Accounting for initial condition uncertainties in COSMO-DE-EPS

Journal of Geophysical Research Atmospheres

Volumn 117, Issue 7, 2012, Pages

  Peralta, C ^a,b,c   Ben Bouallegue Z ^a   Theis, S E ^a   Gebhardt, C ^a   Buchhold, M ^a  

^a DEUTSCHER WETTERDIENST   (Germany)

^b FRAUNHOFER IWES   (Germany)

^c UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL MODELS;

ENSEMBLE FORECASTS; ENSEMBLE PREDICTION SYSTEMS; EXPERIMENTAL SETUP; HIGH RESOLUTION; INITIAL CONDITIONS; INITIAL PERTURBATION; LEADTIME; MODEL PHYSICS; NUMERICAL WEATHER PREDICTION MODELS; PROBABILISTIC SCORES;

FORECASTING;

ATMOSPHERIC MODELING; CLIMATE PREDICTION; ENSEMBLE FORECASTING; EXPERIMENTAL STUDY; PERFORMANCE ASSESSMENT; PROBABILITY; UNCERTAINTY ANALYSIS;

EID: 84859912164     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/2011JD016581     Document Type: Article

References (55)

1. Baldauf, M., A. Seifert, J. Förstner, D. Majewski, M. Raschendorfer, and T. Reinhardt (2011), Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities, Mon. Weather Rev., 139(12), 3887-3905, doi:10. 1175/MWR-D-10-05013. 1.
2. Benoit, R.,M. Desgagné, P. Pellerin, S. Pellerin, Y. Chartier, and S.Desjardins (1997), The Canadian MC2: A semi-Lagrangian, semi-implicit wideband atmospheric model suited for finescale process studies and simulation, Mon. Weather Rev., 125, 2382-2415. (Pubitemid 127657701)
3. Bowler, N. E., A. Arribas, K. R. Mylne, K. B. Robertson, and S. E. Beare (2008), The MOGREPS short-range ensemble prediction system, Q. J. R. Meteorol. Soc., 134, 703-722, doi:10. 1002/qj.234.
4. Brier, G. W. (1950), Verification of forecasts expressed in terms of probability, Mon. Weather Rev., 78, 1-3.
5. Center for Analysis and Prediction of Storms (CAPS) (2011), 2011 CAPS Spring Forecast Experiment Program Plan, report, 20 pp. , NOAA, Boulder, Colo. [Available at http://forecast.caps.ou.edu/SpringProgram2011-Plan- CAPS.pdf.]
6. Clark, A. J., W. A. Gallus,M. Xue, and F. Kong (2009), A comparison of precipitation forecast skill between small convection-allowing and large convection- parameterizing ensembles, Weather Forecasting, 24, 1121-1140, doi:10. 1175/2009WAF2222222. 1.
7. Côté, J., S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth (1998a), The operational CMC MRB Global Environmental Multiscale (GEM) Model. Part I: Design considerations and formulation, Mon. Weather Rev., 126, 1373-1395. (Pubitemid 128597642)
8. Côté, J., J. Desmarais, S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth (1998b), The operational CMC MRB Global Environmental Multiscale (GEM) Model. Part II: Results, Mon. Weather Rev., 126, 1397-1418. (Pubitemid 128597643)
9. Doms, G. (2011), A description of the nonhydrostatic COSMO-Model. Part I: Dynamics and numerics, report, 153 pp. , Consortium for Small-ScaleModell., Dtsch. Wetterdienst, Offenbach, Germany. [Available at http://www.cosmomodel. org/content/model/documentation/core/cosmoDyncsNumcs.pdf.]
10. Doms, G., et al. (2011), A description of the nonhydrostatic COSMO-Model. Part II: Physical parametrizations, Consortium for Small-ScaleModell., Dtsch. Wetterdienst, Offenbach, Germany. [Available at http://www.cosmo-model. org/content/model/documentation/core/cosmoPhysParamtr.pdf.]
11. Du, J., S. L. Mullen, and F. Sanders (1997), Short-range ensemble forecasting of quantitative precipitation, Mon. Weather Rev., 125, 2427-2459. (Pubitemid 127657703)
12. 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. Weather Rev., 121, 1493-1513. (Pubitemid 24429226)
13. Ebisuzaki, W., and E. Kalnay (1991), Ensemble experiments with a new lagged average forecasting scheme, WMO Rep. 15, World Meteorol. Organ., Geneva, Switzerland.
14. EnvironmentalModeling Center (2003), The GFSAtmospheric Model, NCEP Off. Note 442, Natl. Cent. for Environ. Protection, Camp Springs, Md.
15. Fujita, T., D. J. Stensrud, and D. C. Dowell (2007), Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties, Mon. Weather Rev., 135, 1846-1868, doi:10. 1175/MWR3391. 1. (Pubitemid 46839862)
