Long-term trends of hail-related weather types in an ensemble of regional climate models using a Bayesian approach

Journal of Geophysical Research Atmospheres

Volumn 117, Issue 15, 2012, Pages

  Kapsch, M L ^a,b   Kunz, M ^a   Vitolo, R ^c   Economou, T ^c  

^a KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

^b STOCKHOLM UNIVERSITY   (Sweden)

^c UNIVERSITY OF EXETER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; CLIMATE MODELS; STORMS;

BAYESIAN APPROACHES; CLASSIFICATION SCHEME; GERMANY; HAIL DAMAGE; LONG-TERM TREND; LONG-TERM VARIABILITY; MARKOV CHAIN MONTE CARLO; MODEL PREDICTION; OBSERVATION SYSTEMS; PROBABILISTIC PREDICTION; REANALYSIS; REGIONAL CLIMATE MODELS; STATISTICAL MODELS; TIME-PERIODS; WEATHER SERVICES; WEATHER TYPES;

PRECIPITATION (METEOROLOGY);

BAYESIAN ANALYSIS; CLASSIFICATION; CLIMATE MODELING; DATA SET; ENSEMBLE FORECASTING; HAIL; MARKOV CHAIN; MONTE CARLO ANALYSIS; NUMERICAL MODEL; REGIONAL CLIMATE; TREND ANALYSIS; WEATHER FORECASTING;

GERMANY;

EID: 84865330975     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/2011JD017185     Document Type: Article

References (43)

