Start Submission Become a Reviewer

Reading: Comparison of initial perturbation methods for the mesoscale ensemble prediction system of t...

Download

A- A+
Alt. Display

Original Research Papers

Comparison of initial perturbation methods for the mesoscale ensemble prediction system of the Meteorological Research Institute for the WWRP Beijing 2008 Olympics Research and Development Project (B08RDP)

Authors:

Kazuo Saito ,

Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, JP
X close

Masahiro Hara,

Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, JP
X close

Masaru Kunii,

Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, JP
X close

Hiromu Seko,

Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, JP
X close

Munehiko Yamaguchi

Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, JP
X close

Abstract

Different initial perturbation methods for the mesoscale ensemble prediction were compared by the Meteorological Research Institute (MRI) as a part of the intercomparison of mesoscale ensemble prediction systems (EPSs) of the WorldWeather Research Programme (WWRP) Beijing 2008 Olympics Research and Development Project (B08RDP).

Five initial perturbation methods for mesoscale ensemble prediction were developed for B08RDP and compared at MRI: (1) a downscaling method of the Japan Meteorological Agency (JMA)’s operational one-week EPS (WEP), (2) a targeted global model singular vector (GSV) method, (3) a mesoscale model singular vector (MSV) method based on the adjoint model of the JMA non-hydrostatic model (NHM), (4) a mesoscale breeding growing mode (MBD) method based on the NHM forecast and (5) a local ensemble transform (LET) method based on the local ensemble transform Kalman filter (LETKF) using NHM. These perturbation methods were applied to the preliminary experiments of the B08RDP Tier-1 mesoscale ensemble prediction with a horizontal resolution of 15 km. To make the comparison easier, the same horizontal resolution (40 km) was employed for the three mesoscale model-based initial perturbation methods (MSV, MBD and LET).

The GSV method completely outperformed the WEP method, confirming the advantage of targeting in mesoscale EPS. The GSV method generally performed well with regard to root mean square errors of the ensemble mean, large growth rates of ensemble spreads throughout the 36-h forecast period, and high detection rates and high Brier skill scores (BSSs) for weak rains. On the other hand, the mesoscale model-based initial perturbation methods showed good detection rates and BSSs for intense rains. The MSV method showed a rapid growth in the ensemble spread of precipitation up to a forecast time of 6 h, which suggests suitability of the mesoscale SV for short-range EPSs, but the initial large growth of the perturbation did not last long. The performance of the MBD method was good for ensemble prediction of intense rain with a relatively small computing cost. The LET method showed similar characteristics to the MBD method, but the spread and growth rate were slightly smaller and the relative operating characteristic area skill score and BSS did not surpass those of MBD. These characteristic features of the five methods were confirmed by checking the evolution of the total energy norms and their growth rates.

Characteristics of the initial perturbations obtained by four methods (GSV, MSV, MBD and LET) were examined for the case of a synoptic low-pressure system passing over eastern China. With GSV and MSV, the regions of large spread were near the low-pressure system, but with MSV, the distribution was more concentrated on the mesoscale disturbance. On the other hand, large-spread areas were observed southwest of the disturbance in MBD and LET. The horizontal pattern of LET perturbation was similar to that of MBD, but the amplitude of the LET perturbation reflected the observation density.

How to Cite: Saito, K., Hara, M., Kunii, M., Seko, H. and Yamaguchi, M., 2011. Comparison of initial perturbation methods for the mesoscale ensemble prediction system of the Meteorological Research Institute for the WWRP Beijing 2008 Olympics Research and Development Project (B08RDP). Tellus A: Dynamic Meteorology and Oceanography, 63(3), pp.445–467. DOI: http://doi.org/10.1111/j.1600-0870.2010.00509.x
2
Views
1
Downloads
19
Citations
  Published on 01 Jan 2011
 Accepted on 21 Dec 2010            Submitted on 17 May 2010

References

  1. Barkmeijer , J. , Buizza , R. , Palmer , T. N. , Pun , K. and Mahfouf , J . 2001 . Tropical singular vectors computed with linearized diabatic physics . Q. J. R. Meteorol. Soc . 127 , 658 – 708 .  

  2. Beljaars , A. C. M. and Holtslag , A. A. M . 1991 . Flux parameteriza-tion and land surfaces in atmospheric models . J. AppL Meteor . 30 , 327 – 341 .  

  3. Bishop , C. H. , Etherton , B. J. and Majumdar , S. J . 2001 . Adaptive Sampling with Ensemble Transform Kalman Filter. Part I: theoretical aspects. Mon . Wea. Re v . 129 , 420 – 436 .  

