Method for identifying the sensitive areas in targeted observations for tropical cyclone prediction: Conditional nonlinear optimal perturbation

Monthly Weather Review

Volumn 137, Issue 5, 2009, Pages 1623-1639

  Mu, Mu ^a,c   Zhou, Feifan ^a,b   Wang, Hongli ^a,b  

^a INSTITUTE OF ATMOSPHERIC PHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c NONE   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL NONLINEAR OPTIMAL PERTURBATION; HUMIDITY CHANGE; INITIAL STATE; LINEAR SINGULAR VECTOR; NATURAL EXTENSION; NON-LINEAR REGIMES; NONLINEAR EVOLUTIONS; SENSITIVE AREA; SINGULAR VECTORS; SPATIAL STRUCTURE; TROPICAL CYCLONE; TROPICAL CYCLONE PREDICTIONS; TYPE ERRORS;

EXPERIMENTS; HURRICANES; OPTIMIZATION; STORMS; TROPICS;

ERRORS;

CLIMATE PREDICTION; ERROR ANALYSIS; KINETIC ENERGY; OBSERVATIONAL METHOD; OPTIMIZATION; RELATIVE HUMIDITY; SENSITIVITY ANALYSIS; TROPICAL CYCLONE; VECTOR; WEATHER FORECASTING;

EID: 68249155000     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2008MWR2640.1     Document Type: Article

References (65)