16. García-Moya, J., A. Callado, C. Santos, D. Santos-Muñoz, and J. Simarro (2007), Predictability for short-range forecast: A multi-model approach., report, Agencia Estatal de Meteorol., Madrid, Spain.
17. Gebhardt, C., S. E. Theis, M. Paulat, and Z. Ben-Bouallègue (2011), Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries, Atmos. Res., 100, 168-177.
18. Hohenegger, C., A. Walser, W. Langhans, and C. Schär (2008), Cloudresolving ensemble simulations of the August 2005 Alpine flood, Q. J. R. Meteorol. Soc., 134, 889-904, doi:10. 1002/qj.252.
19. Huang, X.-Y., et al. (2009), Four-dimensional variational data assimilation for WRF: Formulation and preliminary results, Mon. Weather Rev., 137, 299-314, doi:10. 1175/2008MWR2577. 1.
20. Janssen, P., and J. Bidlot (2002), ECMWF Wave Model (CY25R1). IFS Documentation cycle CY25r1, report, ECMWF, Reading, U. K. [Available at http://www.ecmwf.int/research/ifsdocs/WAVES/index.html.]
21. Keil, C., and G. Craig (2011), Regime-dependent forecast uncertainty of convective precipitation, Meteorol. Z., 20, 145-151.
22. Kong, F., K. K. Droegemeier, and N. L. Hickmon (2007), Multiresolution ensemble forecasts of an observed tornadic thunderstorm system. Part II: Storm-scale experiments, Mon. Weather Rev., 135, 759-782, doi:10. 1175/ MWR3323. 1. (Pubitemid 46542135)
23. Leoncini, G., R. S. Plant, S. L. Gray, and P. A. Clark (2010), Perturbation growth at the convective scale for CSIP IOP18, Q. J. R. Meteorol. Soc., 136(648), 653-670, doi:10. 1002/qj.587.
24. Majewski, D., D. Liermann, P. Prohl, B. Ritter, M. Buchhold, T. Hanisch, G. Paul, W. Wergen, and J. Baumgardner (2002), The operational global icosahedral hexagonal gridpoint model GME: Description and highresolution tests, Mon. Weather Rev., 130, 319-338. (Pubitemid 34842332)
25. Marsigli, C. (2009), Final report on priority project SREPS (Short Range Ensemble Prediction System), Tech. Rep. 13, 33 pp. ,COSMO, Dtsch. Wetterdienst, Offenbach, Germany. [Available at http://cosmo-model.cscs.ch/ content/model/documentation/techReports/docs/techReport13. pdf.]
26. Marsigli, C., A. Montani, and T. Paccagnella (2008), The COSMO-SREPS ensemble for short-range: System analysis and verification on the MAP D-PHASE DOP, paper presented at Joint MAP D-PHASE Scientific Meeting-COST 731 Mid-term Seminar, ISAC-CNR, Bologna, Italy.
27. Mason, S. J. (2004), On using "climatology" as a reference strategy in the Brier and ranked probability skill scores, Mon. Weather Rev., 132, 1891-1895. (Pubitemid 39052103)
28. Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis (1996), The ECMWF Ensemble Prediction System: Methodology and validation, Q. J. R. Meteorol. Soc., 122, 73-119, doi:10. 1002/qj.49712252905.
29. Montani, A., D. Cesari, C. Marsigli, and T. Paccagnella (2011), Seven years of activity in the field of mesoscale ensemble forecasting by the COSMOLEPS system: Main achievements and open challenges, Tellus, Ser. A, 63, 605-624, doi:10. 1111/j.1600-0870. 2010. 00499. x.
30. Peralta, C., and M. Buchhold (2011), Initial condition perturbations for the COSMO-DE-EPS, COSMO Newsl., 11, 115-123.
31. Radnoti, G., et al. (1995), The spectral limited areamodel ARPEGE/ALADIN, WMO TD 699, pp. 111-117, WorldMeteorol. Organ., Geneva, Switzerland.
32. Reich, H., A. Rhodin, and C. Schraff (2011), LETKF for the nonhydrostatic regional model COSMO-DE, COSMO Newsl., 11, 27-31.
33. Roberts, N. M., and H. W. Lean (2008), Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events, Mon. Weather Rev., 136, 78-97, doi:10. 1175/2007MWR2123. 1. (Pubitemid 351308459)
34. Schaefer, J. T. (1990), The Critical Success Index as an indicator of warning skill, Weather Forecasting, 5, 570-575.
35. Schättler, U. (2009), A description of the nonhydrostatic COSMO-Model. Part V: Preprocessing, report, 50 pp. , Consort. for Small-Scale Modell., Dtsch. Wetterdienst, Offenbach, Germany. [Available at http://www. cosmo-model.org/content/model/documentation/core/cosmoInt2lm.pdf.]
