Comparison of hybrid four-dimensional data assimilation methods with and without the tangent linear and adjoint models for predicting the life cycle of Hurricane Karl (2010)

Monthly Weather Review

Volumn 144, Issue 4, 2016, Pages 1449-1468

  Poterjoy, Jonathan ^a,b   Zhang, Fuqing ^b  

^a NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Data assimilation; Ensembles; Forecasting; Kalman filters; Mathematical and statistical techniques; Models and modeling; Numerical weather prediction forecasting; Variational analysis

Indexed keywords

CLIMATE MODELS; ERROR STATISTICS; ERRORS; FORECASTING; HURRICANES; KALMAN FILTERS; LINEARIZATION; STORMS; VARIATIONAL TECHNIQUES;

DATA ASSIMILATION; ENSEMBLES; NUMERICAL WEATHER PREDICTION/FORECASTING; STATISTICAL TECHNIQUES; VARIATIONAL ANALYSIS;

WEATHER FORECASTING;

COMPARATIVE STUDY; COVARIANCE ANALYSIS; DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; FOUR-DIMENSIONAL MODELING; GEOSTATISTICS; HURRICANE EVENT; KALMAN FILTER; LIFE CYCLE ANALYSIS; LINEARITY; NUMERICAL MODEL; RELATIVE HUMIDITY; TROPICAL CYCLONE; WEATHER FORECASTING;

EID: 84966350551     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-15-0116.1     Document Type: Article

References (64)