  4. Bousquet , O. , Lin , C.A. and Zawadzki , I . 2006 . Analysis of scale de-pendence of quantitative precipitation forecast verification: a case-study over the Mackenzie river basin . Q. J. R. MeteoroL Soc . 132 , 2107 – 2125 .  

  5. Bowler , N. E . 2006 . Comparison of error breeding, singular vectors, ran-dom perturbations and ensemble Kalman filter perturbation strategies on a simple model . Tellus 58A , 538 – 548 .  

  6. Bowler N. E. , Arribas , A. , Mylne , K. R. , Robertson , K. B. and Beare S. E . 2008 . The MOGREPS short-range ensemble prediction system . Q. J. R. MeteoroL Soc . 134 , 703 – 722 .  

  7. Bowler N. E. and Mylne , K. R . 2009 . Ensemble transform Kalman filter perturbations for a regional ensemble prediction system . Q. J. R. MeteoroL Soc . 135 , 757 – 766 .  

  8. Buizza , R. and Palmer , T. N . 1995 . The singular vector structure of the atmospheric global circulation. J. Atmos. Sc i . 52 , 1434 – 1456 .  

  9. Du J. , DiMego , G. , Tracton , M. S. and Zhou , B . 2003 . NCEP short-range ensemble forecasting (SREF) system: multi-IC, multi-model and multi-physics approach . CAS/JSC WGNE Res. Act. Atmos. Ocea. Modell . 33 , 5 . 09-5 . 10 .  

  10. Duan , Y. , Gong , J. , Chen , D. , DiMego , G. , Kuo , B. and co-authors . 2009 . BO8RDP—a WWRP Research and Development Project: Beijing 2008 Olympics Meso-scale Ensemble Prediction Research & Development Project (B08RDP)A Report on the WWRP Research and Development Project BO8RDP to the WWRP Joint Scientific Cornmittee . Available at: http://www.wmo.int/pages/prog/arep/wwrp/new/documents/Doc3_2_3_BO8RDP.doc.  

  11. Ebert , E. E. , Damrath , U. , Wergen , W. and Baldwin , M. E . 2003 . The WGNE assessment of short-term quantitative precipitation forecasts . Bull. Amer Meteor Soc . 84 , 481 – 492 .  

  12. Eckel , F. A. and Mass , C. E 2005 . Aspects of effective short-range ensemble forecasting . Wea. Forecast . 20 , 328 – 350 .  

  13. Ehrendorfer , M. , and Errico , R. M. and Raeder , K. D . 1999 . Singular-vector perturbation growth in a primitive equation model with moist physics.J . Atmos. Sc i . 56 , 1627 – 1648 .  

  14. Fujita , T. , Tsuguti , H. , Miyoshi , T. , Seko , H. and Saito , K . 2009 . Devel-opment of a mesoscale ensemble data assimilation system at JMA. Report of the Grant-in-Aid for Scientific Research (B) (2005-2008), No. 17110035 , 232 – 235 ( in Japanese )  

  15. Hara , M . 2010a . Global singular vector method . Tech. Rep. MRI 62 , 61 – 72 .  

  16. Hara , T . 2010b . Turbulent processes . Tech. Rep. MRI 62 , 168 – 176 .  

  17. Hoffman , R. N. and Kalnay , E . 1983 . Lagged average forecasting, an alternative to Monte Carlo forecasting . Tellus 35A , 100 – 118 .  

  18. Hohenegger , C. and Schar , C . 2007 . Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sc i . 56 , 4467 – 4478 .  

  19. Honda , Y. , Nishijima, M. Koizumi , K. Ohta , Y. , Tamiya , K. and co-authors. 2005. A pre-operational variational data assimilation system for a nonhydrostatic model at Japan Meteorological Agency: formula-tion and preliminary results. Q. J. R. Meteorol. Soc . 131 , 3465-347 5 .  

  20. Houtelcamer , P. L. and Mitchell , H. L . 2005 . Ensemble Kalman filtering . Q. J. R. Meteorol. Soc . 131 , 3269 – 3289 .  

  21. Houtekamer , P. L. , Mitchell , H. L. and Deng , X . 2009 . Model error representation in an operational ensemble Kalman filter. Mon . Wea. Re v . 137 , 2126 – 2143 .  

  22. Hunt , B. R. , Kostelich , E. J. and Szunyogh , I . 2007 . Efficient data As-similation for spatiotemporal chaos: a Local Ensemble Transform Kalman Filter . Physica D 230 , 112 – 126 .  

  23. Japan Meteorological Agency 2007. Outline of the operational nu-merical weather prediction at the Japan Meteorological Agency. Appendix to WMO Numerical Weather Prediction Progress Report. Japan Meteorological Agency, Tokyo, Japan , 194 pp. Available online at http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline-nwp/index.htm  

  24. Jolliffe , I. T. and Stephenson , D. B . 2003 . Forecast Verification: A Prac-titioner’s Guide in Atmospheric Science , John Wiley. Chichester , 245 pp .  