1. Aberson, S. D., 2002: Two years of operational hurricane synoptic surveillance. Wea. Forecasting, 17, 1101-1110.
2. Ancell, B. C., and C. F. Mass, 2006: Structure, growth rates, and tangent linear accuracy of adjoint sensitivities with respect to horizontal and vertical resolution. Mon. Wea. Rev., 134, 2971-2988.
3. Barkmeijer, J., 1996: Constructing fast-growing perturbations for the nonlinear regime. J. Atmos. Sci., 53, 2838-2851.
4. Barkmeijer, J., R. Buizza, T. N. Palmer, K. Puri, and J.-F. Mahfouf, 2001: Tropical singular vectors computed with linearized diabatic physics. Quart. J. Roy. Meteor. Soc., 127, 685-708.
5. Barkmeijer, J., T. Iversen, and T. N. Palmer, 2003: Forcing singular vectors and other sensitive model structures. Quart. J. Roy. Meteor. Soc., 129, 2401-2423.
6. Bergot, T., 1999: Adaptive observations during FASTEX: A systematic survey of upstream flights. Quart. J. Roy. Meteor. Soc., 125, 3271-3298.
7. Bergot, T., G. Hello, A. Joly, and S. Malardel, 1999: Adaptive observations: A feasibility study. Mon. Wea. Rev., 127, 743-765.
8. Birgin, E. G., J. E. Martinez, and R. Marcos, 2001: Algorithm 813: SPG - software for convex-constrained optimization. ACM Trans. Math. Softw., 27, 340-349.
9. Bishop, C. H., and Z. Toth, 1999: Ensemble transformation and adaptive observations. J. Atmos. Sci., 56, 1748-1765.
10. 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.
11. Buizza, R., R. Gelaro, F. Molteni, and T. N. Palmer, 1997: The impact of increased resolution on predictabiity studies with singular vectors. Quart. J. Roy. Meteor. Soc., 123, 1007-1033.
12. Buizza, R., C. Cardinali, G. Kelly, and J. Thépaut, 2007: The value of targeted observations. Part II: The value of observations taken in singular vectors-based target areas. Quart. J. Roy. Meteor. Soc., 133, 1817-1832.
13. Chan, J. C. L., 1995: Tropical cyclone activity in the western North Pacific in relation to the stratospheric quasi-biennial oscillation. Mon. Wea. Rev., 123, 2567-2571.
14. Chan, J. C. L., 2000: Tropical cyclone activity over the western North Pacific associated with El Niño and La Niña events. J. Climate, 13, 2960-2972.
15. Corbosiero, K. L., and J. Molinari, 2003: The relationship between storm motion, vertical wind shear, and convective asymmetries in tropical cyclones. J. Atmos. Sci., 60, 366-376.
16. Duan, W. S., and M. Mu, 2006: Investigating decadal variability of El Niño-Southern Oscillation asymmetry by conditional nonlinear optimal perturbation. J. Geophys. Res., 111, C07015, doi:10.1029/2005JC003458.
17. Duan, W. S., M. Mu, and B. Wang, 2004: Conditional nonlinear optimal perturbations as the optimal precursors for El Niño-Southern Oscillation events. J. Geophys. Res., 109, D23105, doi:10.1029/2004JD004756.
18. Dudhia, J., 1993: A nonhydrostatic version of the Penn State/NCAR Mesoscale Model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121, 1493-1513.
19. Ehrendorfer, M., and R. M. Errico, 1995: Mesoscale predictability and the spectrum of optimal perturbations. J. Atmos. Sci., 52, 3475-3500.
20. Ehrendorfer, M., R. M. Errico, and K. D. Raeder, 1999: Singular-vector perturbation growth in a primitive equation model with moist physics. J. Atmos. Sci., 56, 1627-1648.
21. Errico, R. M., K. D. Raeder, and M. Ehrendorfer, 2004: Singular vectors for moisture-measuring norms. Quart. J. Roy. Meteor. Soc., 130, 963-987.
22. Franklin, J. L., and M. DeMaria, 1992: The impact of omega dropwindsonde observations on barotropic hurricane track forecasts. Mon. Wea. Rev., 120, 381-391.
23. Froude, L. S. R., L. Bengtsson, and K. I. Hodges, 2007: The predictability of extratropical storm tracks and the sensitivity of their prediction to the observing system. Mon. Wea. Rev., 135, 315-333.
24. Gelaro, R., R. H. Langland, G. D. Rohaly, and T. E. Rosmond, 1999: An assessment of the singular-vector approach to targeted observations using the FASTEX dataset. Quart. J. Roy. Meteor. Soc., 125, 3299-3327.
25. Gelaro, R., T. Rosmond, and R. Daley, 2002: Singular vector calculations with an analysis error variance metric. Mon. Wea. Rev., 130, 1166-1186.
26. Hamill, T. M., and C. Snyder, 2002: Using improved background-error covariance from an ensemble Kalman filter for adaptive observations. Mon. Wea. Rev., 130, 1552-1572.
27. Henderson, J.M., R. N. Hoffman, S. M. Leidner, T. Nehrkorn, and C. Grassotti, 2005: A 4D-VAR study on the potential of weather control and exigent weather forecasting. Quart. J. Roy. Meteor. Soc., 131, 3037-3052.
28. Hoffman, R. N., 2006: Using 4d-VAR to move a simulated tropical cyclone in a mesoscale model. Comput. Math. Appl., 52 (8-9), 1193-1204.
29. Hoffman, R. N., J. M. Henderson, S. M. Leidner, C. Grassotti, and T. Nehrkorn, 2006: The response of damaging winds of a simulated tropical cyclone to finite amplitude perturbations of different variables. J. Atmos. Sci., 63, 1924-1937.
30. Hoskins, B. J., and M. M. Coutinho, 2005: Moist singular vectors and the predictability of some high impact European cyclones. Quart. J. Roy. Meteor. Soc., 131, 581-601.
31. Joly, A., and Coauthors, 1997: The Fronts and Atlantic Storm-Track Experiment (FASTEX): Scientific objectives and experimental design. Bull. Amer. Meteor. Soc., 78, 1917-1940.
32. Kim, H. M., M. C. Morgan, and R. E. Morss, 2004: Evolution of analysis error and adjoint-based sensitivities: Implications for adaptive observations. J. Atmos. Sci., 61, 795-812.
33. Kitade, T., 1981: A numerical study of the vortex motion with barotropic models. J. Meteor. Soc. Japan, 59, 801-807.