36. Schättler, U., G. Doms, and C. Schraff (2009), A description of the nonhydrostatic COSMO-Model. Part VII: User's guide, report, 147 pp. , Consort. for Small-Scale Modell., Dtsch. Wetterdienst, Offenbach, Germany. [Available at http://www.cosmo-model.org/content/model/documentation/ core/cosmoUserGuide.pdf.
37. Schraff, C., and R. Hess (2003), A description of the nonhydrostatic COSMO-Model. Part III: Data assimilation, report, 85 pp. , Consort. for Small-Scale Modell., Dtsch. Wetterdienst, Offenbach, Germany. [Available at http://www.cosmo-model.org/content/model/documentation/core/ cosmoAssim.pdf.]
38. Schwartz, C. S., et al. (2010), Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership, Weather Forecasting, 25, 263-280, doi:10. 1175/2009WAF2222267. 1.
39. Skamarock, W., J. B. Klemp, J. Dudhia, D. O. Gill, D. Barker, W. Wang, and J. Powers (2005), A description of the Advanced Research WRF version 2, NCAR/TN468+STR, 100 pp. , NCAR, Boulder, Colo. [Available at http://box.mmm.ucar.edu/wrf/users/docs/arw-v2. pdf.]
40. Staniforth, A., A. White, N. Wood, J. Thuburn, M. Zerroukat, E. Cordero, M. Davies, and T. Diamantakis (2006), Joy of U.M. 6. 3 - Model formulation, Unified Model Doc. Pap. 15, 559 pp. , Dyn. Res., Numer. WeatherPredict., Met Office, Exeter, U. K. [Available at http://cms.ncas.ac.uk/ html-umdocs/.]
41. Stensrud, D. J., and M. S. Wandishin (2000), The correspondence ratio in forecast evaluation, Weather Forecasting, 15, 593-602. (Pubitemid 32013380)
42. Stensrud, D. J., J.-W. Bao, and T. T. Warner (2000), Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems, Mon. Weather Rev., 128, 2077-2107.
43. Stephan, K., S. Klink, and C. Schraff (2008), Assimilation of radar-derived rain rates into the convective-scale model COSMO-DE at DWD, Q. J. R. Meteorol. Soc., 134, 1315-1326, doi:10. 1002/qj.269.
44. Steppeler, J., G. Doms, U. Schättler, H. W. Bitzer, A. Gassmann, U. Damrath, and G. Gregoric (2003), Meso-gamma scale forecasts using the nonhydrostatic model LM, Meteorol. Atmos. Phys., 82, 75-96, doi:10. 1007/s00703- 001-0592-9. (Pubitemid 37450346)
45. Torn, R. D., G. J. Hakim, and C. Snyder (2006), Boundary conditions for limited- area ensemble Kalman filters, Mon. Weather Rev., 134, 2490-2502, doi:10. 1175/MWR3187. 1. (Pubitemid 44445440)
46. Toth, Z., and E. Kalnay (1997), Ensemble forecasting at NCEP and the breeding method, Mon. Weather Rev., 125, 3297-3319. (Pubitemid 127667540)
47. Vié, B., O. Nuissier, and V. Ducrocq (2011), Cloud-resolving ensemble simulations of Mediterranean heavy precipitating events: Uncertainty on initial conditions and lateral boundary conditions, Mon. Weather Rev., 139, 403-423, doi:10. 1175/2010MWR3487. 1.
48. Voigt, R. G., D. Gottlieb, and M. Y. Hussaini (1984), Spectral Methods for Partial Differential Equations, SIAM, Philadelphia, Pa.
49. Walser, A., D. Lüthi, and C. Schär (2004), Predictability of precipitation in a cloud-resolving model, Mon. Weather Rev., 132, 560-577. (Pubitemid 38335450)
50. Wang, Y., A. Kann, M. Bellus, J. Pailleux, and C. Wittmann (2010), A strategy for perturbing surface initial conditions in LAMEPS, Atmos. Sci. Lett., 11, 108-113, doi:10. 1002/asl.260.
51. Weusthoff, T., F. Ament, M. Arpagaus, and M. W. Rotach (2010), Assessing the benefits of convection-permitting models by neighborhood verification: Examples from MAP D-PHASE, Mon. Weather Rev., 138, 3418-3433, doi:10. 1175/2010MWR3380. 1.
52. Wilks, D. S. (1995), Statistical Methods in the Atmospheric Sciences, 467 pp. , Academic, San Diego, Calif.
53. Zhang, D., J. Li, L. Ji, B. Huang, W. Wu, J. Chen, and Z. Song (1995), A global spectral model and test of its performance, Adv. Atmos. Sci., 12, 67-78, doi:10. 1007/BF02661288.
54. Zhang, H., and Z. Pu (2010), Beating the uncertainties: Ensemble forecasting and ensemble-based data assimilation in modern numerical weather prediction, Adv. Meteorol., 2010, 432160, doi:10. 1155/2010/432160.
55. Zupanski, D., S. Q. Zhang, M. Zupanski, A. Y. Hou, and S. H. Cheung (2011), A prototype WRF-based ensemble data assimilation system for dynamically downscaling satellite precipitation observations, J. Hydrometeorol., 12, 118-134, doi:10. 1175/2010JHM1271. 1.