  25. Kadowaki , T . 2005 . A 4-dimensional variational assimilation system for the .TMA Global Spectrum Model . CAS/JSC WGNE Res. Act. Atmos. Ocea. Modell . 34 , 117 – 118 .  

  26. Keenan , T. , Joe , R , Wilson , J. , Collier , C. , Golding , B. and co-authors . 2003. The Sydney 2000 World Weather Research Programme Forecast Demonstration Project. Bull. Amer Meteor Soc . 84 , 1041-105 4 .  

  27. Koizumi , K. , Ishilcawa , Y. and Tsuyuki , T . 2005 : Assimilation of Precip-itation Data to the JMA Mesoscale Model with a Four-dimensional Variational Method and its Impact on Precipitation Forecasts . SOLA 1 , 45 – 48 .  

  28. Kunii , M . 2010 . MSV method . Tech. Rep. MRI 62 , 73 – 77 .  

  29. Kunii , M. , Saito , K. and Seko , H . 2010 . Mesoscale data assimilation experiment in the WWRP BO8RDP . SOLA 6 , 33 – 36 .  

  30. Kunii , M. , Saito , K. , Seko , H. , Hara , M. , Hara , T. and co-authors . 2011 . Verification and intercomparison of mesoscale ensemble prediction systems in the Beijing 2008 Olympics Research and Development Project. Tellus 63A , this issue .  

  31. Li , X. , Charron , M. , Spacek , L. and Candille , G . 2008 . A regional ensemble prediction system based on moist targeted singular vectors and stochastic parameter perturbations. Mon . Wea. Re v . 136 , 443 – 462 .  

  32. Marsigli , C. , Boccanera , F. , Montani , A. and Paccagnella , T . 2005 . The COSMO-LEPS mesoscale ensemble system: validation of the methodology and verification . Nonlin. Processes Geophys . 12 , 527 – 536 .  

  33. Mason , I . 1982 . A model for assessment of weather forecasts . AusL Meteor Mag . 30 , 291 – 303 .  

  34. Miyoshi , T . 2011 . The Gaussian approach to adaptive covariance in-flation and its implementation with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev . 139. https://doi.org/10.1175/2010MWR3570.  

  35. Miyoshi , T. and Aranami , K. 2006. Applying a four-dimensional local ensemble transform Kalman filter (4D-LETKE) to the .TMA nonhy-drostatic model (NHM). SOLA 2 , 128 - 131 .  

  36. Miyoshi , T. , Yamane , S. and Enomoto , T . 2007 . Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF) . SOLA 3 , 89 – 92 .  

  37. Molteni , F. , Buizza , R. , Palmer , T. N. and Petroliagis , T . 1996 . The ECMWF ensemble prediction system: meteorology and validation . Q. J. R. Meteorol. Soc . 122 , 73 – 120 .  

  38. Murphy , A. H . 1988 . Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon . Wea. Re v . 116 , 2417 – 2424 .  

  39. Nakanishi , M. and Niino , H . 2004 . An improved Mellor-Yamada level 3 model with condensation physics : its design and verification . Bound.-Layer Meteor 112 , 1 – 31 .  

  40. Nuret , M. , Lafore , J. P. , Gouget , V. and Ducrocq , V . 2005 . Mesoscale analysis and impact on simulation of I0P14 of the MAP experiment . Q. J. R. Meteorol. Soc . 131 , 2769 – 2793 .  

  41. Nutter , R , Stensrud , D. and Xue , M . 2004 . Application of lateral bound-ary condition perturbations to help restore dispersion in limited-area ensemble forecasts. Mon . Wea. Re v . 132 , 2378 – 2390 .  

  42. Ono , K. , Honda , Y. and Kunii , M . 2010 . Development of a mesoscale ensemble prediction system using a singular vector method . CAS/JSC WGNE Res. Act. Atmos. Ocea. Modell . 40 , 5 . 17-5 . 18 .  

  43. Ott , E. , Hunt , B. R. , Szunyogh , I. , Zimin , A. V. , Kostelich , E. J. and co-authors . 2004 . A local ensemble Kalman filter for atmospheric data assimilation. Tellus 56A , 415 - 428 .  

  44. Saito , K. , Fujita , T. , Yamada , Y. , Ishida , J. , Kumagai , Y. and co-authors . 2006a. The operational .TMA nonhydrostatic mesoscale model. Mon. Wea. Rev . 134 , 1266-129 8 .  

