,2905: AHEKFV.2.0.CO;2.
27. 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, doi:10.1175/ 1520-0493(2001)129,2776:DDFOBE.2.0.CO;2.
28. 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, doi:10.1175/2010MWR3456.1.
29. 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, doi:10.1175/MWR-D-11-00079.1.
30. Hayden, C.M., and R.J. Purser, 1995: Recursive filter objective analysis of meteorological fields: Applications to NESDIS operational processing. J. Appl. Meteor., 34, 3-15, doi:10.1175/ 1520-0450-34.1.3.
31. Houtekamer, P.L., and H.L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811, doi:10.1175/1520-0493(1998)126,0796: DAUAEK.2.0.CO;2.
32. 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, doi:10.1175/MWR-2864.1.
33. 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, doi:10.1016/ j.physd.2006.11.008.
34. Jung, Y., M. Xue, and M. Tong, 2012: Ensemble Kalman filter analyses of the 29-30 May 2004 Oklahoma tornadic thunderstorm using one- and two-moment bulk microphysics schemes, with verification against polarimetric data. Mon. Wea. Rev., 140, 1457-1475, doi:10.1175/MWR-D-11-00032.1.
35. Kleist, D.T., D.F. Parrish, J.C. Derber, R. Treadon, W.-S. Wu, and S. Lord, 2009a: Introduction of the GSI into the NCEP global data assimilation system. Wea. Forecasting, 24, 1691-1705, doi:10.1175/2009WAF2222201.1.
36. Kleist, D.T., D.F. Parrish, J.C. Derber, R. Treadon, R.M. Errico, and R. Yang, 2009b: Improving incremental balance in the GSI 3DVAR analysis system. Mon. Wea. Rev., 137, 1046-1060, doi:10.1175/ 2008MWR2623.1.
37. Le Dimet, F.X., and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 38A, 97-110, doi:10.1111/ j.1600-0870.1986.tb00459.x.
38. Li, Y., X. Wang, and M. Xue, 2012: Assimilation of radar radial velocity data with the WRF ensemble-3DVAR hybrid system for the prediction of Hurricane Ike (2008). Mon. Wea. Rev., 140, 3507-3524, doi:10.1175/MWR-D-12-00043.1.
39. Lin, Y., and K.E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor.Soc., 1.2. [Available online at https://ams.confex.com/ams/Annual2005/ techprogram/paper_83847.htm.]
40. Liu, C., and Q. Xiao, 2013: An ensemble-based four-dimensional variational data assimilation scheme. Part III: Antarctic applications with Advanced Research WRF using real data. Mon. Wea. Rev., 141, 2721-2739, doi:10.1175/MWR-D-12-00130.1.
41. Lin, Y., K.E. Mitchell, and B. Wang, 2008: An ensemble-based fourdimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test. Mon. Wea. Rev., 136, 3363-3373, doi:10.1175/2008MWR2312.1.
42. Lorenc, A.C., 1986: Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 112, 1177-1194, doi:10.1002/qj.49711247414.
43. Lorenc, A.C., 2003: The potential of the ensemble Kalman filter forNWP- A comparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183-3204, doi:10.1256/qj.02.132.
44. Lynch, P., 1997: The Dolph-Chebyshev window: A simple optimal filter. Mon. Wea. Rev., 125, 655-660, doi:10.1175/ 1520-0493(1997)125,0655:TDCWAS.2.0.CO;2.
45. Lynch, P., and X.-Y. Huang, 1992: Initialization of the HIRLAMmodel using a digital filter. Mon. Wea. Rev., 120, 1019-1034, doi:10.1175/1520-0493(1992)120,1019:IOTHMU.2.0.CO;2.
46. Lynch, P., and X.-Y. Huang, 1994: Diabatic initialization using recursive filters. Tellus, 46A, 583-597, doi:10.1034/j.1600-0870.1994.t01-4-00003.x.
47. Meng, Z., and F. Zhang, 2007: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect model experiments. Mon. Wea. Rev., 135, 1403- 1423, doi:10.1175/MWR3352.1.
48. Mizzi, A.P., 2012: Comparison of the GSI/ETKF regional hybrid using WRF/ARW with GSI/LETKF and GSI/EnKF regional hybrids. 16th Conf. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, NewOrleans, LA,Amer.Meteor. Soc., 9.3. [Available online at https://ams.confex.com/ams/92Annual/webprogram/Paper199538. html.]
49. Parrish, D.F., and J.C. Derber, 1992: The NationalMeteorological Center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763, doi:10.1175/1520-0493(1992)120,1747: TNMCSS.2.0.CO;2.
50. Purser, R.J., W.-S. Wu, D.F. Parrish, and N.M. Roberts, 2003a: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131, 1524-1535, doi:10.1175//1520-0493(2003)131,1524: NAOTAO.2.0.CO;2.
51. Purser, R.J., W.-S. Wu, D.F. Parrish, and N.M. Roberts, 2003b: Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131, 1536-1548, doi:10.1175//2543.1.
52. Putnam, B.J., M. Xue, Y. Jung, N.A. Snook, and G. Zhang, 2014: The analysis and prediction of microphysical states and polarimetric variables in a mesoscale convective system using double-moment microphysics, multi-network radar data, and the ensemble Kalman filter. Mon. Wea. Rev., 142, 141-162, doi:10.1175/MWR-D-13-00042.1.
