Intercomparison and coupling of ensemble and four-dimensional variational data assimilation methods for the analysis and forecasting of Hurricane Karl (2010)

Monthly Weather Review

Volumn 142, Issue 9, 2014, Pages 3347-3364

  Poterjoy, Jonathan ^a   Zhang, Fuqing ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HURRICANES; HYBRID SYSTEMS;

DATA ASSIMILATION METHODS; ENSEMBLE KALMAN FILTER; FOUR-DIMENSIONAL VARIATIONAL DATA ASSIMILATION; INTENSITY PREDICTION; RAPID INTENSIFICATION; TROPICAL CYCLONE PREDICTIONS; VARIATIONAL DATA ASSIMILATION SYSTEM; WEATHER RESEARCH AND FORECASTING MODELS;

WEATHER FORECASTING;

EID: 84906888369     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-13-00394.1     Document Type: Article

References (43)

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.
2. Barnes, S. L., 1964:A technique for maximizing details in numerical weather map analysis. J. Appl.Meteor., 3, 396-409.
3. Braun, S. A., and Coauthors, 2013: NASA's Genesis and Rapid Intensification Processes (GRIP) field experiment. Bull. Amer. Meteor. Soc., 94, 345-363.
4. 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.
5. 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.
6. 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.
7. 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.
8. Davis, C. A., and D. A. Ahijevych, 2012: Mesoscale structural evolution of three tropical weather systems observed during PREDICT. J. Atmos. Sci., 69, 1284-1305.
9. Dunkerton, T. J., M. T. Montgomery, and Z. Wang, 2009: Tropical cyclogenesis in a tropical wave critical layer: Easterly waves. Atmos. Chem. Phys., 9, 5587-5646.
10. 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.
11. Fang, J., and F. Zhang, 2011: Evolution of multiscale vortices in the development of Hurricane Dolly (2008). J. Atmos. Sci., 68, 103-122.
12. Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723-757.
13. Halverson, J., and Coauthors, 2007: NASA's Tropical Cloud Systems and Processes experiment: Investigating tropical cyclogenesis and hurricane intensity change. Bull. Amer. Meteor. Soc., 88, 867-882.
14. 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.
15. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
16. Houtekamer, P. L., and H. L. Mitchell, 2005: Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 3269-3289.
17. Huang, X.-Y., and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Wea. Rev., 137, 299-314.
18. Kalnay, E., H. Li, T. Miyoshi, S.-C. Yang, and J. Ballabrera-Poy, 2007: 4D-Var or ensemble Kalman filter? Tellus, 59A, 758- 773.
19. Kuhl,D.D.,T.E.Rosmond,C.H.Bishop, J.McLay, andN.L.Baker, 2013: Comparison of hybrid ensemble/4DVar and 4DVar within the NAVDAS-AR data assimilation framework. Mon. Wea. Rev., 141, 2740-2758.
20. Lorenc, A. C., 2003a: Modeling of error covariances by 4D-Var data assimilation. Quart. J. Roy. Meteor. Soc., 129, 3167-3182.
21. 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.
22. Meng, Z., and F. Zhang, 2008a: Test 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.
23. Meng, Z., and F. Zhang, 2008b: Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part VI: Performance over a warm season month of June 2003. Mon. Wea. Rev., 136, 3671-3682.
24. Meng, Z., and F. Zhang 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.
25. Poterjoy, J., and F. Zhang, 2014: Predictability and genesis of Hurricane Karl (2010) examined through the EnKF assimilation of field observations collected during PREDICT. J. Atmos. Sci., 71, 1260-1275.
26. Reasor, P. D., M. T. Montgomery, and L. F. Bosart, 2005: Mesoscale observations of the genesis of Hurricane Dolly (1996). J. Atmos. Sci., 62, 3151-3171.
27. Sippel, J. A., and F. Zhang, 2008: A probabilistic analysis of the dynamics and predictability of tropical cyclogenesis. J. Atmos. Sci., 65, 3440-3459.
28. Sippel, J. A., and F. Zhang 2010: Factors affecting the predictability of Hurricane Humberto (2007). J. Atmos. Sci., 67, 1759-1778.
29. Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/ TN-4751STR, 113 pp. [Available online at http://www.mmm. ucar.edu/wrf/users/docs/arw_v3_bw.pdf.]
30. Stewart, S. R., 2010: Tropical cyclone report Hurricane Karl 14-18 September 2010. Tech. Rep. AL1312010, National Hurricane Center, 17 pp. [Available online at http://www.nhc.noaa.gov/ pdf/TCR-AL132010_Karl.pdf.]
31. Thepáut, J.-N., P. Courtier, G. Belaud, and G. Lamaître, 1996: Dynamical structure functions in a four-dimensional variational assimilation:Acase study.Quart. J.Roy.Meteor. Soc., 122, 535- 561.
32. 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.
33. Wang, X., C. Snyder, and T. M. Hamill, 2007: On the theoretical equivalence of differently proposed ensemble-3DVARhybrid analysis schemes. Mon. Wea. Rev., 135, 222-227.
34. Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008: A hybrid ETKF-3DVar data assimilation scheme for the WRF model. Part I: Observation system simulation experiment. Mon. Wea. Rev., 136, 5116-5131.
35. Wang, X., D. F. Parrish, and J. S. W. D. Kleist, 2013: GSI 3DVar-based ensemble variational hybrid data assimilation for NCEP Global Forecast System: Single-resolution experiments. Mon. Wea. Rev., 141, 4098-4117.
36. Wang, X., M. T. Montgomery, and C. Fritz, 2012: A first look at the structure of the wave pouch during the 2009 PREDICT-GRIP dry runs of the Atlantic. Mon. Wea. Rev., 140, 1144-1163.
37. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.
38. 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.
39. 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.
40. Zhang, F.,Y.Weng, J. F. Gamache, and F. D. Marks, 2011: Performance of convection-permitting hurricane initialization and prediction during 2008-2010 with ensemble data assimilation of inner-core airborne Doppler radar observations. Geophys. Res. Lett., 38, L15810.
41. 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.
42. Zhang, M., 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.
43. Zhang, M., 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.