  45. Saito , K. , Kyouda , M. and Yamaguchi , M . 2006b . Mesoscale Ensem-ble Prediction Experiment of a Heavy Rain Event with the JMA Mesoscale Model . CAS/JSC WGNE Res. Act. Atmos. Ocea. Modell . 36 , 5 . 49-5 . 50 .  

  46. Saito , K. and Hara , T . 2010 . Numerical model for the 2008 experiment . Tech. Rep. MRI 62 , 40 – 50 .  

  47. Saito , K. , Ishida , J. , Aranami , K. , Hara , T. , Segawa , T. and co-authors . 2007 . Nonhydrostatic atmospheric models and operational develop-ment at .TMA.J. Meteor Soc. Japan 85B , 271 - 304 .  

  48. Saito , K. , Kunii , M. , Hara , M. , Seko , H. , Hara , T. and co-authors . 2010a . WWRP Beijing 2008 Olympics Forecast Demonstration/Research and Development Project (B08FDP/RDP). Tech. Rep. MRI 62 , 210 pp. Available at: http://www.mri-jma.go.jp/Publish/Technical/DATANOL_62/62_en.html .  

  49. Saito , K. , Kuroda , T. , Kunii , M. and Kohno , N . 2010b . Numerical simulations of Myanmar Cyclone Nargis and the associated storm surge. Part 2: ensemble prediction . J. Meteor. Soc. Japan 88 , 547 – 570 .  

  50. Saito , K. , Seko , H. , Kunii , M. , Hara , M. and Miyoshi , T . 2009 . Influence of lateral boundary perturbations on the mesoscale EPS using BGM and LETKF . CAS/JSC WGNE Res. Activities Atmos. Oceanic Modell . 39 , 5 . 21-5 . 22 .  

  51. Seko , H . 2010 . Local ensemble transform Kalman filter (LET) method . Tech. Rep. MRI 62 , 80 – 84 .  

  52. Seko , H. , Miyoshi , T. , Shoji , Y. and Saito , K . 2011 . Data assimilation experiments of precipitable water vapor using the LETKF system: intense rainfall event over Japan 28 July 2008. Tellus 63A, this issue. Seko, H., Saito, K., Kunii, M. and Kyouda, M. 2009. Mesoscale en-semble experiments on potential parameters for Tornado formation . SOLA 5 , 57 – 60 .  

  53. Shapiro , MA. and Thorpe , A.J . 2004 . THORPEX International Sci-ence Plan . Version 3. WMO/TD-No. 1246 , WWRP/T’HORPEX No. 2 , 51 pp .  

  54. Simon , H. and Paden , B. 1980 . Solving Ax = b using the Lanczos algorithm with selective orthogonalization . University of California Berkeley .  

  55. Stensrud , D. J. and Yussouf , N . 2007 . Reliable probabilistic quantitative precipitation forecasts from a short-range ensemble forecasting system . Wea. Forecast . 22 , 3 – 17 .  

  56. Torn , R. D. , Hakim , G. J. and Snyder , C . 2006 . Boundary conditions for limited-area ensemble Kalman filters. Mon . Wea. Re v . 134 , 2490 – 2502 .  

  57. Toth , Z. and Kalnay , E . 1993 . Ensemble forecasting at NMC: the generation of perturbations . Bull. Amer Meteor. Soc . 74 , 2317 – 2330 .  

  58. Toth , Z. and Kalnay , E . 1997 . Ensemble forecasting at NCEP and the breeding method. Mon . Wea. Re v . 125 , 3297 – 3319 .  

  59. Wang , X. and Bishop , C. H . 2003 . A comparison of breeding and en-semble transform Kalman filter ensemble forecast schemes. J. Atmos. Sc i . 60 , 1140 – 1158 .  

  60. Wang , Y. , Bellus , M. , Wittmann , C. , Steinheimer , M. , Weidle , F. and co-authors . 2011 . The Central European limited area ensemble fore-casting system: ALADIN-LAEF. Quart. J. Roy. Meteor Soc . 137 . https://doi.org/10.1002/qj.751 .  

  61. Yamaguchi , M. and Majumdar , S. J . 2010 . Using TTGGE data to diagnose initial perturbations and their growth for tropical cyclone ensemble forecasts. Mon . Wea. Re v . 138 , 3634 – 3655 .  

  62. Yamaguchi , M. , Sakai , R. , Kyoda , M. , Komori , T. and Kadowaki , T . 2009 . Typhoon ensemble prediction system developed at the Japan Meteorological Agency. Mon . Wea. Re v . 137 , 2592 – 2604 .  

  63. Yoden , S . 2007 . Atmospheric predictability . J. Meteor Soc. Jpn . 85B , 77 – 102 .  

comments powered by Disqus