53. Rabier, F., H. Jarvinen, E. Klinker, J.-F. Mahfouf, and A. Simmons, 2000: The ECMWF operational implementation of fourdimensional variational assimilation. I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126, 1143-1170, doi:10.1002/qj.49712656415.
54. Raynaud, L., L. Berre, and G. Desroziers, 2011: An extended specification of flow-dependent background error variances in the Meteo-France global 4D-Var system. Quart. J. Roy. Meteor. Soc., 137, 607-619, doi:10.1002/qj.795.
55. Schwartz, C.S., and Z. Liu, 2014: Convection-permitting forecasts initialized with continuously cycling limited-area 3DVAR, ensemble Kalman filter, and ''hybrid'' variational-ensemble data assimilation systems. Mon. Wea. Rev., 142, 716-738, doi:10.1175/MWR-D-13-00100.1.
56. Skamarock, W.C., J.B. Klemp, J. Dudhia, D.O. Gill, D.M. Barker, W. Wang, and J.D. Powers, 2005:Adescription of the advanced research WRF version 2. NCAR Tech. Note NCAR/TN-4681STR, 88 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v2.pdf.]
57. Talagrand, O., and P. Courtier, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation. Part I: Theory. Quart. J. Roy. Meteor. Soc., 113, 1311- 1328, doi:10.1002/qj.49711347812.
58. 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, doi:10.1175/1520-0493(2003)131,1485: ESRF.2.0.CO;2.
59. Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 1789-1807, doi:10.1175/MWR2898.1.
60. Wang, X., 2010: Incorporating ensemble covariance in the Gridpoint Statistical Interpolation variational minimization: A mathematical framework. Mon. Wea. Rev., 138, 2990-2995, doi:10.1175/2010MWR3245.1.
61. Wang, X., 2011: Application of the WRF hybrid ETKF-3DVAR data assimilation system for hurricane track forecasts. Wea. Forecasting, 26, 868-884, doi:10.1175/WAF-D-10-05058.1.
62. Wang, X., C. Snyder, and T.M. Hamill, 2007: On the theoretical equivalence of differently proposed ensemble/VAR hybrid analysis schemes. Mon. Wea. Rev., 135, 222-227, doi:10.1175/ MWR3282.1.
63. Wang, X., D.M. Barker, C. Snyder, and T.M. Hamill, 2008a: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116-5131, doi:10.1175/2008MWR2444.1.
64. Wang, X., D.M. Barker, C. Snyder, and T.M. Hamill, 2008b: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiment. Mon. Wea. Rev., 136, 5132-5147, doi:10.1175/2008MWR2445.1.
65. 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 errors due to unresolved scales. Mon. Wea. Rev., 137, 3219-3232, doi:10.1175/2009MWR2923.1.
66. Wang, X., D. Parrish, D. Kleist, and J. Whitaker, 2013: GSI 3DVarbased ensemble-variational hybrid data assimilation for NCEP Global Forecast System: Single-resolution experiments. Mon. Wea. Rev., 141, 4098-4117, doi:10.1175/ MWR-D-12-00141.1.
67. Whitaker, J.S., and T.M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924, doi:10.1175/1520-0493(2002)130,1913:EDAWPO.2.0.CO;2.
68. 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, doi:10.1175/2007MWR2018.1.
69. Whitaker, J.S., D.T. Kleist, X. Wang, and T. Hamill, 2011: Tests of a hybrid variational-ensemble global data assimilation system for hurricane prediction. 24th Conf. on Weather Forecasting/20th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., J16.2. [Available online at https://ams.confex.com/ams/91Annual/webprogram/Paper178847.html.]
70. Wu,W.-S., R.J. Purser, and D.F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 2905-2916, doi:10.1175/ 1520-0493(2002)130,2905:TDVAWS.2.0.CO;2.
71. Xue, M., Y. Jung, and G. Zhang, 2010: State estimation of convective storms with a two-moment microphysics scheme and an ensemble Kalman filter: Experiments with simulated radar data. Quart. J. Roy. Meteor. Soc., 136, 685-700, doi:10.1002/ qj.593.
72. Xue, M., J. Schleif, F. Kong, K.K. Thomas, Y. Wang, and K. Zhu, 2013: Track and intensity forecasting of hurricanes: Impact of cloud-resolving resolution and ensemble Kalman filter data assimilation on 2010 Atlantic season forecasts. Wea. Forecasting, 28, 1366-1384, doi:10.1175/WAF-D-12-00063.1.
73. Zhang, F., M. Zhang, 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, doi:10.1175/MWR-D-12-00075.1.
74. 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, doi:10.1175/MWR-D-11-00023.1.
75. Zhu, K., Y. Pan, M. Xue, X. Wang, J.S. Whitaker, S.G. Benjamin, S.S. Weygandt, and M. Hu, 2013: A regional GSI-based ensemble Kalman filter data assimilation system for the rapid refresh configuration: Testing at reduced resolution. Mon. Wea. Rev., 141, 4118-4139, doi:10.1175/MWR-D-13-00039.1.