Ensemble statistics for diagnosing dynamics: Tropical cyclone track forecast sensitivities revealed by ensemble regression

Monthly Weather Review

Volumn 140, Issue 8, 2012, Pages 2647-2669

  Gombos Dr , Daniel ^a   Hoffman, Ross N ^a   Hansen, James A ^b  

^a ATMOSPHERIC AND ENVIRONMENTAL RESEARCH   (United States)

^b NAVAL RESEARCH LABORATORY   (United States)

Author keywords

Empirical orthogonal functions; Ensembles; Hurricanes typhoons; Potential vorticity; Principal components analysis; Regression analysis

Indexed keywords

ATMOSPHERIC FIELDS; DYNAMICAL PROCESS; EMPIRICAL ORTHOGONAL FUNCTION; ENSEMBLE FORECASTS; ENSEMBLE MEMBERS; ENSEMBLE MODELS; ENSEMBLE STATISTICS; ENSEMBLES; FORECAST SENSITIVITY; GEOPOTENTIAL HEIGHT; JAPAN METEOROLOGICAL AGENCIES; LINEAR INVERSE TECHNIQUES; MULTIVARIATE REGRESSION; OPERATIONAL FORECASTERS; POTENTIAL VORTICITY; PRINCIPAL COMPONENTS; PRINCIPAL COMPONENTS ANALYSIS; REAL TIME; STATISTICAL SENSITIVITY; TRAINING SAMPLE; TROPICAL CYCLONE;

DYNAMICS; MULTIVARIANT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; VORTICITY;

WEATHER FORECASTING;

ENSEMBLE FORECASTING; HURRICANE; POTENTIAL VORTICITY; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; TYPHOON; WEATHER FORECASTING;

EID: 84865628152     PISSN: 00270644     EISSN: 15200493     Source Type: Journal    
DOI: 10.1175/MWR-D-11-00002.1     Document Type: Article

References (29)

1. Ancell, B., and G. Hakim, 2007: Comparing adjoint- and ensemblesensitivity analysis with application to observation targeting. Mon. Wea. Rev., 135, 4117-4134.
2. Anderson, J., 2007: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D, 230, 99-111.
3. Barnett, T., and R. Preisendorfer, 1987: Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis. Mon. Wea. Rev., 115, 1825-1850.
4. Bishop, C., B. Etherton, and S. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420-436.
5. Bougeault, P., and Coauthors, 2010: The THORPEX Interactive Grand Global Ensemble. Bull. Amer. Meteor. Soc., 91, 1059-1072.
6. Davis, C., and K. Emanuel, 1991: Potential vorticity diagnostics of cyclogenesis. Mon. Wea. Rev., 119, 1929-1953.
7. Ertel, H., 1942: Ein neuer hydrodynamischer wirbelsatz. Meteor. Z., 59, 271-281.
8. Etherton, B., 2007: Preemptive forecasts using an ensemble Kalman filter. Mon. Wea. Rev., 135, 3484-3495.
9. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10143-10162.
10. Gombos, D., 2009: Ensemble regression: Using ensemble model output for atmospheric dynamics and prediction. Ph.D. thesis, Massachusetts Institute of Technology, 190 pp.
11. Gombos, D., and J. Hansen, 2008: Potential vorticity regression and its relationship to dynamical piecewise inversion. Mon. Wea. Rev., 136, 2668-2682.
12. Hakim, G., and R. Torn, 2008: Ensemble synoptic analysis. Synoptic-Dynamic Meteorology and Weather Analysis and Forecasting: A Tribute to Fred Sanders, Meteor. Monogr., No. 55, Amer. Meteor. Soc., 147-162.
13. Hawblitzel, D., F. Zhang, Z. Meng, and C. Davis, 2007: Probabilistic evaluation of the dynamics and predictability of the mesoscale convective vortex of 10-13 June 2003. Mon. Wea. Rev., 135, 1544-1563.
14. Holton, J., 1992: An Introduction to Dynamic Meteorology. Academic Press, 511 pp.
15. Hoskins, B., M. McIntyre, and A. Robertson, 1985: On the use and significance of isentropic potential vorticity maps. Quart. J. Roy. Meteor. Soc., 111, 877-946.
16. Jazwinski, A., 1970: Stochastic Processes and Filtering Theory. Academic Press, 276 pp.
17. Molteni, F., R. Buizza, T. Palmer, and T. Petroliagis, 1996: The new ECMWF Ensemble Prediction System: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73-119.
18. Nakagawa, M., 2009: Outline of the High Resolution Global Model at the Japanese Meteorological Agency. RSMC Tokyo- Typhoon Center Tech. Rev. 11, 13 pp.
19. Nicholls, N., 1987: The use of canonical correlation to study teleconnections. Mon. Wea. Rev., 115, 393-399.
20. Penland, C., 1989: Random forcing and forecasting using principal oscillation pattern analysis. Mon. Wea. Rev., 117, 2165-2185.
21. Penland, C., and P. Sardeshmukh, 1995: The optimal growth of sea surface temperature anomalies. J. Climate, 8, 1999-2024.
22. Penland, C., and L. Matrosova, 1998: Prediction of tropical Atlantic sea surface temperatures using linear inverse modeling. J. Climate, 11, 483-496.
23. Rabier, E., H. Järvinen, E. Klinker, J. 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.
24. Sampson, C., and A. Schrader, 2000: The Automated Tropical Cyclone Forecasting System (version 3.2). Bull. Amer. Meteor. Soc., 81, 1231-1240.
25. Sippel, J., and F. Zhang, 2008: A probabilistic analysis of the dynamics and predictability of tropical cyclogenesis. J. Atmos. Sci., 65, 3440-3459.
26. Torn, R., and G. Hakim, 2008: Ensemble-based sensitivity analysis. Mon. Wea. Rev., 136, 663-677.
27. Velden, C., and L. Leslie, 1991: The basic relationship between tropical cyclone intensity and the depth of the environmental steering layer in the Australian region. Wea. Forecasting, 6, 244-253.
28. Wilks, D., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Elsevier, 672 pp.
29. Zhang, F., 2005: Dynamics and structure of mesoscale error covariance of a winter cyclone estimated through short-range ensemble forecasts. Mon. Wea. Rev., 133, 2876-2893.