34. Langland, R. H., 2005: Issues in targeted observing. Quart. J. Roy. Meteor. Soc., 131, 3409-3425.
35. Langland, R. H., and Coauthors, 1999a: The North Pacific Experiment (NORPEX-98): Targeted observations for improved North American weather forecasts. Bull. Amer. Meteor. Soc., 80, 1363-1384.
36. Langland, R. H., R. Gelaro, G. D. Rohaly, and M. A. Shapiro, 1999b: Targeted observations in FASTEX: Adjoint based targeting procedures and data impact experiments in IOP/7 and IOP/8. Quart. J. Roy. Meteor. Soc., 125, 3241-3270.
37. Lorenz, E. N., and K. A. Emanuel, 1998: Optimal sites for supplementary weather observations: Simulation with a small model. J. Atmos. Sci., 55, 399-414.
38. Morss, R. E., K. A. Emanuel, and C. Snyder, 2001: Idealized adaptive observation strategies for improving numerical weather prediction. J. Atmos. Sci., 58, 210-232.
39. Mu, M., and W. S. Duan, 2003: A new approach to studying ENSO predictability: Conditional nonlinear optimal perturbation. Chin. Sci. Bull., 48, 747-749.
40. Mu, M., and Z. Y. Zhang, 2006: Conditional nonlinear optimal perturbations of a barotropic model. J. Atmos. Sci., 63, 1587-1604.
41. Mu, M., and Z. N. Jiang, 2008: A new method to generate the initial perturbations in ensemble forecast: Conditional nonlinear optimal perturbations. Chin. Sci. Bull., 53, 2062-2068.
42. Mu, M., L. Sun, and D. A. Hank, 2004: The sensitivity and stability of the ocean's thermohaline circulation to finite amplitude perturbations. J. Phys. Oceanogr., 34, 2305-2315.
43. Mu, M., H. L. Wang, and F. F. Zhou, 2007a: A preliminary application of conditional nonlinear optimal perturbation to adaptive observation. Chin. J. Atmos. Sci., 31, 1102-1112.
44. Mu, M., H. Xu, and W. S. Duan, 2007b: A kind of initial errors related to "spring predictability barrier" for El Niño events in Zebiak-Cane model. Geophys. Res. Lett., 34, L03709, doi:10.1029/2006GL027412.
45. Oortwijn, J., 1998: Predictability of the onset of blocking and strong zonal flow regimes. J. Atmos. Sci., 55, 973-994.
46. Oortwijn, J., and J. Barkmeijer, 1995: Perturbations that optimally trigger weather regimes. J. Atmos. Sci., 52, 3932-3944.
47. Palmer, T. N., R. Gelaro, J. Barkmeijer, and R. Buizza, 1998: Singular vectors, metrics, and adaptive observations. J. Atmos. Sci., 55, 633-653.
48. Petersen, G. N., R. Dumelow, and A. J. Thorpe, 2006: Impact of NATReC observations on global model forecasts. Geophysical Research Abstracts, Vol. 8, Abstract 08999.
49. Pu, Z. X., E. Kalnay, J. Sela, and I. Szunyogh, 1997: Sensitivity of forecast errors to initial conditions with a quasi-inverse linear method. Mon. Wea. Rev., 125, 2479-2503.
50. Puri, K., J. Barkmeijer, and T. N. Palmer, 2001: Ensemble prediction of tropical cyclones using targeted diabatic singular vectors. Quart. J. Roy. Meteor. Soc., 127, 709-731.
51. Reynolds, C. A., and T. E. Rosmond, 2003: Nonlinear growth of singular vector-based perturbations. Quart. J. Roy. Meteor. Soc., 129, 3059-3078.
52. Riehl, H., W. H. Haggard, and R. W. Sanborn, 1956: On the prediction of 24-hour hurricane motion. J. Meteor., 13, 415-420.
53. Rivière, O., G. Lapeyre, and O. Talagrand, 2008: Nonlinear generalization of singular vectors: behavior in a baroclinic unstable flow. J. Atmos. Sci., 65, 1896-1911.
54. Rosenthal, S. L., 1978: Numerical simulation of tropical cyclone development with latent heat release by the resolvable scales. I: Model description and preliminary results. J. Atmos. Sci., 35, 258-271.
55. Schade, L. R., and K. A. Emanuel, 1999: The ocean's effect on the intensity of tropical cyclones: Results from a simple coupled atmosphere-ocean model. J. Atmos. Sci., 56, 642-651.
56. Simmons, A. J., R. Mereau, and T. Petroliagis, 1995: Error growth and estimates of predictability from the ECWMF forecasting system. Quart. J. Roy. Meteor. Soc., 121, 1739-1771.
57. Snyder, C., 1996: Summary of an informal workshop on adaptive observations and FASTEX. Bull. Amer. Meteor. Soc., 77, 953-961.
58. Sun, L., M. Mu, D. J. Sun, and X. Y. Yin, 2005: Passive mechanism of decadal variation of thermohaline circulation. J. Geophys. Res., 110, C07025, doi:10.1029/2005JC002897.
59. Szunyogh, I., Z. Toth, S. Majumdar, R. Morss, B. Etherton, and C. Bishop, 2000: The effect of targeted observations during the 1999 Winter Storm Reconnaissance program. Mon. Wea. Rev., 128, 3520-3537.
60. Szunyogh, I., Z. Toth, A. V. Zimin, S. J. Majumdar, and A. Persson, 2002: Propagation of the effect of targeted observations: The 2000 Winter Storm Reconnaissance Program. Mon. Wea. Rev., 130, 1144-1165.
61. Terwisscha van Scheltinga, A. D., and H. A. Dijkstra, 2008: Conditional nonlinear optimal perturbations of the double-gyre ocean circulation. Nonlinear Processes Geophys., 15, 727-734.
62. Toth, Z., and Coauthors, 2002: Adaptive observations at NCEP: Past, present and future. Preprints, Symp. on Observations, Data Assimilation, and Probabilistic Prediction, Orlando, FL, Amer. Meteor. Soc., 7.1. [Available online at http://ams.confex. com/ams/ pdfpapers/26036.pdf.]
63. Wu, C. C., and Coauthors, 2005: Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOSTAR): An overview. Bull. Amer. Meteor. Soc., 86, 787-790.
64. Wu, C. C., J. H. Chen, P. H. Lin, and K. H. Chou, 2007: Targeted observations of tropical cyclone movement based on the adjoint-derived sensitivity steering vector. J. Atmos. Sci., 64, 2611-2626.
65. Zou, X., F. Vandenberghe, M. Pondeca, and Y.-H. Kuo, 1997: Introduction to adjoint techniques and the MM5 adjoint modeling system. NCAR Tech. Note, NCAR/TN-435 STR, 111 pp.