1. Barker, D., and Coauthors, 2012: The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA. Bull. Amer. Meteor. Soc., 93, 831-843, doi:10.1175/BAMS-D-11-00167.1.
2. Barnes, S. L., 1964: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor., 3, 396-409, doi:10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2.
3. Bishop, C. H., and D. Hodyss, 2011: Adaptive ensemble covariance localization in ensemble4D-VAR state estimation. Mon. Wea. Rev., 139, 1241-1255, doi:10.1175/2010MWR3403.1.
4. Bishop, C. H., and E. A. Satterfield, 2013: Hidden error variance theory. Part I: Exposition and analytic model. Mon. Wea. Rev., 141, 1454-1468, doi:10.1175/MWR-D-12-00118.1.
5. Braun, S. A., and Coauthors, 2013: NASA's Genesis and Rapid Intensification Processes (GRIP) field experiment. Bull. Amer. Meteor. Soc., 94, 345-363, doi:10.1175/BAMS-D-11-00232.1.
6. 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.
7. Buehner, M., P. L. Houtekamer, C. Charette, H. 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.
8. Buehner, M., P. L. Houtekamer, C. Charette, H. 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.
9. Buehner, M., and Coauthors, 2015: Implementation of deterministic weather forecasting systems based on ensemble-variational data assimilation at Environment Canada. Part I: The global system. Mon. Wea. Rev., 143, 2532-2559, doi:10.1175/MWR-D-14-00354.1.
10. Clayton, A. M., A. C. Lorenc, and D. M. Barker, 2013: 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.
11. Courtier, P., J.-N. Thepáut, and A. Hollingsworth, 1994:Astrategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367-1387, doi:10.1002/qj.49712051912.
12. Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale twodimensional model. J. Atmos. Sci., 46, 3077-3107, doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.
13. 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.
14. Evensen, G., and P. J. van Leeuwen, 2000: An ensemble Kalman smoother for nonlinear dynamics. Mon. Wea. Rev., 128, 1852-1867, doi:10.1175/1520-0493(2000)128<1852:AEKSFN>2.0.CO;2.
15. Fisher, M., and E. Andersson, 2001: Developments in 4D-Var and Kalman filtering. Tech. Memo. 347, ECMWF, 36 pp.
16. 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.
17. Gustafsson, N., and J. Bojarova, 2014: Four-dimensional ensemble variational (4D-En-Var) data assimilation for the High Resolution Limited Area Model. Nonlinear Processes Geophys., 21, 745-762, doi:10.5194/npg-21-745-2014.
18. Hendricks, E. A., M. T. Montgomery, and C. A. Davis, 2004: The role of vortical hot towers in the formation of Tropical Cyclone Diane (1984). J. Atmos. Sci., 61, 1209-1232, doi:10.1175/1520-0469(2004)061<1209:TROVHT>2.0.CO;2.
19. Honda, Y., M. Nishijima, K. Koizumi, Y. Ohta, K. Tamiya, T. Kawabata, and T. Tsuyuki, 2005: A pre-operational variational data assimilation system for a nonhydrostatic model at Japan Meteorological Agency: Formulation and preliminary results. Quart. J. Roy. Meteor. Soc., 131, 3465-3475, doi:10.1256/qj.05.132.
20. Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice-microphysical processes for the bulk parameterization of cloud and precipitation. Mon. Wea. Rev., 132, 103-120, doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.
21. Huang, X., and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Wea. Rev., 137, 299-314, doi:10.1175/2008MWR2577.1.
22. Hunt, B. R., and Coauthors, 2004: Four-dimensional ensemble Kalman filtering. Tellus, 56A, 273-277, doi:10.1111/j.1600-0870.2004.00066.x.
23. Kleist, D. T., and K. Ide, 2015: An OSSE-based evaluation of hybrid variational-ensemble data assimilation for NCEP GFS. Part II: 4DEnVar and hybrid variants. Mon. Wea. Rev., 143, 452-470, doi:10.1175/MWR-D-13-00350.1.
24. Kuhl, D. D., T. E. Rosmond, C. H. Bishop, J. McLay, and N. L. Baker, 2013: Comparison of hybrid ensemble/4DVar and 4DVar within the NAVDAS-AR data assimilation framework. Mon. Wea. Rev., 141, 2740-2758, doi:10.1175/MWR-D-12-00182.1.
25. Liu, C., and Q. Xiao, 2013: An ensemble-based fourdimensional 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.
26. Liu, C., Q. Xiao, and B. Wang, 2008: An ensemble-based four-dimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test. Mon. Wea. Rev., 136, 3363-3373, doi:10.1175/2008MWR2312.1.
27. Liu, C., Q. Xiao, and B. Wang, 2009: An ensemble-based four-dimensional variational data assimilation scheme. Part II: Observing system simulation experiments with advanced research WRF (ARW). Mon. Wea. Rev., 137, 1687-1704, doi:10.1175/2008MWR2699.1.
28. Lorenc, A. C., 2003a: Modeling of error covariances by 4D-Var data assimilation. Quart. J. Roy. Meteor. Soc., 129, 3167-3182, doi:10.1256/qj.02.131.
29. Lorenc, A. C., 2003b: The potential of the ensemble Kalman filter for NWP: A comparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183-3203, doi:10.1256/qj.02.132.
30. Lorenc, A. C., N. E. Howler, A. M. Clayton, S. R. Pring, and D. Fairbairn, 2015: Comparison of hybrid-4DEnVar and hybrid-4DVar data assimilation methods for global NWP. Mon. Wea. Rev., 143, 212-229, doi:10.1175/MWR-D-14-00195.1.
31. Lorenz, E. N., 1996: Predictability: A problem partly solved. Proc. Seminar on Predictability, Vol. 1, ECMWF, Reading, United Kingdom, 1-18.
32. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 663-16 682, doi:10.1029/97JD00237.
33. Monin, A. S., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere. Tr. Akad. Nauk SSSR Geophiz. Inst., 24, 163-187.
34. Montgomery, M. T., and Coauthors, 2012: The Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) experiment: Scientific basis, new analysis tools, and some first results. Bull. Amer. Meteor. Soc., 93, 153-172, doi:10.1175/BAMS-D-11-00046.1.
35. Munsell, E. B., F. Zhang, and D. P. Stern, 2013: Predictability and dynamics of a nonintensifying tropical storm: Erica (2009). J. Atmos. Sci., 70, 2505-2524, doi:10.1175/JAS-D-12-0243.1.
