Book contents
- Dynamics and Predictability of Large-Scale, High-Impact Weather and Climate Events
- Special publications of the International Union of Geodesy and Geophysics Series
- Dynamics and Predictability of Large-Scale High-Impact Weather and Climate Events
- Copyright page
- Contents
- Preface
- Frontispiece
- Book part
- Contributors
- Part I Diagnostics and prediction of high-impact weather
- 1 Global prediction of high-impact weather: diagnosis and performance
- 2 Severe weather diagnosis from the perspective of generalized slantwise vorticity development
- 3 Probabilistic extreme event attribution
- 4 Observed and projected changes in temperature and precipitation extremes
- Part II High-impact weather in mid latitudes
- Part III Tropical cyclones
- Part IV Heat waves and cold-air outbreaks
- Part V Ocean connections
- Part VI Asian monsoons
- Index
- References
1 - Global prediction of high-impact weather: diagnosis and performance
from Part I - Diagnostics and prediction of high-impact weather
Published online by Cambridge University Press: 05 March 2016
Book contents
- Dynamics and Predictability of Large-Scale, High-Impact Weather and Climate Events
- Special publications of the International Union of Geodesy and Geophysics Series
- Dynamics and Predictability of Large-Scale High-Impact Weather and Climate Events
- Copyright page
- Contents
- Preface
- Frontispiece
- Book part
- Contributors
- Part I Diagnostics and prediction of high-impact weather
- 1 Global prediction of high-impact weather: diagnosis and performance
- 2 Severe weather diagnosis from the perspective of generalized slantwise vorticity development
- 3 Probabilistic extreme event attribution
- 4 Observed and projected changes in temperature and precipitation extremes
- Part II High-impact weather in mid latitudes
- Part III Tropical cyclones
- Part IV Heat waves and cold-air outbreaks
- Part V Ocean connections
- Part VI Asian monsoons
- Index
- References
- Type
- Chapter
- Information
- Publisher: Cambridge University PressPrint publication year: 2016
References
Bauer, P., Geer, A. J., Lopez, P., and Salmond, D. (2010). Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation. Q. J. R. Meteorol. Soc., 136, 1868–1885, doi: 10.1002/qj.659.CrossRefGoogle Scholar
Berner, J., Shutts, G. J., Leutbecher, M., and Palmer, T. N. (2009). A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603–626, doi: 10.1175/2008JAS2677.1.CrossRefGoogle Scholar
Bonavita, M., Raynaud, L., and Isaksen, L. (2011). Estimating background-error variances with the ECMWF Ensemble of Data Assimilations system: Some effects of ensemble size and day-to-day variability. Q. J. R. Meteorol. Soc., 137, 423–434, doi: 10.1002/qj.756.CrossRefGoogle Scholar
Buizza, R., Miller, M., and Palmer, T. N. (1999). Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc., 125, 2887–2908, doi: 10.1002/qj.49712556006.CrossRefGoogle Scholar
Buizza, R. and Palmer, T. N. (1995). The singular-vector structure of the atmospheric global circulation. J. Atmos. Sci., 52, 1434–1456, doi: 10.1175/1520–0469(1995)052<1434:TSVSOT>2.0.CO;2.2.0.CO;2>CrossRefGoogle Scholar
Courtier, P., Thépaut, J.-N., and Hollingsworth, A. (1994). A strategy for operational implementation of 4D-Var, using an incremental approach. Q. J. R. Meteorol. Soc., 120, 1367–1387, doi: 10.1002/qj.49712051912.Google Scholar
Dee, D. P., and Co-authors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553–597, doi: 10.1002/qj.828.CrossRefGoogle Scholar
Dee, D. P. and Uppala, S. (2009). Variational bias correction of satellite radiance data in the ERA-Interim reanalysis. Q. J. R. Meteorol. Soc., 135, 1830–1841, doi: 10.1002/qj.493.CrossRefGoogle Scholar
Derber, J. C. and Wu, W.-S. (1998). The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287–2299, doi: 10.1175/1520–0493(1998)126<2287:TUOTCC>2.0.CO;2.2.0.CO;2>CrossRefGoogle Scholar
Fisher, M., Trémolet, Y., Auvinen, H., Tan, D., and Poli, P., 2011: Weak-constraint and long window 4D-Var. ECMWF Tech. Memo., 655, ECMWF, Reading, United Kingdom, 47 pp.Google Scholar
Forbes, R. M., Tompkins, A. M., and Untch, A. (2011). A new prognostic bulk microphysics scheme for the IFS. ECMWF Tech, Memo., 649, ECMWF, Reading, United Kingdom, 30 pp.Google Scholar
Geer, A. J., Bauer, P., and Lopez, P. (2010). Direct 4D-Var assimilation of all-sky radiances. Part II: Assessment. Q. J. R. Meteorol. Soc., 136, 1886–1905, doi: 10.1002/qj.681.CrossRefGoogle Scholar