1. Bissolli, P., and E. Dittmann (2001), The objective weather types classification of the German Weather Service and its possibilities of application to environmental and meteorological investigations, Meteorol. Z., 10, 253-260. (Pubitemid 32997340)
2. Bissolli, P., J. Grieser, N. Dotzek, and M. Welsch (2007), Tornadoes in Germany 1950-2003 and their relation to particular weather conditions, Global Planet. Change, 57, 124-138. (Pubitemid 46660559)
3. Brooks, H. E., J. W. Lee, and J. Craven (2003), The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data, Atmos. Res., 67, 73-94. (Pubitemid 37141987)
4. Cao, Z. (2008), Severe hail frequency over Ontario, Canada: Recent trend and variability, Geophys. Res. Lett., 35, L14803, doi:10.1029/2008GL034888.
5. Changnon, S. A. (1970), Hailstreaks, J. Atmos. Sci., 27, 109-125.
6. Clarke, G. M., and D. Cooke (2004), A Basic Course in Statistics, 492 pp., John Wiley, London.
7. DeRubertis, D. (2006), Recent trends in four common stability indices derived from US radiosonde observations, J. Clim., 19, 309-323.
8. Dessens, J. (1986), Hail in southwestern France: Hailfall characteristics and hailstorm environment, J. Clim. Appl. Meteorol., 25, 35-47.
9. Doswell III, C. A. (1987), The distinction between large-scale and meso-scale contributions to severe convection: A case study example, Weather Forecast., 2, 3-16.
10. Egli, T. (2007), Elementarschutzregister Hagel, technical report, 35 pp., Kantonale Gebäudeversicherungen, Bern.
11. Ehmann, C. (2009), Analyse der vorherrschenden Großwetterlagen während Hagelereignissen in Baden-Württemberg, seminar thesis, 72 pp. Inst. for Meteorol. and Clim. Res., Karlsruhe Inst. for Technol., Karlsruhe, Germany.
12. Emanuel, K. (1994), Atmospheric Convection, 580 pp., Oxford Univ. Press, Oxford, U. K.
13. Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn (2011), Regional climate models add value to global model data-A review and selected examples, Bull. Am. Meteorol. Soc., 92, 1181-1192.
14. Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin (2004), Bayesian Data Analysis, 689 pp., Chapman and Hall, Boca Raton, Fla.
15. Hagedorn, R., F. J. Doblas-Reyes, and T. N. Palmer (2005), The rationale behind the success of multi-model ensembles in seasonal forecasting-I. Basic concept, Tellus, Ser. A, 57, 219-233. (Pubitemid 40659620)
16. Haklander, A. J., and A. van Delden (2003), Thunderstorm predictors and their forecast skill for the Netherlands, Atmos. Res., 67-68, 273-299. (Pubitemid 37141998)
17. Hanafin, J., R. McGrath, T. Semmler, S. Wang, P. Lynch, S. Steele-Dunne, and P. Nolan (2011), Air flow and stability indices in GCM future and control runs, Int. J. Climatol., 31, 1240-1247.
18. Harris, G. R., M. Collins, D. M. H. Sexton, J. M. Murphy, and B. B. B. Booth (2010), Probabilistic projections for 21st century European climate, Nat. Hazards Earth Syst. Sci., 10, 2009-2020.
19. Hawkins, E. and R. Sutton (2009), The potential to narrow uncertainty in regional climate predictions, Bull. Am. Meteor. Soc., 90, 1095-1107.
20. Hess, P., and H. Brezowsky (1977) Katalog der Grosswetterlagen Europas, Tech. Rep. 113, 14 + 54 pp., Ber. Dtsch. Wetterdienst., Potsdam, Germany.
21. IPCC (2007), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., 996 pp., Cambridge Univ. Press, Cambridge, U. K.
22. Kendall, M. (1975), Rank Correlation Methods, 260 pp., Griffin, London.
23. Kunz, M. (2007), The skill of convective parameters and indices to predict isolated and severe thunderstorms, Nat. Hazards Earth Syst. Sci., 7, 327-342. (Pubitemid 46710928)
24. Kunz, M., and M. Puskeiler (2010), High-resolution assessment of the hail hazard over complex terrain from radar and insurance data, Meteorol. Z., 19, 427-439.
25. Kunz, M., J. Sander, and C. Kottmeier (2009), Recent trends of thunderstorm and hailstorm frequency and their relation to atmospheric characteristics in southwest Germany, Int. J. Climatol., 29, 2283-2297.
26. Lautenschlager, M., K. Keuler, C. Wunram, E. Keup-Thiel, M. Schubert, A. Will, B. Rockel, and U. Boehm (2009a), Climate Simulation with CLM. Climate of the 20th Century run no. 1. Data Stream 3: European region MPI-M/MaD, http://dx.doi.org/10.1594/WDCC/CLM/C20/1/D3, World Data Cent. for Clim., Hamburg, Germany.
27. Lautenschlager, M., K. Keuler, C. Wunram, E. Keup-Thiel, M. Schubert, A. Will, B. Rockel, and U. Boehm (2009b), Climate Simulation with CLM. Climate of the 20th Century run no. 2. Data Stream 3: European region MPI-M/MaD, http://dx.doi.org/10.1594/WDCC/CLM/C20/2/D3, World Data Cent. for Clim., Hamburg, Germany.
28. Leslie, L. M., M. Leplastrier, and B. W. Buckley (2007), Estimating future trends in severe hailstorms over the Sydney Basin: A climate modelling study, Atmos. Res., 87, 37-51. (Pubitemid 350227175)
29. Mahoney, K., M. A. Alexander, G. Thompson, J. J. Barsugil, and J. D. Scott (2012), Changes in hail and flood risk in high-resolution simulations over Colorado's mountains, Nat. Clim. Change, 2, 125-131.
30. Mann, H. B. (1945), Nonparametric tests against trend, Econometrica, 13, 245-259.
31. Marsh, P. T., H. E. Brooks, and D. J. Karoly (2009) Preliminary investigation into the severe thunderstorm environment of Europe simulated by the Community Climate System Model 3, Atmos. Res., 93, 607-618.
32. Noppel, H., U. Blahak, A. Seifert, and K. D. Beheng (2010), Simulations of a hailstorm and the impact of CCN using an advanced two-moment cloud microphysical scheme, Atmos. Res., 96, 286-301.
33. Rockel, B., A. Will, and A. Hense (2008), The Regional Climate Model COSMO-CLM (CCLM), Meteorol. Z., 17, 347-348.
34. Roeckner, E., et al. (2003), The atmospheric general circulation model ECHAM5. Part I: Model description. MPI-Rep. 349, Max-Planck-Inst. für Meteorol., Hamburg, Germany.
35. Roeckner, E., R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, L. Kornblueh, E. Manzini, U. Schlese, and U. Schulzweida (2006), Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model, J. Clim., 19, 3771-3791. (Pubitemid 44405143)
36. Sauvageot, H. (1992), Radar Meteorology, 366 pp., Artech House, Norwood, Mass.
37. Schiesser, H. (2003), Extremereignisse und Klimaänderung, chap. 2.6, pp. 65-68, OcCC, Bern.
38. Spiegelhalter, D. J., A. Thomas, N. G. Best, and D. Lunn (2003), WinBUGS Version 1.4 user manual, MRC Biostat. Unit, Inst. of Public Health, Cambridge, U. K. [Available at http://www.mrc-bsu.cam.ac.uk/bugs].
39. Trapp, R. J., E. D. Robinson, M. E. Baldwin, N. S. Diffenbaugh, and B.R. J. Schwedler (2011), Regional climate of hazardous convective weather through high-resolution dynamical downscaling, Clim. Dyn., 37, 677-688.
40. Uppala, S. M., et al. (2005), The ERA-40 re-analysis, Q. J. R. Meteorol. Soc., 131, 2961-3012. (Pubitemid 43186171)
41. Van Klooster, S. L., and P. J. Roebber (2009), Surface-based convective potential in the contiguous United States in a business-as-usual future climate, J. Clim., 22, 3317-3330.
42. Yarnal, B., A. C. Comrie, B. Frakes, and D. P. Brown (2001) Review: Developments and prospects in synoptic climatology, Int. J. Climatol., 21, 1923-1950. (Pubitemid 34161912)
43. Yue, S., P. Pilon, B. Phinney, and G. Cavadias (2002) The influence of autocorrelation on the ability to detect trend in hydrological series, Hydrol. Process., 16, 1807-1829. (Pubitemid 34749913)