36. Noh, Y., W.-G. Cheon, and S.-Y. Hong, 2003: Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor., 107, 401-427, doi:10.1023/A:1022146015946.
37. Nolan, D. S., 2007: What is the trigger for tropical cyclogenesis? Aust. Meteor. Mag., 56, 241-266.
38. Nolan, D. S., and M. G. McGauley, 2012: Tropical cyclogenesis in wind shear: Climatological relationships and physical processes. Cyclones: Formation, Triggers and Control, K. Oouchi and H. Fudeyasu, Eds., Nova Science Publishers, 1-34.
39. Parrish, D. F., and J. C. Derber, 1992: The National Meteorological 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.
40. Poterjoy, J., and F. Zhang, 2014a: Inter-comparison and coupling of ensemble and variational data assimilation approaches for the analysis and forecasting of Hurricane Karl (2010). Mon. Wea. Rev., 142, 3347-3364, doi:10.1175/MWR-D-13-00394.1.
41. Poterjoy, J., and F. Zhang, 2014b: Tropical cyclogenesis predictability examined through ensemble analyses and forecasts for Hurricane Karl (2010) during PREDICT. J. Atmos. Sci., 71, 1260-1275, doi:10.1175/JAS-D-13-0291.1.
42. Poterjoy, J., and F. Zhang, 2015: Systematic comparison of four-dimensional data assimilation methods with and without a tangent linear model using hybrid background error covariance: E4DVar versus 4DEnVar. Mon. Wea. Rev., 143, 1601-1621, doi:10.1175/MWR-D-14-00224.1.
43. Rabier, F., H. Järvinen, E. Klinker, J.-F. Mahfouf, and A. Simmons, 2000: The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126, 1143-1170, doi:10.1002/qj.49712656415.
44. Rappin, E. D., and D. S. Nolan, 2012: The effect of vertical shear orientation on tropical cyclogenesis. Quart. J. Roy. Meteor. Soc., 138, 1035-1054, doi:10.1002/qj.977.
45. Schecter, D.A., M. T. Montgomery, and P.D. Reasor, 2002:Atheory for the vertical alignment of a quasigeostrophic vortex. J. Atmos. Sci., 59, 150-168, doi:10.1175/1520-0469(2002)059<0150: ATFTVA>2.0.CO;2.
46. Sippel, J. A., and F. Zhang, 2008: A probabilistic analysis of the dynamics and predictability of tropical cyclogenesis. J. Atmos. Sci., 65, 3440-3459, doi:10.1175/2008JAS2597.1.
47. Sippel, J. A., and F. Zhang, 2010: Factors affecting the predictability of Hurricane Humberto (2007). J. Atmos. Sci., 67, 1759-1778, doi:10.1175/2010JAS3172.1.
48. Skamarock, W. C., and Coauthors., 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-4751STR, 113 pp., doi:10.5065/D68S4MVH. Stewart, S. R., 2010: Tropical cyclone report Hurricane Karl (AL1312010) 14-18 September 2010. Tech. Rep. AL1312010, National Hurricane Center, 17 pp. [Available online at http://www.nhc.noaa.gov/data/tcr/AL132010_Karl.pdf.].
49. Stewart, S. R., 2010: Tropical cyclone report Hurricane Karl (AL1312010) 14-18 September 2010. Tech. Rep. AL1312010, National Hurricane Center, 17 pp. [Available online at http://www.nhc.noaa.gov/data/tcr/AL132010_Karl.pdf.].
50. Sun, J., and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 1642-1661, doi:10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2.
51. Torn, R. D., and D. Cook, 2013: The role of vortex and environmental errors in genesis forecasts of Hurricanes Danielle and Karl (2010). Mon. Wea. Rev., 141, 232-251, doi:10.1175/MWR-D-12-00086.1.
52. Wang, X., and T. Lei, 2014: GSI-based four-dimensional ensemblevariational (4DEnsVar) data assimilation: Formulation and single-resolution experiments with real data for NCEP Global Forecast System. Mon. Wea. Rev., 142, 3303-3325, doi:10.1175/MWR-D-13-00303.1.
53. Wang, X., D. Barker, C. Snyder, and T. M. Hamill, 2008a: A hybrid ensemble transform Kalman filter (ETKF)-3DVAR data assimilation scheme for WRF. 11th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), San Antonio, TX, Amer. Meteor. Soc., P4.10. [Available online at https://ams.confex.com/ams/87ANNUAL/techprogram/paper_116592.htm.].
54. Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008b: A hybrid ETKF-3DVar data assimilation scheme for the WRF model. Part I: Observation system simulation experiment. Mon. Wea. Rev., 136, 5116-5131, doi:10.1175/2008MWR2444.1.
55. Wang, X., D. Parrish, D. Kleist, and J. Whitaker, 2013: GSI 3DVAR-based 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.
56. Wang, Z., M. T. Montgomery, and C. Fritz, 2012:Afirst look at the structure of the wave pouch during the 2009 PREDICT-GRIP dry runs of the Atlantic. Mon. Wea. Rev., 140, 1144-1163, doi:10.1175/MWR-D-10-05063.1.
57. Zhang, F., and D. Tao, 2013: Effects of vertical wind shear on the predictability of tropical cyclones. J. Atmos. Sci., 70, 975-983, doi:10.1175/JAS-D-12-0133.1.
58. Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter.Mon.Wea. Rev., 132, 1238-1253, doi:10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.
59. Zhang, F., M. Zhang, and J. A. Hansen, 2009: Coupling ensemble Kalman filter with four-dimensional variational data assimilation. Adv. Atmos. Sci., 26, 1-8, doi:10.1007/s00376-009-0001-8.
60. 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.
61. Zhang, M., and F. Zhang, 2012: E4DVar: Coupling an ensemble Kalman filter with four-dimensional data assimilation in a limited-area weather prediction model. Mon. Wea. Rev., 140, 587-600, doi:10.1175/MWR-D-11-00023.1.
62. Zhang, X., X.-Y. Huang, and N. Pan, 2013: Development of the upgraded tangent linear and adjoint of the Weather Research and Forecasting (WRF) Model. J. Atmos. Oceanic Technol., 30, 1180-1188, doi:10.1175/JTECH-D-12-00213.1.
63. Zhang, X., X.-Y. Huang, J. Liu, J. Poterjoy, Y. Weng, F. Zhang, and H. Wang, 2014: Development of an efficient regional four-dimensional variational data assimilation system for WRF. J. Atmos. Oceanic Technol., 31, 2777-2794, doi:10.1175/JTECH-D-13-00076.1.
64. Zupanski, M., 2005: Maximum likelihood ensemble filter: Theoretical aspects. Mon. Wea. Rev., 133, 1710-1726, doi:10.1175/MWR2946.1.