Isaksen, L., Bonavita, M., Buizza, R., et al. (2010). Ensemble of Data Assimilations at ECMWF. ECMWF Tech. Memo., 636, ECMWF, Reading, United Kingdom, 48 pp.Google Scholar
Hoskins, B. J., McIntyre, M. E., and Robertson, A. W. (1985). On the use and significance of isentropic potential vorticity maps. Q. J. R. Meteorol. Soc., 111, 877–946, doi: 10.1002/qj.49711147002.CrossRefGoogle Scholar
Janisková, M., and Lopez, P. (2012). Linearized physics for data assimilation at ECMWF. ECMWF Tech. Memo., 666, ECMWF, Reading, United Kingdom, 26 pp.Google Scholar
Joos, H. and Wernli, H. (2012). Influence of microphysical processes on the potential vorticity development in a warm conveyor belt: a case-study with the limited-area model COSMO. Q. J. R. Meteorol. Soc., 138, 407–418, doi: 10.1002/qj.934.CrossRefGoogle Scholar
Jung, T., Balsamo, G., Bechtold, P., et al. (2010). The ECMWF model climate: Recent progress through improved physical parametrizations. Q. J. R. Meteorol. Soc., 136, 1145–1160, doi: 10.1002/qj.634.CrossRefGoogle Scholar
Jung, T., and Co-authors (2012). High-resolution global climate simulations with the ECMWF model in project Athena: Experimental design, model climate, and seasonal forecast skill. J. Climate, 25, 3155–3172, doi:10.1175/JCLI-D-11-00265.1.CrossRefGoogle Scholar
Klinker, E., and Sardeshmukh, P. D. (1992). The diagnosis of mechanical dissipation in the atmosphere from large-scale balance requirements. J. Atmos. Sci., 49, 608–627, doi:10.1175/1520–0469(1992)049<0608:TDOMDI>2.0.CO;2.2.0.CO;2>CrossRefGoogle Scholar
Klocke, D. and Rodwell, M. J. (2014). A comparison of two numerical weather prediction methods for diagnosing fast-physics errors in climate models. Q. J. R. Meteorol. Soc., 140, 517–524, doi: 10.1002/qj.2172.CrossRefGoogle Scholar
Leroy, S. S., and Rodwell, M. J. (2014). Leveraging highly accurate data in diagnosing errors in atmospheric models, Bull. Am. Meteorol. Soc., doi: 10.1175/BAMS-D-12-00143.1.CrossRefGoogle Scholar
Leutbecher, M., and Palmer, T. (2008). Ensemble forecasting. J. Comp. Phys., 227, 3515–3539, doi: 10.1016/j.jcp.2007.02.014.CrossRefGoogle Scholar
Lorenz, E. N. (1963). Deterministic non-periodic flow. J. Atmos. Sci., 20, 130–141, doi:10.1175/1520–0469(1963)020<0130:DNF>2.0.CO;2.2.0.CO;2>CrossRefGoogle Scholar
Magnusson, L., Bidlot, J.-R., Lang, S., Thorpe, A., and Wedi, N. (2014). Evaluation of medium-range forecasts for hurricane Sandy. Mon. Wea. Rev., 142, 1962–1981, doi:10.1175/MWR-D-13-00228.1.CrossRefGoogle Scholar
Mapes, B. E. and Bacmeister, J. T. (2012). Diagnosis of tropical biases and the MJO from patterns in the MERRA analysis tendency fields. J. Climate, 25, 6202–6214, doi: 10.1175/JCLI-D-11-00424.1.CrossRefGoogle Scholar
Madonna, E. (2013). Warm conveyor belts: Climatology and forecast performance. PhD thesis. Diss, 21315, ETH, Zurich, Switzerland, 143 pp.Google Scholar
Rodwell, M. J. and Jung, T. (2008). Understanding the local and global impacts of model physics changes: an aerosol example. Q. J. R. Meteorol. Soc., 134, 1479–1497. doi: 10.1002/qj.298.CrossRefGoogle Scholar
Rodwell, M. J., Magnusson, L., Bauer, P., et al. (2013). Characteristics of occasional poor medium-range weather forecasts for Europe. Bull. Am. Meteorol. Soc., 94, 1393–1405, doi: 10.1175/BAMS-D-12-00099.1.CrossRefGoogle Scholar
Rodwell, M. J. and Palmer, T. N. (2007). Using numerical weather prediction to assess climate models. Q. J. R. Meteorol. Soc., 133, 129–146, doi: 10.1002/qj.23.CrossRefGoogle Scholar
Shutts, G., Leutbecher, M., Weisheimer, A., et al. (2011). Representing model uncertainty: Stochastic parametrization at ECMWF. ECMWF Newsletter, 129, ECMWF, Reading, United Kingdom, 19–24.Google Scholar
Simmons, A. J., Burridge, D. M., Jarraud, M., Girard, C., and Wergen, W. (1989). The ECMWF medium-range prediction models development of the numerical formulations and the impact of increased resolution. Meteor. Atmos. Phys., 40, 28–60, doi: 10.1007/BF01027467.CrossRefGoogle Scholar
Thorpe, A. J. (1986). Synoptic scale disturbances with circular symmetry. Mon. Wea. Rev., 114, 1384–1389, doi: 10.1175/1520–0493(1986)114<1384:SSDWCS>2.0.CO;2.2.0.CO;2>CrossRefGoogle Scholar
Trémolet, Y. (2007). Model-error estimation in 4DVar. Q. J. R. Meteorol. Soc., 133, 1267–1280, doi:10.1002/qj.94.CrossRefGoogle Scholar